Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 90 appraisals
International urology and nephrology
Acute kidney injury prediction and prognostication using machine learning
Acute Kidney Injury (AKI) is a major health concern with high costs and poor outcomes, partly due to late diagnosis. This paper reviews the application of artificial intelligence (AI) and machine learning (ML) to overcome these limitations by enabling earlier and more accurate prediction and prognostication of AKI. Many studies on AI and ML models used to predict and prognosticate AKI were included in the review. The focus was on models that analyze complex datasets, including real-time data streams like novel biomarkers and continuous vital signs, to achieve earlier and more accurate predictions than conventional methods. ML models show high predictive accuracy for AKI onset and outcomes across various clinical settings, including intensive care units, sepsis, and postoperative and postcontrast situations, with key findings like the successful integration of real-time data to reflect the evolving nature of kidney injury. AI and ML offer a powerful, proactive solution for AKI management. By leveraging diverse data, these technologies can significantly improve patient outcomes and reduce healthcare costs. While their promising performance warrants further exploration, successful clinical integration will require user-friendly platforms and continued validation.
1 Aug 2026
Read appraisal →Journal of robotic surgery
Robot-assisted versus open kidney transplantation: an umbrella review of systematic reviews and meta-analyses
Robot-assisted kidney transplantation (RAKT) has emerged as a minimally invasive alternative to open kidney transplantation (OKT). However, the comparative benefits and limitations of both approaches remain unclear. To compare the outcomes of RAKT and OKT through an umbrella review of published systematic reviews and meta-analyses. This umbrella review was written following PRISMA guidelines. Only meta-analysis and systematic review were included. Many outcomes were studied such as warm (WIT) and cold ischemia times (CIT), rewarming time, total ischemia time, blood loss, blood transfusion, delayed graft function, surgical site infections (SSI), hospital stay, graft rejection, graft failure, all-cause mortality, operation time, incision length, hospital readmission, vascular and ureteral anastomosis time, and other complications. We assessed methodological quality via the AMSTAR-2 tool. Consistency and strength of evidence were also evaluated. 6 studies were included: four meta-analyses and two systematic reviews. Studies were of low to moderate quality. Robotic kidney transplant surgery yields better outcomes in terms of blood loss (MDs ranging from - 16 to - 55 mL across studies), post-operative pain, incision length, SSI and ureteral leak. On the other hand, CIT, rewarming time, total ischemia time, operative time and ileus rate were favored in open surgery. Same rejection rates, graft failure, mortality and hospital readmission rate were found between both techniques. RAKT appears to improve several perioperative outcomes while maintaining comparable graft and patient survival outcomes to OKT. However, the available evidence remains limited by the low-to-moderate quality of existing reviews, highlighting the need for high-quality prospective studies.
27 July 2026
Read appraisal →Journal of robotic surgery
Transperitoneal versus retroperitoneal single-port robot-assisted partial nephrectomy: systematic review and meta-analysis of perioperative and functional outcomes
To systematically compare the safety and efficacy of the transperitoneal (TP) and retroperitoneal (RP) approaches in single-port robot-assisted partial nephrectomy (SP-RAPN). PubMed, Web of Science, and Embase were searched for comparative studies published before October 2025. The primary evaluated outcomes were grouped as Perioperative Outcomes (operative time, blood loss, ischemia time, positive surgical margin, postoperative eGFR, and hospital stay) and Surgical Safety (complication rate). Study inclusion and exclusion standards followed the PICOS guidelines. Four comparative studies with 384 patients (188 TP and 196 RP) were analyzed. Compared with the RP group, the TP group had higher intraoperative blood loss (WMD = 27.95 mL, 95% CI = 4.90-51.02, p = 0.018), longer hospital stay (WMD = 0.56 days, 95% CI = 0.34-0.78, p < 0.001), and higher absolute postoperative eGFR (WMD = 6.87, 95% CI = 2.65-11.10, p = 0.001). Operative time did not differ significantly in the main analysis (p = 0.356); an exploratory sensitivity analysis suggested a possible small difference (≈ 12 min) that requires confirmation. Warm ischemia time, positive surgical margin rate, and complication rate showed no significant differences between the two groups. Sensitivity analysis confirms the robustness of these findings, with overall low heterogeneity. Both transperitoneal and retroperitoneal SP RAPN are safe and feasible based on this limited evidence. Preliminary findings suggest the RP approach may offer advantages in blood loss, hospital stay, and renal preservation, but these results are hypothesis generating. Importantly, tumor location strongly influenced approach selection, and observed differences may be attributable to tumor location rather than the approach itself; causal inference is not possible from this observational data. Validation in larger, prospective, and preferably randomized studies is required.
21 July 2026
Read appraisal →Renal failure
Effects on mortality of different blood purification techniques in sepsis patients: an umbrella review of systematic reviews and meta-analyses
Sepsis remains a leading cause of mortality worldwide, and extracorporeal blood purification has been widely implemented despite ongoing uncertainty regarding its survival benefit. We conducted an umbrella review of 42 systematic reviews and meta-analyses evaluating mortality across major extracorporeal blood purification modalities. Mortality estimates generally favored extracorporeal blood purification over conventional care, but effect sizes and consistency varied substantially by modality, and the overall certainty of evidence for mortality was low. At the review level, polymyxin B hemoperfusion showed a relatively consistent direction toward lower mortality, whereas renal replacement therapy-based strategies were typically closer to the null. However, most included reviews were of low or critically low methodological quality, with downgrading driven by risk of bias, inconsistency, and potential publication bias. Therefore, these findings should be interpreted cautiously and should not be considered definitive evidence of survival benefit. They highlight persistent evidentiary fragility and underscore the need for rigorously designed, phenotype-informed trials to clarify modality-specific effects in sepsis. What is known: Extracorporeal blood purification has been widely used as an adjunctive therapy for sepsis, but its mortality benefit remains uncertain across different modalities.What this study adds: This umbrella review synthesizes evidence from 42 systematic reviews and meta-analyses and shows that mortality estimates generally favor extracorporeal blood purification, although effects vary substantially by modality and the overall certainty of evidence remains low.Potential impact: These findings support cautious, modality- and phenotype-informed interpretation of extracorporeal blood purification in sepsis and highlight the need for rigorously designed trials with standardized protocols.
19 July 2026
Read appraisal →Health care management science
Improving hard-to-place kidney allocation: A machine learning approach to center ranking
Kidney transplantation is the preferred treatment for end-stage renal disease, yet donor scarcity and inefficiencies in allocation systems create major bottlenecks, resulting in prolonged wait times and alarming mortality rates. Despite the severe shortage of donor kidneys, timely and effective interventions to prevent non-utilization of life-saving organs remain limited. Expedited out-of-sequence placement of hard-to-place kidneys to centers with a high likelihood of acceptance has been recommended in the literature as a strategy to improve placement success. However, in practice, this process remains nonstandardized and relies heavily on the subjective judgment of decision-makers. We propose a data-driven, machine learning-based ranking policy for out-of-sequence allocation of hard-to-place kidneys that prioritizes transplant centers using predicted center-level acceptance probabilities. Using national deceased-donor and kidney-offer data, we construct a unique offer-level dataset with donor- and center-specific features. We also employ machine learning interpretability tools to provide insight into the factors influencing kidney allocation decisions. Our analysis demonstrates that the proposed policy can reduce the average number of centers considered before placement by fourfold for all kidneys and tenfold for the subset of hard-to-place kidneys. These results highlight the potential of the proposed framework to improve the efficiency of expedited placement and support more timely utilization of hard-to-place kidneys.
17 July 2026
Read appraisal →Critical care explorations
Drug Clearance and Dosing During CytoSorb Hemoadsorption: A Systematic Review
OBJECTIVE: CytoSorb (CS) hemoadsorption is increasingly used in critical care and cardiac surgery to remove inflammatory mediators and select drugs. This systematic review aimed to summarize available evidence on intentional drug removal as well as unintentional drug removal during CS therapy. DATA SOURCES: PubMed, congress abstracts, and CytoSorbents online literature database. STUDY SELECTION: Eligible studies included benchtop, animal, and human pharmacokinetic investigations of drug removal during CS therapy as well as clinical reports describing CS use for drug overdose. DATA EXTRACTION: Data from the eligible studies were extracted independently by two authors into Microsoft Excel. DATA SYNTHESIS: Data were identified for drugs across multiple therapeutic classes, including analgesics, antiarrhythmics, anticonvulsants, antidepressants, antihypertensives, anti-infectives, antithrombotics, anxiolytics, and immunosuppressants. Drugs were categorized into low (< 30%), moderate (30-60%), or high (> 60%) removal rate, or by the relative increase in clearance attributable to CS: negligible (< 25%), low (25-100%), moderate (100-400%), or high (> 400%). Based on the assembled data and considering drug-, patient-, and circuit-specific factors, general dosing recommendations for concomitant medications were developed. CONCLUSIONS: CS therapy can alter drug concentrations for select agents with variable clinical relevance. CS can reduce perioperative bleeding risk through antithrombotic removal and treat certain drug overdoses. Unintentional drug removal during CS therapy may occur and practical guidance is provided for drugs with known susceptibility to removal.
16 July 2026
Read appraisal →Food chemistry
Decoding the neural mechanisms of salty peptide perception via electroencephalography and machine learning
Excessive sodium intake poses major public health risks, driving the search for salt substitutes that preserve desirable flavor. Salty peptides have emerged as promising low-sodium alternatives, yet their neural perception, distinct from the ion-dependent mechanism of sodium chloride, remains poorly understood. This study integrated electroencephalography (EEG) and machine learning to elucidate the neurophysiological mechanisms of salty peptide perception. By extracting temporal, spectral, and spatial EEG features and combining them with source localization, the study identified delta-band predominance and a hierarchical cortical activation cascade extending from the prefrontal to the insular and parietal cortices, reflecting both primary gustatory processing and higher-order integration. Among the tested classifiers, XGBoost achieved the highest performance (AUC = 0.815), demonstrating that EEG features effectively distinguish neural responses between salty and non-salty peptides. These findings provide electrophysiological evidence for salty perception and offer a neural framework for reduced-sodium food design.
16 July 2026
Read appraisal →Metabolomics : Official journal of the Metabolomic Society
A metabolomic signatures in hyperuricemia: a systematic review
BACKGROUND: Hyperuricemia (HUA) is traditionally viewed as a disorder of purine metabolism. However, its broader metabolic alterations remain incompletely understood. Metabolomics provides a useful approach for exploring metabolite changes associated with HUA, but a comprehensive synthesis of existing findings is still lacking. AIM OF REVIEW: This systematic review and meta-analysis aimed to characterize the systemic metabolic signature of HUA beyond purine pathways. By synthesizing data from 27 metabolomics studies involving 12,335 participants, the study sought to identify consistent metabolite biomarkers and key dysregulated pathways to provide new insights for diagnosis and therapeutic targeting. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review included 27 metabolomics studies involving 12,335 participants and identified 1,187 metabolites reported in association with HUA. Qualitative synthesis showed 54 consistently elevated and 20 consistently decreased blood metabolites, mainly involving amino acids, lipid-related metabolites, energy-related compounds, vitamins and their derivatives, and purine nucleoside metabolites. The meta-analysis was limited to two eligible studies, with one study contributing most of the statistical weight; it suggested higher levels of Alanine, Leucine, Phenylalanine, and Tyrosine and lower Histidine levels in HUA. Pathway enrichment analysis highlighted "One carbon pool by folate," "Arginine biosynthesis," "Glutathione metabolism," and related amino acid and energy metabolism pathways. Overall, these findings suggest that HUA may be associated with metabolic perturbations beyond purine metabolism alone, but the candidate metabolites and pathways require further validation in longitudinal, standardized, and mechanistic studies.
13 July 2026
Read appraisal →Journal of epidemiology and community health
Analysis of the potential association between physical activity and skin autofluorescence: a systematic review.
INTRODUCTION: Accumulation of advanced glycation end products, measured by skin autofluorescence (SAF), has been shown to be associated with several chronic non-communicable diseases, particularly cardiovascular diseases (CVDs). The promotion of physical activity (PA) as a strategy for the prevention of CVD by modifying healthy habits has been widely studied. AIM: To assess the evidence for the association between PA and SAF in the general adult population. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Synthesis Without Meta-analysis framework. A search was performed in MEDLINE (via PubMed), Web of Science, Scopus, Cochrane Library and SportDiscuss (via EBSCOhost), from inception to September 2024. Study quality was assessed using the National Heart, Lung and Blood Institute tools and the certainty of evidence was evaluated with Grading of Recommendations, Assessment Development and Evaluation. Vote counting based on the direction of effect was used as the standardised synthesis metric. RESULTS: In the systematic review, 17 studies were included. The qualitative synthesis showed a predominant consistency in favour of a beneficial association. Specifically, 58.8% of the studies reported a statistically significant inverse association, indicating that higher levels of PA or exercise frequency are correlated with lower SAF levels. The remaining studies (41.2%) reported non-significant results, though several showed favourable trends. No studies reported a positive association between PA and SAF. The quality of the studies was generally fair, and the certainty of evidence low. CONCLUSIONS: PA is inversely associated with SAF. Therefore, while causality cannot be proven, it is hypothesised that PA may reduce SAF and thus have a positive impact on health.
11 July 2026
Read appraisal →BMC research notes
From manual entry to machine precision: challenges and evolution of metadata schema development in collaborative research centers
OBJECTIVE: Metadata standardization in collaborative biomedical research must balance interoperability with domain-specific detail. We describe a parent-template approach in which a baseline schema from the nephrology-focused CRC 1453 NephGen was adapted for the tumor-immunology CRC OncoEscape and the perinatal-immunology CRC Pilot. RESULTS: The derivation process produced three structurally compatible yet vocabulary-divergent schemas. Pilot required the highest granularity (324 levels), followed by NephGen (287) and OncoEscape (283). Vocabulary reuse from the NephGen baseline was limited: 134 of 283 OncoEscape levels (47%) and 113 of 324 Pilot levels (35%) were retained unchanged. The main adaptations were not only expanded level lists, such as cell lines and mouse lines, but also new CRC-specific query dimensions, including "Oncogenes" in OncoEscape and "Timeline" in Pilot. In the context of AI-assisted extraction, we use the term instruction set to denote a schema that specifies target fields, expected granularity, example values, and validation resources for each metadata dimension, rather than a simple drop-down form or a free-text prompt template.
8 July 2026
Read appraisal →Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers
Chronic kidney disease (CKD) is a systemic condition that leads to progressive renal failure and metabolic imbalances that may be detected in the keratinized bio-tissues of the body such as fingernails. Nevertheless, still its early detection is difficult due to the invasive nature of current clinical screening approaches. This research paper provides a precise, non-invasive, and AI-enhanced diagnostic model of CKD screening through the combination of Laser-Induced Breakdown Spectroscopy (LIBS) and the cutting-edge ensemble machine learning to determine the elemental patterns of fingernail biomarkers. In this study, 55 participants with chronic kidney disease and 45 healthy controls were taken as the subjects and their emission spectra were measured with a nanosecond LIBS spectrometer using a laser of a fundamental wavelength @ 1064 nm. To handle the high-dimensional spectral data, Principal Component Analysis (PCA) was used as a feature extraction approach, forming the basis for novel machine-learning models. A sophisticated ensemble learning algorithm specifically Extreme Gradient Boosting (XGBoost) was applied to examine the biochemical change between CKD and control samples and the predictive quality was tested by a relative comparison with a Support Vector Machine (SVM) algorithm. Using 10-fold cross-validation, the XGBoost model outperformed SVM, attaining 97% accuracy, 98% sensitivity, 96% precision and specificity, and a 97% F1-Score. Moreover, external validation on a separate dataset showed that the model is robust and generalizable with 95% accuracy, 97% sensitivity, and an F1-score of 95%. The results conclude that fingernail-based LIBS integrated with ensemble machine learning is a potential and non-invasive instrument for CKD classification.
6 July 2026
Read appraisal →Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
Clinical applications of artificial intelligence in autosomal dominant polycystic kidney disease
Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic kidney disorder leading to kidney failure. Recent advancements in artificial intelligence (AI) are transforming the diagnosis, risk stratification, management and prognostication in ADPKD by enabling more accurate assessments and individualized care. AI-powered imaging tools enhance the measurement of total kidney volume (TKV), improving the precision and efficiency of monitoring disease progression and more reliable assessment of therapeutic response. Machine learning algorithms can integrate genetic, imaging and clinical data to predict kidney function decline, facilitating personalized treatment strategies. In addition, AI is being used to identify genetic variants and to refine genotype-phenotype relationships, offering deeper insights into disease variability. AI-enabled monitoring technologies can support longitudinal tracking of TKV measurements obtained through magnetic resonance imaging or computed tomography, thereby improving clinical decision-making and management. Furthermore, AI can optimize clinical trial design by improving patient selection, prediction of treatment responses and safety monitoring. Despite these promising developments, integrating AI into the clinical practice remains controversial and may pose several challenges. This review highlights the emerging clinical applications of AI in ADPKD, emphasizing its potential to advance precision medicine and improve patient outcomes.
1 July 2026
Read appraisal →Science advances
Smart contact lens-trained digital twin for device-free personalized uric acid prediction
Tears contain valuable biomarkers and offer potential for noninvasive disease monitoring. However, the lack of correlation analysis between serum uric acid (SUA) and tear uric acid (TUA) has limited their clinical application in personalized medicine. Here, we present a wireless smart contact lens capable of real-time, noninvasive monitoring of TUA as an alternative to blood-based UA testing. We validated the device through correlation analysis in rabbits and human participants, including individuals with hyperuricemia and gout. Daily-life monitoring enabled personalized characterization of TUA fluctuations in response to food intake and physical activity, together with individualized lag time profiling. A strong linear relationship allowed development of a regression model to derive estimated SUA from TUA. Building on these temporal profiles, lifestyle-informed digital twins were constructed to predict daily uric acid dynamics without continuous lens wear. This digital twin-enabled, device-free framework provides a practical route toward unobtrusive and personalized metabolic health monitoring.
27 June 2026
Read appraisal →BMJ open
Caregiver experiences of social isolation and loneliness in chronic kidney disease: systematic review of qualitative studies.
OBJECTIVES: This qualitative systematic review aimed to describe the experiences and perspectives of loneliness and social isolation among informal caregivers of people with chronic kidney disease (CKD). METHODS: Terms for caregivers, qualitative research, loneliness and social isolation and CKD were entered into MEDLINE, Embase, CINAHL and PsycINFO and searched from inception to May 2025. Qualitative studies that described social isolation and loneliness among caregivers of people with CKD were included in this review. Study characteristics were extracted into Microsoft Excel. Qualitative data from each study were imported into HyperRESEARCH and analysed using thematic synthesis. RESULTS: We included 19 articles involving 598 caregivers of people with CKD from 28 countries. Four major themes with subthemes were identified: confined by the patient's needs (social withdrawal due to unrelenting demands, separated from communities due to treatment, torn between work and caregiving responsibilities, foregoing social outings with family and friends, restricted by patient's diet, feeling protective against infection risk); limited care assistance exacerbating isolation (inadequate familial support, absence of respite care, lacking accessible guidance from health professionals); disrupting relationships and social roles (sacrificing social needs and identity, family conflicts worsening isolation, withdrawing to avoid stigma and ridicule, grappling with hopelessness, reluctance to share struggles); and coping with support resources (connecting with other families, seeking assistance from support services, finding support through faith). CONCLUSIONS: Caregivers of people with CKD experience restricted social participation and loss of social roles and identity, which can exacerbate feelings of loneliness and social isolation. Support services are needed to prevent and address social isolation and loneliness in CKD caregivers. PROSPERO REGISTRATION NUMBER: CRD420250637194.
26 June 2026
Read appraisal →Rheumatology international
Mortality and survival in patients with systemic lupus erythematosus in the Middle East and North Africa: a systematic review and meta-analysis
Systemic lupus erythematosus (SLE) is associated with increased mortality compared with the general population, with outcomes influenced by ethnicity and organ involvement. Data from the Middle East and North Africa (MENA) remain limited, particularly regarding long-term survival and mortality predictors. To estimate and compare all-cause mortality and survival rates among adult patients with SLE in the MENA region and identify predictors of mortality. A systematic review and meta-analysis of cohort studies reporting survival and mortality in patients with SLE in the MENA region was conducted. Eligible studies were identified through a comprehensive database search and screened according to PRISMA guidelines. Data on treatment exposures and clinical predictors of mortality were extracted. Twelve cohort studies, including 3,478 adults with SLE from five MENA countries (Türkiye, Iran, Egypt, Israel, and Tunisia), were analyzed. The pooled all-cause mortality rate was 9.9% (95% CI: 7.2%-14.0%; I² = 89.7%). Survival remained high at 5 years (93.9%) and 10 years (90.0%), but declined at 15 years (85.5%) and 20 years (71.7%), with long-term estimates highly sensitive to single-study exclusion. Leave-one-out analysis slightly increased survival estimates and reduced heterogeneity. Major mortality predictors included renal involvement, cardiovascular disease, infections, hypertension, diabetes, hematologic abnormalities, and male sex. Higher cumulative corticosteroid and cyclophosphamide exposure was linked to increased mortality, whereas hydroxychloroquine and azathioprine appeared protective. Infection was the leading cause of death, followed by active disease, renal failure, and cardiovascular complications. Patients with SLE in the MENA region show high short-term survival, but long-term outcomes remain variable. Mortality is mainly driven by renal, cardiovascular, and infectious complications, with treatment exposure contributing to risk. These findings highlight the need for multicenter registries, standardized reporting, and strategies to reduce infection burden and optimize nephritis care.
23 June 2026
Read appraisal →Urolithiasis
A validated custom pipeline for three-dimensional kidney stone renderings to create an open access repository
Three-dimensional (3D) rendering of urologic pathology plays an important role in simulation-based education, surgical training, and computer vision research; however, a standardized, open-access repository of high-fidelity kidney stone models stratified by chemical composition is lacking. We developed and validated a reproducible photogrammetry-based pipeline to generate realistic 3D kidney stone renderings. Chemically characterized human stones composed of calcium oxalate monohydrate (COM) (n = 11), uric acid (UA) (n = 5), cystine (n = 4), magnesium ammonium phosphate hexahydrate/carbonate apatite (MAPH/CA) (n = 2), and calcium hydrogen phosphate dihydrate (CHPD) (n = 3) were photographed using a custom-built rotating stage and dual fixed 4 K cameras. Rendered models were sent to 25 endourologists using a 5-point Likert-scale survey assessing geometric and surface texture fidelity. Successful 3D renderings were obtained for 8/11 COM stones, 5/5 UA stones, 2/2 MAPH/CA fragments, and 3/3 CHPD fragments, while all cystine stones failed to render. Across stone types, mean fidelity scores were highest for UA and COM stones (mean 3.8-3.9), intermediate for calcium phosphate stones (mean 3.6-3.8), and lowest for struvite stones (mean 3.0-3.3). Geometry scores were higher than texture scores overall, though this difference was not significant. Significant differences in geometric fidelity were observed across stone compositions (χ² = 9.30, p = 0.026). Inter-rater reliability was poor for individual evaluators (ICC = 0.10) but moderate for aggregated mean ratings (ICC = 0.67). This validated workflow enables the creation of generally realistic, open-access 3D kidney stone models (github.com/uro-glidar/3d-rendering-diverse-stones) for simulation, education, and future machine learning applications in endourology.
21 June 2026
Read appraisal →Lancet (London, England)
Atrasentan in patients with IgA nephropathy (ALIGN): final 2·5-year results from a randomised, double-blind, placebo-controlled, phase 3 trial
BACKGROUND: Atrasentan, a selective endothelin A receptor antagonist, reduced proteinuria in the prespecified interim analysis (week 36) of the ALIGN trial in adults with IgA nephropathy. We assessed whether atrasentan slowed estimated glomerular filtration rate (eGFR) decline after 2·5 years' treatment. METHODS: We performed a randomised, double-blind, placebo-controlled, phase 3 trial (133 sites, 20 countries). Adults with biopsy-proven IgA nephropathy, eGFR of at least 30 mL/min per 1·73 m2, and urinary protein excretion of at least 1·0 g/day while receiving renin-angiotensin system inhibition were enrolled (main stratum). An exploratory stratum enrolled participants also receiving an SGLT2 inhibitor. Participants were randomly assigned (1:1) to oral atrasentan 0·75 mg once daily or placebo for 132 weeks, followed by a 4-week off-treatment follow-up. The key secondary endpoint was change from baseline to week 136 in eGFR in the main stratum, assessed in all randomised patients (excluding data after intercurrent events, defined as initiation of restricted or alternative IgA nephropathy medications or kidney replacement therapy). Safety was assessed in all randomised patients who received at least one dose of study treatment. ALIGN is registered with ClinicalTrials.gov, NCT04573478; the double-blind section is complete, and the open-label extension is ongoing. FINDINGS: Between March 15, 2021, and April 28, 2023, 404 participants were randomly assigned (main stratum: 340; SGLT2 inhibitor stratum: 64). In the main stratum, change from baseline in eGFR at week 136 was -7·5 mL/min per 1·73 m2 (95% CI -9·2 to -5·8) with atrasentan and -9·9 mL/min per 1·73 m2 (-11·7 to -8·1) with placebo (between-group difference 2·4 mL/min per 1·73 m2; 95% CI -0·1 to 4·8; p=0·057). The between-group difference in eGFR change from baseline at end of treatment (week 132) was 2·6 mL/min per 1·73 m2 (95% CI 0·1-5·0), and the difference in total eGFR slope (baseline to week 136) was 1·4 mL/min per 1·73 m2 per year (95% CI 0·5-2·3). In the SGLT2 inhibitor stratum, the difference in eGFR change from baseline at week 136 between atrasentan and placebo was 9·1 mL/min per 1·73 m2 (95% CI 3·0-15·2). Treatment-emergent adverse events were well balanced between atrasentan and placebo, with no new safety signals. In the main stratum, fluid retention adverse events occurred in 24 (14%) of 169 patients receiving atrasentan and 20 (12%) of 170 receiving placebo. INTERPRETATION: Atrasentan reduced proteinuria and preserved kidney function after 2·5 years. This effect was present with or without concomitant SGLT2 inhibitor use. Atrasentan was well tolerated. FUNDING: Novartis.
14 June 2026
Read appraisal →Critical care explorations
Artificial Liver Support Effectively Removes Immunosuppressive Bile Acids From Circulation in Patients With Severe Liver Failure: A Proof of Concept Study
OBJECTIVE: Bile acids that induce immunosuppression through activation of the Takeda G protein-coupled receptor 5 (TGR5) have recently been identified in the circulation of patients with liver failure and sepsis. These bile acids are associated with increased susceptibility to infection and a high short-term mortality. Furthermore, TGR5 activation by bile acids has been implicated in the development of pruritus. However, therapies targeting TGR5-activating bile acids in patients have not yet been reported. This study investigates the effects of two artificial liver support systems, the Molecular Adsorbent Recirculating System (MARS) and the open albumin dialysis (OPAL). DESIGN: Proof of concept study, multicenter study. SETTING: University Hospital Jena, University Hospital Ulm, and University Hospital Münster, Germany. PATIENTS: Patients with severe liver failure, as defined by the Sequential Organ Failure Assessment hepatic sub-score (i.e., bilirubin > 12 mg/dL). INTERVENTIONS: Patients with severe liver failure were randomized to receive one session of MARS or OPAL for 8 hours, followed by crossover to the other treatment on the next day. MEASUREMENTS AND MAIN RESULTS: Bilirubin, circulating total and individual bile acids, albumin function, and TGR5 activation induced by circulating bile acids were assessed before and after each treatment session. Both OPAL and MARS significantly removed bilirubin, as well as individual and total bile acids from circulation. OPAL was superior to MARS in removing protein-bound lipophilic bile acids and restoring patients' albumin function (i.e., albumin-binding capacity, detoxification efficiency, and binding efficiency). TGR5 activation induced by circulating bile acids was reduced by 60% with OPAL compared with 39% with MARS (median values, p = 0.051). CONCLUSIONS: Artificial liver support systems effectively remove immunosuppressive bile acids from circulation. The clinical implications of bile acid reduction, however, require further study.
11 June 2026
Read appraisal →Neurology
Acute Kidney Injury and Risk of Adverse Neurocognitive Outcomes: A Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Chronic kidney disease is a recognized risk factor for adverse neurocognitive outcomes, but the effect of acute kidney injury (AKI) on brain health remains less well defined. We conducted a systematic review and meta-analysis to evaluate associations between AKI and subsequent risk of stroke, delirium, and dementia. METHODS: Eligible studies were identified by searching Ovid MEDLINE and Embase from inception (Ovid: January 1946; Embase: January 1970) until April 2025. Studies were included if they reported quantitative estimates with measures of precision for the association between AKI and delirium, stroke, or dementia in adult populations. Two reviewers independently screened and extracted data, and study quality was assessed using standardized criteria. Study characteristics, participant demographics, and adjusted effect estimates (hazard ratios [HRs] or odds ratios [ORs]) with 95% CIs were extracted. Pooled HRs and ORs with 95% CIs were calculated using random-effects models. Heterogeneity was evaluated with the χ2 test and I2 statistic, and sources of heterogeneity were explored through prespecified subgroup analyses and meta-regression. RESULTS: We identified 49 studies comprising 11,253,825 participants with 1,279,145 events. Individuals with AKI were at increased risk of stroke (pooled adjusted HR 1.35, 95% CI 1.20-1.52), delirium (pooled adjusted OR 1.76; 1.42-2.17), and dementia (pooled adjusted HR 1.64, 1.41-1.89). A gradient of risk across increasing AKI stages was demonstrated for stroke (stage 1: HR 1.11; 1.00-1.23; combined stages 2 and 3: HR 1.57; 1.35-1.81). AKI was also associated with higher in-hospital and 90-day mortality poststroke (pooled HR 2.13, 1.56-2.90, and 4.81, 2.55-9.08, respectively) and with 90-day disability (pooled adjusted OR 1.47, 1.22-1.76). Associations between AKI and all outcomes were directionally consistent across sensitivity analyses and pooled propensity score-matched studies. DISCUSSION: In this systematic review and meta-analysis, AKI was consistently associated with increased short-term and long-term neurocognitive risk, including stroke, delirium, and dementia. These findings suggest that AKI may identify individuals vulnerable to both acute and chronic brain injury. Further studies are needed to clarify mechanisms linking AKI to brain injury and to identify strategies to mitigate neurocognitive risk in this high-risk population.
9 June 2026
Read appraisal →Journal of proteome research
AI-Based Digital Pathology-Enabled Spatial-Omics Data Analyses of the Human Kidney
Identification of tissue-region-specific changes in glycosylation is crucial for understanding the pathogenesis of kidney diseases, yet it remains a great challenge. We developed a workflow that combines matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) data with AI-based digital pathology annotations of kidney functional tissue units (FTUs) to profile N-glycan distributions within biopsy tissues. This approach can generate molecular-level data relevant to diverse pathological outcomes, thereby aiding in the elucidation of disease mechanisms. As a proof-of-concept, we demonstrate that this AI-based digital pathology approach to MALDI data segmentation enables the detection and differentiation of N-glycosylation within FTUs of healthy kidney tissue. We then elucidated differences in N-glycosylation between the diseased kidney tissue samples from patients with different diagnoses. Sialic acid N-glycans, which have been linked to various kidney diseases, displayed enrichment in the glomeruli and tubules of tissues from patients diagnosed with diabetic kidney disease (DKD), whereas they were enriched in the tubules and arteries from patients with acute kidney injury (AKI), in comparison to healthy tissue. Furthermore, we found that polylactosamine N-glycans were enriched only in the AKI samples, indicating their potential roles in tubular injury and inflammation. This workflow has the potential to bridge the gap between region-specific glycosylation and its implications on FTUs in diseases, paving the way for targeted molecular imaging studies in the kidney and other tissues.
7 June 2026
Read appraisal →BMJ health & care informatics
Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury
OBJECTIVES: Monitoring kidney function after acute kidney injury (AKI) hospitalisation is essential for identifying patients at risk of rapid progression. This study developed a machine learning (ML) model to predict membership in a high-risk longitudinal estimated glomerular filtration rate (eGFR) trajectory associated with subsequent kidney replacement therapy (KRT) initiation. METHODS: We conducted a cohort study of 88 632 adults with ≥2 post-discharge eGFR measurements between 2010 and 2017. Joint latent class mixed models were used to identify dynamic eGFR trajectories from 665 499 measurements. Patient-level data splitting (80/20) was strictly enforced. Extreme gradient boosting (XGBoost) and random forest models were trained using clinical features within 3 months after discharge to predict membership in the highest-risk eGFR trajectory class. RESULTS: Three eGFR trajectories were identified: Steady Low (high-risk phenotype), Slow Decline and Recovery. The Steady Low group showed the strongest association with KRT. The 10-variable XGBoost model demonstrated excellent discrimination (AUROC 0.974, sensitivity 0.913, specificity 0.919). The eGFR slope from discharge to 3 months was the most influential predictor of high-risk progression. DISCUSSION: Although the study was limited to a single healthcare system, this ML framework identified patients at risk of rapid kidney function decline using routinely available clinical data. The model supports early risk stratification after AKI and may enable more timely kidney-protective interventions. External validation, calibration assessment and benchmarking against simpler eGFR-based rules remain important next steps. CONCLUSION: A 10-variable XGBoost model accurately identifies patients with deteriorative eGFR trajectories (Steady Low) early after discharge, providing a practical framework for personalised post-AKI kidney care.
29 May 2026
Read appraisal →Renal failure
Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients
Objective: This study aimed to develop and validate a time-updated, explainable machine learning (ML) early-warning system for short-term mortality risk in patients with continuous ambulatory peritoneal dialysis (CAPD). Methods: Multiple supervised ML techniques were validated to stratify individuals at high risk of 6-month death, classifiers were trained, internally validated and independently temporally validated in this retrospective study of 1,484 CAPD patients in our PD center. We collected time-updated markers such as patient demographics, clinical characteristics, and laboratory data to inform the ML models and the performance of eight ML models was assessed via the area under the curve (AUC) and accuracy. The SHapley Additive exPlanation (SHAP) method was selected to further interpret the predictive models and link findings to clinical actionability. Results: In internal validation cohort, the light gradient boosting machine (lightGBM) model demonstrated best performance with AUC of 0.888 and highest accuracy of 0.879. In temporal validation cohort, the lightGBM model also exhibited good classification performance, achieving AUC of 0.850 and good accuracy of 0.874. Moreover, SHAP analysis identified several key features that contributed to the accurate prediction of the lightGBM model. Conclusions: The time-updated lightGBM early-warning system helps clinicians to early identify CAPD patients with high risk of death within six months based on easily accessible and time-updated data and provides a basis for personalized treatment. We developed and validated a time-updated, explainable ML early-warning system that accurately predicts 6-month all-cause mortality in CAPD patients using routinely, available laboratory data.The LightGBM model achieved excellent discriminative performance, with an AUC of 0.888 in internal validation and 0.850 in independent temporal validation.This model provides a scalable, low-cost early-warning tool for routine CAPD care, with a standardized risk-stratified intervention framework to guide timely, targeted care and advance care planning.
29 May 2026
Read appraisal →BMC nephrology
Roxadustat for CKD-related anemia in patients undergoing peritoneal dialysis: a systematic review and meta-analysis
BACKGROUND: CKD-related anemia remains a major complication in patients receiving peritoneal dialysis (PD), with limited treatment options beyond erythropoiesis-stimulating agents. Roxadustat, an oral hypoxia-inducible factor prolyl hydroxylase inhibitor, has shown promise in correcting CKD-related anemia and modulating iron and metabolic parameters. However, its evidence in PD remains limited. METHODS: We conducted a systematic search of PubMed, Scopus, and Web of Science from inception to July 20, 2025. We included studies reporting the efficacy and safety of roxadustat in PD. Statistical analysis was performed using Review Manager (RevMan 5.4 for Windows) and R Studio. RESULTS: Eight studies were included in the meta-analysis (n = 607). Roxadustat significantly increased hemoglobin at all time points (MD 0.35 g/dL, 95% CI 0.28-0.41; p < 0.00001), serum iron (MD 0.95 µmol/L, 95% CI 0.02-1.89; p = 0.05), and total iron-binding capacity (MD 6.25 µmol/L, 95% CI 3.95-8.55; p < 0.00001), and reduced hepcidin (MD - 12.28 ng/mL, 95% CI - 21.06 to - 3.50; p = 0.006). No significant effects were observed for ferritin (p = 0.49), transferrin saturation (p = 0.45), cholesterol (p = 0.07), LDL (p = 0.14), HDL (p = 0.27), triglycerides (p = 0.26), CRP (p = 0.75), systolic blood pressure (p = 0.10), or diastolic blood pressure (p = 0.08). Heterogeneity was low to moderate for most outcomes. CONCLUSION: Roxadustat is effective in improving hemoglobin and iron metabolism in patients with PD, while exerting neutral effects on lipids, inflammation, and blood pressure. These findings support its role as a promising therapy for CKD-related anemia in PD. CLINICAL TRIAL NUMBER: Not applicable.
28 May 2026
Read appraisal →The Cochrane database of systematic reviews
Steroid avoidance or withdrawal for kidney transplant recipients.
RATIONALE: Steroid-sparing strategies aim to reduce the adverse effects associated with steroid use. The main concern has been a potential increase in the risk of rejection, as reported in previous systematic reviews. In recent years, improvements in immunosuppressive regimens and a significant decline in the incidence of acute rejection have renewed interest in steroid-sparing strategies. More contemporary trials suggest that reducing steroid exposure might be safe. This is an updated review, previously published in 2009 and 2016. OBJECTIVES: To evaluate the benefits and harms of steroid withdrawal or avoidance for kidney transplant recipients. SEARCH METHODS: We searched the Cochrane Kidney and Transplant Specialised Register, CENTRAL, MEDLINE, Embase and two trials registers up to 29 April 2025. ELIGIBILITY CRITERIA: All randomised and quasi-randomised controlled trials that evaluated steroid avoidance or withdrawal after kidney transplantation. OUTCOMES: Critical outcomes were all-cause death, graft loss and biopsy-proven acute rejection. Important outcomes were post-transplant diabetes mellitus, cardiovascular events, infection, malignancy, and kidney function measures. RISK OF BIAS: Risk of bias was assessed with the Cochrane RoB 1 tool. SYNTHESIS METHODS: Assessment of risk of bias and data extraction were performed independently by at least two authors, and disagreements were resolved through discussion. Statistical analyses were performed using the random-effects model, and dichotomous outcomes were reported as risk ratios (RRs) and continuous outcomes as mean differences (MDs) with 95% confidence intervals (CIs). The certainty of the evidence was assessed using the GRADE approach. INCLUDED STUDIES: This updated review includes 53 studies (261 reports, 8317 randomised kidney transplant recipients). Of these, three studies were conducted in children (204 randomised participants). SYNTHESIS OF RESULTS: The overall certainty of the evidence of the included studies was low, ranging from moderate to very low. Factors downgraded on GRADE were primarily risk of bias and imprecision. Over 50% of the included studies did not adequately report random sequence generation or allocation concealment. Only five of 53 studies blinded participants and investigators. Incomplete outcome data was judged to be low in 26 studies, and 41 were free of selective reporting. Studies in adult kidney transplant recipients Steroid avoidance versus steroid maintenance The evidence is very uncertain about the effect of steroid avoidance on all-cause death up to one year (RR 0.84, 95% CI 0.51 to 1.39; 13 studies, 2126 participants; very-low-certainty evidence). Steroid avoidance may reduce death by one to five years (RR 0.61, 95% CI 0.39 to 0.96; 9 studies, 1607 participants; low-certainty evidence), and has little to no effect on graft loss censored for death up to one year (RR 0.95, 95% CI 0.62 to 1.46; moderate-certainty evidence). Steroid avoidance may increase the risk of biopsy-proven acute rejection up to one year (RR 1.58, 95% CI 1.11 to 2.25; 10 studies, 1845 participants; low-certainty evidence), although subgroup analysis showed no increase in risk when steroid avoidance was used in combination with tacrolimus, an antimetabolite and induction therapy. Steroid avoidance may reduce the risk of post-transplant diabetes mellitus up to five years (RR 0.70, 95% CI 0.59 to 0.84; 13 studies, 2390 participants; low-certainty evidence), but may have little to no effect on cardiovascular events (RR 0.73, 95% CI 0.46 to 1.18; 6 studies, 1417 participants; low-certainty evidence), infection (all) (RR 0.96, 95% CI 0.87 to 1.05; 11 studies, 2426 participants; low-certainty evidence), and probably has little to no effect on cytomegalovirus (CMV) infection (RR 1.01, 95% CI 0.80 to 1.27; 9 studies, 2155 participants; moderate-certainty evidence). Steroid withdrawal versus steroid maintenance Compared to steroid maintenance, steroid withdrawal may have little to no effect on all-cause death up to one year (RR 0.69, 95% CI 0.35 to 1.37; 9 studies, 1884 participants; low-certainty evidence) or one to five years (RR 1.17, 95% CI 0.67 to 2.03; 8 studies, 1189 participants; low-certainty evidence), and probably has little to no effect on graft loss censored for death up to one year (RR 1.19, 95% CI 0.72 to 1.96; 8 studies, 1854 participants; moderate-certainty evidence). Steroid withdrawal may have little to no effect on biopsy-proven acute rejection up to one year (RR 1.41, 95% CI 0.89 to 2.23; 5 studies, 1406 participants; low-certainty evidence). Steroid withdrawal probably has little to no effect on post-transplant diabetes mellitus up to five years (RR 0.82, 95% CI 0.55 to 1.21; 8 studies, 1647 participants; moderate-certainty evidence), and may have little to no effect on cardiovascular events up to five years (RR 0.91, 95% CI 0.42 to 1.97; 3 studies, 712 participants; low-certainty evidence) or infection (all) up to five years (RR 1.06, 95% CI 0.83 to 1.36; 6 studies, 1924 participants; low-certainty evidence). Steroid withdrawal probably has little to no effect on CMV infection up to five years (RR 1.10, 95% CI 0.85 to 1.43; 7 studies, 1956 participants; moderate-certainty evidence). Steroid avoidance versus steroid withdrawal The evidence is very uncertain regarding the effects of steroid avoidance compared with steroid withdrawal (4 studies, 466 randomised participants). Studies in paediatric kidney transplant recipients The evidence is very uncertain regarding the effects of steroid-sparing regimens in paediatric kidney transplant recipients (3 studies, 204 randomised participants). AUTHORS' CONCLUSIONS: There was no evidence to suggest a difference in patient death or graft loss up to one year after transplantation with steroid avoidance or withdrawal compared to maintenance, but steroid avoidance may reduce patient death at five years in adult kidney transplant recipients. Steroid avoidance may increase the risk of biopsy-proven acute rejection, but the risk may be similar when steroid avoidance is paired with tacrolimus, antimetabolite and induction therapy. The risk of biopsy-proven acute rejection in steroid withdrawal regimens may be similar to steroid maintenance regimens. We found no evidence to suggest differences in cardiovascular events or infection, but steroid avoidance compared to steroid maintenance may reduce the risk of post-transplant diabetes mellitus. FUNDING: LO is supported by a Río Hortega predoctoral fellowship from the Instituto de Salud Carlos III. REGISTRATION: Protocol and previous versions available via https://doi.org/10.1002/14651858.CD005632, https://doi.org/10.1002/14651858.CD005632.pub2, and https://doi.org/10.1002/14651858.CD005632.pub3.
27 May 2026
Read appraisal →Renal failure
Development and validation of an early prediction model for bee-sting-induced acute kidney injury using machine learning
Acute kidney injury (AKI) is a severe and frequent complication following bee stings, with a reported incidence of 30-50%. Early identification is clinically challenging due to the delayed rise in serum creatinine. This retrospective study aimed to develop and validate an interpretable machine learning model for the early prediction of AKI in bee-sting patients. We included 305 patients admitted between January 2018 and September 2025, of whom 99 (32.5%) developed AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection from 33 baseline variables, identifying eight key predictors, including lactate, creatinine, and dark urine. Six machine learning algorithms - logistic regression, random forest, support vector machine, neural network, extreme gradient boosting (XGBoost), and light gradient boosting machine - were developed and evaluated. The XGBoost model demonstrated superior performance, achieving an area under the receiver operating characteristic curve of 0.964 and an accuracy of 92.6% on the test set. SHapley Additive exPlanations analysis was applied to interpret the optimal model, highlighting creatinine, lactate dehydrogenase, lactate, and dark urine as the most influential predictors. A clinically applicable nomogram was constructed and showed good calibration and utility. In conclusion, this study successfully developed a high-performance and interpretable XGBoost model for early prediction of bee-sting-induced AKI. The validated nomogram provides an accessible bedside decision-support tool to facilitate timely risk stratification and intervention, potentially improving patient outcomes.
26 May 2026
Read appraisal →Renal failure
Complication rates of 16- and 18-gauge needles for native kidney biopsies: a systematic review and proportional meta-analysis
This systematic review and meta-analysis evaluated complication rates and diagnostic yield reported in studies of adult native kidney biopsy using 16-gauge (16 G) or 18-gauge (18 G) needles. We included randomized trials and cohort studies of real-time ultrasound-guided biopsies, including case series. MEDLINE, Embase, and CENTRAL were searched through October 2024. Two reviewers independently performed study selection, data extraction, and risk of bias assessment using Joanna Briggs Institute tools. Random-effects meta-analyses estimated pooled proportions with 95% confidence intervals (CI), and univariable random-effects meta-regression explored study-level associations with major complications, transfusion, or gross hematuria. We screened 4,499 titles and abstracts and reviewed 319 full-text articles; 62 studies comprising 68 biopsy series were included. The pooled major complication rate in studies using 16 G needles was 1.83% (95% CI: 1.20-2.79) and 1.29% (95% CI: 0.78-2.13) in studies using 18 G needles, with no statistically significant difference. Mean glomerular yield was 18.8 with 16 G and 17.5 with 18 G needles. In study-level meta-regression, studies with higher prevalence of acute kidney injury, lower mean estimated glomerular filtration rate, or lower mean hemoglobin reported higher pooled complication rates. Most studies were single-arm cohorts; between-needle differences therefore reflect indirect study-level contrasts. Interpretation is limited by retrospective design and heterogeneity across studies. Overall, studies using both needle sizes reported low complication rates and similar diagnostic yield, although definitions and reporting varied. Direct comparative studies are needed to determine whether meaningful differences exist. What was knownKidney biopsy is essential for diagnosing many medical kidney diseases and guiding treatment, requiring adequate tissue samples for subsequent analyses.Modern biopsy techniques use spring-loaded, ultrasound-guided devices with smaller gauge needles, but optimal needle size remains uncertain.While 14G needles are associated with higher complication risk, evidence on whether 16G or 18G needles differ in safety or diagnostic yield is conflicting.This study addsIn this systematic review and meta-analysis of 62 studies involving 22,208 adult native kidney biopsies, we found no statistically significant difference in major complication rates between studies using 16G and 18G needles.In study-level meta-regression analyses, studies with a higher prevalence of acute kidney injury, lower mean estimated glomerular filtration rate, and lower mean hemoglobin reported higher pooled major complication rates.Potential impactStudies using both 16G and 18G needles reported similarly low major complication rates, although comparisons are based on indirect evidence with substantial heterogeneity.Findings support the ethical feasibility and clinical relevance of a large, randomized controlled trial to definitively guide needle size selection in native kidney biopsies.
26 May 2026
Read appraisal →Renal failure
Prognostic factors in malaria patients with acute kidney injury: a systematic review and meta analysis
This systematic review and meta-analysis aims to evaluate the predictors of mortality in malaria patients with AKI. Studies were searched in PubMed, Scopus, Ebsco, and ScienceDirect. We included observational studies that showed the association between clinical or laboratory abnormalities and mortality, reported in odds ratio. The quality of each study was assessed using the Newcastle-Ottawa Scale. Meta-analysis was performed using R. Sixteen studies with 1,104 patients were analyzed. Factors significantly associated with mortality were respiratory distress syndrome (OR: 18.50, p < 0.001), neurological involvement (OR: 12.83, p < 0.001), disseminated intravascular coagulation (OR: 12.00, p < 0.001), hypotension (OR: 18.23, p < 0.001), oliguria (OR: 3.41, p = 0.002), ventilator requirement (OR: 30.35, p < 0.001), leukocytosis (OR: 4.87, p = 0.003), hyponatremia (OR: 3.67, p = 0.048), acidosis (OR: 4.88, p < 0.001), hyperkalemia (OR: 3.99, p < 0.001), and jaundice (OR: 5.03, p = 0.001). ARDS, neurological involvement, DIC, hypotension, oliguria, ventilator requirement, leukocytosis, hyponatremia, acidosis, hyperkalemia, and jaundice were associated with mortality in malaria patients with AKI. The findings underscored the importance of heightened awareness and vigilance in managing patients with these conditions.
26 May 2026
Read appraisal →Critical care explorations
External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data
BACKGROUND: Acute kidney injury (AKI) is common in critically ill children and is associated with high morbidity and mortality. Risk prediction models designed for clinical decision support implementation can facilitate early identification and proactive mitigation of AKI risk. Existing models have primarily been validated using single-center data, partly because of the lack of appropriately detailed multicenter datasets. OBJECTIVE: To determine the performance of a single-center model to predict new AKI at 72 hours of ICU admission in children across two multicenter datasets and refine this model to improve prediction performance while maintaining acceptable alert burden. DERIVATION AND VALIDATION COHORTS: We analyzed two datasets: the Pediatric Learning Health System Network-Virtual Pediatric Systems (PEDSNET-VPS) dataset, created through the linkage of PEDSnet electronic health record (EHR) extraction with VPS (LLC, http://www.myvps.org), and the PICU Data Collaborative dataset, created through EHR extraction and harmonization from eight participating institutions. We divided each dataset into a derivation and test split. PREDICTION MODEL: We first recalibrated an existing single-center model and measured discrimination (area under the receiver operating characteristic curve [AUROC] and area under the precision-recall curve [AUPRC]) and performance at multiple cutpoints. We next added features available at 12 hours of ICU admission, optimizing by precision and recall. We measured discrimination and performance at multiple cutpoints and identified the features contributing most to the risk score. RESULTS: In total we analyzed 186,540 ICU admissions. We found early AKI by serum creatinine criteria within 72 hours of admission in 2.2-2.7%. Initial recalibration of an existing single-center model demonstrated poor discrimination (AUROC 0.65-0.78; AUPRC 0.10-0.12). Following the addition of new features, the model had higher AUROC (0.80-0.88) and AUPRC (0.13-0.22). CONCLUSIONS: In this first use of two new multicenter datasets, we found improved performance in a model designed using features available at 12 hours of ICU admission, balancing sensitivity and precision to predict patients at risk for AKI development.
26 May 2026
Read appraisal →Renal failure
Unveiling the role of air pollution in diabetic kidney disease: an integrated study combining network toxicology, machine learning, and Mendelian randomization
Diabetic kidney disease (DKD) is a major and severe complication associated with diabetes. Air pollution is not only an independent risk factor for metabolic disorders but also an "accelerator" of DKD progression. This study seeks to investigate the molecular pathways connecting air pollution to DKD. Multiple databases were integrated to obtain potential target genes of 10 common air pollutants. The gene expression omnibus (GEO) database was employed to acquire DKD datasets. Differential expression analysis and weighted correlation network analysis (WGCNA) were performed to identify DKD-related genes. 12 machine learning algorithms were utilized to generate 113 unique predictive models, which were employed to select hub genes. Subsequently, MR, single-cell, gene set enrichment analysis (GSEA), and immune infiltration analyses were performed, followed by molecular docking of hub genes with air pollutants. A total of 714 targets were identified from 10 air pollutants, and 80 potential targets were identified from DKD transcriptomic data. Machine learning methods identified 5 hub genes that are closely associated with DKD. Mendelian randomization (MR) analysis indicated that, among the five hub genes, only NOS3 demonstrated a statistically significant causal association with DKD. Immune infiltration analysis found that hub genes were closely related to immune cells. Molecular docking validation indicated that certain air pollutants can stably bind with hub genes such as NOS3 and PTGS2. Air pollutants may be linked to alterations in various biological processes, potentially involving key genes such as ADH5, CASP3, NOS3, PTGS2, and SDHB, including potential metabolic reprogramming, inflammatory processes, and immune microenvironment changes.
26 May 2026
Read appraisal →World journal of urology
Retrograde intrarenal surgery versus miniaturized percutaneous nephrolithotomy for renal calculi: a grade-assessed meta-analysis of randomized controlled trials with trial sequential analysis
BACKGROUND: The optimal management of medium-sized (1-3 cm) renal stones remains a clinical dilemma, with retrograde intrarenal surgery (RIRS) and miniaturized percutaneous nephrolithotomy (mPCNL) serving as competing minimally invasive options. To overcome the selection bias inherent in previous meta-analyses that included observational data, we aimed to compare the efficacy and safety of both techniques by exclusively analyzing randomized controlled trials (RCTs). METHODS: A systematic literature search of five databases was conducted up to February 2026. Only RCTs comparing RIRS and mPCNL were included. The primary outcomes were the single session stone-free rate (SFR) and overall complication rate. Secondary outcomes included stone clearance-related outcomes, operative performance outcomes, and recovery-related outcomes. Data were pooled using a random-effects model, and trial sequential analysis (TSA) was applied. RESULTS: Thirty RCTs encompassing 4173 patients were included. mPCNL demonstrated a significantly higher single-session SFR compared to RIRS (R.R: 0.92, 95% C.I: 0.88-0.96, p < 0.001). RIRS showed a trend toward a lower overall complication rate although the difference did not reach statistical significance (RR = 0.79, 95% CI: 0.63-1.01; p = 0.057), reduced blood transfusion requirements, smaller hemoglobin drops, and shorter hospitalization times. However, RIRS required significantly more postoperative auxiliary procedures. Operative times and high-grade Clavien-Dindo III-V complication rates were comparable between both techniques. TSA confirmed that the cumulative evidence for single-session SFR was sufficient and conclusive, whereas TSA findings for overall complication rate should be interpreted cautiously. CONCLUSIONS: mPCNL achieves significantly higher SFR than RIRS in the management of 1-3 cm renal calculi. However, RIRS was associated with lower bleeding-related morbidity and shorter hospitalization, although no statistically significant difference was observed in the overall complication rate. Therefore, the choice between RIRS and mPCNL should be individualized, balancing maximal SFR against procedural invasiveness according to patient characteristics and stone complexity.
25 May 2026
Read appraisal →Journal of medical systems
Machine Learning-Predicted Risk Trajectories for Incident Chronic Kidney Disease and Associations with Post-CKD Outcomes
Chronic kidney disease (CKD) is a progressive condition where risk accumulates before clinical onset. However, strategies for evaluating CKD risk remain limited, and the trajectories of risk over time are poorly understood. We enrolled 51,156 individuals without CKD in the University of California health system between 2012 and 2024. A debiased XGBoost model was developed to predict CKD risk, and the predicted probabilities from this model at each visit were used to derive risk trajectories through latent class growth mixture modeling. The model achieved a time-dependent area under the curve of 0.95 (95% confidence interval (CI), 0.94-0.96), a C-index of 0.93 (0.92-0.94), and a Brier score of 0.027 (0.026-0.028) for predicting 5-year CKD occurrence. Similar predictive performance was observed for 1 to 4 years. Longitudinal patterns of predicted CKD risk revealed three distinct trajectories: gradual (75.9%), progressive (10.1%), and rapid increase (14.0%). Compared to the gradual trajectory, the progressive trajectory was associated with a higher risk of estimated glomerular filtration rate (eGFR) decline of ≥ 25% (adjusted hazard ratio (HR) 1.31; 95% CI, 1.06-1.61) and ≥ 50% (HR 1.59; 95% CI, 1.09-2.31) following CKD onset. The progressive and rapid trajectory was linked to an increased risk of cardiovascular events (HR 1.84; 95% CI 1.18-2.91) and all-cause mortality (HR 2.36; 95% CI 1.49-3.73). Our machine learning model demonstrated strong predictive performance for incident CKD risk and identified three distinct risk trajectories that were associated with different post-CKD outcomes, highlighting the importance of early risk monitoring.
25 May 2026
Read appraisal →Current urology reports
Association Between Ambient Temperature and Urolithiasis: A Systematic Review and Meta-analysis
PURPOSE OF REVIEW: The global incidence of urolithiasis has risen in recent decades. Emerging evidence suggests that higher ambient temperatures are linked to an increased risk of kidney-stone-related events, including acute renal colic, likely through physiological mechanisms such as increased urinary calcium excretion and greater supersaturation of calcium oxalate and calcium phosphate. This study aims to systematically review existing evidence on the relationship between ambient temperature and urolithiasis risk. RECENT FINDINGS: Of 892 records identified, 46 studies met the inclusion criteria for the systematic review, and 16 were included in the meta-analysis. Most studies reported a positive association between higher ambient temperatures and increased kidney stone-related events, with seasonal peaks occurring during summer months in the Northern Hemisphere and from December to March in Australia. Considerable heterogeneity was observed in exposure definitions, meteorological data sources, outcome ascertainment, and lag structures. Reported effect estimates ranged from RR 1.10 to 2.54. Meta-analysis showed that higher ambient temperature was associated with an increased risk of urolithiasis presentation (pooled RR 1.31, 95% CI 1.21-1.43; I²=86.7%). Subgroup analyses showed pooled RRs of 1.27 (95% CI 1.18-1.37) for stone colic, 1.33 (95% CI 1.06-1.67) for emergency visits, and 1.36 (95% CI 0.98-1.90) for hospitalizations. Associations were stronger at medium and long lag periods than at short lags. Most studies were rated as high risk of bias, primarily because of confounding and outcome measurement concerns. Sensitivity analyses supported the robustness of the findings. This systematic review highlights an association between higher ambient temperatures and increased presentations for kidney stone disease. Addressing environmental factors may be a valuable strategy for reducing nephrolithiasis incidence.
25 May 2026
Read appraisal →Lasers in medical science
Acute effect of photobiomodulation therapy in quadriceps strength, fatigue and pain of patients with chronic kidney failure: a randomized, double-blind, placebo controlled crossover clinical trial
Patients with chronic kidney failure (CKF) present changes in physical capacity and low exercise tolerance. Photobiomodulation therapy (PBMT) has shown an ergogenic effect on exercise in studies with different populations and may be an adjuvant treatment in rehabilitation programs for CKF patients. This study aimed to evaluate the acute effects of different doses of PBMT on quadriceps strength, fatigue and muscle pain in CKF patients. This study is a randomized, placebo-controlled, double-blind, crossover clinical trial. Adult patients with CKF received four PBMT applications with different energy doses (30J, 60J, 90J, or placebo), administered in a random order. A one-week interval was maintained between each dose, and an 830nm laser was applied to six points of the quadriceps muscle. Immediately after PBMT, maximal isometric quadriceps muscle strength was assessed by dynamometry. Before the intervention and after the assessment of muscle strength, muscle fatigue (Borg rating of perceived exertion scale and blood lactate) and pain perception in the lower limbs (visual analogue scale) were measured. Fifteen patients were randomized and fourteen completed the study. There was no significant difference between the doses tested for maximal isometric muscle strength in the right (p=0.053) and left (p=0.509) quadriceps. The perception of fatigue assessed by the modified Borg scale was not altered (p=0.703) nor were blood lactate levels, regardless of the PBMT dose and measurement time (t0: p=0.126; t3: p=0.667; t6: p=0.700). There was no change in pain perception after quadriceps irradiation for any of the PBMT doses tested (p=0.566). A single application of PBMT with a dose of 30J, 60J, or 90J did not alter maximal isometric quadriceps muscle strength, fatigue, or muscle pain perception in the lower limbs of CKF patients on HD.
24 May 2026
Read appraisal →The AAPS journal
A Mechanistic Framework Integrating Renal QSP-PK-PD and Machine Learning for Baseline-Informed Stratification of Diuretic Resistance
Diuretic resistance represents a major source of heterogeneity in loop diuretic response and remains a key barrier to effective decongestion in heart failure. A key clinical challenge is the early identification of patients at high risk of an inadequate response to standard-dose furosemide in order to inform timely treatment intensification or alternative decongestive strategies. However, current approaches remain reactive and rely on post-treatment response, limiting prospective risk stratification and treatment decision-making. To address this problem, we integrated a mechanistic renal quantitative systems pharmacology (QSP) model with furosemide pharmacokinetics and pharmacodynamics to enable baseline-informed risk stratification of diuretic resistance. The model was calibrated using published clinical pharmacokinetic and renal response data and applied to generate a physiologically constrained virtual patient population capturing heterogeneity in renal function, tubular sodium handling, and neurohormonal activation. Machine learning methods were incorporated as complementary analytical tools to identify and validate baseline physiological determinants that define risk categories of diuretic response within the mechanistic simulation framework. Model-based analyses indicated that diuretic resistance arises from the combined effects of reduced filtration capacity, enhanced tubular sodium avidity, diminished pharmacodynamic sensitivity and sustained neurohormonal activation. Across analytical approaches, baseline fractional excretion of sodium and glomerular filtration rate emerged as integrative biomarkers that stratify patients into distinct risk categories of diuretic response, reflecting a mechanism-informed reduction of underlying physiological variability. These findings provide a mechanistically grounded framework for baseline-informed risk stratification and may inform earlier identification of patients at risk of inadequate response, supporting model-informed evaluation of treatment intensification or alternative decongestive strategies.
24 May 2026
Read appraisal →Studies in health technology and informatics
Assessing AI-Based Decision Support in Early Sepsis and AKI Recognition
Sepsis and acute kidney injury (AKI) remain among the most critical conditions in acute care, associated with high morbidity and mortality. Early risk recognition is essential but often hampered by nonspecific symptoms. Recent studies have demonstrated that AI- and ML-based Clinical Decision Support Systems (CDSS) can enhance the early detection of sepsis and AKI and support clinical decision-making [1-3]. In May 2025, the University Hospital Augsburg (UKA) integrated an externally developed AI-based CDSS to provide real-time risk predictions for sepsis and AKI. This study evaluates the effectiveness of the system in routine clinical practice using retrospective data from periods before and after its implementation. Primary endpoints include diagnostic accuracy. The results will indicate whether the CDSS contributes to improved early risk recognition and enhanced patient safety in a real-world hospital environment.
24 May 2026
Read appraisal →Journal of medical Internet research
Human-AI Interaction in Kidney Transplant Decision Support Systems: Qualitative Study of Patient and Support Person Expectations
BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (DSS) can enhance early risk detection and treatment optimization. However, the perspectives of patients and their support persons on AI-assisted DSS in clinical care, particularly regarding shared decision-making (SDM), remain underexplored. OBJECTIVE: This study investigates the expectations, informational needs, and perceptions of patients who underwent kidney transplantation and their support persons regarding AI-assisted DSS and its influence on SDM in posttransplant care. METHODS: In a longitudinal qualitative study, 36 semistructured interviews were conducted with patients who underwent kidney transplantation and their support persons at a German kidney transplant center. Participants were asked about their views on AI's role in follow-up care, its impact on communication, trust, and decision-making, as well as their informational needs regarding AI-assisted DSS. Interviews were transcribed, pseudonymized, and analyzed using framework analysis. RESULTS: Participants recognized AI's potential to support clinicians by identifying risks of transplant loss, rejection, and infection, and by providing data-driven treatment recommendations. However, they emphasized that final decisions should remain with physicians. A majority of participants (n=28, 78%) expressed concern that AI might depersonalize care and diminish physician-patient communication due to a lack of "human touch." Participants demonstrated limited understanding of AI-based DSS functionality and highlighted the need for simple, accessible educational materials (eg, leaflets) explaining AI operations. While most doubted AI could replicate human empathy, some acknowledged that AI might be perceived as more attentive than time-pressured physicians, offering consistent monitoring and support. Participants consistently stressed that AI should augment, not replace, clinical decision-making. CONCLUSIONS: Patients who underwent kidney transplantation and support persons endorse the integration of AI in follow-up care when it enhances clinical decision-making without supplanting the physician's role. Acceptance and trust depend on transparency, accountability, and preserving the "human touch" in care. The development of educational tools to communicate AI functions and limitations is crucial to empower patients and support persons in SDM processes and to ensure AI complements, rather than undermines, patient-centered care.
23 May 2026
Read appraisal →Journal of robotic surgery
Emerging robotic platforms in partial nephrectomy: a comparative systematic review and network meta-analysis
Robotic-assisted partial nephrectomy (RAPN) is the preferred minimally invasive nephron-sparing technique, with outcomes comparable to open and laparoscopic surgery. While most evidence derives from the da Vinci system, newer robotic platforms such as Hugo, Hinotori, KangDuo and MP1000 are emerging. This systematic review and network meta-analysis evaluated perioperative outcomes across available RAPN platforms. A systematic search of PubMed, Cochrane Library, CINAHL, and Scopus identified studies reporting RAPN outcomes across available robotic platforms. Eligible studies included randomized and observational cohorts. Risk of bias was assessed with ROBINS-I. Outcomes included in the analysis operative time (OT), estimated blood loss (EBL), and length of hospital stay (LOS). A frequentist NMA using the "netmeta" R package generated standardized mean differences (SMD) with 95% CIs, and P-scores ranked platform performance. The review was registered on PROSPERO (ID: CRD420251032233). This review was conducted in accordance with PRISMA guidelines. Thirteen studies (n = 2,450) were included. Under fixed-effects model, da Vinci SP (SMD - 0.63, 95% CI - 0.87 to - 0.40) and Xi (-0.52, 95% CI - 0.67 to - 0.38) revealed shorter operative times; however, under random-effects models these differences were not statically significant due to substantial heterogeneity. Heterogeneity (I² ≈ 84%) attenuated these effects. For ischemia time, Hugo RAS (-0.88, 95% CI - 1.45 to - 0.30) and Xi (-0.58, 95% CI - 0.73 to - 0.44) outperformed Si, though differences lost significance under random-effects (I² >90%). No significant differences were found for length of stay, blood loss, or R.E.N.A.L. nephrometry scores, although ranking differences were observed. Preoperative outcomes were revealed a comparable outcome using emerging robotic system with the established system. Favourable trends in operative and ischemia times were showed in some platforms; however, these differences were not vigorous under random-effects models and are limited by substantial heterogeneity. This raised the need for long-term, comparative follow-up studies, as the current evidence remains elementary.
22 May 2026
Read appraisal →Cell systems
GFETM: Genome foundation-based embedded topic model for scATAC-seq modeling
Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) enables investigation of open chromatin landscapes at single-cell resolution. However, analyzing scATAC-seq data remains challenging due to inherent sparsity and noise. Genome foundation models (GFMs), pre-trained on extensive DNA sequence datasets, have demonstrated effectiveness in genome analysis. Because open chromatin regions (OCRs) harbor salient sequence features, we hypothesized that leveraging GFMs' sequence embeddings could enhance scATAC-seq modeling accuracy and generalizability. We introduce the genome foundation embedded topic model (GFETM), an interpretable deep learning framework combining GFMs with the embedded topic model (ETM) for scATAC-seq analysis. By integrating DNA sequence embeddings extracted by a GFM from OCRs, GFETM demonstrates superior accuracy and generalizability and captures cell-state-specific transcription factor (TF) activity with both zero-shot inference and attention-mechanism analysis. Finally, the topic mixtures inferred by GFETM reveal biologically meaningful epigenomic signatures of kidney diabetes. A record of this paper's transparent peer review process is included in the supplemental information.
22 May 2026
Read appraisal →Renal failure
Machine learning-based risk prediction model for postoperative acute kidney injury in surgical patients with chronic kidney disease (CKD): development, validation, and SHAP-based explainability
This study aimed to develop machine learning models to predict postoperative acute kidney injury (AKI) in surgical patients with pre-existing chronic kidney disease (CKD), a serious complication associated with high morbidity and accelerated disease progression. Using data from 3,851 surgical patients with CKD from the MIMIC-IV database, nine supervised machine learning models were developed to predict AKI, as defined by the KDIGO criteria. Postoperative AKI occurred in 24.2% (932/3,851) of the patients. The XGBoost model demonstrated the best predictive performance, achieving an AUC of 0.844, accuracy of 0.791, sensitivity of 0.79, and F1-score of 0.73, followed by Random Forest and LightGBM. The calibration curve showed a good agreement between the predicted and observed risks. For interpretability, SHAP analysis was used to identify SOFA score, estimated glomerular filtration rate (eGFR), systolic blood pressure, albumin, and phosphorus as the most influential predictors. In conclusion, machine learning models, particularly XGBoost, provide accurate and interpretable predictions of postoperative AKI in this high-risk population. The integration of explainable AI facilitates transparent risk stratification, which may support personalized perioperative care and the development of real-time clinical decision support systems.
21 May 2026
Read appraisal →BMJ open
What are the views of cancer care administrators and clinicians in England on the use of a machine learning clinical decision support system (ML-CDSS) to predict patients' risk of hepatic and renal deterioration during chemotherapy? A qualitative study.
OBJECTIVE: To explore administrators' and clinicians' views on the factors that influence their use and adoption of a machine learning clinical decision support system (ML-CDSS) to predict patients' risk of hepatic and renal deterioration during chemotherapy. METHODS AND ANALYSIS: This was a qualitative study that used purposive sampling. 18 participants with administration and clinical backgrounds working in cancer care in England were recruited. Qualitative data were collected by conducting semi-structured interviews and a focus group. Data were analysed thematically using the framework method to identify key themes. RESULTS: Participants acknowledged that monitoring blood chemistry is a core component of chemotherapy as it helps clinicians assess patient fitness and treatment response. The ML-CDSS was perceived as a potentially valuable tool for identifying patients at increased risk of hepatic and renal deterioration, supporting clinical decision-making and enhancing care efficiency. However, several concerns were raised regarding its potential implementation in practice. Participants questioned clinicians' willingness and capacity to integrate the tool into their existing workflows. Participants also believed it was important to demonstrate the ML-CDSS's sensitivity, specificity and validity in accurately predicting patients' risk to build clinicians' trust in the tool, demonstrating evidence of its efficacy and effectiveness in practice. CONCLUSION: Administrators and clinicians recognised the potential benefits of the ML-CDSS to enhance the delivery of chemotherapy by identifying patients at risk for hepatic and renal deterioration. Successful adoption in practice depends on building trust with the tool by being transparent in its development, its effectiveness and impact. Future work should demonstrate the ML-CDSS being used in practice to generate real-world evidence.
20 May 2026
Read appraisal →World journal of urology
Supine mini-PCNL using pneumatic lithotripsy for pediatric renal stones: safety and efficacy in low-income countries
BACKGROUND: Mini-percutaneous nephrolithotomy (mini-PCNL) is an effective surgical technique for pediatric renal stones. However, data comparing different lithotripsy modalities in this population are limited. PURPOSE: To compare the efficacy, safety, and acute renal parenchymal impact of pneumatic lithotripsy versus Holmium: YAG (Ho: YAG) laser dusting during supine mini-percutaneous nephrolithotomy (mini-PCNL) in pediatric patients. PATIENTS AND METHODS: A double-masked, randomized clinical trial was conducted between March 2023 and October 2024. A total of 104 pediatric patients (ages 3-10 years) with solitary renal stones (10-25 mm) were randomized into two groups: Group 1 (n = 53) underwent mini-PCNL using a pneumatic lithotripter, and Group 2 (n = 51) underwent mini-PCNL using Ho: YAG laser dusting. Primary outcomes included operative time, stone-free rate (SFR), complications (Clavien-Dindo), and biochemical markers of renal injury, including Kidney Injury Molecule-1 (KIM-1) and albumin/creatinine (A/C) ratio. RESULTS: Pneumatic lithotripsy demonstrated significantly shorter mean operative times (59.5 vs. 67.6 min, p = 0.007) and lithotripsy times (p < 0.001) compared to the laser group. The stone-free rates were comparable between the pneumatic and laser groups (92.45% vs. 94.1%, p > 0.05). Group 1 (pneumatic) had a significantly shorter hospital stay (2.2 vs. 2.6 days, p = 0.004). While both groups showed postoperative elevations in injury markers, the laser group exhibited significantly higher and more sustained levels of urinary KIM-1 and A/C ratios, as well as higher postoperative RBC counts in nephrostomy samples (p < 0.05). No major complications or perforations occurred in either group. CONCLUSIONS: Pneumatic lithotripsy is a highly effective and safe alternative to laser lithotripsy for pediatric mini-PCNL. It offers comparable operative efficiency, reduced hospital stays, and potentially lower acute tubular injury as evidenced by lower KIM-1 levels. Given its lower cost and comparable stone-free rates, pneumatic lithotripsy is a favorable option for managing pediatric renal stones in resource-conscious settings.
18 May 2026
Read appraisal →European journal of preventive cardiology
Effect of intensive blood pressure control in patients with resistant hypertension: insights from the STEP trial
AIMS: Resistant hypertension is defined as uncontrolled blood pressure (BP) despite the use of ≥3 antihypertensive drugs or controlled BP with ≥4 antihypertensive drugs. Whether intensive BP control is beneficial for the management of resistant hypertension is unknown. METHODS AND RESULTS: Intensive (<130 mmHg) BP control in patients is compared with standard (130-150 mmHg) BP control in patients with resistant hypertension, using the Strategy of Blood Pressure Intervention in Older Hypertensive Patients (STEP) trial data. Patients were divided into those with and without resistant hypertension. The primary outcome was a composite of cardiovascular events. Hazard ratios (HRs) were calculated using the Fine-Gray sub-distribution hazard model for primary and secondary events and the Cox regression model for all-cause death. In patients with resistant hypertension, intensive BP control was associated with a lower risk of primary outcomes (HR, 0.36; 95% CI, 0.14-0.93; P = 0.035). Results were consistent across different BP thresholds used to define resistant hypertension and in patients with true resistant hypertension. The benefit of intensive BP control was consistent across resistant hypertension status. Intensive BP control was not associated with an increased risk of adverse events in patients with resistant hypertension. Patients with resistant hypertension had increased cardiovascular risk compared with those without (HR, 1.78; 95% CI, 1.13-2.81; P = 0.012). CONCLUSION: Intensive BP control is associated with reduced risk of cardiovascular events in patients with resistant hypertension without increased risk of adverse events. REGISTRATION: https://www.clinicaltrials.gov/ct2/show/NCT03015311. The present study is a secondary analysis of the effect of intensive blood pressure control in patients with resistant hypertension in the STEP trial. Resistant hypertension is characterized as elevated blood pressure (BP) despite three antihypertensive agents or controlled BP with four or more agents. Intensive BP control in patients with resistant hypertension may reduce the risk of cardiovascular events. Further research is needed to investigate multifaceted treatment approaches, including both pharmacological and non-pharmacological interventions.Intensive blood pressure (BP) control is associated with a reduced risk of cardiovascular events and death in patients with resistant hypertension, compared with standard BP control, without increasing the risk of adverse events.Patients with resistant hypertension remain at higher risk of cardiovascular events compared with patients without resistant hypertension. Intensive BP control in this population may lower the risk of cardiovascular events.
18 May 2026
Read appraisal →PloS one
Effect of intra-dialytic pedaling exercise on dialysis adequacy: A randomized controlled trial
BACKGROUND: In patients with chronic kidney disease undergoing hemodialysis, physical activity and rehabilitation are crucial for preventing declines in muscle strength and functional capacity. This study aimed to assess the impact of physical activity during hemodialysis on dialysis adequacy in patients undergoing hemodialysis. METHODS: This randomized controlled trial (RCT) investigated the impact of pedaling exercise on dialysis effectiveness in 84 hemodialysis patients at hospitals in Bushehr. Participants were randomly assigned to either an experimental group (n = 42) that performed 30 minutes of pedaling exercise during their 4-hour dialysis sessions or a control group (n = 42) that received routine hemodialysis. Dialysis adequacy was assessed by comparing pre- and post-dialysis blood samples obtained from the arterial line. A conservative intradialytic exercise protocol, blood samples, and patient weight were measured using a calibrated digital scale. Data analysis was performed using SPSS version 24 software. RESULTS: The experimental and control groups were similar in demographic characteristics, except for age (X2 = -3.84, p = 0.001) and education levels (X2 = 10.100, p = 0.006). While there was no significant difference in weight between the groups before and after the intervention (t = 0.223, p = 0.82 before; t = 0.203, p = 0.84 after), both groups showed a substantial weight reduction overall (p < 0.0001). There were no statistically significant differences in weight change (t = 0.80, p = 0.25), BUN (t = 0.13, p = 1.52), or Kt/V (t = 1.62, p = 0.11) between the experimental and control groups. CONCLUSION: This study found that incorporating pedaling exercise during hemodialysis did not significantly improve dialysis effectiveness, as measured by weight change, BUN levels, or Kt/V. While both groups showed weight loss, there were no statistically significant differences between them. However, the study was limited by its small sample size and the specific exercise protocol employed. Further research with larger cohorts and a broader range of physical activities is needed to determine whether physical activity during hemodialysis can improve dialysis adequacy and overall patient outcomes. TRIAL REGISTRATION: IRCT code number 20150529022466N15 and trial Code of Ethics IR.BPUMS.REC.1398.130E.
16 May 2026
Read appraisal →Scientific reports
Exploring the psychological and physiological effects of a virtual reality-based bicycle exercise in hemodialysis patients: a randomized controlled trial
This study aimed to evaluate the efficacy of a virtual reality (VR)-based bicycle exercise in psychological distress (depression and anxiety) and selected biochemical parameters among patients receiving maintenance hemodialysis (MHD). A total of 70 patients from the dialysis station at Changzhou Medical District of the No. 904th Hospital were randomly allocated into study group(VR-based bicycle exercise, n = 35) and control group(routine nursing care, n = 35). The depression and anxiety levels were assessed by Self-rating Depression Scale (SDS), and Self-rating Anxiety Scale (SAS) respectively. Physiological indicators, including serum creatinine (Scr) and blood urea nitrogen (BUN) levels, were also analyzed. Following the intervention, the study group exhibited significant reductions in SDS and SAS scores (both P < 0.05). The levels of Scr and BUN in peripheral blood were also observed to be significantly decreased in study group (P < 0.05). Correlation analysis revealed a significant positive correlation between SAS/SDS scores with both Scr and BUN levels (P < 0.05 or P < 0.01). Furthermore, regression models identified SAS scores as significant predictors of Scr and BUN levels, accounting for 27.8% and 31.9% of their variance, respectively (P < 0.05 or P < 0.01). VR-based bicycle exercise can improve psychological well-being, and was associated with beneficial changes in pre-dialysis serum biomarkers (Scr and BUN) in MHD patients. This integrated intervention represents a promising non-pharmacological strategy to complement standard care in MHD patients.
15 May 2026
Read appraisal →Renal failure
Comparative efficacy and safety of apixaban and rivaroxaban versus warfarin in atrial fibrillation patients with end-stage renal disease: a systematic review and meta-analysis
This study aimed to evaluate the comparative efficacy and safety of apixaban and rivaroxaban versus vitamin K antagonists (VKAs) in anticoagulation management in a dialysis population. PubMed, Embase, and the Cochrane Library were searched for studies comparing apixaban or rivaroxaban with VKAs in patients with atrial fibrillation (AF) undergoing dialysis. The primary efficacy endpoints included stroke/systemic embolism (SSE) and all-cause mortality. Safety outcomes encompassed major bleeding, intracranial hemorrhage, and gastrointestinal bleeding. Risk ratios (RR) with 95% confidence intervals (CI) were synthesized using random-effects models. The meta-analysis included three randomized controlled trials (RCTs) and eight observational studies. Pooled analyses showed that apixaban and rivaroxaban were associated with lower risks of major bleeding (RR 0.57, 95% CI: 0.51-0.63), gastrointestinal bleeding (RR 0.66, 95% CI: 0.57-0.76), and intracranial hemorrhage (RR 0.54, 95% CI: 0.36-0.83) compared with VKAs. Additionally, apixaban and rivaroxaban were associated with reduced risk of SSE (RR 0.57, 95% CI: 0.46-0.72) and all-cause mortality (RR 0.73, 95% CI:0.63-0.83), although substantial heterogeneity was present. Exploratory dose-stratified analyses suggested both standard- and low-dose apixaban regimens were associated with favorable efficacy and hemostatic safety relative to warfarin. Consistent numerical trends were observed in the RCT-only analysis, though none reached statistical significance owing to limited sample size. In conclusion, apixaban and rivaroxaban are associated with lower risks of bleeding compared with VKAs in patients with AF and ESRD. However, evidence regarding their efficacy in preventing SSE, all-cause mortality and the optimal apixaban dosing regimen remains inconclusive and requires validation in large, dedicated RCTs.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Global Hypertension 2000 to 2020: Trends, Disparities, and Progress in Awareness, Treatment, and Control
BACKGROUND: Hypertension is the leading preventable risk factor for premature death worldwide. Accurate estimates of prevalence and control are critical for developing public health priorities. OBJECTIVE: This study sought to examine global variations in hypertension prevalence, awareness, treatment, and control by world economic region and compare secular changes from 2000 to 2020. METHODS: A systematic search of MEDLINE from January 1995 to November 2024 was supplemented with manual searches of article references. Population-based cross-sectional surveys reporting age- and sex-specific hypertension prevalence and using standardized blood pressure measurement methods were included. Sex- and age-specific prevalence by country was applied to population counts to calculate the number with hypertension regionally and worldwide. Country-specific percentages of awareness, treatment, and control were applied to numbers of adults with hypertension to obtain regional and global estimates. Hypertension was defined as mean systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or use of antihypertensive medication. RESULTS: We included 287 studies conducted in 6,060,567 adults from 119 countries. In 2020, 33.0% (95% CI: 32.5%-33.5%), or 1.71 (95% CI: 1.65-1.77) billion, of the world's adults had hypertension: 30.1% (95% CI: 29.4%-30.9%), or 395 (95% CI: 394-396) million, in high-income countries and 33.4% (95% CI: 32.8%-33.9%), or 1.32 (95% CI: 1.26-1.38) billion, in low- and middle-income countries. From 2000 to 2020, age-standardized prevalence decreased by 2.7% and absolute burden increased by 76 million in high-income countries compared with increases of 5.8% in prevalence and 651 million in absolute burden in low- and middle-income countries. From 2000 to 2020 in high-income countries, awareness increased from 57.7% to 69.2%, treatment from 42.9% to 66.3%, and control from 16.4% to 40.2%. More modest increases were observed in low- and middle-income countries: awareness from 29.1% to 46.1%, treatment from 20.7% to 30.8%, and control from 6.4% to 13.6%. CONCLUSIONS: The burden of hypertension is increasing, as are disparities between high-income countries and low- and middle-income countries. Urgent efforts are needed to address hypertension worldwide, particularly in low- and middle-income countries.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Blood Pressure Levels and Outcomes in Type 2 Diabetes: Dose-Response Meta-Analysis of 5.87 Million Cohort Participants
BACKGROUND: The dose-response relationships between systolic and diastolic blood pressure (SBP and DBP) levels and risks of all-cause mortality, cardiovascular and renal outcomes in type 2 diabetes remain poorly characterized, particularly at lower blood pressure (BP) levels. OBJECTIVE: This study aimed to examine risk patterns of major clinical outcomes across a wide range of BP levels in participants with type 2 diabetes. METHODS: We systematically searched PubMed, Embase, and Web of Science from inception to November 30, 2024, for cohort studies assessing associations of BP levels with all-cause mortality, cardiovascular, and renal outcomes. One-stage mixed-effects dose-response meta-analysis was conducted to assess the curvilinear associations. RESULTS: A total of 89 cohorts from 113 articles of 5,875,364 participants with type 2 diabetes were identified. A J-shaped association was observed for SBP with all-cause mortality. J-shaped associations were also observed for SBP with cardiovascular events and for DBP with all-cause mortality, with flattened risks at lower BP levels. After excluding studies with participants who had baseline cardiovascular diseases or cancer, a significantly lower risk of cardiovascular events was observed at lower SBP levels, as well as a flattened risk of all-cause mortality. Positive linear or monotonic trends were observed for renal events, estimated glomerular filtration rate decline, and development or progression of albuminuria. CONCLUSIONS: In type 2 diabetes, BP is linearly or monotonically associated with most cardiovascular and renal outcomes. Low SBP does not appear to be associated with an elevated risk of all-cause mortality compared with higher BP targets, suggesting that previously reported associations are likely due to reverse causation and unmeasured confounding.
15 May 2026
Read appraisal →Journal of the American College of Cardiology
Natriuretic Peptide Receptor-1 Agonist for Resistant Hypertension: A Randomized Phase 2 Trial
BACKGROUND: XXB750 is a potent and long-acting human monoclonal antibody agonist of the natriuretic peptide receptor-1 (NPR-1), the receptor for atrial natriuretic peptide and B-type natriuretic peptide. Activation of NPR-1 may lower blood pressure (BP) in patients with resistant hypertension through increases in the second messenger cyclic guanosine monophosphate. OBJECTIVES: The aim of this study was to determine the effect of NPR-1 agonism on 24-hour BP. METHODS: This phase 2, multicenter, randomized, double-blind, placebo-controlled trial evaluated 4 once-monthly subcutaneous doses of XXB750 (30 mg, 60 mg, 120 mg, and 240 mg) administered at baseline, week 4, and week 8 in patients with resistant hypertension. Resistant hypertension was defined as 24-hour systolic BP (SBP) ≥135 mm Hg despite treatment with 3 or 4 guideline-directed antihypertensive agents. The primary endpoint was the dose-response relationship of XXB750 vs placebo in reducing the mean 24-hour SBP from baseline to week 12. RESULTS: The study population (N = 189) had a mean age of 61 years, was 69% male, 61% White, 20% Asian, and 14% Black/African American, and had a baseline mean 24-hour ambulatory BP of 148/81 ± 11/10 mm Hg. Plasma concentrations of XXB750 dose-dependently increased in the XXB750 groups, and XXB750 dose-dependently increased plasma and urine cyclic guanosine monophosphate concentrations by week 12. However, paradoxically, there was no significant dose-dependent change from baseline in mean 24-hour SBP at week 12. In fact, none of the doses of XXB750 had a greater BP-lowering effect than placebo. Changes in ambulatory BP were not affected by the development of anti-drug antibodies. Although there were no changes from baseline in urinary sodium excretion, there were small increases in plasma renin levels in the XXB750 groups. Adverse events with XXB750 were numerically higher than with placebo. Adherence to background antihypertensive medications, assessed by drug assays in 88 patients with available plasma samples, was consistent across all treatment groups at both baseline and week 12. CONCLUSIONS: XXB750, an NPR-1 agonist, had a neutral effect on 24-hour ambulatory SBP in patients with resistant hypertension, despite showing a robust dose-response relationship for engagement of the target of pharmacologic action as indicated by increases in cyclic guanosine monophosphate levels. (An Efficacy, Safety, Tolerability and Dose Finding Study of XXB750 in Resistant Hypertension Patients; NCT05562934).
14 May 2026
Read appraisal →Journal of the American College of Cardiology
Efficacy of Tonlamarsen in Patients With Uncontrolled Hypertension: The KARDINAL Phase 2 Randomized Clinical Trial
BACKGROUND: Angiotensinogen production represents the rate-limiting step in activation of the renin-angiotensin-aldosterone system. Tonlamarsen is an investigational antisense oligonucleotide directed against hepatic angiotensinogen synthesis; its efficacy and safety among patients with hypertension are unknown. OBJECTIVES: The aim of this study was to assess the safety and efficacy of 90 mg tonlamarsen administered subcutaneously once monthly for 5 months compared with a single dose of tonlamarsen and subsequent placebo. METHODS: This randomized, placebo-controlled trial enrolled adults with office systolic blood pressure (BP) >135 mm Hg receiving 2 to 5 antihypertensive medications. Participants entered a 3-part treatment period with monthly administration of the study drug, consisting of a 4-week placebo lead-in, followed by 4-week active run-in with a single dose of tonlamarsen and subsequent randomization to 4 additional doses of tonlamarsen or matching placebo for 16 weeks. The coprimary endpoints were the between-group differences in the change from baseline to week 20 in plasma angiotensinogen and in office systolic BP. RESULTS: A total of 279 participants received placebo lead-in, 206 received 90 mg tonlamarsen during the active run-in, and 198 were randomized. The mean BP among randomized participants before and after the placebo lead-in was 147/90 and 147/89 mm Hg, respectively. Following active run-in with tonlamarsen, the mean BP was 140/87 mm Hg. Twenty weeks following the first dose of tonlamarsen, least squares (LS) mean percentage changes in plasma angiotensinogen levels were -23.0% (95% CI: -27.8% to -18.2%) with a single dose of tonlamarsen and subsequent placebo and -67.2% (95% CI: -71.9% to -62.4%) with monthly tonlamarsen administration, with a LS mean difference of -44.1% (97.5% CI: -51.9% to -36.4%; P < 0.0001). The LS mean changes in office systolic BP were -6.7 mm Hg (95% CI: -9.8 to -3.5 mm Hg) for participants following a single dose of tonlamarsen and subsequent placebo and -6.7 mm Hg (95% CI: -9.8 to -3.6 mm Hg) for participants treated with monthly tonlamarsen, with a LS mean difference of -0.1 mm Hg (95% CI: -4.5 to -4.4 mm Hg; P = 0.97). Serious adverse events were infrequent and similar between treatment groups. CONCLUSIONS: Among individuals with uncontrolled hypertension, monthly tonlamarsen administration was more effective at lowering plasma angiotensinogen compared with a single tonlamarsen dose, but there was no additional BP reduction. (A Study to Investigate Tonlamarsen for the Treatment of Adults With Uncontrolled Hypertension [KARDINAL]; NCT06864104).
14 May 2026
Read appraisal →World journal of urology
Complications of thulium fiber laser lithotripsy: a systematic review and meta-analysis by the EAU-YAU endourology and urolithiasis working group
BACKGROUND: Thulium Fiber Laser (TFL) has emerged as a promising alternative to Holmium: YAG for lithotripsy, yet its safety profile remains fragmented across various trials. This study aims to synthesize available evidence to determine the pooled complication rates associated with TFL lithotripsy . METHODS: A systematic review was conducted following PRISMA guidelines. We searched PubMed, Cochrane CENTRAL, and ClinicalTrials.gov for studies reporting complications of TFL in adult patients with urolithiasis. The primary outcome was the overall complication rate. Secondary outcomes included Clavien-Dindo grading, specific adverse events (fever, sepsis, injury), and stone-free rate (SFR). A random-effects meta-analysis was performed using the Freeman-Tukey double-arcsine transformation. RESULTS: Thirty-eight studies involving 3,329 patients were included. The pooled overall complication rate was 10.42% (95% CI: 8.01%-13.10%; I²=81.9%). Major complications (Clavien-Dindo III-V) were rare, with a pooled rate of 1.19% (95% CI: 0.68%-1.84%). Specific event rates included fever (2.45%), ureteral injury (1.37%), bleeding (1.07%), urosepsis (0.77%), ureteral strictures (0.70) and acute pyelonephritis (0.59%). Meta-regression identified operative time as a significant predictor of complications (p = 0.041), whereas stone size was not (p = 0.358). Subgroup analysis showed higher complication rates in pure renal stone treatments (13.83%) compared to ureteral stones (10.64%). The pooled Stone-Free Rate (SFR) was 87.61% (95% CI: 83.61%-91.12%). CONCLUSION: Thulium Fiber Laser lithotripsy demonstrates a pretty favorable safety profile with a very low incidence of high-grade complications (< 1.2%) and high efficacy.
14 May 2026
Read appraisal →Pediatric surgery international
Effect of anticholinergics vs. alpha-1 blockers vs. combination therapy on bladder function in patients with posterior urethral valve after transurethral fulguration: a pilot randomized controlled trial
PURPOSE: To identify preliminary efficacy signals of anticholinergics, alpha-1 blockers, and their combination on bladder and renal function in boys with bladder dysfunction following posterior urethral valve (PUV) ablation, and to assess the feasibility of conducting a definitive randomized, controlled trial. STUDY DESIGN: In this single-center, prospective, randomized pilot trial boys under 12 years who had undergone cystoscopic valve ablation were randomized into three groups: anticholinergic therapy (Group I), alpha-1 blocker therapy (Group II), and combination therapy (Group III). The study was designed to detect within-group preliminary efficacy signals in urodynamic parameters following 6 months of pharmacotherapy. Secondary outcomes included changes in patient-reported symptom scores, renal function, and feasibility metrics. Between-group comparisons were performed as exploratory analyses. Feasibility and safety outcomes were analyzed in the intention-to-treat population, while efficacy analyses were conducted in participants who received intervention and had evaluable baseline and follow-up data. RESULTS: Thirty-five eligible patients were randomized (Group I, n = 12; Group II, n = 11; Group III, n = 12). and their clinical and biochemical analyses at baseline were recorded. Two deaths in Group II occurred prior to baseline urodynamics and pharmacotherapy initiation; efficacy analyses, therefore, included 33 patients (Group I, n = 12; Group II, n = 9; Group III, n = 12) with evaluable data. Urodynamic evaluation demonstrated favourable within-group trends: Group I showed a significant improvement in voided volume, while changes in voiding time, cystometric capacity (CC), static compliance, and maximum detrusor pressure (Pdetmax) were favourable but not uniformly statistically significant. Group II showed modest, favourable effects on Qmax, Time@Qmax, CC, and Pdetmax, with wide confidence intervals and no clear significant changes across most UDS measures. Group III demonstrated the most consistent favourable pattern, particularly for CC and Pdetmax, although most confidence intervals overlapped the null. Baseline-adjusted exploratory analyses did not demonstrate statistically significant between-group differences. Symptom scores improved across all groups. Feasibility outcomes were robust, with 81% recruitment, 91% urodynamic study completion, and 94% participant retention. CONCLUSION: This pilot trial demonstrates the feasibility of conducting urodynamic-guided pharmacotherapy studies in young boys with PUV-related bladder dysfunction. The observed within-group efficacy signals across all treatment arms-particularly with combination therapy-are hypothesis-generating and provide effect size and variability estimates to inform the design of a definitive randomized controlled trial.
13 May 2026
Read appraisal →The aging male : the official journal of the International Society for the Study of the Aging Male
SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome
BACKGROUND: While the single-point insulin sensitivity estimator (SPISE) shows promise as an insulin resistance biomarker, its association with cardiovascular disease (CVD) in early CKM stages (0-3) remains underexplored. METHODS: We analyzed 6480 participants with CKM stage 0-3 from the China Health and Retirement Longitudinal Study. CVD outcomes were assessed relative to SPISE index levels. An ensemble machine learning model was employed to predict CVD risk. RESULTS: 6480 subjects were enrolled, of whom 967 developed CVD. After stratifying participants into SPISE quartiles (Q1-Q4) and adjusting for covariates, higher quartiles were linked to a lower CVD risk. This study developed an LR+GMM (Logistic Regression + Gaussian Mixture Model) ensemble model to predict CVD risk using five strong predictors: SPISE, high-density lipoprotein cholesterol (HDL-c), diastolic blood pressure (DBP), body mass index (BMI), and glycated hemoglobin (HbA1c). The model performed well, achieving an accuracy (ACC) of 0.986 and an area under the receiver operating characteristic curve (AUC) of 0.932. CONCLUSIONS: The SPISE index is a significant inverse predictor of CVD risk in individuals with stage 0-3 CKM syndrome. The LR+GMM ensemble model, incorporating the SPISE index and four clinical metrics, demonstrated outstanding predictive performance.
12 May 2026
Read appraisal →European journal of clinical pharmacology
Effects of hypoxia-inducible factor prolyl hydroxylase inhibitors on lipid profiles in patients with chronic kidney disease: a systematic review and meta-analysis
BACKGROUND: Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are novel oral agents for treating anemia in chronic kidney disease (CKD), with potential effects on lipid modulation. We aimed to systematically evaluate the effects of HIF-PHIs on lipid profiles and cardiovascular outcomes in CKD patients. MATERIALS AND METHODS: PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), and Embase (Ovid) were searched for randomized controlled trials comparing HIF-PHIs with erythropoiesis-stimulating agents (ESAs) or placebo in dialysis-dependent (DD) or nondialysis-dependent (NDD) CKD patients. Primary outcomes included changes in low-density lipoprotein cholesterol (LDL-C), total cholesterol, triglycerides, and high-density lipoprotein cholesterol (HDL-C). Secondary outcomes included cardiovascular outcomes, including cardiovascular death, myocardial infarction, stroke, and all-cause mortality. RESULTS: A total of 20 trials involving 12,155 patients were analyzed in this review. Roxadustat significantly reduced LDL-C (mean difference [MD], -16.07 mg/dL; 95% CI, -17.92 to -14.21; 14 randomized controlled trials [RCTs], 10,510 patients), total cholesterol (MD, -25.25 mg/dL; 95% CI, -29.70 to -20.81; 10 RCTs, 5,538 patients), triglycerides (MD, -19.70 mg/dL; 95% CI, -30.78 to -8.61; 9 RCTs, 4,616 patients), but also decreased HDL-C (MD, -4.91 mg/dL; 95% CI, -6.80 to -3.02; 9 RCTs, 5,132 patients). Desidustat significantly reduced LDL-C and total cholesterol, but showed no significant effects on triglycerides or HDL-C, whereas molidustat showed no significant lipid-lowering effects. Overall, treatment with HIF-PHIs was not associated with significant differences in cardiovascular death (RR, 1.00; 95% CI, 0.84 to 1.18; 10 RCTs, 9,371 patients), myocardial infarction (RR, 1.12; 95% CI, 0.90 to 1.38; 15 RCTs, 11,265 patients), stroke (RR, 1.18; 95% CI, 0.86 to 1.61; 14 RCTs, 11,136 patients), or all-cause mortality (RR, 1.06; 95% CI, 0.96 to 1.17; 19 RCTs, 11,903 patients), compared with ESAs or placebo. CONCLUSION: Roxadustat showed the most substantial lipid-lowering effects, while desidustat showed significant reductions in LDL-C and total cholesterol but no significant effects on triglycerides or HDL-C, and molidustat showed no significant effects. Despite these changes in lipid profiles, no significant differences in cardiovascular outcomes were observed for these three HIF-PHIs, compared with ESAs or placebo.
11 May 2026
Read appraisal →International journal of medical informatics
Development of an interpretable machine learning model for predicting 4-year chronic kidney disease risk in elderly hypertensive patients
INTRODUCTION: Age and hypertension are key drivers of renal impairment, predisposing older hypertensive adults to faster kidney function decline and higher mortality. We aim to develop an interpretable machinelearning model to predict 4-year chronic kidney disease (CKD) risk in this population. METHODS: Our study incorporated 4,142 hypertensive patients from the Health and Retirement Study (HRS) 2010 and 2012 cohorts for model development and internal validation, with additional temporal validation performed within the HRS 2006 and 2008 cohorts. External validation was conducted using three distinct subcohorts derived from the China Health and Retirement Longitudinal Study (CHARLS) database. Feature selection was implemented through an integrated LASSO-Boruta algorithm, followed by model construction using eight machine learning approaches. Discriminative performance was rigorously evaluated through multiple metrics, including receiver operating characteristic (ROC) curve analysis, accuracy, sensitivity, specificity, and Brier score. The optimal model underwent interpretability analysis via SHapley Additive exPlanations (SHAP) to elucidate decision-making mechanisms and was subsequently deployed as a web-based clinical prediction tool. RESULTS: Using a combined LASSO-Boruta strategy, we identified nine routinely available predictors for model development. In the training set, SVM achieved the highest AUC (0.735), closely followed by XGBoost (0.734); notably, in the temporal validation cohort, XGBoost was the only model with an AUC > 0.700 (0.702). Overall performance metrics derived from confusion matrices, together with Brier scores, suggested that XGBoost provided a favorable balance between sensitivity and specificity while maintaining acceptable probabilistic calibration. Calibration curves further suggested that XGBoost showed relatively stable agreement between predicted and observed risks across datasets, supporting its selection for subsequent SHAP-based interpretation and web deployment; SHAP identified age as the leading contributor to CKD risk. CONCLUSIONS: We developed an interpretable model using routine clinical indicators to predict 4-year CKD risk in elderly hypertensive adults, with applicability across Asian and Caucasian populations.
9 May 2026
Read appraisal →Journal of the American Society of Nephrology : JASN
Streamlining the Histopathologic Workflow in Diabetic Kidney Disease with Artificial Intelligence
KEY POINTS: Artificial intelligence models effectively generalized across studies and animal models and reduced translational gaps when applied to human biopsies. Artificial intelligence assistance reduced study evaluation turnaround times by up to 90% versus manual whole slide imaging scoring, matching expert-level performance. Self-supervised learning captured diabetic kidney disease-relevant features and mitigated expert-specific bias. BACKGROUND: Assessment of pathology end points in animal models of diabetic kidney disease is time-consuming and prone to expert bias. In addition, the sparsity of human kidney biopsy data hinders the development of translational models from animals to humans. METHODS: We developed an artificial intelligence (AI)-driven workflow to streamline histopathologic assessments in animal models of diabetic nephropathy. Our approach ( 1 ) detected glomeruli in whole slide images, ( 2 ) enabled fast expert scoring through an annotation tool, and ( 3 ) automated scoring. By leveraging unlabeled preclinical data for self-supervised learning, we enhanced AI scoring performance, reduced expert bias, and enabled the translation of AI scoring from animal models to human biopsies. To translate AI models from preclinical studies to human biopsies, we introduced a method that adjusted the feature extractor to human-specific features during inference without the need for annotated examples. RESULTS: Our annotation tool streamlined glomerular scoring, reducing turnaround time by 80%. Supervised AI models outperformed expert agreement and further reduced turnaround time by 90%, demonstrating generalization across studies involving both the same and different animal models. Without supervision, the self-supervised model achieved a κ value of 0.78, effectively identifying glomerular changes without guidance. Incorporating self-supervised learning into supervised training improved performance to κ=0.84 and reduced bias compared with individual experts ( P < 0.001). Our translational approach achieved a κ value of 0.63 on human glomeruli, although the model was trained exclusively on mouse glomeruli scores, reducing the translational gap by 45%. CONCLUSIONS: In this study, we accelerated and enhanced pathology readouts in a real-life pharmaceutical industry setting. We show that AI-assisted scoring reduced pathologists' workload and expedited study assessments. Self-supervised learning captured intrinsic properties of kidney morphology without expert annotation and reduced expert bias and translational discrepancies, greatly facilitating translational activities in drug development for patients with diabetic kidney disease.
5 May 2026
Read appraisal →The Journal of dermatological treatment
Modulation of mu-opioid pathway for the treatment of pruritus: a systematic review of 1,146 patients
BACKGROUND: Pruritus is a debilitating symptom and conventional therapies often provide incomplete relief. The μ-opioid receptor (MOR) pathway is a key regulator of itch, and μ-pathway-modulating agents are increasingly used off-label for refractory pruritus. AIM: This systematic review evaluates the efficacy, safety, and real-world use of μ-opioid pathway modulators for pruritus of dermatologic and systemic origin. METHODS: Following PRISMA guidelines (PROSPERO CRD420251207101), a comprehensive search of four databases identified 3,274 records, of which 69 studies met inclusion criteria, comprising 14 randomized controlled trials (RCTs; n = 795) and 55 non-randomized studies (n = 351). Owing to substantial clinical and methodological heterogeneity, findings were narratively synthesized. RESULTS: Five RCTs evaluated dermatologic conditions, including atopic dermatitis, prurigo nodularis, chronic urticaria, and lichen planopilaris, while nine RCTs assessed systemic pruritus, predominantly cholestatic and uremic pruritus. RCTs investigated oral or topical naltrexone, intravenous naloxone, nalmefene, and extended-release nalbuphine, with most demonstrating clinically meaningful reductions in pruritus intensity using validated itch scales. Non-randomized studies reported improvement across a wide range of dermatologic and systemic conditions, with adverse events generally mild and well tolerated. CONCLUSIONS: Overall, μ-opioid pathway modulators show promising efficacy with acceptable short-term safety across diverse pruritic conditions, although larger high-quality RCTs are required to define optimal dosing and support clinical translation.
2 May 2026
Read appraisal →Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Cardiac adverse events associated with axitinib: A real-world pharmacovigilance study based on the Japanese adverse drug event report database
OBJECTIVE: To evaluate the disproportionality, times to onset, and outcomes of cardiac adverse events (AEs) associated with axitinib, using data from the Japanese Adverse Drug Event Report (JADER) database. METHODS: We analyzed data for the period from April 2004 to December 2024. Those on cardiac AEs were extracted and the disproportionality of axitinib-associated AEs was assessed by calculating reporting odds ratios (RORs). RESULTS: Of the 2,988,681 reports with sex and age information, we identified 4788 reports of AEs associated with axitinib, including 508 cardiac AEs. Signals were detected for 16 cardiac AEs after adjustment for sex and age. Fatal outcomes were reported for five of these events with particularly high rates observed for cardiac failure, myocarditis, and immune-mediated myocarditis. Median times to onset indicated that cardiac AEs associated with axitinib occurred 14 to 420 days after administration. Weibull distributions showed that seven AEs (blood pressure increased, myocardial infarction, acute myocardial infarction, blood pressure systolic increased, oedema peripheral, oedema, and generalised oedema) occurred constantly throughout the exposure period (random failure type), and stress cardiomyopathy and immune-mediated myocarditis developed in a dose-dependent manner (wear-out failure-type). CONCLUSIONS: We focused on cardiac AEs associated with axitinib as post-marketing AEs. Some with fatal outcomes may occur not only early in treatment but also later in the course. Continuous monitoring is essential to ensure timely detection and management of these potentially serious AEs in clinical practice.
28 Apr 2026
Read appraisal →Journal of ethnopharmacology
Salvia plebeia R.Br.: A comprehensive review of its ethnopharmacology, phytochemistry, pharmacological activities, quality control, and current applications.
ETHNOPHARMACOLOGICAL RELEVANCE: Salvia plebeia R.Br. (Lamiaceae), the dried aerial parts, has been traditionally used across China and East/South Asia to treat sore throat, bronchitis, nephritis edema, abscesses, mastitis, hemorrhoids, and bleeding disorders, consistent with traditional indications of heat-clearing, detoxifying, cooling blood, and promoting diuresis. AIM OF THE REVIEW: This study provides a systematic overview of the ethnopharmacology, phytochemistry, pharmacological activities, quality control, and current applications of S. plebeia, while highlighting current research gaps and future perspectives. MATERIALS AND METHODS: A comprehensive literature search was conducted using multiple scientific databases, including Web of Science, PubMed, Elsevier, ACS, CNKI, and Google Scholar, covering publications from database inception to October 2025. Relevant information on the ethnopharmacology, phytochemistry, pharmacological activities, quality control, and applications of S. plebeia was systematically collected, screened, and synthesized according to predefined inclusion/exclusion criteria. RESULTS: To date, 325 constituents have been identified, dominated by flavonoids, phenolic acids, terpenoids, volatile oils, sterols, lignans, and others. Extracts and purified compounds show tradition-relevant anti-inflammatory, antiviral, immunoregulatory, and antioxidant activities, while other reported effects (hepatoprotective, hypolipidemic, cardiovascular-protective, sedative/anticonvulsant, anticancer, and anti-photoaging) are presented as exploratory findings. Quality evaluation evolved from UV and HPLC to UPLC-MS fingerprinting using flavonoids and sesquiterpenes as markers. In practical applications, S. plebeia has been used in medicinal preparations for the management of pharyngitis, bronchitis, nephritis, dermatitis, and anorectal inflammation. In food sector, it has been developed into functional products, including health teas, chewable or effervescent tablets, composite beverages, and co-fermented vinegars. CONCLUSION: Although S. plebeia shows broad pharmacological potential, current investigations remain constrained by incomplete chemical space coverage, limited pharmacokinetic and toxicological characterization, and insufficient well-designed clinical evidence. Future research should focus on comprehensive constituent profiling, mechanism-oriented validation aligned with traditional indications, standardized quality markers/fingerprints, and rigorous PK-toxicology-clinical translation, to promote safe, effective, and evidence-based utilization of S. plebeia in medicine and functional food development.
27 Apr 2026
Read appraisal →ACS sensors
Laser-Engineered MXene Heterostructure for Wearable Ammonia Sensors
Exhaled ammonia is a vital biomarker for clinical diagnosis of hepatorenal dysfunction and metabolic disorders. However, conventional sensors are often limited by complex fabrication processes, high operating temperatures, and particularly, compromised selectivity under high-humidity conditions. To overcome these challenges, we developed a laser-assisted heterostructure engineering strategy that enables in situ construction of Schottky barrier in MXene/polyacrylonitrile (PANF) nanofiber membranes, termed as a laser-engineered MXene (LEM) sensor. The laser micropatterning technique provides three key innovations: (1) kinetically controlled deposition of MXene (Ti3C2Tx) with minimal oxidation, (2) self-aligned formation of Schottky barriers through interfacial TiO2/MXene heterojunctions, and (3) creation of hierarchical gas transport channels within the 3D nanofibrous matrix. The optimized LEM sensor exhibits outstanding sensitivity (2.5% ppm-1), a low detection limit (0.2 ppm) at room temperature (25 °C), and humidity operation stability (> 10% response at 90% relative humidity). This performance surpasses that of conventional MXene-based sensors by fivefold in sensitivity without the need for thermal activation. Mechanism studies reveal that the laser-induced heterointerface facilitates charge transfer-dominated ammonia adsorption and desorption kinetics, while the nanofibrous architecture ensures molecular accessibility to active sites. This ambient-processable and scalable fabrication method paves the way for advanced wearable breath analyzers by combining manufacturing versatility, humidity-resistant excellent selectivity, and energy-efficient operation.
26 Apr 2026
Read appraisal →European journal of hospital pharmacy : science and practice
Algorithm-managed dosing and pharmacist-managed dosing of erythropoietin stimulating agents in renal anaemia: a systematic review
OBJECTIVES: The goal of this systematic review was to identify and summarise algorithm-managed and pharmacist-managed dosing of erythropoietin stimulating agents (ESA) in patients with renal anaemia and to determine the effects on available outcome parameters. METHODS: We followed the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines for systematic reviews. Studies investigating algorithm-managed and pharmacist-managed dosing of ESA in adult patients with renal anaemia were evaluated for inclusion. No restrictions were set on outcome parameters. Observational and interventional studies available as full-text articles with a control group and follow-up ≥6 months were eligible for inclusion. Relevant databases were searched from their inception through August 2024. Two independent reviewers evaluated all studies. The risk of bias was assessed by the ROBINS-I and RoB1 tools. The protocol of this study was registered in PROSPERO (CRD42021243678). RESULTS: After screening 140 articles, 17 articles and 4313 patients could be included. Available evidence was of low to moderate quality with a high risk of bias. Data were summarised and tabulated. Meta-analysis was not possible due to the substantial heterogeneity in participants, study design, interventions, comparisons, and outcome parameters. However, standardised metrics could be identified and calculated for haemoglobin and ESA dose. The percentage in target range for haemoglobin varied between 3.5% lower (95% CI -18.67% to +11.67%) to 32.0% higher (95% CI 14.07% to 49.93%) in the pharmacist-managed group versus the control group (n=1401). The range in reduction in ESA dose was 5.45% (95% CI -7.97% to +18.87%) to 49.97% (95% CI 20.32% to 79.61%) in the pharmacist-managed group versus the control group (n=2115). CONCLUSION: Low-quality data with high risk of bias suggest that pharmacist-managed renal anaemia may improve the percentage of haemoglobin within target range and reduce the ESA dose. However, meta-analysis was impossible due to substantial heterogeneity. Therefore, no definite conclusions could be drawn on the effectiveness of pharmacist-managed dosing of ESA in renal anaemia. PROSPERO REGISTRATION NUMBER: CRD42021243678.
26 Apr 2026
Read appraisal →Renal failure
Dapagliflozin improves hemoglobin and anemia in chronic kidney disease: a systematic review and meta-analysis
Anemia becomes increasingly prevalent as kidney function declines. Current treatments carry multiple safety risks. Sodium-glucose cotransporter 2 inhibitors (SGLT2i), dapagliflozin, have emerged as background anemia modulator, a promising alternative, showing protective effects against anemia. This study synthesizes evidence on dapagliflozin's potential to improve anemia in chronic kidney disease (CKD) patients. A systematic review and meta-analysis was performed according to Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, including eight studies identified from six databases. Risk of bias was assessed using tools appropriate for each study design (RoB 2, NOS, JBI). Data were pooled using a random-effects model with RevMan 5.4. The primary analysis for hemoglobin demonstrated significant increase (MD = 4.37; 95% CI = 0.71-8.03; p = 0.02) and remained significant in the sensitivity analysis (MD = 4.11; 95% CI = 0.19-8.04; p = 0.04). A sensitivity analysis for hematocrit revealed a highly significant increase (MD = 2.15; 95% CI = 1.86-2.44; p < 0.00001). In the sensitivity analysis for adverse events dapagliflozin significantly reduced the overall risk (RR = 0.77; 95% CI = 0.59-0.99; p = 0.04). This finding was associated with significantly lower mortality (RR = 0.67; p = 0.02), while no significant differences were observed for cardiovascular (p = 0.22) or genitourinary events (p = 0.70). Dapagliflozin is associated with clinically meaningful improvements in hemoglobin and anemia outcomes in CKD. These findings suggest a potential erythropoietic benefit beyond glycemic control, although dedicated anemia-focused trials are needed to confirm clinical applicability.
25 Apr 2026
Read appraisal →Renal failure
Factors influencing patency after percutaneous transluminal angioplasty for autogenous arteriovenous fistulae: a systematic review and meta-analysis
This systematic review and meta-analysis identified factors influencing patency after percutaneous transluminal angioplasty (PTA) for autogenous arteriovenous fistulae (AVF) in hemodialysis patients. Literature through July 2025 was searched for observational and randomized trials. Two reviewers independently performed study selection, quality assessment (Newcastle-Ottawa Scale/Cochrane tool), and data extraction. Meta-analyses used fixed-/random-effects models in R (per I2 statistic), with sensitivity and publication bias analyses. Fifty-two studies (n = 6,407) were included, 45 of high quality. For primary patency, independent risk factors were diabetes (OR = 1.02, 95% CI 1.01-1.02), lesion length >2 cm (OR = 2.87, 1.38-5.96), previous intervention (OR = 3.13, 1.69-5.79), failed AVF history (OR = 1.69, 1.22-2.35), brachiocephalic configuration (vs. radiocephalic, OR = 1.73, 1.33-2.26), vascular calcification, and multiple comorbidities. High post-PTA flow was protective, and drug-coated balloons outperformed conventional angioplasty. Secondary patency was compromised by diabetes (OR = 1.05, 95% CI 1.02-1.08), longer lesions (OR = 1.01, 95% CI 1.01-1.02), and residual stenosis (OR = 1.02, 95% CI 1.01-1.04), with procedural success protective. Intimal hyperplasia strongly predicted failure. Restenosis risk was elevated by diabetes (OR = 1.72, 95% CI 1.28-2.31) and hypertension (OR = 1.59, 95% CI 1.04-2.44), while medical therapy with nitrates (OR = 0.20, 95% CI 0.05-0.79) and higher albumin were protective. Conventional biomarkers had limited value. Long-term AVF patency after PTA depends on anatomical, historical, and pathological factors, emphasizing the need to target modifiable procedural and hemodynamic variables in clinical practice.
25 Apr 2026
Read appraisal →Renal failure
Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study
Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and accurate prediction remains challenging due to the lack of reliable biomarkers. Urinary small-molecule metabolomics offers a promising approach to capture the dynamic metabolic changes during the progression of S-AKI. In this two-center prospective observational study, we enrolled 360 children from both centers. Urine samples were collected within 24h after hospitalized children diagnosed with sepsis, stored at -80 °C, and analyzed using gas chromatography-mass spectrometry (GC-MS). Based on urinary metabolic fingerprints (U-MF), we developed and validated a machine learning model for early prediction of S-AKI. The Shapley Additive Explanations (SHAP) algorithm was applied to visually explain the optimal model. A panel of 10 metabolites was selected as common discriminative features. Among the 4 machine learning models evaluated, the support vector machine (SVM) demonstrated the best performance in both the discovery cohort (AUC 0.94, 95% CI: 0.91-0.98) and the external validation cohort (AUC 0.89, 95% CI: 0.82-0.96), enabling early prediction of S-AKI within 24 h. Furthermore, the U-MF panel was integrated into an open-access online platform to facilitate clinical translation. Our findings suggest that U-MF combined with machine learning holds promise as a robust and noninvasive approach with potential utility for early prediction of S-AKI in pediatric patients.
24 Apr 2026
Read appraisal →Renal failure
Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal dialysis
Peritoneal dialysis (PD) is a common treatment for end-stage renal disease, yet cardiovascular disease (CVD) remains a major cause of morbidity. Inadequate fluid management can elevate CVD risk, and bioelectrical impedance spectroscopy (BIS) is increasingly used to assess fluid status. This study aimed to develop an artificial intelligence model integrating BIS measurements and medical history to predict major adverse cardiovascular events (MACEs) within six months in clinically stable PD patients. Data were stratified by MACE occurrence and subject, with 80% assigned to training and 20% to testing. Class imbalance was addressed using the synthetic minority over-sampling technique. Four algorithms - logistic regression, random forest, XGBoost, and deep neural networks - were trained using fivefold cross-validation and grid search for optimal hyperparameters. Model performance was evaluated with area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Feature ablation experiments compared models using all 15 features, only 11 BIS-recorded features, and only 4 medical history features. The random forest model achieved the highest performance (AUC = 0.88), with CVD history as the most influential predictor. Isotonic regression calibration improved probability alignment (brier score = 0.0451). DCA suggested potential clinical benefit. While the model relied on four medical history features, its initial sensitivity was modest (0.68). Integrating BIS features significantly enhanced diagnostic sensitivity (0.84). The random forest model, based on 15 clinically accessible features, accurately predicts the risk of MACE within six months in clinically stable PD patients, demonstrating strong discriminatory ability with a performance reaching 88%.
24 Apr 2026
Read appraisal →Renal failure
Decoding the renal-cochlear axis: explainable machine learning and phenotype clustering reveal high-risk hearing loss subtypes in CKD
This study develops a dual-level machine learning framework for risk stratification and phenotyping of hearing loss (HL) in patients with chronic kidney disease (CKD) using data from the National Health and Nutrition Examination Survey (NHANES). From a cohort of 3,402 CKD patients, feature selection via univariate and multivariate logistic regression identified key predictors, which were used to construct predictive models with nine machine learning algorithms. The eXtreme Gradient Boosting (XGBoost) model demonstrated superior performance, with mean area under the curve (AUC) values of 0.984 (training), 0.984 (validation), and 0.939 (testing). SHapley Additive exPlanations (SHAP) interpretation identified age as the predominant risk determinant. Subsequent Gaussian mixture modeling (GMM) clustered patients into two distinct subtypes: a low-risk subgroup (n = 1,075) with a 1.58% HL prevalence and a high-risk subgroup (n = 2,316) characterized by older age, elevated blood urea nitrogen and bicarbonate levels, and a 48.2% HL prevalence. A classifier trained on these subtypes achieved discrimination (AUC = 0.99974). A clinically a web-based tool was also developed based on the six most influential features. The findings establish a dual-level predictive framework integrating explainable ML and unsupervised clustering for HL risk assessment in CKD. This approach provides a robust strategy for the precision screening of high-risk subpopulations and supports the integration of hearing assessments into routine CKD.
24 Apr 2026
Read appraisal →Annals of medicine
The effect of nutrition education interventions on dialysis patients' outcomes: a systematic review and meta-analysis
BACKGROUND: Non-adherence to dietary and fluid restrictions among dialysis patients is associated with adverse clinical outcomes. Quantifying the effectiveness of nutrition education interventions can inform practice and policy. METHODS: We searched MEDLINE, EMBASE, CINAHL, CENTRAL, PsycINFO, Web of Science, and Scopus up to 15 July 2025, supplemented by trial registries and Google Scholar, with an updated search through 8 April 2026. Eligible studies included randomized and non-randomized trials evaluating nutritional education interventions in adult dialysis populations. Risk of bias was assessed using RoB-2 and ROBINS-I, certainty graded using GRADE, and random-effects meta-analyses conducted alongside subgroup, sensitivity, and meta-regression analyses. Publication bias was assessed with Egger and Begg tests and trim-and-fill where applicable. RESULTS: Forty-four studies comprising 4,106 participants were included. Nutrition education significantly improved knowledge (SMD = 1.09; 95% CI: 0.67-1.51) and health-related quality of life (SMD = 1.43; 95% CI: 0.86-2.00; I² = 0%), and reduced serum potassium (SMD = -0.52; 95% CI: -0.91 to -0.14; I² = 92%) and serum phosphate (SMD = -0.35; 95% CI: -0.56 to -0.15; I² = 81%). Results for albumin, creatinine, sodium, calcium, and BUN were inconsistent and non-significant. Most outcomes were rated low or very low certainty by GRADE, reflecting inconsistency, indirectness, and imprecision. Potential publication bias was identified for certain outcomes. CONCLUSIONS: Nutrition education consistently improves knowledge and quality of life and may modestly reduce serum phosphate and potassium in dialysis patients. High-quality registered trials with standardized outcomes and longer follow-up are needed to establish effectiveness and sustainability. PROSPERO REGISTRATION: CRD420251119567.
24 Apr 2026
Read appraisal →Critical care explorations
The Effects of Conservative Fluid Management and Active Deresuscitation on Markers of Tissue Perfusion, Kidney Injury, and Vascular Injury in Critically Ill Adults: A Secondary Analysis of the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) Trial
OBJECTIVES: Test the hypothesis that conservative fluid management with active deresuscitation would not adversely affect tissue perfusion or kidney injury and would be associated with reduced vascular injury compared with usual care. DESIGN: Secondary analysis of the Role of Active Deresuscitation After Resuscitation-2 (RADAR-2) trial. SETTING: ICUs. PATIENTS OR SUBJECTS: Critically ill patients enrolled in the RADAR-2 trial. INTERVENTIONS: Conservative fluid management with active deresuscitation vs. usual care. MEASUREMENTS AND MAIN RESULTS: Measures of tissue hypoperfusion (whole blood lactate), acute kidney injury (AKIRisk score and urinary cystatin-C), and vascular injury (plasma hyaluronan, syndecan-1, and angiopoietin-2) were compared between groups. For each analyte, change from baseline was compared between groups and the median inter-group difference at each timepoint was estimated with bootstrapped CIs. Exploratory logistic regression examined associations between plasma biomarker levels (including N-terminal pro-B-type natriuretic peptide [NT-proBNP]), 28-day mortality, and treatment allocation. Whole blood lactate levels were similar between groups at all timepoints. Using change from baseline comparisons, no statistically detectable between-group differences were observed in AKIRisk scores or urinary cystatin-C levels. Plasma vascular injury biomarkers showed no statistically detectable between-group differences at any timepoint. High baseline hyaluronan (adjusted odds ratio [aOR], 5.75; 95% CI, 1.94-17.02; p = 0.002), syndecan-1 (aOR, 8.82; 95% CI, 2.67-29.15; p < 0.001), and NT-proBNP greater than 2500 pg/mL (aOR, 21.48; 95% CI, 3.57-129.41; p < 0.001) were independently associated with increased 28-day mortality. There was no evidence of differential treatment response based on these biomarker levels. CONCLUSIONS: Conservative fluid management and active deresuscitation were not associated with worsening tissue perfusion or acute kidney injury. A reduction in vascular injury markers was not observed. Given the modest sample size and resultant imprecision, clinically important effects cannot be excluded.
23 Apr 2026
Read appraisal →EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
Impact of renal function on edoxaban antithrombotic therapy in patients with atrial fibrillation and stable coronary artery disease: a prespecified analysis of the EPIC-CAD trial
BACKGROUND: Renal function is a critical factor of ischaemic and bleeding risks in patients with atrial fibrillation (AF) receiving antithrombotic therapy. AIMS: This study aimed to evaluate the impact of renal dysfunction in patients with AF and stable coronary artery disease (CAD) undergoing antithrombotic therapy. METHODS: The Edoxaban Versus Edoxaban With antiPlatelet Agent In Patients With Atrial Fibrillation and Chronic Stable Coronary Artery Disease (EPIC-CAD) trial randomised patients to edoxaban monotherapy or dual antithrombotic therapy (edoxaban plus a single antiplatelet agent). In this prespecified analysis, patients were stratified by creatinine clearance into low (<50 mL/min) or high (≥50 mL/min) groups according to edoxaban dose-reduction criteria. The primary endpoint was net adverse clinical events (NACE: death from any cause, myocardial infarction, stroke, systemic embolism, urgent revascularisation, or major/clinically relevant non-major bleeding) at 12 months. RESULTS: Of 1,040 randomised patients, 252 (24.2%) had low creatinine clearance; these patients were older and had more comorbidities compared with the 788 patients (75.8%) with high creatinine clearance. Patients with low creatinine clearance experienced higher risks of NACE (hazard ratio [HR] 1.72, 95% confidence interval [CI]: 1.19-2.49; p=0.004), ischaemic events (HR 2.70, 95% CI: 1.09-6.70; p=0.032), and bleeding (HR 1.54, 95% CI: 1.01-2.34; p=0.046). At 12 months, edoxaban monotherapy reduced NACE compared with dual therapy in both the low (12.1% vs 21.7%, HR 0.52, 95% CI: 0.28-0.98; p=0.042) and high creatinine clearance groups (5.2% vs 14.5%, HR 0.40, 95% CI: 0.25-0.65; p<0.001), with no interaction (p for interaction=0.53). CONCLUSIONS: In patients with AF and stable CAD, edoxaban monotherapy led to a lower risk of primary NACE than dual antithrombotic therapy, regardless of renal function. (ClinicalTrials.gov: NCT03718559).
21 Apr 2026
Read appraisal →Journal of clinical oncology : official journal of the American Society of Clinical Oncology
CALYPSO: Final Results of Savolitinib and Durvalumab Combination in Metastatic Papillary Renal Cancer
PURPOSE: The CALYPSO study demonstrated activity of savolitinib and durvalumab in MET-driven papillary renal cancer (PRC). We report final efficacy outcomes and exploratory circulating tumor DNA (ctDNA) biomarker analysis. METHODS: This single-arm phase II study evaluated savolitinib and durvalumab in treatment-naïve or pretreated PRC. End points included overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). FoundationOne CDx assessed DNA alterations and MET/PD-L1 or MET/tumor mutational burden (TMB) copositivity. ctDNA collected at baseline and on treatment was correlated with outcomes. RESULTS: At 41-months median follow-up, ORR was 34% (95% CI, 20.0 to 51.0) in the intention-to-treat (ITT) population (N = 41) and 53% (95% CI, 28.0 to 77.0) in MET-driven patients (n = 17). Median PFS was 6.5 (95% CI, 2.7 to 12.0) versus 13.9 months (95% CI, 2.9 to 23.8), and OS was 18.3 (95% CI, 7.3 to 30.7) versus 27.4 months (95% CI, 9.3 to 37.4), in the ITT population and the MET-driven population, respectively. PD-L1 (66% positive) and TMB (median 2.5 mut/Mb) status did not correlate with response. Baseline ctDNA positivity (10/21) correlated with shorter OS (median 7.3 v 33.3 months), while ctDNA clearance and mean variant allele frequency reduction correlated with improved OS (median 31.3 v 7.2 and 31.3 v 15.5 months, respectively). CONCLUSION: Savolitinib plus durvalumab shows OS in MET-driven PRC, supporting the ongoing SAMETA RIII trial (ClinicalTrials.gov identifier: NCT05043090). ctDNA may be a useful predictive biomarker.
21 Apr 2026
Read appraisal →PloS one
Treatment sequence patterns of urate-lowering therapy in Korean patients with gout: A common data model-based study
BACKGROUND: A treat-to-target strategy involving treatment modification improves outcomes in gout, but evidence remains limited regarding the optimal approach when initial urate-lowering therapy (ULT) fails. This study aimed to investigate real-world ULT sequence patterns and evaluate treatment retention based on the initial agent, modification type, and comorbidities. METHOD: We analyzed electronic health record data collected from 2010 to 2022 from the common data model databases of two hospitals. Adults aged 18 years or older diagnosed with gout who initiated ULT and were followed for at least 2 years were included. Outcomes included the frequency and sequence of ULT prescriptions. Treatment modification, defined as switching to another ULT or adding an additional agent, was considered the end of retention for the previous regimen. Subgroup analyses were performed based on comorbidity. RESULTS: Among 2220 patients, febuxostat was the most common first-line agent (51.4%), with 90.9% maintaining therapy. Among those who modified febuxostat therapy, switchers and add-on users continued treatment similarly (91.5% vs. 86.8%, P = 0.33). Of allopurinol initiators, 55.8% changed therapy, mainly switching to febuxostat or benzbromarone rather than adding another agent (51.4% vs. 4.1%, P < 0.001), with similar retention rates (91.5% vs. 86.8%, P = 0.33). Among benzbromarone initiators, 57.2% changed therapy (switchers, 56.7%; add-on users, 0.5%; P < 0.001), and retention rates were 90.5% and 100%, respectively (P = 1.00). Chronic kidney disease was associated with low variability in ULT sequence. CONCLUSIONS: ULT demonstrated durable retention when used as first- or second-line treatment, with switching being more common than add-on therapy and maintaining similar retention rates.
19 Apr 2026
Read appraisal →Computers in biology and medicine
Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: application to renal cancer CT scans
Deep learning survival (DLS) modeling has shown significant promise across multiple disease conditions for predicting patient outcomes via biomedical data, but remain relatively under-explored in conjunction with medical imaging. This poses the question of what methodological choices promote model generalization, especially when predicting continuous survival outcomes in external validation cohorts. In this work, we systematically examined the impact of data partitioning, data order, model initialization, and augmentation strategies on the generalization of CT-based DLS survival models in renal cancers. A multi-institutional CT cohort was assembled, leveraging public repositories such as TCGA-KIRC, TCGA-KICH, TCGA-KIRP, and KITS19; totaling 525 patients from across 9 institutions. A 3D ResNet-18 model was trained to predict overall patient survival with a Cox loss function and evaluated across three controlled experiments: (i) random versus covariate-balanced data partitioning, (ii) randomizing vs fixing data ordering and initial model weights, and (iii) varying intensity, spatial, and noise based augmentations; evaluated in terms of concordance index (c-index) and hazard ratio (HR). Intelligent covariate-balanced data partitioning demonstrated optimal performance on an external validation set (c-index 0.74, HR 5.08) compared to random partitioning (c-index 0.56, HR 0.29). Different model initializations significantly impacted the average performance and variance across replicate trainings, while data ordering had little to no effect. Combining multiple augmentations produced the best performance on external validation (c-index +4.76%, HR +44.39%) compared to no augmentations, while most impactful individual augmentation type was additive gaussian noise (c-index +3.17%, HR +23.47%). Our results demonstrate that careful selection of strategies for data partitioning and ordering, weight initialization, and image augmentation are critical for developing robust and generalizable DL models for continuous survival prediction using medical imaging data.
17 Apr 2026
Read appraisal →Journal of the International Society of Sports Nutrition
Effects of race distance and probiotics intervention on kidney, muscle, and gut injury and inflammation biomarker responses during running.
INTRODUCTION: Prolonged intense physical activity, such as long-distance running, may lead to systemic inflammation and cause organ injury, particularly to the kidneys. This study aimed to assess the impact of trail race running on kidney and muscle injury and gut inflammation biomarkers, and the potential mitigating effects of Lactiplantibacillus plantarum 299v (Lp299v) supplementation. METHODS: This randomized, double-blind, placebo-controlled study included 34 participants who completed 42 races, ranging from 20 to 164 km. Participants were divided into two groups, receiving either 40 bn CFU Lp299v or a placebo for 4 weeks before the race. Urine and fecal samples were collected pre- and post-race (immediately after, morning after and 24 h after the race) to measure biomarkers of muscle and kidney injury, and gut inflammation. Principal component analysis was used to create a single tubular kidney injury biomarker component variable (TKIBC1) positively associated with five separate tubular kidney injury biomarkers (MCP-1, KIM-1, GST-π, clusterin, and calbindin) at the three post-race time points. RESULTS: Running led to increased tubular and glomerular kidney injury markers, increased levels of fecal calprotectin, and, in some cases, elevated urine myoglobin levels. These effects were more pronounced in races ≥107 km (ultradistance). While Lp299v supplementation did not significantly influence TKIBC1, it was associated with a protective effect against gut inflammation. CONCLUSION: These findings suggest that prolonged intense exercise induces kidney and muscle injury as well as gut inflammation, with more severe effects observed in ultra-distance running. Lp299v may have some protective effects, particularly against gut inflammation, but further studies are needed to confirm these findings and explore the underlying mechanisms linking gut health and kidney injury during extreme physical exertion.
16 Apr 2026
Read appraisal →Clinica chimica acta; international journal of clinical chemistry
Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions.
Artificial intelligence (AI) and machine learning (ML) applications have emerged as transformative technologies in nephrology, particularly in dialysis care. The availability of multimodal datasets including electronic health records, hemodialysis machine data, laboratory values, and imaging has enabled the development of sophisticated prognostic, diagnostic, and treatment support tools. This comprehensive review narratively examines current AI/ML applications in dialysis, evaluates their clinical performance, and identifies future research directions. We conducted a comprehensive literature search of PubMed, Web of Science, and other major databases from 2020 to 2025, focusing on peer-reviewed studies that employed AI/ML techniques in dialysis care. Studies were categorised by application domain and analysed for methodology, performance metrics, and clinical implications. Our analysis identified five major application domains: (1) prediction and prognosis, including intradialytic hypotension (IDH) prediction with AUROC values ranging 0.89-0.95, mortality prediction achieving C-indices up to 0.83, and hospitalisation risk assessment; (2) early detection of chronic kidney disease (CKD) progression and dialysis risk; (3) clinical decision support for anaemia management and treatment optimisation; (4) vascular access monitoring with AI-driven image analysis achieving AUROC ≈ 0.96; and (5) natural language processing (NLP) applications for symptom detection. Federated learning (FL) approaches are emerging to enable multi-centre collaboration while preserving data privacy. AI/ML technologies demonstrate significant promise in enhancing dialysis care through improved prediction accuracy, personalised treatment approaches, and clinical decision support. However, widespread clinical adoption remains limited due to challenges including data privacy concerns, model interpretability issues, regulatory complexity, and the need for diverse, representative datasets.
16 Apr 2026
Read appraisal →Renal failure
Benefits of physical exercises in chronic kidney disease: a systematic review and meta-analysis of randomized controlled trials
Physical exercise has been proven to be beneficial for chronic kidney disease (CKD) patients; however, there is a lack of a comprehensive summary of existing literature, especially the results from randomized controlled trials (RCTs). We had conducted a systematic review and meta-analysis to evaluate the benefits of physical exercise in chronic kidney disease. Outcomes were classified into all-cause mortality, cardiovascular, pulmonary, and renal outcomes, inflammatory indices, laboratory tests, and physical indices. The risk of bias was assessed using the Cochrane criteria. Relative risk ratios with 95% confidence intervals (CIs) for categorical variables, and weighted mean difference or standardized mean differences with 95% CIs for continuous variables were pooled using a random-effects model. Publication bias was assessed using Funnel plot analysis and Egger's Test. At last, 82 studies were included, and the results of the meta-analysis show that the physical exercises can significantly reduce all-cause mortality, particularly in dialysis dependent patients. Cardiovascular function, reflected by 6-minute walk test and blood pressure and pulmonary function, reflected by peak oxygen uptake, forced expiratory volume in 1 second, and forced vital capacity, all improved after physical exercise interventions. Physical exercises helped to increase the dialysis adequacy and preserve the residual renal function. C-reactive protein and interleukin-6 levels decreased, and hemoglobin and albumin levels increased upon physical exercise interventions. The fat mass decreased after physical exercise interventions in non-dialysis patients. We concluded that physical exercise is beneficial for CKD patients, independent of the modes or locations of exercise.
16 Apr 2026
Read appraisal →International immunopharmacology
Machine learning derived proliferating T cell-related signature: a novel biomarker for prognosis and treatment efficacy in clear cell renal cell carcinoma
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits profound molecular heterogeneity, and reliable biomarkers for predicting clinical outcomes are urgently needed. Proliferating T cells (Tprolif) are central to immune system activation but their related signatures in predicting prognosis and therapeutic effect of ccRCC patients remains unexplored. METHODS: We developed a Tprolif-related RCC score (TRRS) using an integrative machine learning framework. Transcriptomic data from the CheckMate025 trial formed the discovery cohort. The model was validated across multiple independent cohorts, including IMmotion151, JAVELIN Renal 101, TCGA-KIRC, and West China Hospital (WCH) cohort from our center. Multi-omics analyses, including spatial and single-cell transcriptomics, were employed to investigate the associated biology and identify key mediators. RESULTS: The final TRRS model, built from 7 genes, demonstrated robust performance in stratifying patients for overall survival (OS) and progression-free survival (PFS) in training and all validation sets. TRRS was a powerful predictor of improved outcomes not only for immune checkpoint inhibitor (ICI) monotherapy but also for ICI-based combination therapy and targeted therapies. Biologically, a high TRRS was associated with aggressive tumor hallmarks, a distinct metabolic profile favoring aerobic glycolysis and glutamine metabolism, and an immunosuppressive tumor microenvironment despite high immune cell infiltration based on WCH cohort transcriptome expression profile. Through multi-omics screening, we identified CST3 as a key target, with spatial and single-cell analyses confirming its role in promoting tumor malignancy and enhancing cell-cell communication among microenvironment components. CONCLUSION: The TRRS is a novel, validated biomarker that effectively predicts prognosis and therapeutic responses in advanced ccRCC. It reflects critical biological features of tumor aggressiveness and immune evasion, with CST3 emerging as a potential central mediator and therapeutic target.
15 Apr 2026
Read appraisal →A&A practice
Hypotension Prediction Index-Guided Hemodynamic Management on Postoperative Organ Outcome: A Systematic Review and Meta-Analysis of Randomized Clinical Trials
Intraoperative hypotension (IOH) is a frequent occurrence during noncardiac surgery. Hypotensive episodes can compromise tissue perfusion, and cumulative exposure has been associated with adverse outcomes such as acute kidney injury (AKI), myocardial injury, and increased mortality. The Hypotension Prediction Index (HPI) is an artificial intelligence tool designed to predict hypotensive events before they occur, allowing clinicians to intervene proactively to prevent or mitigate hypotension. Although randomized trials and pooled analyses have shown its effectiveness in reducing both the incidence and duration of IOH, its impact on patient-centered outcomes remains unclear. From 1946 to August 8, 2025, we prospectively queried MEDLINE (PubMed), Embase, Cochrane Library, and Web of Science. We included randomized controlled trials in adult surgical patients with invasive arterial monitoring, comparing HPI-guided perioperative hemodynamic management to standard care. Primary outcome was postoperative AKI, and secondary outcomes were postoperative myocardial injury, pneumonia, arrhythmia, stroke, postoperative cognitive dysfunction, including delirium, surgical site infection, length of hospital stay, and mortality. Two authors independently evaluated the risk of bias according to the methods described in the Cochrane Handbook for Systematic Reviews of Interventions. A meta-analysis was performed using RevMan 5.4.1 software. Dichotomous data were presented as risk ratio, 95% confidence interval [CI]; continuous data were presented as mean difference, 95% CI. All results were pooled using random-effects models. Of 883 imported references, 14 studies (N = 2030) were included for final analysis. No significant difference in incidence of postoperative AKI was observed between HPI-guided management and standard care, RR = 0.87, 95% CI (0.71-1.07). Postoperative myocardial injury was significantly reduced in HPI-guided management group, RR = 0.61, 95% CI (0.39-0.96), although exclusion-based sensitivity analysis suggests that this finding may not be robust. No other significant differences were observed for hospital length of stay, arrhythmia, stroke, cognitive dysfunction (including delirium), surgical site infection, pneumonia, or mortality. Although HPI-guided management is associated with a reduction in postoperative myocardial injury, its significance does not withstand sensitivity analysis. All other postoperative complications, including AKI incidence, are not reduced in the intervention groups. This study has limitations inherent to the heterogeneity of included patient populations, surgical interventions performed, standard of care practices, and reporting of postoperative outcomes.
15 Apr 2026
Read appraisal →Human mutation
Use of Single-Cell Data and scPagwas Analysis to Identify T Cell Subsets and Construct a Prognostic Model for Clear Cell Renal Cell Carcinoma
BACKGROUND: Clear cell renal cell carcinoma (KIRC), the most prevalent pathological renal cell carcinoma (RCC) subtype, makes up approximately 75%-84% of total cases. KIRC is characterized by high heterogeneity, high metastasis rates, and a poor prognosis. Its incidence rate has continued to rise in recent years. We sought to construct new prognostic models to optimize treatment decisions, improve clinical benefits, and explore potential therapeutic targets. METHODS: This study integrated various omics data including single-cell RNA seq (GSE171306), TCGA-KIRC, GWAS, and validation datasets (GSE29609 and E-MTAB-1980). The scPagwas algorithm combines GWAS with scRNA-seq to identify immune subgroups with high feature correlation. Key genes are identified through the combination of weighted correlation network analysis (WGCNA) with differentially expressed genes (DEGs). We built a clinical prognostic model by using machine learning algorithms and validated it through survival rate and receiver operating characteristic (ROC) analysis. We used cancer drug sensitivity genomics data to analyze drug sensitivity and performed molecular docking to identify potential therapeutic drugs. RESULTS: Using single-cell RNA seq data, we identified T cell subsets as characteristic cell subsets in KIRC through scPagwas analysis. In single-cell analysis, key genes in T cell subsets and genes with PCC values > 0.05 were combined with the core genes in DEGs and WGCNA modules, thus yielding 86 intersecting genes. These genes were significantly enriched in immune-related pathways. We established a clinical prognostic model containing seven risk genes. Low-risk patients exhibited substantial survival advantages. Time-dependent ROC analysis indicated the prognostic model's excellent clinical predictive value. Functional enrichment, immune infiltration, and somatic mutation analyses highlighted different biological mechanisms among risk populations. The SHAP values of the XGBoost and LightGBM machine learning algorithms indicated DOCK8 as a potential biomarker. Drug prediction and molecular docking predicted five potential drugs targeting DOCK8 (finasteride, nocodazole, palonosetron, pifithrin alpha, and topiramate). CONCLUSION: Our systematic analysis of the immune microenvironment, key genes, and prognosis of KIRC highlighted the critical roles of T cell subsets. We additionally established an effective clinical prognostic model. Our findings provide new insights and potential targets for the precise diagnosis and targeted KIRC therapy.
11 Apr 2026
Read appraisal →Journal for immunotherapy of cancer
Simlukafusp alfa (FAP-IL2v) plus atezolizumab with or without bevacizumab in unresectable, metastatic renal cell carcinoma: a randomized, open-label phase Ib study
BACKGROUND: Simlukafusp alfa (FAP-IL2v) was engineered to preferentially activate CD8+ T and natural killer (NK) cells in tumor microenvironments overexpressing fibroblast activation protein (FAP). Checkpoint inhibitors combined with antiangiogenic agents are standard therapy for metastatic renal cell carcinoma (mRCC), which overexpresses FAP. Here, we explored the efficacy, safety, and pharmacodynamic effects of FAP-IL2v in combination with atezolizumab with or without bevacizumab in patients with mRCC. METHODS: Patients with treatment-naïve or pretreated clear cell and/or sarcomatoid mRCC were eligible. Dose escalation explored FAP-IL2v every 2 weeks (Q2W) with atezolizumab Q2W (doublet, arm A), and with atezolizumab and bevacizumab Q2W (triplet, arm B) in patients treated with up to one prior systemic therapy. Dose extension explored in untreated patients the recommended FAP-IL2v dose administered Q2W (doublet, arm A; and triplet, arm B) or 3-weekly (doublet, arm C; and triplet, arm D). Primary objectives were the recommended FAP-IL2v dose and antitumor activity. Secondary objectives included safety, pharmacodynamics, and exploratory biomarkers in peripheral blood and paired biopsies. RESULTS: By the data cut-off date (31 August, 2021), 66 patients were enrolled. The median duration of treatment was 11.0 months. Objective response rates (ORRs) were 25% for the doublet and 47% for the triplet, and median progression-free survival was 6.3 and 18.3 months, respectively. Safety profiles were consistent with the individual drugs, including expected interleukin-2 (IL-2) class-specific adverse events (AEs). Two deaths were recorded caused by AEs related to study treatment (acute kidney injury, n=1; pancytopenia, n=1). Expansion and activation of NK and T cells, but not regulatory T cells, was observed in peripheral blood, leading to increased tumor infiltration and inflammation in paired biopsies. The addition of bevacizumab led to a reduced angiogenesis signature score and reduced vessel density. CONCLUSION: The combination of FAP-IL2v plus atezolizumab with or without bevacizumab was consistent with the known safety profile of the individual drugs. The maximum tolerated dose was not reached, with a recommended FAP-IL2v dose of 10 mg. ORR was higher among patients receiving the triplet therapy compared with the doublet. Pharmacodynamic results were consistent with the mechanism of action of IL-2, supporting further research in this field.
11 Apr 2026
Read appraisal →Lupus science & medicine
Unsupervised clustering of clinical and laboratory features of systemic lupus erythematosus: insights from a multicentre cohort
OBJECTIVE: This study explored the relationship between clinical phenotypes and immuno-molecular features of systemic lupus erythematosus (SLE) using unsupervised machine learning and multi-omics integration. METHODS: A multicentre cohort of 1065 SLE patients from five teaching hospitals was studied. Unsupervised clustering was performed using integrated clinical and laboratory data to identify distinct phenotypic clusters. Multi-omics profiling, including proteomics and transcriptomics, was conducted for each cluster, comparing with healthy controls. In addition, 899 patients were prospectively followed for over 1 year to assess therapeutic responses and remission outcomes. RESULTS: Unsupervised clustering identified five distinct clusters of SLE based on clinical phenotypes and immunological signatures, and an intrinsic association was found between each cluster and immune-inflammatory features. Cluster 1 (renal; 14.27%) had predominant renal involvement (87.5%) and high disease activity. Cluster 2 (mucocutaneous; 39.53%) exhibited mainly cutaneous manifestations (72.45%) with mild visceral involvement and the highest rates of achieving low disease activity state (46.42%). Cluster 3 (neuropsychiatric; 14.74%) had a high frequency of neuropsychiatric manifestations (96.18%) and anti-U1 ribonucleoprotein antibodies positivity (42.04%). Cluster 4 (haematologic; 22.63%) was characterised by haematologic involvement, predominantly cytopenia (95.85%), with frequent antiphospholipid antibody positivity and direct Coombs positivity (87.55%). Cluster 5 (cardiopulmonary; 8.83%) exhibited prominent cardiopulmonary involvement (97.87%) and serositis, with enrichment of inflammatory CD161+ regulatory T cells. Transcriptomic and proteomic analyses confirmed distinct molecular signatures and cluster-specific enrichment of biological pathways. Key molecule apolipoprotein A4 was validated using enzyme-linked immunosorbent assay. Regarding therapeutic outcomes at 1 year follow-up, Cluster 2 had the highest proportions achieving lupus low disease activity state (46.42%) and tapering glucocorticoids. Cyclophosphamide use was associated with greater clinical improvement (r=0.284; p=0.023) in Cluster 3, while in Cluster 5, rituximab use was associated with favourable responses (r=0.286; p=0.019). CONCLUSIONS: We identified five SLE clusters with distinct clinical, immunological features and treatment outcomes, demonstrating an intrinsic link between clinical involvement and immune-inflammatory alterations.
9 Apr 2026
Read appraisal →JMIR human factors
e-Mental Health Intervention Preferences Among Informal Caregivers of Adults Living with Chronic Kidney Disease: Semistructured Interview Study
BACKGROUND: Informal caregivers of people living with chronic kidney disease commonly experience mental health difficulties. However, there is currently limited access to scalable psychological interventions such as those using e-mental health platforms. Furthermore, there is an absence of such interventions tailored for this group of caregivers, which may limit acceptability. OBJECTIVE: To inform future e-mental health intervention development, this study aimed to explore preferences for e-mental health interventions among informal caregivers of people living with chronic kidney disease. METHODS: A total of 13 informal caregivers of people living with chronic kidney disease participated in semistructured interviews. Eligible informal caregivers lived in the United Kingdom and were recruited via nonprofit organizations and social media advertisements. Semistructured interviews followed a topic guide exploring caregivers' e-mental health intervention preferences. Data were analyzed using manifest content analysis. RESULTS: A total of 4 categories and 13 subcategories were generated. "Design preferences" illustrates key intervention design features that were important to caregivers, such as involving caregivers and other interest-holders in the design process. "Content preferences" describes the information caregivers valued in an intervention, such as information about chronic kidney disease and resources to support caregivers. "Support preferences" reflects the characteristics of support caregivers preferred if support was available as part of the intervention, such as peer support and providing support that accommodates individual preferences. Lastly, "Dissemination preferences" describes methods caregivers recommended to promote intervention uptake, such as engaging communication strategies and trusted and accessible sources of information. CONCLUSIONS: Caregivers identified a number of e-mental health intervention design and delivery preferences that should be considered when developing e-mental health interventions for caregivers of people living with chronic kidney disease. To ensure such interventions are acceptable, feasible, and relevant, co-design approaches should be adopted during the intervention development process.
8 Apr 2026
Read appraisal →Journal of the American Heart Association
Development and Validation of a Machine Learning Model for Incident Heart Failure Prediction in Chronic Kidney Disease: A Multicenter Cohort Study
BACKGROUND: Chronic kidney disease (CKD) and heart failure (HF) share pathophysiological mechanisms, rendering HF one of the most burdensome cardiovascular complication in CKD. Current HF prediction models, derived from the general population, exhibit limited accuracy in CKD, thus necessitating a CKD-specific risk model and clinical implementation. METHODS: The development set comprised 52 251 patients with CKD from the China Renal Data System (70% training; 30% internal validation). External validation used 21 798 patients from independent Chinese hospitals and 3323 UK Biobank participants. Outcome was 5-year new-onset HF. Five machine learning models were developed, with performance assessed via area under the curve and compared using DeLong test. The top-performing extreme gradient boosting model was simplified via forward stepwise selection; feature importance quantified using Shapley additive explanations. RESULTS: In the Chinese external validation cohort, the extreme gradient boosting model outperformed others (area under the curve, 0.879 [95% CI, 0.871-0.887]; DeLong P<0.05), with an area under the curve of 0.851 (95% CI, 0.831-0.870) in the UK Biobank cohort. The model was simplified to 9 predictors; Shapley additive explanations analysis ranked estimated glomerular filtration rate and albuminuria as among the most important features. In Chinese and UK Biobank external validation sets, the simplified extreme gradient boosting model showed a ΔC-statistic of 0.050 (0.045-0.056) and 0.006 (-0.013-0.024) versus the Atherosclerosis Risk in Communities risk score, and 0.194 (0.183-0.204) and 0.145 (0.107-0.182) versus the Predicting Risk of Cardiovascular Disease Events Equation, respectively. A web-based calculator was deployed (https://clinician.shinyapps.io/HF_Risk_Predictor_for_CKD/). CONCLUSIONS: The 9-variable extreme gradient boosting model tailored for patients with CKD may help predict HF risk in this high-risk population. Validation across diverse populations is warranted to confirm its generalizability.
8 Apr 2026
Read appraisal →Medical science monitor : international medical journal of experimental and clinical research
AI-Assisted Dialysis Decision-Making: Assessing Agreement Between ChatGPT and Nephrologist in Initial Dialysis Indication and Prescription in Emergency and ICU Settings
BACKGROUND Artificial intelligence (AI) is increasingly explored as a clinical decision-support tool in nephrology; however, its real-world applicability for dialysis decision-making in emergency and intensive care unit (ICU) settings remains insufficiently studied. Hemodialysis initiation and prescription are complex, time-sensitive, and dynamic processes that require expert clinical judgment. MATERIAL AND METHODS This retrospective observational study evaluated agreement between AI-generated (ChatGPT) and nephrologist-made dialysis decisions in emergency and ICU settings. Adult patients undergoing first-time dialysis were included. Agreement was assessed for dialysis initiation, modality selection, and key prescription parameters. To ensure clinical relevance, continuous prescription variables were categorized into predefined ranges. Agreement was quantified using Gwet's AC1 coefficient and Cramer's V statistic. RESULTS Eighty-four patients were included. AI demonstrated 100% agreement with nephrologists regarding dialysis initiation. Overall agreement for dialysis modality selection was 92.9% (Cramer's V=0.87, P<0.001). Agreement for core dialysis prescription parameters - including blood flow rate, dialysate sodium, potassium, and calcium concentrations - was high across modalities (all P<0.001). Lower agreement was observed for ultrafiltration-related parameters, particularly ultrafiltration duration, reflecting the individualized and dynamic nature of volume management during dialysis. CONCLUSIONS AI-assisted decision support demonstrated high agreement with nephrologist decisions for initial dialysis initiation, modality selection, and core prescription parameters in emergency and ICU settings. Discrepancies were primarily confined to ultrafiltration-related decisions, underscoring the necessity of ongoing bedside clinical judgment. These findings support the role of AI as a decision-support tool rather than a replacement for clinician-led dialysis management.
8 Apr 2026
Read appraisal →Journal of ethnopharmacology
Evaluation of the association of pseudoaldosteronism with licorice-containing herbal medicine using the Japanese adverse drug event report (JADER) database
ETHNOPHARMACOLOGICAL RELEVANCE: Herbal medicine in Japan is known as Kampo preparation, a traditional medicine that combines multiple medicinal herbs and is widely used in medical practice. Pseudoaldosteronism is an issue associated with herbal medicines that contain licorice. However, the relationship between licorice intake and pseudoaldosteronism is not fully understood and is debatable. AIM OF THE STUDY: This study evaluates the association between herbal medicine preparations containing licorice and pseudoaldosteronism using data from the Japanese Adverse Drug Reaction Reporting System (JADER) database. MATERIALS AND METHODS: The JADER database contained 961,171 reports published between April 2004 and December 2024. The association between herbal medicines and pseudoaldosteronism was evaluated using the pharmacovigilance index as the reporting odds ratio (ROR) and logistic regression models. RESULTS: There were 537 cases of pseudoaldosteronism (PT code: 10037113). The reporting ratio of pseudoaldosteronism was higher in the licorice-containing herbal medicines group than in the overall herbal medicines group. The RORs of the top 20 herbal medicines with the highest number of pseudoaldosteronism reports, all of which contained licorice, exceeded one, and a signal was detected. The risk of pseudoaldosteronism increased with increasing licorice intake. According to the receiver operating characteristic (ROC) curve for pseudoaldosteronism (area under the curve (AUC) = 0.71, sensitivity = 0.8493, 1-specificity = 0.5530), the cutoff value was 1.125 g/day. CONCLUSIONS: This study is the first to clarify the association between licorice intake from herbal medicines and pseudoaldosteronism using Japan's spontaneous adverse event-reporting database. These findings will be useful and informative for medical professionals considering treatments with herbal medicines.
6 Apr 2026
Read appraisal →PloS one
Real-world safety of aliskiren in primary hypertension: A cross-database study
Hypertension is one of the main causes of cardiovascular diseases worldwide, affecting over one billion people. Although aliskiren offers a valuable option for inhibiting the renin-angiotensin system, its safety profile in the real world remains insufficiently explored, especially for rare or under-recognized adverse events (AEs), which have not been fully clarified. Therefore, leveraging large-scale post-marketing surveillance data is crucial for identifying rare AEs and guiding safer clinical practice. This study aims to elucidate pharmacovigilance signals associated with aliskiren (an antihypertensive drug) by systematically analyzing the characteristics of adverse events (AEs) from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database and WHO-VigiAccess database, which provides a reliable scientific basis for clinical practice and regulatory decision-making. We conducted a retrospective quantitative analysis of aliskiren-related AE reports from the aforementioned two databases, employing the Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms for signal detection. The results indicate that there were 5,596 and 5,549 aliskiren-related reports in the FAERS and WHO-VigiAccess databases, respectively. The median duration of these AEs during the observation period was 62 days, with an interquartile range (IQR) of 7-282 days. In both databases, signals for aliskiren were distributed across 28 System Organ Classes (SOCs), among which investigations, cardiac disorders, renal and urinary disorders, vascular disorders, and metabolism and nutrition disorders exhibited significant signals based on specific criteria applied across the four algorithms. A total of 607 preferred terms (PTs) with significant disproportionality signals were detected using the four algorithms, including potential AEs not previously well-documented, such as palpitations, myalgia, proteinuria, muscular weakness, pulmonary edema, and pollakiuria. This study not only confirms the known adverse reactions of aliskiren but also uncovers new potential risks, highlighting the importance of strengthening drug safety monitoring to enhance therapeutic efficacy and reduce the risk of adverse reactions. It provides valuable safety insights for physicians considering the use of aliskiren in the management of primary hypertension.
5 Apr 2026
Read appraisal →Scientific reports
Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing.
Large language models (LLMs) are increasingly applied in clinical contexts; however, to our knowledge, their performance in acute kidney injury (AKI) has not been directly compared with physician performance on standardized clinical knowledge assessments. We conducted a cross-sectional study to evaluate the performance of 13 publicly available LLMs and 123 volunteer participants at the 131st Annual Congress of the German Society of Internal Medicine in Wiesbaden, Germany. Both groups completed an identical AKI knowledge assessment consisting of two case vignettes and 15 single-best-answer multiple-choice questions. LLMs achieved a mean score of 13.5 out of 15 (90%), with several models reaching a perfect score, while human participants averaged 7.3 out of 15 (48.7%). Only 16.3% of participants scored 11 points or higher. As an illustrative example, ChatGPT-4o completed the test in approximately 0.5 minutes, whereas humans required a mean of 7.3 minutes. These findings demonstrate that LLMs substantially outperformed a heterogeneous group of medical professionals in AKI knowledge assessments and did so with markedly greater efficiency. While this highlights the potential of LLMs as rapid, cost-effective tools for clinical knowledge support, their role in real-world patient care remains undetermined, and human clinical judgment remains essential to ensure safe, context-sensitive, and patient-centered care.
5 Apr 2026
Read appraisal →Clinical chemistry
Discovery of an Artificial Intelligence Label Feedback Loop: How the Success of a Clinically Implemented Artificial Intelligence Algorithm Has Created an Unforeseen Challenge to Algorithm Retraining
BACKGROUND: Artificial intelligence (AI) augmented laboratory tests can improve quality, efficiency, cost-effectiveness, and staff satisfaction. However, the clinical success of these tests can introduce unforeseen challenges for model retraining. This study describes the discovery of an "AI label feedback loop" in a clinically implemented AI-augmented kidney stone composition test. METHODS: An AI-augmented kidney stone composition test (V1) has been previously deployed for clinical kidney stone characterization. After several years of clinical use, a retrained model (V2) was developed using 6 times more data. Model performance of both V1 and V2 were evaluated across 3 datasets: a recent production validation (hold-out) set (mostly AI-influenced labels), the original V1 validation set (pre-AI, entirely human-labeled), and a subset of recent cases with exclusively human-generated or human-corrected labels. RESULTS: V2 demonstrated a 10% lower concordance rate than V1 when evaluated on the recent production hold-out set, despite a much larger training dataset. Performance between V1 and V2 was similar when applied to the pre-AI validation set. Notably, V2 outperformed V1 on the recent subset of cases with human-only or human-corrected labels, particularly for less-common stone types. These findings revealed an AI label feedback loop, confounding retraining and evaluation. CONCLUSIONS: The integration of AI into clinical practice can potentially influence reported test results, complicating the development and evaluation of future models. To mitigate AI label feedback loops, ongoing human annotation and careful validation set construction are essential. These strategies can ensure reliable performance assessment and support the safe evolution of clinical AI systems.
4 Apr 2026
Read appraisal →Clinical chemistry
Towards Clinical Integration of Deep Learning-Based Classification of Urinary Sediment Particles from Digital Microscopy Images: A Prospective Study
BACKGROUND: Urinalysis is a standard clinical test that includes the microscopic examination of urinary sediment to identify formed elements. Manual evaluation by laboratory technicians is time-intensive and subject to human error. Automated analysis using digital microscopy images presents a potential alternative. This study evaluates the integration of a deep learning approach to automatically classify urinary sediment images in the clinical laboratory, including independent prospective validation of its performance. METHODS: An annotated data set comprising 13 classes of urinary sediment elements was created from a database of Sysmex UD-10 digital microscope images. An EfficientNet-based model was trained and tested across three experimental scenarios to evaluate the effects of data collection strategies on performance. Uncertainty calibration was examined. The model's robustness and interpretability were examined using gradient-weighted class activation mapping (Grad-CAM) to visualize influential image regions and t-distributed stochastic neighbor embedding (t-SNE) to analyze learned feature embeddings. Lastly, a graphical user interface was developed for a prospective evaluation in the laboratory. RESULTS: The model achieved approximately 97% overall accuracy on the test set. Experiments revealed sensitivity to data set variability, suggesting that performance may improve by integrating additional training examples. Confidence scores aligned with accuracy, and interpretability analyses showed that the model focused on relevant image regions and learned embeddings demonstrated clear class separation. In the prospective evaluation, top 1 and top 3 accuracies decreased to approximately 78% and 92%, respectively. CONCLUSIONS: Our results indicate that a lightweight deep learning model can achieve high performance in classifying urine particles. Analysis of discrepancies between retrospective and prospective evaluations provides important insights toward reliable clinical application.
4 Apr 2026
Read appraisal →Kidney360
Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration
Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: ( 1 ) rigorous multicenter validation focused on clinical outcomes, ( 2 ) integration of uncertainty quantification and causal modeling into AI tools, ( 3 ) development of clinician-centered interfaces that minimize cognitive load, and ( 4 ) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.
1 Mar 2026
Read appraisal →Gut Microbes
Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease
Diabetic kidney disease (DKD) is a serious healthcare dilemma. Nonetheless, the interplay between the functional capacity of gut microbiota and their host remains elusive for DKD. This study aims to elucidate the functional capability of gut microbiota to affect kidney function of DKD patients. A total of 990 subjects were enrolled consisting of a control group (n = 455), a type 2 diabetes mellitus group (DM, n = 204), a DKD group (n = 182) and a chronic kidney disease group (CKD, n = 149). Full-length sequencing of 16S rRNA genes from stool DNA was conducted. Three findings are pinpointed. Firstly, new types of microbiota biomarkers have been created using a machine-learning (ML) method, namely relative abundance of a microbe, presence or absence of a microbe, and the hierarchy ratio between two different taxonomies. Four different panels of features were selected to be analyzed: (i) DM vs. Control, (ii) DKD vs. DM, (iii) DKD vs. CKD, and (iv) CKD vs. Control. These had accuracy rates between 0.72 and 0.78 and areas under curve between 0.79 and 0.86. Secondly, 13 gut microbiota biomarkers, which are strongly correlated with anthropometric, metabolic and/or renal indexes, concomitantly identified by the ML algorithm and the differential abundance method were highly discriminatory. Finally, the predicted functional capability of a DKD-specific biomarker, Gemmiger spp. is enriched in carbohydrate metabolism and branched-chain amino acid (BCAA) biosynthesis. Coincidentally, the circulating levels of various BCAAs (L-valine, L-leucine and L-isoleucine) and their precursor, L-glutamate, are significantly increased in DM and DKD patients, which suggests that, when hyperglycemia is present, there has been alterations in various interconnected pathways associated with glycolysis, pyruvate fermentation and BCAA biosynthesis. Our findings demonstrate that there is a link involving the gut-kidney axis in DKD patients. Furthermore, our findings highlight specific gut bacteria that can acts as useful biomarkers; these could have mechanistic and diagnostic implications.
1 Dec 2025
Read appraisal →Cardiovasc Diabetol
Association of estimated glucose disposition rate with aging acceleration and mortality risk in individuals with cardiovascular-kidney-metabolic syndrome: evidence from two large national population-based studies
OBJECTIVE: This study investigated the relationship between estimated glucose disposal rate (eGDR), aging acceleration (AgeAccel), and mortality in adults diagnosed with cardiovascular-kidney-metabolic (CKM) stages 1 to 4. METHODS: The study utilized data from 4,826 adults with CKM syndrome stages 1 to 4, collected from the National Health and Nutrition Examination Survey (NHANES) conducted during the 2005-2010 survey cycles. The assessment of AgeAccel was performed using two complementary measures: phenotypic AgeAccel (PhenoAgeAccel) and biological AgeAccel (BioAgeAccel). Survey-weighted logistic regression and Cox proportional hazards models were used to assess the associations of eGDR with AgeAccel and mortality risk, respectively. To assess the prognostic value of eGDR for mortality risk, we implemented a suite of nine distinct machine learning models. Additionally, a nomogram was developed to enhance the clinical applicability of our findings. Furthermore, we performed causal mediation analysis to quantify the proportion of the total effect of eGDR on mortality that was mediated through AgeAccel. To ensure the robustness of the results, we replicated our primary analyses using data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) cohort. RESULTS: Our analysis included 4,826 NHANES participants, among whom we documented 831 all-cause mortality events and 208 cardiovascular disease (CVD)-specific deaths during follow-up. In multivariable-adjusted Cox regression models, each unit increase in eGDR was significantly associated with a 10% reduction in all-cause mortality risk (Hazard ratio [HR] = 0.90, 95% Confidence interval [CI] 0.86-0.93) and a 13% decrease in CVD mortality risk (HR = 0.87, 95% CI 0.81-0.93). Additionally, eGDR showed a negative association with AgeAccel, including both BioAgeAccel (odds ratio [OR] = 0.85, 95% CI 0.82-0.87) and PhenoAgeAccel (OR = 0.78, 95% CI 0.75-0.80). For predicting all-cause mortality from eGDR, the K-Nearest Neighbors (KNN) showed superior discrimination (Area Under the Curve [AUC]: 0.926), exceeding the performance of other machine learning algorithms in a comparative evaluation. Mediation analysis revealed that the protective effect of higher eGDR was partially explained by slower PhenoAgeAccel, with mediation effects accounting for 23.53% and 15.73% of the total impact on all-cause and CVD mortality, respectively. CONCLUSIONS: In the CKM population, lower eGDR levels may be associated with both AgeAccel and an increased risk of mortality, with AgeAccel potentially mediating the relationship between eGDR and mortality. These findings suggested that eGDR could serve as a potential predictor and intervention target for delaying aging and reducing mortality risk.
1 Oct 2025
Read appraisal →