Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 23 appraisals
Journal of the American Medical Informatics Association : JAMIA
Alert fatigue measurement in clinical decision support: a systematic review
BACKGROUND: Alert fatigue is defined as alert dismissals due to excessive or irrelevant alerts and is frequently cited as a barrier to clinical decision support system use and impact. However, the criteria for determining the presence or absence of alert fatigue are poorly defined. The objective of this systematic review of systematic reviews was to identify operationalized definitions and measures of alert fatigue or alert-related metrics. METHODS: Systematic reviews reporting at least one alert-related metric or measure/operationalization of alert fatigue for physician-directed electronic alerts were included. The Cochrane Library, Embase, and PubMed were searched from database start to 2024. The Revised Assessment of Multiple Systematic Reviews was used to assess study quality and risk of bias. Data were synthesized narratively and with descriptive statistics. RESULTS: A total of 22 studies were included in the review. Studies reported between 1 and 11 alert metrics. Studies were most often of medium quality. Reporting of primary study characteristics was frequently judged to be insufficient. Only one article reported an operational definition of alert fatigue. The most common alert metrics were quantity, override rate, and acceptance rate. DISCUSSION: Alert fatigue measurement methods are not clearly or consistently defined in systematic reviews related to alert fatigue in clinical decision support. Reporting of other primary study characteristics is often limited. We recommend that future efforts use a significant, sustained decrease in appropriate alert response rates from an established baseline as a measure of alert fatigue.
2 Aug 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Utility of Repeating Blood Cultures in Candidemia: Insights From 2 Large Clinical Data Repositories
We evaluated utility of repeating blood culture for candidemia using 2 electronic health record datasets. Among 1386 candidemia episodes, repeated blood culture detected 23% of episodes at Houston Methodist Hospital and 45% in Medical Information Mart for Intensive Care IV after the initial negative cultures. These findings support repeating blood cultures when candidemia is highly suspected.
24 July 2026
Read appraisal →ACS synthetic biology
LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides
Antimicrobial peptides (AMPs) represent promising alternatives to conventional antibiotics, yet hemolytic toxicity remains a critical barrier to clinical translation, with approximately 70% of known AMPs exhibiting high or moderate hemolytic activity. Existing computational prediction methods are often constrained by deficiencies, including high computational complexities, the inability to capture multiscale sequence patterns, and insufficient generalization across diverse datasets. We present LysePred, a multiscale convolutional neural network to address these deficiencies concurrently by employing parallel branches with exponentially spaced kernel sizes to simultaneously capture local amino acid motifs (bigrams, 4-g) and longer-range amphipathic patterns (8- to 32-g). LysePred achieves top-tier performance on six benchmark datasets, exceeding the performance of the second-best method by 9.13% in MCC and 3.65% in ACC on average, while maintaining exceptional stability (MCC CV < 6.92%, ACC CV < 2.73%). Furthermore, independent validation on the HemoPI2 dataset demonstrates that LysePred delivers highly competitive results with a computationally parsimonious design (∼0.55 M parameters). This represents a reduction in parameter density of 1 to 2 orders of magnitude compared to Transformer-based approaches, such as the 8M-parameter ESM-2 or the 110M-parameter BERT Base while maintaining linear complexity for high-throughput screening. Ablation studies validate that the multiscale architecture contributes meaningfully to performance, with single-scale variants showing up to 13.35% MCC degradation. Interpretability analyses via t-SNE visualization and SHAP feature importance reveal that LysePred learns biologically meaningful representations integrating both local sequence motifs and global structural patterns. LysePred offers a practical, efficient, and interpretable tool for rapid hemolytic toxicity prediction in antimicrobial peptide development. Code and data are available at https://github.com/lincubator/LysePred.
20 July 2026
Read appraisal →ACS infectious diseases
Machine Learning Approaches Using High-Throughput Profiling Data for Antibiotic Discovery
Antibiotic-resistant bacterial infections continue to increase globally, creating an urgent need for new antibacterials with novel mechanisms of action. Early stages of antibiotic discovery are often limited by the difficulty of identifying compounds that act through previously unrecognized pathways and by challenges in determining their mechanisms. Machine learning (ML) integrated with high-throughput profiling now provides systematic approaches to overcome these barriers. Beyond initial hit discovery, multilayer profiling using morphological phenotyping, transposon sequencing, transcriptomics, proteomics, and metabolomics captures cellular responses that reflect the mechanisms of action. Because profiling data sets are typically high-dimensional and contain defined features and variables, ML can extract complex patterns associated with pathway-level responses and predict mechanisms for unknown compounds. In this review, we summarize current progress in high-throughput profiling and describe how ML applied to each data set can accelerate the identification of antibacterials with new mechanisms. These approaches accelerate the transition from large-scale compound screening to mechanistic validation and enable effective prioritization of lead compounds in early-stage antibiotic discovery.
11 July 2026
Read appraisal →Cell host & microbe
Mining the code of life for new antibiotics.
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
10 July 2026
Read appraisal →Cell host & microbe
Predicting antimicrobial resistance for precision medicine
Antibiotics are among medicine's greatest successes, but resistance evolution threatens their continued efficacy. Decades of research have deepened our understanding of the mechanisms and evolutionary dynamics of antimicrobial resistance. More recently, advances in machine learning (ML) and artificial intelligence (AI) show promise in predicting antimicrobial resistance in pathogens based on rapid whole-genome sequencing and other accessible data. In this perspective, we highlight advances in understanding the mechanisms and spread of antimicrobial resistance. We discuss how this knowledge, coupled with ML- and AI-based approaches, can inform the prediction of resistance and a precision-medicine strategy that targets pathogenic bacteria specifically, thereby limiting resistance evolution and collateral damage to the microbiome. These accurate predictions of bacterial vulnerabilities will enable the adaptation of classical antimicrobial treatments with adjuvants, as well as the use of novel, narrow-spectrum therapeutics. Implementing these strategies, while also identifying key challenges, will help bring this strategy into clinical practice.
10 July 2026
Read appraisal →The Kurume medical journal
Development of Machine Learning Models to Predict Candidemia in Hospitalized Adult Patients
PURPOSE: We aimed to develop a generalizable machine learning model that leverages electronic medical record (EMR) data to predict candidemia using information available on or before the date of blood culture collection. METHODS: This study included adult patients admitted to Shin Koga Hospital (April 2014-March 2022) who underwent blood cultures at least 14 days after admission. We prepared two datasets: a "Complete case dataset" (13 variables, no missing values) and a "Imputed full-variable dataset" (36 variables, single imputation). We evaluated the discriminative performance of four machine learning models, including extreme gradient boosting, random forest, least absolute shrinkage and selection operator (LASSO) logistic regression, and logistic regression using stratified 5-fold cross-validation by comparing their area under the receiver operating characteristic curve (AUROC). The model with the highest mean AUROC was selected, assessed on the entire dataset, and internally validated using 1,000 bootstrap replicates. RESULTS: A total of 919 patients were included, of whom 47 (5.1%) had candidemia. The LASSO logistic regression demonstrated the best performance and was selected as the final model. The AUROC (95% CI) was 0.880 (0.836-0.924) for the Complete case dataset and 0.913 (0.878-0.948) for the Imputed full-variable dataset. The optimism-corrected AUROCs were 0.851 and 0.876, respectively. CONCLUSION: We developed two versions of the LASSO logistic regression model using only information automatically extracted from EMR, considering real-world data availability. These models have the potential to serve as practical tools to support early diagnosis and therapeutic intervention for candidemia.
3 July 2026
Read appraisal →JAMA
Fast Antimicrobial Susceptibility Testing for Gram-Negative Bacteremia: The FAST Randomized Clinical Trial
IMPORTANCE: Novel blood culture diagnostics provide rapid antimicrobial susceptibility testing (AST) results for bacteria causing bloodstream infections (BSIs) but have unclear clinical impact. OBJECTIVE: To evaluate clinical outcomes of blood culture testing using a rapid AST method compared with standard AST in patients with BSIs caused by gram-negative bacilli in regions with high prevalence of antimicrobial resistance. DESIGN, SETTING, AND PARTICIPANTS: Open-label randomized clinical trial that enrolled participants from December 2023 to May 2025 (final follow-up, June 18, 2025) at 7 medical centers in Greece (n = 2), India (n = 1), Israel (n = 3), and Spain (n = 1). Hospitalized patients (adults and children) with BSIs caused by gram-negative bacilli were eligible. Statistical analysis was conducted from August 2025 to January 2026. INTERVENTION: Patients were randomized to undergo blood culture evaluation using rapid, phenotypic AST directly from positive blood cultures plus standard susceptibility testing (n = 413) vs standard susceptibility testing alone (n = 437). Local antimicrobial stewardship teams reviewed all patients and provided treatment recommendations. MAIN OUTCOMES AND MEASURES: The primary outcome was a desirability of outcome ranking (DOOR) at day 30, with 3 categories (alive without deleterious events, alive with deleterious events, and death). The difference between rapid and standard testing was summarized as the probability that the DOOR outcomes were more desirable in the rapid testing group. Superiority was concluded if the lower limit of the 95% CI exceeded 50%. Secondary outcomes included 30-day mortality, length of hospitalization, intensive care unit admission, acquisition of hospital-acquired infections, time to effective antibiotic therapy within 3 days, and time to antibiotic escalation or deescalation within 3 days. RESULTS: Of the 899 patients randomized, 850 were included in the primary outcome analysis (median [IQR] age, 72 [21] years; 43% female). The probability that DOOR outcomes were more favorable in the rapid testing group was 48.8% (95% CI, 45.3%-52.4%). Median time to effective antibiotic therapy was not different between the groups in the as-randomized population. Median time to antibiotic escalation or deescalation was faster in the rapid testing group by 14 hours (95% CI, 6-22) in the as-randomized population. There were no differences between the groups in other secondary outcomes. In the prespecified subgroup with carbapenem-resistant infections, median time to effective therapy was 9.5 hours in the rapid testing group vs 28 hours in the standard testing group (difference, -18 hours [95% CI, -42 to 6]). CONCLUSIONS AND RELEVANCE: Among patients with gram-negative bacilli BSIs, rapid blood culture AST was not superior to standard testing by DOOR. When considered with other efficacy and safety outcomes, these findings may help inform the use of rapid susceptibility testing in clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06174649.
29 May 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Cephalosporins Versus Non-cephalosporin Antibiotics for Perioperative Prophylaxis in Primary Arthroplasty: A Systematic Review and Meta-Analysis
BACKGROUND: The number of arthroplasty procedures and infection-related complications, including prosthetic joint infections (PJIs) and surgical site infections (SSIs), continues to rise. Although current guidelines recommend cefazolin or cefuroxime as the first-line perioperative prophylactic antibiotic, substitutions with non-cephalosporins remain common, especially among patients with reported β-lactam allergies. These substitutions may increase infection risk and healthcare costs. Evidence comparing outcomes across antibiotic classes remains variable. METHODS: We conducted a systematic review and meta-analysis of studies comparing single-agent cephalosporin versus single-agent non-cephalosporin prophylaxis in adults undergoing primary arthroplasty. Cochrane, Embase, Medline, Scopus, and Web of Science were searched from database inception through January 2025. Random-effect or fixed-effect models were used to estimate pooled odds ratios (OR) and 95% confidence intervals (CI). Subgroup analyses stratified results by cephalosporin type, study era, and risk of bias. RESULTS: Twenty-three studies (7 randomized controlled trials and 16 observational studies) were included, encompassing 191 527 arthroplasties in the cephalosporin arm and 20 058 in the non-cephalosporin arm. The odds of PJI were lower with cephalosporins (OR 0.59; 95% CI, .46-.75). No overall difference was observed for SSI (OR 0.70; 95% CI, .36-1.36), though post-2013 studies and the cefazolin subgroup demonstrated a significant protective effect. Randomized trial estimates were limited and heterogeneous and did not reach statistical significance. Certainty of evidence was graded as moderate. CONCLUSIONS: Cephalosporins, particularly cefazolin or cefuroxime, remain the preferred prophylactic agents for primary arthroplasty. This quantitative synthesis reinforces their protective association against prosthetic joint infection and supports adherence to guideline-endorsed prophylaxis.
22 May 2026
Read appraisal →Emerging microbes & infections
Clinical outcomes of carbapenem therapy in OXA-48-producing Enterobacterales infections: a French multicentre cohort, systematic review, and meta-analysis
OXA-48-producing Enterobacterales (OXA-48-PE) represent a growing global health threat. Most of OXA-48-PE remain categorized susceptible to carbapenems, which might encourage their use despite uncertain clinical efficacy. This study evaluated clinical outcomes of infections caused by OXA-48-PE treated with carbapenem compared with alternative active therapies. The analyses were performed at three levels: a descriptive analysis of the French cohort, a comparative descriptive analysis of all published studies, and a meta-analysis restricted to studies providing direct comparisons between carbapenem-based and alternative active regimens. Between September 2021 and March 2023, 59 patients with monomicrobial OXA-48-PE infections were included in a French multicenter retrospective cohort. In parallel, a systematic review was conducted to identify clinical studies published through 31 December 2024, reporting outcomes of OXA-48-PE infections. In the French cohort, the overall 30-day mortality was 49.1%. Clinical failure occurred in 57.1% of patients receiving meropenem monotherapy, including patients infected with isolates exhibiting meropenem MICs within the susceptible range. Across 12 clinical studies (817 patients), carbapenem therapy was associated with high and variable crude mortality (52%), whereas newer agents active against OXA-48-PE, particularly ceftazidime-avibactam, were associated with lower and more consistent crude mortality (30.7%). In the primary meta-analysis of 6 human comparative studies, carbapenem therapy was associated with an increased risk of clinical failure compared with alternative active regimens (OR = 2.02; 95% CI = 1.05-3.88). Despite apparent in vitro susceptibility, carbapenem therapy was consistently associated with unfavourable clinical outcomes in OXA-48-PE infections, supporting the prioritization of alternative active agents whenever available.
20 May 2026
Read appraisal →Lancet (London, England)
Efficacy and safety of cefepime-nacubactam and aztreonam-nacubactam compared with imipenem-cilastatin for complicated urinary tract infection or acute uncomplicated pyelonephritis (Integral-1): a double-blind, randomised phase 3 trial
BACKGROUND: Nacubactam (OP0595) is a newly developed diazabicyclooctane β-lactamase inhibitor used in combination with cefepime or aztreonam. We assessed the efficacy and safety of cefepime-nacubactam and aztreonam-nacubactam versus imipenem-cilastatin in complicated urinary tract infection (cUTI) or acute uncomplicated pyelonephritis. METHODS: The Integral-1 global, phase 3, multicentre, randomised, double-blind study recruited adults (aged ≥18 years) with cUTI or acute uncomplicated pyelonephritis at 79 sites in Bulgaria, China, Czech Republic, Estonia, Georgia, Japan, Latvia, Lithuania, and Slovakia. Patients were randomly assigned (2:1:1) to receive intravenous cefepime (2 g) plus nacubactam (1 g), aztreonam (2 g) plus nacubactam (1 g), or imipenem (1 g) plus cilastatin (1 g) every 8 h for 5-14 days. Randomisation was stratified by diagnosis and geographical region. The primary endpoint was the proportion of patients achieving composite clinical and microbiological success at test of cure in the microbiological modified intention-to-treat population-all patients who were randomly assigned, received any amount of the study drug, and had a baseline qualifying pathogen that was susceptible to imipenem and meropenem. The prespecified non-inferiority margin was more than 15 percentage points difference; the superiority margin was more than zero percentage points difference, for the lower bound of the two-sided 95% CI for imipenem-cilastatin. Safety was assessed in all patients who received at least one dose of study drug. This study is registered with ClinicalTrials.gov, NCT05887908. FINDINGS: Between May 22, 2023, and Nov 26, 2024, 614 patients were randomly assigned and 431 were included in the primary efficacy analysis (cefepime-nacubactam [n=214], aztreonam-nacubactam [n=112], or imipenem-cilastatin [n=105]); 228 patients (53%) were male and 203 (47%) were female. The primary endpoint was achieved by 176 (82%) of 214, 81 (72%) of 112, and 64 (61%) of 105 patients in the cefepime-nacubactam, aztreonam-nacubactam, and imipenem-cilastatin groups, respectively. The percentage difference in the success rate versus imipenem-cilastatin was 21·3% (95% CI 10·9 to 32·0) for cefepime-nacubactam (non-inferior and superior), and 11·4% (-1·2 to 23·7) for aztreonam-nacubactam (non-inferior). Treatment-emergent adverse events were reported in 100 (33%) of 306, 45 (30%) of 152, and 65 (43%) of 150 patients in the cefepime-nacubactam, aztreonam-nacubactam, and imipenem-cilastatin groups, respectively. No treatment-related deaths occurred. INTERPRETATION: Cefepime-nacubactam and aztreonam-nacubactam are potential treatment options for Gram-negative cUTI and acute uncomplicated pyelonephritis, including infections caused by antimicrobial-resistant strains. FUNDING: Meiji Seika Pharma and Japan Agency for Medical Research and Development.
18 May 2026
Read appraisal →Talanta
SERS technology helps bacterial inactivation in vivo: including detection, identification, and evaluation.
The problem of bacterial infection and antimicrobial resistance has become a major challenge threatening to human health. Detection, identification, and real-time evaluation of bacteria during the sterilization process are of great significance for interpreting the sterilization mechanism and ensuring the effectiveness of sterilization. With the advantages of high sensitivity, fast detection speed, and real-time monitoring, Surface-enhanced Raman Scattering (SERS) technology provides a revolutionary solution to in vivo bacterial detection, identification, and antibacterial effect evaluation through functional substrate optimization, microfluidic chip integration, and deep collaboration of machine learning algorithms. This review focuses on the prominent applications of SERS technology in chemical, biological, and physical sterilization processes. By analyzing the changes in bacterial SERS fingerprint patterns, precise detection and identification of sensitive/resistant bacteria have been achieved, deepening the interpretation of drug resistance mechanisms at the gene, cell, and biofilm levels. Meanwhile, SERS technology provides an ideal tool for monitoring and evaluating the biological sterilization process. In the process of physical sterilization, enhancing the substrate material not only helps to improve SERS signals, but also promotes bacterial inactivation. By monitoring real-time changes in bacterial metabolism, the sterilization process can be precisely regulated. This review summarizes the innovative application of SERS technology in accurately guiding the sterilization process in vivo, providing important support for optimizing antibacterial treatment strategies.
18 May 2026
Read appraisal →JMIR medical informatics
Development Process of a Clinical Decision Support System for Empiric Antibiotic Therapies in Patients With Sepsis: Case Study
BACKGROUND: Antibiotic therapies are the main treatment for bacterial infections, but growing antibiotic resistance is a major global health threat, severely impacting patients with sepsis. Rapid selection of the most effective antibiotic therapy is critical for survival and for preventing further resistance. Physicians must consider numerous factors for proper empiric treatment selection. A clinical decision support system (CDSS) aims to support physicians in this process, facilitating rapid and targeted therapy. OBJECTIVE: The purpose of this work is to explore the extent to which the realization of a CDSS is possible based on the data available to us and to document insights gained during the development of a foundational model designed to assist physicians in determining empiric treatment options for patients with sepsis. In this regard, rather than aiming to develop a CDSS for clinical application, we highlight the importance of close interprofessional collaboration between scientists from various disciplines and analyze the effects of data quality and quantity on the performance of our statistical models. METHODS: Empirical scientists conducted interviews with medical practitioners to acquire the medical knowledge required to develop sound statistical models. We developed and applied 2-step cross-sectional, as well as time-series classification models, to carefully preprocessed data of patients with sepsis admitted to the intensive care unit of a German hospital. RESULTS: We identified several factors as crucial information for valid decisions on empiric therapy for treating patients with sepsis. These include the patients' core data, especially the infection focus. To prevent further resistance, individual risk factors such as travel history and professional background should be considered. The evaluation of a therapy's effectiveness is mainly based on the patient's general condition and blood values such as procalcitonin and interleukin 6. One key factor in the acceptance of a CDSS is the explainability of the results produced by the applied methods. Our models demonstrated mainly weak predictive ability for all considered empiric antibiotic therapies. However, they are not yet suitable for use in clinical practice, especially as they are based on prescribing habits rather than on optimal treatment decisions. CONCLUSIONS: This work highlights the importance of interprofessional collaboration between medical experts and model developers, ensuring that data quality and clinical relevance are central to the process. It emphasizes the urgent need for high-quality, comprehensive data to overcome challenges such as data discontinuity and improve model performance, particularly through enhanced digitization in health care. This feasibility study will facilitate future efforts to develop a CDSS for treating patients with sepsis and to translate it into clinical use.
15 May 2026
Read appraisal →Journal of clinical microbiology
Prediction of KPC-producing Klebsiella pneumoniae by MALDI-TOF MS, ensemble learning, and spectral peak annotation
The increasing prevalence of carbapenem-resistant Klebsiella pneumoniae represents a serious global health challenge. Rapid and accurate detection methods are essential to inform early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of resistant isolates. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been evaluated for resistance detection, but its clinical application remains limited. In this study, MALDI-TOF MS was integrated with machine learning (ML) to improve detection of Klebsiella pneumoniae carbapenemase (KPC) among K. pneumoniae isolates harboring KPC carbapenemase. Ensemble learning strategies were applied to 435 clinical isolates from different geographic locations (Spain, United States, South America, and Asia), with validation performed using external data sets. By constructing two peak matrices and applying four ML algorithms, as well as combinations of two and three in ensemble models, 92 different classifiers were tested. Ensemble combinations increased the specificity of classifiers to over 95%, while sensitivity reached 72%, being significantly higher than that of the MALDI Biotyper KPC module. Low sensitivity may be affected by technical variability during spectral acquisition. The first annotated MALDI-TOF MS spectrum for K. pneumoniae by in silico prediction of protein masses was developed to enable peak identification, including peaks potentially related to antimicrobial resistance. Overall, the results of this study show that combining MALDI-TOF MS with ensemble learning can enhance KPC detection performance.IMPORTANCERapid and accurate detection of Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae informs early and correct use of antimicrobial therapy to idealize patient outcomes and limit the spread of antimicrobial resistance. In this study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and ensemble machine learning were employed to detect KPC-producing isolates, using diverse isolate collections, obtaining excellent specificity. An annotated K. pneumoniae MALDI-TOF MS profile is provided with peak annotation, providing a resource for peak analysis.
14 May 2026
Read appraisal →mSystems
Short-chain fatty acid-producing microbes differentiate non-infectious and infectious neutropenic fever in leukemia.
UNLABELLED: Neutropenic fever (NF) is often the first sign of infection in patients with hematologic malignancies, but its cause is frequently unknown, leading to broad-spectrum antibiotic use without confirmed infections. Although research links gut microbiome disruptions to treatment-related infections, it typically examines NF as the outcome, leaving a gap in understanding how the microbiome and metabolic factors distinguish infectious from non-infectious cases. Stool samples from acute myeloid leukemia patients were analyzed to characterize gut microbiome composition and fecal metabolites at baseline and at fever onset. Machine learning models, network analyses, and functional profiling were used to differentiate infectious NF vs non-infectious NF at baseline and at fever onset. The baseline model (area under the receiver operating characteristic [AUROC] = 0.769) identified higher levels of Akkermansia, Enterobacter, Escherichia-Shigella, and Flavonifractor as predictors of infectious NF, while Collinsella, Lachnospiraceae, Coprococcus, and acetic acid were linked to non-infectious cases. At fever onset, Enterococcus was enriched in infectious NF, whereas Gemella, Butyrate, Lachnospiraceae, Ruminococcaceae, and Eisenbergiella abundances favored non-infectious NF outcomes (AUROC = 0.752). Network analyses also revealed greater functional diversity and microbiome-metabolome connectivity in non-infectious cases at fever onset. This study suggests that gut microbiota and metabolites may serve as biomarkers for distinguishing infectious from non-infectious neutropenic fever, warranting further validation in larger cohorts. IMPORTANCE: Our study tackles the challenge of managing neutropenic fever (NF) in immunocompromised patients whose numbers have increased due to various immunodeficiencies and treatments that suppress immune function. Fever is often the only sign of a serious infection in these patients, yet there are neither clear patterns linking risk factors to infection nor biomarkers reliable for ruling out non-infectious causes. As a result, febrile patients are typically empirically treated for major pathogens, even in the absence of confirmed infections, which propagates antimicrobial resistance and gut dysbiosis. Our research utilizes gut microbiome and targeted metabolomic profiling from two cohorts of patients with acute myeloid leukemia undergoing chemotherapy and employs a machine learning framework to distinguish between infectious and non-infectious NFs at baseline and upon fever onset.
23 Apr 2026
Read appraisal →PloS one
Prevalence of antimicrobial resistance in Tanzania: A systematic review and meta-analysis
Antimicrobial resistance (AMR) threatens global health, and understanding resistance patterns aids in effective treatment and promotes responsible antimicrobial use. Despite the urgency of resistant pathogens, systematic reviews focusing specifically on Tanzania are limited, and while several studies report resistance patterns for individual pathogens, a consolidated analysis of overall prevalence is needed to inform policymaking and public health interventions. Therefore, this review and meta-analysis assessed the prevalence of antimicrobial resistance among clinically relevant pathogens in Tanzania, providing a comprehensive overview to support surveillance, infection control, and stewardship efforts. A total of 1865 studies identified from Google Scholar (1600), PubMed (13), and Science Direct (252) underwent screening and full article review. Finally, 28 studies were included. A subgroup analysis was performed to evaluate the resistance patterns within antibiotic classes for specific pathogens. Descriptive statistics were used to describe the characteristics of the studies, while the prevalence of antimicrobial resistance was estimated through Meta-analysis. Inconsistency and heterogeneity between studies were quantified by the I2 index. Among the included studies, most isolates (25.0%) were obtained from urine samples. Of these studies, 75% were cross-sectional studies and 92.9% were conducted in hospital settings. The analysis revealed high resistance to penicillin, particularly amoxicillin-clavulanic and ampicillin, with Klebsiella pneumoniae (0.96 [0.83-0.99]), Acinetobacter baumannii (0.94 [0.67-0.99]) and Escherichia coli (0.90 [0.81-0.95]). Similarly, erythromycin resistance was most prevalent in Campylobacter spp. (0.85 [0.80-0.89]). Ciprofloxacin resistance was highest in Acinetobacter baumannii (0.54 [0.33-0.73]), whereas amikacin resistance was highest in Proteus spp. (0.86 [0.35-0.99]). Ceftriaxone resistance was particularly high in Acinetobacter baumannii (0.91 [0.70-0.98]) and Pseudomonas aeruginosa (0.85 [0.74-0.92]). Meropenem resistance was lowest among Escherichia coli (0.04 [0.01-0.10]) and Klebsiella spp. (0.07 [0.03-0.15]), while the pooled resistance across ESKAPE-E pathogens was (0.11[0.06-0.19]). Imipenem and clindamycin each had an overall pooled resistance of (0.06[0.02-0.14]) against both Escherichia coli and Klebsiella pneumoniae. The findings highlight widespread resistance among bacterial pathogens, ESKAPE-E, particularly in the Access and Watch groups of antibiotics. The variability in resistance patterns underscores the need for the Ministry of Health to re-evaluate empirical treatment protocols (STG/NEMLIT) to ensure effective treatment regimens, strengthen antimicrobial stewardship, enhance surveillance systems, and promote rational antibiotic use.
23 Apr 2026
Read appraisal →PloS one
Comparison of OneChoice AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru
Bacteremia is a major contributor to global morbidity and mortality, particularly in low- and middle-income countries where diagnostic delays and empirical antimicrobial misuse exacerbate resistance. This study assessed the accuracy of OneChoice®, an artificial intelligence (AI)-based Clinical Decision Support System (CDSS), in guiding antimicrobial therapy for bloodstream infections (BSIs) in Lima, Peru. A cross-sectional, observational design was used, comparing therapeutic recommendations generated by OneChoice®-based on molecular (FilmArray®) and phenotypic (MALDI-TOF MS, VITEK2) data-with the clinical decisions of 94 physicians (35 infectious disease [ID] specialists and 59 non-specialists) across 366 survey-based evaluations of bacteremia cases. Concordance between CDSS and physician decisions was analyzed using Cohen's Kappa and logistic regression. The overall concordance rate was 96.1% when considering any suggested treatment, and 74.6% for the top recommendation, with a substantial agreement (κ = 0.70). ID specialists showed significantly higher concordance (κ = 0.78) than non-ID physicians (κ = 0.61), and specialization was the strongest predictor of agreement (OR = 2.26, p = 0.001). Escherichia coli cases had the highest concordance, while Pseudomonas aeruginosa showed the lowest. The CDSS reduced inappropriate antibiotic use, particularly unnecessary carbapenem prescriptions. These findings support the utility of AI-CDSS tools in enhancing antimicrobial stewardship and standardizing care, especially in resource-limited healthcare settings.
20 Apr 2026
Read appraisal →PloS one
Impact of prophylactic oral azithromycin during labor on Azithromycin Resistance (AMR) in nasal Staphylococcus aureus and Streptococcus pneumoniae in women and infants in the multi-country Azithromycin Prevention in Labor Use Study (A-PLUS)
BACKGROUND: Prophylactic oral azithromycin vs. placebo reduced maternal, but not neonatal, mortality/sepsis in the A-PLUS Randomized Trial. While prophylactic intrapartum azithromycin reduces maternal mortality/sepsis, it may promote antimicrobial resistance (AMR) in commensal bacteria,. METHODS: Randomly selected women and their infants participating in A-PLUS were enrolled in a longitudinal cross-sectional sub-study to assess the presence of azithromycin resistance in selected bacteria in nasal cultures. Staphylococcus aureus and Streptococcus pneumoniae were cultured on selective agar, then azithromycin-containing agar to select for azithromycin resistant bacteria, identified biochemically. Azithromycin susceptibility was assessed by E-test. Nasal cultures were collected from women and infants between August 11, 2021 and September 18, 2023 during labor/day 1, day 7, 6 weeks, and 3, 6 and 12 months after delivery. RESULTS: The study enrolled 911 women and 915 liveborn infants at 8 sites in 7 countries. Azithromycin resistance in S aureus was higher and azithromycin susceptibility was lower in women receiving azithromycin compared with those receiving placebo on day 7 (P < 0.001), 6 weeks (P < 0.001) and 3 months (P = 0.009) after delivery. Azithromycin resistance in S aureus was also higher and azithromycin susceptibility was lower 6 weeks after delivery (P < 0.001) in infants born to women receiving azithromycin, Azithromycin resistance in S. pneumoniae was too sparse to interpret. CONCLUSIONS: There was an increase in prevalence of azithromycin resistance (or reduction in azithromycin susceptibility) in commensal nasal S. aureus between day 7, 6 weeks and 3 months in women exposed to azithromycin vs. placebo and only at 6 weeks in infants exposed to azithromycin vs. placebo. These differences between the azithromycin and placebo groups were no longer detected at 6 and 12 months post-partum in the women and after 6 weeks through 12 months in the infants.
20 Apr 2026
Read appraisal →BMJ open respiratory research
Personalised management of community-acquired pneumonia and the role of Clinical Decision Support Software
Community-acquired pneumonia (CAP) is one of the most common causes of hospital admission and is associated with significant morbidity and mortality. National and international guidelines are available to guide the management of CAP, including antibiotic prescribing. However, these guidelines are often not adhered to and there is significant overprescribing of broad-spectrum antibiotics, contributing to the growing pandemic of antimicrobial resistance. Clinical Decision Support Software (CDSS) are electronic tools that use individual patient data to generate patient specific assessments or recommendations, that can then be acted on by the patient or clinical decision maker. This article reviews the evidence surrounding the initial management of CAP in hospital and considers the potential of CDSS to support CAP management.
17 Apr 2026
Read appraisal →European journal of pediatrics
7-days versus 14-days antibiotic therapy in uncomplicated culture proven neonatal sepsis: a randomized control assessor-blinded trial
UNLABELLED: The trial aimed to establish the non-inferiority of a 7-day antibiotic therapy for uncomplicated neonatal sepsis when compared to the standard 14-day therapy. This study was a parallel-group, randomized non-inferiority assessor-blinded trial conducted in a tertiary Neonatal Intensive Care Unit in Central India. Neonates weighing ≥ 1000 g with suspected sepsis were screened and those meeting criteria were enrolled. Exclusions included babies with CNS (central nervous system) infections, septic arthritis, and life-threatening congenital malformations. Participants were observed for 7 days on antibiotics and re-evaluated; those with positive blood cultures were then randomized to receive either 7 or 14 days of antibiotics. The primary outcome was the relapse of sepsis, and a sample size of 70 in each arm was calculated based on a non-inferiority margin. Follow-ups were conducted for 48 h post-antibiotic treatment and weekly for 35 days to monitor any recurrence of illness. During the study, 917 babies with suspected sepsis were admitted, of which 256 had culture-positive sepsis. After excluding those with meningitis, staphylococcus, and fungal infections, 140 babies showed improvement at day 5 and were randomized into two groups: one receiving antibiotics for 7 days and the other for 14 days, each consisting of 70 babies. Klebsiella pneumoniae was the prevalent organism. The 7-day group had a shorter hospital stay (p < 0.05) and less respiratory support (p < 0.05). Outcomes revealed a low incidence of probable relapse in both groups, with no fatalities or definitive relapses recorded. CONCLUSIONS: A 7-day antibiotic regimen for uncomplicated neonatal sepsis is not inferior to a 14-day regimen. WHAT IS KNOWN: • Unregulated use of antibiotics can lead to a myriad of problems, especially in neonates. • There is some evidence that uncomplicated neonatal sepsis can be treated with short-course antibiotics. WHAT IS NEW: • Data is lacking-especially from Central India. • This trial checks if it is possible to reduce the duration of antibiotic therapy in uncomplicated neonatal sepsis.
16 Apr 2026
Read appraisal →PloS one
Clinical impact of broad- versus narrow-spectrum empiric therapy in acute cholangitis: A Japanese claims database study
The clinical benefit of broad-spectrum empiric therapy in patients with acute cholangitis is unclear. We aimed to evaluate the impact of broad-spectrum and narrow-spectrum empiric therapies on patient outcomes using a Japanese claims database. The study included patients who were diagnosed with acute cholangitis between April 2014 and August 2022, aged 18-99 years, received antibiotics, had blood cultures collected, and underwent biliary drainage. Patients who received empiric therapy with carbapenems, piperacillin/tazobactam, or fourth-generation cephalosporins were included in the broad-spectrum group, whereas others were included in the narrow-spectrum group. Of the 4,755 eligible patients, 3,377 were categorized into the narrow-spectrum group and 1,378 into the broad-spectrum group. In the multivariate logistic regression analysis, older age, higher Charlson Comorbidity Index, the presence of sepsis, and intensive care unit admission were associated with increased 30-day in-hospital mortality, whereas the receipt of broad-spectrum empiric therapy was not (adjusted odds ratio, 1.37 [95% confidence interval {CI}, 0.84-2.23]). In the propensity score matching analysis, there was also no association between the receipt of broad-spectrum empiric therapy and 30-day in-hospital mortality (odds ratio, 1.43 [95% CI, 0.82-2.50]). Furthermore, in the propensity score-matched cohort, the broad-spectrum group had longer duration of intravenous antibiotic therapy (median interquartile range [IQR]: 8 [6 -11] day vs. 9 [7 -13] day; difference 1 day [95% CI, 0.31-1.69 day]) and length of hospital stay (median [IQR]: 13 [9 -20] day vs. 16 [11 -25] day; difference 3 day [95% CI, 1.87-4.13 day]), compared with the narrow-spectrum group. In this large-scale study using a Japanese claims database, broad-spectrum empiric therapy was not associated with improved clinical outcomes, compared with narrow-spectrum empiric therapy. Therefore, the necessity of broad-spectrum empiric therapy may be limited in clinical practice, and narrow-spectrum empiric therapy may represent an effective treatment strategy for acute cholangitis.
5 Apr 2026
Read appraisal →Intensive Care Med
Invasive candidiasis in intensive care medicine: shaping the future of diagnosis and therapy
BACKGROUND: Invasive candidiasis (IC) remains one of the most challenging infections in critical care, contributing significantly to morbidity, prolonged organ support, and mortality among intensive care unit (ICU) patients. The clinical landscape of IC is evolving, with increasing recognition of Candida non-albicans species and other yeasts formerly classified as Candida spp., alongside emerging multidrug resistance and growing complexity in host immune profiles. OBJECTIVES: This contemporary, multidisciplinary narrative review-authored by intensivists, infectious diseases specialists, clinical microbiologists, and pharmacologists-aims to provide ICU clinicians with practical, up-to-date insights into the diagnosis and management of IC. To maintain clinical focus, the review excludes non-IC fungal infections and non-ICU patient populations. METHODS: Relevant literature and expert consensus were critically reviewed to summarize current diagnostic and therapeutic approaches for IC in critically ill patients. Emphasis was placed on pragmatic clinical application, diagnostic limitations, antifungal stewardship, and personalized therapeutic decision-making. RESULTS: Despite the availability of novel antifungal agents with improved pharmacokinetic properties, treatment success in IC depends equally on timely and accurate diagnosis and individualized, context-aware therapy. Blood cultures continue to demonstrate limited sensitivity (~40%). Non-culture assays, including β-D-glucan and molecular diagnostics, provide faster detection and high negative predictive value but suffer from low positive predictive value and inconsistent adoption in clinical practice. The absence of validated host-derived biomarkers further limits risk stratification, antifungal discontinuation decisions, and personalized care. CONCLUSIONS: Emerging antifungal agents, stewardship strategies, and multidisciplinary care models are essential to improve clinical outcomes and reduce antifungal resistance. This review underscores the need for integrated, team-based diagnostic and therapeutic approaches to close persistent gaps in IC management, ultimately promoting more effective, timely, and individualized care for critically ill patients with Candida spp.
1 Nov 2025
Read appraisal →Cell
A generative deep learning approach to de novo antibiotic design
The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders. Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection. We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space.
1 Oct 2025
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