Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
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 →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 →Annals of medicine
Construction of an XGBoost-SHAP-based malignant transformation risk prediction model for gallbladder polyps
BACKGROUND: To develop and validate a risk prediction model for malignant transformation in patients with gallbladder polyps (GBPs) using an interpretable machine learning framework and evaluate its predictive accuracy. METHODS: A retrospective cohort of 1,027 surgical patients was enrolled from Yantai Yuhuangding Hospital (training set: n = 933) and Shanghai Eastern Hepatobiliary Surgery Hospital (validation set: n = 94). Feature selection for the training set was performed using the least absolute shrinkage and selection operator (LASSO) regression method. A predictive model was constructed with the XGBoost machine learning algorithm and evaluated using Shapley Additive exPlanation (SHAP). RESULTS: LASSO regression identified five significant risk factors for malignant transformation in GBPs: presence of concomitant cholecystitis, polyp count, polyp base width, age, and maximum polyp diameter. The area under the receiver operating characteristic curve (AUC) was 0.862 (95% confidence interval [CI]: 0.8342-0.8893) in the training set and 0.777 (95% CI: 0.6804-0.8737) in the validation set. SHAP analysis illustrated the contribution of each factor. CONCLUSION: This study developed and validated a risk prediction model for malignant transformation in patients with GBPs. The model demonstrated favorable discrimination, calibration, accuracy, and clinical applicability. Integration with SHAP technology may assist clinicians in optimizing treatment and management strategies.
27 Apr 2026
Read appraisal →Journal of neurology
Predicting disease progression in multiple sclerosis with clinically accessible information and technology
BACKGROUND: Predicting disease progression at the individual level is essential for personalized medicine. We previously developed machine-learning tools to estimate 5-year progression risk in people with multiple sclerosis (PwMS). Such models should account for disease-modifying therapy (DMT) and objective outcome definitions. METHODS: In a retrospective multicenter case-control study, we evaluated adults with relapsing-remitting multiple sclerosis (RRMS) at baseline. Using machine-learning, we developed two complementary tools for individualized 5-year risk estimation: DAAE-M, optimized for transparency, software-neutral use, and mitigation of indication bias, and ELIE, optimized for dynamic landmark-based modeling, complex treatment histories, and mitigation of immortal-time bias. Disease progression was defined using both a clinical outcome (RRMS-to-progressive MS) and an objective outcome (late-stage confirmed progression independent of relapse activity). RESULTS: Among 34,510 people with RRMS (72.6% female, mean age = 37.1, mean disease duration = 5.8), 9.8% and 21% met clinical and objective progression criteria, respectively, over five years. Both models demonstrated good calibration across risk-groups (Brier scores 0.06-0.16). DAAE-M provided patient-level risk estimates with monotonic risk escalation across risk-groups for clinical (3.1%/11.2%/22.6%/33.0%) and objective (8.4%/14.5%/23.3%/38.8%) progression. For DAAE-M, high-efficacy DMT was associated with approximately half the progression risk compared with low-efficacy DMT (risk-ratios: 0.42-0.59; p < 0.01). ELIE also showed good calibration across risk deciles with increasing incidence for both clinical (0.3%/1.2%/1.7%/2.5%/3.7%/5.5%/7.2%/10.2%/14.3%/21.5%) and objective (0.9%/1.6%/2.5%/4.0%/5.8%/7.8%/10.2%/15.3%/20.9%/32.5%) outcomes. CONCLUSION: We developed two well-calibrated machine-learning-based tools for individualized 5-year prediction of clinically- and objectively-defined MS progression, each with distinct strengths in usability, bias handling, and treatment modeling. These findings support future tool use in personalized risk stratification and secondary prevention.
21 Apr 2026
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