ArticleBMC nephrology2025
Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for malnutrition in dialysis patients in China: a systematic review and meta-analysis.Renal failure · 2026Pooled it
- Applying machine learning to associate clinical factors with malnutrition risk in peritoneal dialysis patients: an internally validated interpretable model.Frontiers in nutrition · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundProtein-energy wasting (PEW) is a common complication of patients on maintenance haemodialysis (MHD) and is strongly associated with poor clinical outcomes; early identification and timely nutritional interventions are essential. The aim of this study was to develop and validate an explainable machine learning model to identify the risk of PEW in patients on MHD.
methodsFrom January 2024 to June 2024, a total of 908 patients on MHD were enrolled. The data were randomly divided into training (n = 635) and test (n = 273) sets at a 7:3 ratio. The optimal features were selected via least absolute shrinkage and selection operator (LASSO) regression. Seven machine learning (ML) algorithms were used to develop and validate models to predict PEW risk, and the performance of the models was evaluated via evaluation indicators such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, F1 score, and Brier score to compare the performance of the prediction models in the test set. The best-performing model was interpreted via Shapley additive explanations (SHAPs) and deployed as a web application via Shiny R.
resultsAmong the seven ML models, the AUC values ranged from 0.710 to 0.827 in the test set. The XGBoost model had the best predictive performance; achieved the highest AUC (AUC = 0.827, 95% CI, 0.772-0.883); and demonstrated good sensitivity (0.727), specificity (0.762), accuracy (0.755), and F1 score (0.544) and a lower Brier score (0.125). The SHAP method ranks the feature importance in descending order: predialysis creatinine level, handgrip strength, non-high-density lipoprotein cholesterol (non-HDL-C) level, urea clearance index (Kt/V), and high-sensitivity C-reactive protein.
conclusionThe explainable machine learning model developed in this study is a practical tool for healthcare professionals to identify the risk of PEW in patients on MHD and provide decision support for early implementation of personalized nutrition management strategies.
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