ArticleBMC medical informatics and decision making2025
Exploring prognostic factors on vascular outcomes among maintenance dialysis patients and establishing a prognosis prediction model using machine learning methods.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer.Frontiers in medicine · 2026Article
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6 authors.
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Abstract
backgroundCardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in end-stage kidney disease patients, with persistently high rates of major adverse cardiovascular events (MACEs). Traditional risk factors such as diabetes and hypertension have limited predictive value in this population, while chronic kidney disease (CKD)-specific factors including inflammation, disordered mineral metabolism, and vascular calcification play significant roles. Therefore, we developed a machine learning-based model incorporating traditional and CKD-specific variables to improve MACEs risk predictions and facilitate early intervention.
methodsWe retrospectively enrolled 412 adults undergoing maintenance hemodialysis (MHD) at a single center between October and December 2018, with follow-up until December 2021 or censoring. Enrolled patients were classified by MACEs occurrences. An elastic-net regularized Cox regression with backward selection was used to identify key MACE predictors, integrating traditional and CKD-specific risk factors. The model performance was validated via leave-one-out cross-validation and area under the receiver operating characteristic curve (AUROC). Standard statistical tests were applied for group comparisons.
resultsThe elastic-net regression identified 46 key predictors from a high-dimensional dataset. Patients who developed MACEs were older and had higher prevalences of diabetes, hypertension, coronary disease, heart failure, and lower albumin/cholesterol. A Cox regression with backward selection was used to refine the model to 13 predictors. The final model demonstrated excellent predictive performance, (AUROC = 0.864) (95% CI, 0.8131– 0.9148), effectively stratifying patients into high- and low-risk groups with significant survival differences (log-rank p < 0.001).
conclusionsThe machine learning-based model demonstrated high predictive accuracy for MACEs in MHD patients by integrating both traditional and CKD-specific risk factors, offering a potential tool for early identification and clinical decision-making. CLINICAL TRIAL NUMBER: Not applicable.
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