Prediction Model of Intradialytic Hypertension in Hemodialysis Patients Based on Machine Learning

作者：Wang Y, Zhou H, Guo Q, Wang K, Luo Y, Luan S, Tang D, Dong S, Yin L, Dai Y · 发表于：Journal of medical systems · 年份：2025 · DOI：10.1007/s10916-025-02237-5 · 研究领域：End-stage renal disease、Hemodialysis、Light gradient boosting machine、Machine learning

The escalating global burden of chronic kidney disease (CKD), particularly end-stage renal disease (ESRD), has intensified reliance on hemodialysis (HD), imposing substantial financial and operational burdens on healthcare systems and patients. Intradialytic hypertension (IDH), a critical complication during HD, is associated with life-threatening cardiovascular and neurological sequelae if unmanaged. This study aims to develop a machine learning (ML)-driven early-alert system for IDH risk prediction by integrating demographic profiles and dialysis session records, enabling clinicians to preemptively identify high-risk patients and prioritize targeted monitoring. Two clinical prediction models (IDH-1 and IDH-2) were developed using Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), and TabNet algorithms. IDH-1 estimates immediate hypertension risk by analyzing pre-dialysis vital signs and longitudinal treatment patterns, whereas IDH-2 predicts subsequent session risks by synthesizing real-time dialysis parameters with historical biomarkers. Model performance was rigorously validated using standardized metrics, including AUC-ROC, sensitivity, accuracy, and F1 score, to ensure clinical applicability. 185,125 HD sessions as training set and 71,427 sessions as testing set were used in this study. For IDH-1, the LGBM model demonstrated superior discriminative capacity (AUC: 0.87; recall: 0.73; F1 score: 0.36), outperforming SVM and TabNet. Similarly, LGBM achiev...