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<p>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.&#xa0;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.&#xa0;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 achieved the highest performance for IDH-2 (AUC: 0.74; recall: 0.56; F1 score: 0.26). Most significant parameters in IDH-1 Predictor with LGBM were pre-dialysis diastolic pressures, historical mean arterial pressure, and historical average IDH episodes. For the IDH-2 model with LGBM, historical average IDH episodes and post-dialysis systolic pressures were most important parameters.&#xa0;This study provides two kinds of superior discriminative capacity LGBM model for IDH predicting. The proposed models offer a scalable framework for personalized risk stratification, potentially mitigating adverse outcomes in hemodialysis populations.</p>

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

  • 期刊论文

  • 发表日期：

    2025年9月11日

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  • Yu Wang,
  • Hongming Zhou,
  • Qi Guo,
  • Kang Wang,
  • Yehua Luo,
  • Shaodong Luan,
  • Donge Tang,
  • Shuangyong Dong,
  • Lianghong Yin,
  • Yong Dai

摘要

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 achieved the highest performance for IDH-2 (AUC: 0.74; recall: 0.56; F1 score: 0.26). Most significant parameters in IDH-1 Predictor with LGBM were pre-dialysis diastolic pressures, historical mean arterial pressure, and historical average IDH episodes. For the IDH-2 model with LGBM, historical average IDH episodes and post-dialysis systolic pressures were most important parameters. This study provides two kinds of superior discriminative capacity LGBM model for IDH predicting. The proposed models offer a scalable framework for personalized risk stratification, potentially mitigating adverse outcomes in hemodialysis populations.