Real-Time Prediction and Management of Intradialytic Hypotension with Machine Learning

Intradialytic hypotension (IDH) is associated with high mortality and morbidity. This study evaluates the application of machine learning (ML) for IDH diagnosis and management. This study consisted of a prospective real-world study and a pilot randomized controlled trial (RCT). Clinical data from 167 hemodialysis patients (2018–2020) were randomly divided into a training set (75%) and a validation set (25%). ML models (RNN, XGBoost, LightGBM) were assessed under three IDH definitions. The optimal XGBoost model, which utilized a stratified systolic blood pressure (SBP) threshold, achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.933, demonstrating robust predictive performance. In the RCT, 32 patients were allocated to AI-assisted IDH management or conventional care. Compared to controls, the AI-assisted group had a significantly greater reduction in IDH events (MD − 8.13, 95% CI: − 15.64 to − 0.62, P = 0.034) and a more marked improvement in cumulative SBP decline at IDH onset (MD − 108.69, 95% CI: − 209.83 to − 7.56, P = 0.036). The AI-assisted intervention, based on the XGBoost model predicting IDH risk using a stratified SBP threshold, significantly reduces IDH events, offering a novel strategy for the precise prevention and management of hypotension during dialysis. Clinical Trial registry name and registration number Research on Machine Learning-Based Information Systems for Predicting and Mitigating the Occurence of Intradialytic Hypotension, ChiCTR2000036973