Key result
A comprehensive machine learning model using XGBoost accurately predicted intradialytic hypotension and hypertension with an AUROC of 0.7412.
Why the study?
Intradialytic hypotension and hypertension are major complications of maintenance hemodialysis that impact morbidity and mortality, making effective, explainable prediction necessary to improve management.
Can explainable machine learning models accurately predict intradialytic hypotension and hypertension in maintenance hemodialysis patients?
Cohort (n=193)
No
Can explainable machine learning models accurately predict intradialytic hypotension and hypertension in maintenance hemodialysis patients?
Effect estimate: AUROC 0.7412 (95% CI 0.7336-0.7485)
Explainable machine learning models utilizing pre-dialysis and dialysis parameters can accurately predict the risk of intradialytic hypotension and hypertension, potentially aiding in proactive clinical management.
Machine learning models may aid IDH/IDHTN prediction in hemodialysis; leaves open prospective validation before clinical adoption.
BACKGROUND: Intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN) are major complications of maintenance hemodialysis (MHD) that significantly impact patient morbidity and mortality. Effective, explainable prediction of IDH and IDHTN can improve their management. METHODS: This study introduces a dual-model system for predicting IDH and IDHTN, using SHAP (SHapley Additive exPlanations) to enhance explainability. We analyzed data from maintenance dialysis patients at the Second Hospital of Lanzhou University, covering treatments from February 2019 to August 2023. Two models were developed: Model A, with a small set of easily obtainable features, and Model B, with a comprehensive set of multidimensional features. RESULTS: The study cohort included 193 patients and 45,825 dialysis samples, with an average age of 54 years and 66.32% male. Model A used 12 features, while Model B used 51. Models were trained using XGBoost, Random Forest, logistic regression, and KNN. Random Forest achieved the highest AUROC of 0.7160 in Model A. XGBoost reached an AUROC of 0.7412 in Model B. SHAP analysis identified key predictors such as pre-dialysis blood pressure, lactate dehydrogenase, and age. Older patients (>60 years) were at higher risk for hypotension. A larger gradient between plasma sodium and dialysate sodium was associated with increased IDH risk and required more aggressive ultrafiltration. Adjusting the sodium gradient through dialysate sodium concentration may help manage IDHTN risk. CONCLUSIONS: This study demonstrates that explainable AI models can predict IDH and IDHTN risks accurately before treatment, potentially reducing severe adverse events and improving patient outcomes. CLINICAL TRIAL NUMBER: Not applicable.
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Li et al. (2025) conducted a cohort in End-stage renal disease on maintenance hemodialysis (n=193). Machine learning prediction models was evaluated on Prediction of intradialytic hypotension and hypertension (AUROC) (AUROC 0.7412, 95% CI 0.7336-0.7485). A comprehensive machine learning model using XGBoost accurately predicted intradialytic hypotension and hypertension with an AUROC of 0.7412.
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