Clinical kidney journal · 2025
Predicting blood pressure variability in hemodialysis using an explainable boosting machine model.
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Huang Wun-Yi, Lin Cheng-Jui, Zheng Yu-Xiang, Chen Hung-Ting, Sun Fang-Ju, Wu Chih-Jen, Wu Kun-Pin
Аннотация
BACKGROUND: Intradialytic hypotension and hypertension significantly increase cardiovascular risk in hemodialysis patients, highlighting the need for effective monitoring systems. By predicting systolic blood pressure (SBP) values, monitoring capabilities can be improved beyond traditional event classification. Despite this potential advantage, current predictive models report a consistent error range (11-13 mmHg) and operate as black boxes. This study aims to develop a transparent SBP prediction model and evaluate its generalizability.
METHODS: This retrospective study analyzed data from 524 patients across metropolitan and suburban hospital branches (2016-19), collecting hemodialysis parameters, vital signs and predialytic measurements. The Explainable Boosting Machine (EBM) was used as the primary model to predict SBP changes. Cross-branch validation was implemented to evaluate model generalizability and perform feature selection. The EBM model was then compared with five other common machine learning methods.
RESULTS: EBM with 16 dynamic features achieved competitive performance compared with other machine learning methods (mean absolute error: 10.57-11.33 mmHg, Pearson's r: 0.80-0.83) in cross-validation between branches. A waterfall plot visualized the model's additive scoring process, showing how each feature quantitatively contributes to the final SBP prediction. Error analysis of the models revealed strong positive correlations (Pearson's r: 0.81-0.87) between patients' blood pressure variability and prediction errors.
CONCLUSIONS: The EBM models demonstrated excellent cross-branch generalizability while providing feature-level transparency that reflects clinical understanding of dialysis physiology, unlike black-box models that require additional post-hoc explanation methods such as SHapley Additive exPlanations (SHAP). These findings provide a foundation for developing patient-specific solutions for individuals with highly variable blood pressure patterns.
METHODS: This retrospective study analyzed data from 524 patients across metropolitan and suburban hospital branches (2016-19), collecting hemodialysis parameters, vital signs and predialytic measurements. The Explainable Boosting Machine (EBM) was used as the primary model to predict SBP changes. Cross-branch validation was implemented to evaluate model generalizability and perform feature selection. The EBM model was then compared with five other common machine learning methods.
RESULTS: EBM with 16 dynamic features achieved competitive performance compared with other machine learning methods (mean absolute error: 10.57-11.33 mmHg, Pearson's r: 0.80-0.83) in cross-validation between branches. A waterfall plot visualized the model's additive scoring process, showing how each feature quantitatively contributes to the final SBP prediction. Error analysis of the models revealed strong positive correlations (Pearson's r: 0.81-0.87) between patients' blood pressure variability and prediction errors.
CONCLUSIONS: The EBM models demonstrated excellent cross-branch generalizability while providing feature-level transparency that reflects clinical understanding of dialysis physiology, unlike black-box models that require additional post-hoc explanation methods such as SHapley Additive exPlanations (SHAP). These findings provide a foundation for developing patient-specific solutions for individuals with highly variable blood pressure patterns.