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Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective
Fengda Li1, Anmin Chen2, Zeyi Li3
1Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
Insights
Machine learning accurately predicts intracerebral hemorrhage (ICH) risk in hemodialysis patients. XGBoost model identifies key factors like LDL, HDL, and blood pressure for early intervention.
Area of Science:
- Nephrology
- Neurology
- Artificial Intelligence
Background:
- Intracerebral hemorrhage (ICH) is a severe complication for chronic kidney disease (CKD) patients on long-term hemodialysis, leading to high mortality and disability.
- Early ICH prediction is crucial for timely intervention and improved patient outcomes.
- This study focuses on developing an interpretable machine learning model for ICH risk prediction in hemodialysis patients.
Purpose of the Study:
- To build and evaluate machine learning models for predicting intracerebral hemorrhage (ICH) risk in patients undergoing long-term hemodialysis.
- To identify key clinical factors contributing to ICH risk in this patient population.
- To develop an interpretable model for aiding clinical decision-making.
Main Methods:
- Retrospective analysis of clinical data from 393 end-stage kidney disease patients undergoing hemodialysis.
- Development and comparison of five machine learning algorithms: SVM, XGBoost, CNB, KNN, and LR.
- Model performance evaluation using Area Under the Curve (AUC) and interpretation via SHAP analysis.
Main Results:
- The XGBoost model demonstrated the highest predictive performance with an AUC of 0.979 in the validation dataset.
- Key predictors for ICH identified by SHAP analysis include LDL, HDL, CRP, HGB levels, and pre-hemodialysis blood pressure.
- The XGBoost model significantly outperformed other tested algorithms in predicting ICH risk.
Conclusions:
- The developed XGBoost model effectively predicts ICH risk in hemodialysis patients with uremia.
- The model supports individualized and rational clinical decisions for managing ICH risk.
- ICH events in hemodialysis patients are significantly associated with specific serum lipid, inflammatory, hematologic, and blood pressure markers.
Background:
Intracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.
Methods:
The clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.
Results:
A total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.
Conclusion:
The XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.
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