Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study

Fengda Li; Anmin Chen; Zeyi Li; Longyuan Gu; Qiyang Pan; Pan Wang; Yuechao Fan; Jinhong Feng

doi:10.3389/fneur.2023.1139096

Machine learning-based prediction of cerebral hemorrhage in patients with hemodialysis: A multicenter, retrospective study

Front Neurol. 2023 Apr 3:14:1139096. doi: 10.3389/fneur.2023.1139096. eCollection 2023.

Authors

Fengda Li¹, Anmin Chen², Zeyi Li³, Longyuan Gu⁴, Qiyang Pan⁵, Pan Wang³, Yuechao Fan⁴, Jinhong Feng⁶

Affiliations

¹ Department of Neurosurgery, Changshu Hospital Affiliated to Soochow University, Changshu, China.
² Department of Nephrology, The First People's Hospital of Jintan, Changzhou, China.
³ School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
⁴ Department of Neurosurgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
⁵ Faculty of Informatics, Università della Svizzera italiana, Lugano, Ticino, Switzerland.
⁶ Department of Nephrology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.

Abstract

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.

Keywords: Shapley additive explanations; hemodialysis; intracerebral hemorrhage; machine learning; predictive models; uremia.

Grants and funding

The research was sponsored by the National Natural Science Foundation (General Program) Grant No. 61972211, China and the National Key Research and Development Project Grant No. 2020YFB1804700, China.