Predicting hyperkalemia in patients with advanced chronic kidney disease using the XGBoost model

Hsin Hsiung Chang, Jung Hsien Chiang, Chun Chieh Tsai, Ping Fang Chiu

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)


Background: Hyperkalemia is a common complication of chronic kidney disease (CKD). Hyperkalemia is associated with mortality, CKD progression, hospitalization, and high healthcare costs in patients with CKD. We developed a machine learning model to predict hyperkalemia in patients with advanced CKD at an outpatient clinic. Methods: This retrospective study included 1,965 advanced CKD patients between January 1, 2010, and December 31, 2020 in Taiwan. We randomly divided all patients into the training (75%) and testing (25%) datasets. The primary outcome was to predict hyperkalemia (K+ > 5.5 mEq/L) in the next clinic vist. Two nephrologists were enrolled in a human-machine competition. The area under the receiver operating characteristic curves (AUCs), sensitivity, specificity, and accuracy were used to evaluate the performance of XGBoost and conventional logistic regression models with that of these physicians. Results: In a human-machine competition of hyperkalemia prediction, the AUC, PPV, and accuracy of the XGBoost model were 0.867 (95% confidence interval: 0.840–0.894), 0.700, and 0.933, which was significantly better than that of our clinicians. There were four variables that were chosen as high-ranking variables in XGBoost and logistic regression models, including hemoglobin, the serum potassium level in the previous visit, angiotensin receptor blocker use, and calcium polystyrene sulfonate use. Conclusions: The XGBoost model provided better predictive performance for hyperkalemia than physicians at the outpatient clinic.

期刊BMC Nephrology
出版狀態Published - 2023 12月

All Science Journal Classification (ASJC) codes

  • 腎臟病學


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