Using a machine learning model to predict the development of acute kidney injury in patients with heart failure - Liu, Wen Tao; Liu, Xiao Qi; Jiang, Ting Ting; Wang, Meng Ying; Huang, Yang; Huang, Yu Lin; Jin, Feng Yong; Zhao, Qing; Wu, Qin Yi; Liu, Bi Cheng; Ruan, Xiong Zhong; Ma, Kun Ling (Frontiers in Cardiovascular Medicine, 2022)
Background: Heart failure (HF) is a life-threatening complication of cardiovascular disease. HF patients are more likely to progress to acute kidney injury (AKI) with poor prognosis. However, it is difficult for doctors to distinguish which patient will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients. Methods: The data of HF patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was retrospectively analyzed. A ML model was established to predict AKI development using decision tree, random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR) algorithms. Thirty-nine demographic, clinical, and treatment features were used for model establishment. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the ML algorithms. Results: A total of 2678 HF patients were engaged in this study, of whom 919 developed AKI. Among 5 ML algorithms, the RF algorithm exhibited the highest performance with the AUROC of 0.96. In addition, Gini index showed that Sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO2), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, in order to facilitate clinical application, a simple model was constructed using the 10 features screened by the Gini index. The RF algorithm also exhibited the highest performance with the AUROC of 0.95. Conclusions: Using the ML model could accurately predict the development of AKI in HF patients.