Using a machine learning model to predict the development of acute kidney injury in patients with heart failure

Abstract

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 a poor prognosis. However, it is difficult for doctors to distinguish which patients will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients.

Materials and 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 2,678 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, the Gini index showed that the sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO₂), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, 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.

Conclusion: Using the ML model could accurately predict the development of AKI in HF patients.

Keywords: acute kidney injury; artificial intelligence; heart failure; machine learning; prediction model.

FIGURE 1

Process of establishing the prediction model. AKI, acute kidney injury.

FIGURE 2

Consort flow chart. A total of 2,678 patients were selected from the database with 20,915 patients. ICU, intensive care unit; HF, heart failure; Scr, serum creatinine; eGFR, estimated glomerular filtration rate; AKI, acute kidney injury.

FIGURE 3

Receiver operating characteristic (ROC) curves of the prediction model. RF, random forest; SVM, support vector machine; KNN, K-nearest neighbor; LR, logistic regression.

FIGURE 4

Contribution of features of AKI in HF patients (Top 10 displayed). SOFA, sequential organ function assessment score; PaO₂, partial pressure of oxygen; eGFR, estimated glomerular filtration rate; Scr, serum creatinine.

FIGURE 5

Receiver operating characteristic (ROC) curves of the prediction model using ten selected features. RF, random forest; SVM, support vector machine; KNN, K-nearest neighbor; LR, logistic regression.