Identification of Risk Factors and Prediction Models for Acute Kidney Injury in Hospitalized Elderly Patients with Heart Failure

Background

Elderly heart failure (HF) patients have an increased risk of acute kidney injury (AKI) during hospitalization. Identifying risk factors and developing a prediction model are clinically important. Study method: We retrospectively enrolled 7076 elderly HF patients hospitalized from January 2019 to December 2023. All predictors were restricted to data available within 24 hours of admission and prior to AKI occurrence. Candidate predictors were first screened by univariate analysis (P<0.05) and then further refined by LASSO regression, and the selected variables were ultimately entered into multivariable logistic regression. Thirteen machine learning models were constructed and compared to identify the optimal model. Model performance was evaluated using the area under the ROC curve, calibration curves, and decision curve analysis.