A Predictive Model of Early Readmission for Patients with Heart Failure

Abstract

Background: Readmission within 30 days of discharge for heart failure (HF) has become a challenging public health issue. Predicting the risk of 30-day readmission may assist clinicians in making individualized treatment plans for HF patients. Methods: A total of 2254 patients were enrolled in this study. The risk predictors associated with 30-day readmission were selected using the least absolute shrinkage and the selection operator regression model. The performance of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow (HL) test, and decision curve analysis (DCA). Results: The 30-day all-cause readmission rate was 7.1%. Thirteen clinical parameters were identified as the risk predictors, including age, cystatin C, albumin, red cell distribution width coefficient variation, neutrophils, N-terminal pro-B-type natriuretic peptide, high-sensitivity cardiac troponin T, myoglobin, sex, dyslipidaemia, left ventricular ejection fraction, left ventricular end-diastolic dimension, and atrial fibrillation. The nomogram showed good discrimination, with an area under the ROC curve of 0.653 (95% confidence interval: 0.608–0.698) and good calibration results (HL test p = 0.328). The DCA showed that the nomogram would have good clinical utility. Conclusions: This predictive model based on clinical data makes it simple for clinicians to assess the 30-day HF readmission risk.