Predicting the risk of 1-year mortality among patients hospitalized for acute heart failure in China

Objectives We aimed to develop and validate a model to predict 1-year mortality risk among patients hospitalized for acute heart failure (AHF), build a risk score and interpret its application in clinical decision making. Methods By using data from China Patient-Centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study, which prospectively enrolled patients hospitalized for AHF in 52 hospitals across 20 provinces, we used multivariate Cox proportional hazard model to develop and validate a model to predict 1-year mortality. Results There were 4875 patients included in the study, 857 (17.58%) of them died within 1-year following discharge of index hospitalization. 13 predictors were selected to establish the prediction model, including age, medical history of chronic obstructive pulmonary disease and hypertension, systolic blood pressure, Kansas City Cardiomyopathy Questionnaire-12 score, ACEI/ARB at discharge, discharge symptom, N-terminal pro-brain natriuretic peptide, high-sensitivity troponin T, serum creatine, albumin, blood urea nitrogen and highly sensitive C-reactive protein. The model showed a high performance on discrimination (C-index was 0.759 (95% confidence interval [CI]: 0.739, 0.778) in development cohort and 0.761 (95% CI: 0.731, 0.791) in validation cohort), accuracy, calibration, and outperformed than several existed risk scores. A point-based risk score was built to stratify low- (0-12), intermediate- (13-16) and high-risk group (≥17) among patients. Conclusions A prediction model using readily available predictors was developed and internal validated to predict 1-year mortality risk among patients hospitalized for AHF. It may serve as a useful tool for individual risk stratification and informing decision making to improve clinical care.