Corrigendum to ‘Forecasting the risk of heart failure hospitalization after acute coronary syndromes: the CORALYS HF score’ [American Journal of Cardiology 206 (2023) 320-329]

The present study aimed to identify patients at a higher risk of hospitalization for heart failure (HF) in a population of patients with acute coronary syndrome (ACS) treated with percutane- ous coronary revascularization without a history of HF or reduced left ventricular (LV) ejection fraction before the index admission. We performed a Cox regression multivariable analysis with competitive risk and machine learning models on the incideNce and predictOrs of heaRt fAiLure After Acute coronarY Syndrome (CORALYS) registry (NCT 04895176), an interna- tional and multicenter study including consecutive patients admitted for ACS in 16 European Centers from 2015 to 2020. Of 14,699 patients, 593 (4.0%) were admitted for the development of HF up to 1 year after the index ACS presentation. A total of 2 different data sets were ran- domly created, 1 for the derivative cohort including 11,626 patients (80%) and 1 for the valida- tion cohort including 3,073 patients (20%). On the Cox regression multivariable analysis, several variables were associated with the risk of HF hospitalization, with reduced renal func- tion, complete revascularization, and LV ejection fraction as the most relevant ones. The area under the curve at 1 year was 0.75 (0.72 to 0.78) in the derivative cohort, whereas on validation, it was 0.72 (0.67 to 0.77). The machine learning analysis showed a slightly inferior performance. In conclusion, in a large cohort of patients with ACS without a history of HF or LV dysfunction before the index event, the CORALYS HF score identified patients at a higher risk of hospitali- zation for HF using variables easily accessible at discharge. Further approaches to tackle HF development in this high-risk subset of patients are needed. © 2023 Elsevier Inc. All rights reserved. (Am J Cardiol 2023;206:320−329)