Predicting new-onset heart failure hospitalization of patients with atrial fibrillation: development and external validations of a risk score.

Aim: Atrial fibrillation (AF) is a well-known risk factor for heart failure (HF). We sought to develop and externally validate a risk model for new-onset HF admission in patients with AF and those without a history of HF.
Methods and Results: Using two multicentre, prospective, observational AF registries, RAFFINE (2857 patients, derivation cohort) and SAKURA (2516 patients without a history of HF, validation cohort), we developed a risk model by selecting variables with regularized regression and weighing coefficients by Cox regression with the derivation cohort. External validity testing was used for the validation cohort. Overall, 148 (5.2%) and 104 (4.1%) patients in the derivation and validation cohorts, respectively, developed HF during median follow-ups of 1396 (interquartile range [IQR]: 1078-1820) and 1168 (IQR: 844-1309) days, respectively. In the derivation cohort, age, haemoglobin, serum creatinine, and log-transformed brain natriuretic peptide were identified as potential risk factors for HF development. The risk model showed good discrimination and calibration in both derivations (area under the curve [AUC]: 0.80 [95% confidence interval (CI) 0.76-0.84]; Hosmer-Lemeshow, P = 0.257) and validation cohorts (AUC: 0.78 [95%CI 0.74-0.83]; Hosmer-Lemeshow, P = 0.475).
Conclusion: The novel risk model with four readily available clinical characteristics and biomarkers performed well in predicting new-onset HF admission in patients with AF.
(© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)