A novel prognostic tool to predict mortality in patients with atrial fibrillation: The BASIC-AF risk score

Abstract

BackgroundThis study sought to develop and validate a risk score to predict mortality in patients with atrial fibrillation (AF) after a hospitalization for cardiac reasons. MethodsThe new risk score was derived from a prospective cohort of hospitalized patients with concurrent AF. The outcome measures were all-cause and cardiovascular mortality. Random forest was used for variable selection. A risk points model with predictor variables was developed by weighted Cox regression coefficients and was internally validated by bootstrapping. ResultsIn total, 1130 patients with AF were included. During a median follow-up of 2 years, 346 (30.6%) patients died and 250 patients had a cardiovascular cause of death. N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin-T were the most important predictors of mortality, followed by indexed left atrial volume, history and type of heart failure, age, history of diabetes mellitus, and intraventricular conduction delay, all forming the BASIC-AF risk score (Biomarkers, Age, ultraSound, Intraventricular conduction delay, and Clinical history). The score had good discrimination for all-cause (c-index = 0.85 and 95% CI 0.82–0.88) and cardiovascular death (c-index = 0.84 and 95% CI 0.81-0.87). The predicted probability of mortality varied more than 50-fold across deciles and adjusted well to observed mortality rates. A decision curve analysis revealed a significant net benefit of using the BASIC-AF risk score to predict the risk of death, when compared with other existing risk schemes. ConclusionsWe developed and internally validated a well-performing novel risk score for predicting death in patients with AF. The BASIC-AF risk score included routinely assessed parameters, selected through machine-learning algorithms, and may assist in tailored risk stratification and management of these patients.