Selective screening for atrial fibrillation using multivariable risk models

Abstract

Objective: Atrial fibrillation can lead to stroke if untreated, and identifying those at higher risk is necessary for cost-effective screening for asymptomatic, paroxysmal atrial fibrillation. Age has been proposed to identify those at risk, but risk models may provide better discrimination. This study compares atrial fibrillation risk models with age for screening for atrial fibrillation.

Methods: Nine atrial fibrillation risk models were compared using the Atherosclerosis Risk in Communities study (11 373 subjects, 60.0±5.7 years old). A new risk model (Screening for Asymptomatic Atrial Fibrillation Events-SAAFE) was created using data collected in the Monitoring Disparities in Chronic Conditions study (3790 subjects, 58.9±15.3 years old). The primary measure was the fraction of incident atrial fibrillation subjects who should receive treatment due to a high CHA₂DS₂-VASc score identified when screening a fixed number equivalent to the age criterion. Secondary measures were the C statistic and net benefit.

Results: Five risk models were significantly better than age. Age identified 71 (61%) of the subjects at risk for stroke who subsequently developed atrial fibrillation, while the best risk model identified 96 (82%). The newly developed SAAFE model identified 95 (81%), primarily based on age, congestive heart failure and coronary artery disease.

Conclusions: Use of a risk model increases identification of subjects at risk for atrial fibrillation. One of the best performing models (SAAFE) does not require an ECG for its application, so that it could be used instead of age as a screening criterion without adding to the cost.

Keywords: atrial fibrillation; cardiac risk factors and prevention; stroke.