Systematic, randomised atrial fibrillation screening using detailed phenotyping with a risk-prediction model combined with patch electrocardiogram in a Swedish population aged 65 years or older: The CONSIDERING-AF trial

Background and aims Atrial fibrillation (AF), often asymptomatic and underdiagnosed, is an independent risk factor for ischemic stroke. A knowledge gap remains regarding the optimal target population, and method to use for AF screening. We aimed to test whether screening for AF using a machine learning-based risk-prediction model (RPM) and 14-day continuous patch electrocardiogram (ECG) (Philips ePatch) in high-risk individuals ≥65 years is more effective than standard care. Methods Individuals ≥65 years were assigned to general or RPM cohort. The general cohort was randomised to control or invitation. In the RPM cohort, high-risk individuals, identified by RPM, were randomised to control or invitation. The primary outcome was six-month AF incidence, analysed as intention-to-invite, comparing RPM + invitation with general + control. Results Of 2,960 randomised individuals, participation was 43% (632/1480) in invitation arms. AF incidence was higher in RPM + invitation than in general + control arm (3.8%, 28/740 versus 0.7%, 5/740; p<0.001), yielding a risk ratio of 5.6, (95% confidence interval [2.2, 14.4]), and a number needed to invite of 32. AF was more often detected in RPM + invitation than in general + invitation arm (1.1%, 8/740; p<0.001), but not more often than in RPM + control arm (2.2%, 16/740; p=0.07). No difference was found between general + invitation and general + control arms (1.1%, 8/740 versus 0.7%, 5/740; p=0.40). Conclusion Among high-risk individuals ≥65 years, the combination of a machine learning-based risk-prediction model and long-term ECG recording was superior to standard care in identifying new AF cases.