Abstract

One-time screening trials for atrial fibrillation (AF) have produced mixed results; however, it is unclear if there is a subset of individuals for whom screening would be effective. Identifying such a subgroup would support targeted screening. We conducted a secondary analysis of VITAL-AF, a randomized trial of one-time, single-lead ECG screening during primary care visits. We tested two approaches to identify a subgroup where screening is effective. First, we developed an effect-based model for heterogeneous screening effects using a T-learner. Specifically, we separately predicted the likelihood of AF diagnosis under screening and usual care conditions using LASSO, a penalized regression method. The difference between these probabilities was the predicted screening effect. Second, we used the CHARGE-AF score, a validated AF risk model, to test for a heterogeneous screening effect. We used interaction testing to determine if observed AF diagnosis rates in the screening and usual care groups differed when stratified by decile of the predicted screening effect and predicted AF risk. Baseline characteristics were similar between the screening (n=15187) and usual care (n=15078) groups (mean age 74 years, 59% female). On average, screening did not significantly increase the AF diagnosis rate (2.55 vs. 2.30 per 100 person-years, rate difference 0.24, 95%CI -0.18 to 0.67). In the effect-based analysis, in the highest decile of predicted screening efficacy (n=3026), AF diagnosis rates were higher in the screening group (6.50 vs. 3.06 per 100 person-years, rate difference 3.45, 95%CI 1.62 to 5.28). In this group, the mean age was 84 years, 68% were female, and 55% had vascular disease. The risk-based analysis did not identify a subgroup where screening was more effective. Predicted screening effectiveness and predicted baseline AF risk were poorly correlated and demonstrated a U-shaped relationship (Spearman coefficient 0.13). In a secondary analysis of the VITAL-AF trial, we identified a small subgroup where one-time screening was associated with increased AF diagnoses using an effect-based approach. In this study, predicted AF risk was a poor proxy for predicted screening efficacy. These data caution against the assumption that high AF risk is necessarily correlated with high screening efficacy.

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