Lowres N, Olivier J, Chao TF, et al. – The exact age distribution and calculated stroke risk of screen-detected atrial fibrillation (AF) is unknown, making it impossible to determine the number needed to screen (NNS) to identify one treatable new AF case (NNS-Rx) in each age stratum. Thus, researchers investigated the exact yield and calculated stroke-risk profile of screen-detected AF and NNS-Rx in 5-year age strata. Via performing a systematic review, they identified 24 studies that performed a single time point screen for AF in a general ambulant population, including people aged ≥ 65 years. They invited the authors from eligible studies to collaborate and share patient-level data. Statistical analysis was performed via random effects logistic regression for AF detection rate; Poisson regression modelling was used for CHA₂DS₂-VASc scores. A total of 19 studies collaborated—with 141,220 participants screened and 1,539 new AF cases. The researchers found that pooled yield of screening was greater in males across all age strata, and age/sex-adjusted detection rate for screen-detected AF in those aged ≥ 65 years was 1.44%. Among those aged < 65 years, the age/sex adjusted detection rate for screen-detected AF was 0.41%. New AF detection rate increased progressively with age from 0.34% (< 60 years) to 2.73% (≥ 85 years). Neither the choice of screening methodology or device, the geographical region, nor the screening setting influenced the detection rate of AF. Mean CHA₂DS₂-VASc scores (n = 1,369) increased with age from 1.1 (< 60 years) to 3.9 (≥ 85 years); 72% of those aged ≥ 65 years had ≥ 1 additional stroke risk factor other than age/sex. All new AF cases aged ≥ 75 years and 66% between 65 and 74 years had a Class-1 OAC recommendation. The NNS-Rx was found to be 83 for individuals aged ≥ 65 years, 926 for 60–64 years of age; and 1,089 for < 60 years of age. The researchers noted that one of main limitations of their study was the insufficient data on sociodemographic variables of the populations and possible ascertainment biases to explain the variance in the samples.