Background: The Cohorts for Heart and Aging Research in Genomic Epidemiology - Atrial Fibrillation (CHARGE-AF) score is a validated tool for estimating 5-year risk of atrial fibrillation (AF). We aimed to evaluate the utility of repeated CHARGE-AF scores for improving AF risk prediction. Methods: We analyzed participants from the Atherosclerosis Risk in Communities (ARIC) study with complete data from the first four clinic visits (9-year period) and with no prevalent AF by visit 4 (analysis baseline; N = 10,188). CHARGE-AF scores were calculated for each visit using clinical and demographic variables. Incident AF was determined from electrocardiograms, hospital discharge codes, and death certificates over a median follow-up of 19.5 years. Four Cox regression models were assessed: model 1 included only the visit 4 CHARGE-AF score, and subsequent models added prior CHARGE-AF scores in stepwise fashion. C-statistics were used to evaluate model discrimination, and comparison of observed versus predicted risk was employed to evaluate calibration. Secondary analysis restricted follow-up to five years. Results: During follow-up, 2,519 participants developed AF (14.2 cases per 1,000 person-years). The mean age of participants at start of follow-up was 62.8 (standard deviation 5.6) years. In the primary analysis, each 1% increase in the visit 4 CHARGE-AF score was associated with incident AF (Model 1 HR = 1.14, 95% CI 1.13-1.15). Addition of scores from prior visits did not significantly improve model discrimination (C-statistic: 0.702-0.703 for all models). Sex modified the association between a 1% increase in CHARGE-AF score and incident AF, with a stronger association among females (Model 1 HR = 1.21, 95% CI: 1.19-1.22) than among males (HR for model 1 = 1.12, 95% CI: 1.11-1.13). Similar patterns were observed in the secondary (5-year restricted) analysis. Conclusions: A single measurement of the CHARGE-AF score provided strong predictive value for incident AF, with the addition of prior scores offering limited incremental benefit. These findings suggest that, in clinical settings with longitudinal data, the most recent assessment is sufficient for AF risk prediction.