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Repeated CHARGE-AF Score Assessments for Predicting Incident Atrial Fibrillation: A Longitudinal Analysis from the
Medrxiv : the Preprint Server for Health Sciences
|July 30, 2026
Summary
A single Cohorts for Heart and Aging Research in Genomic Epidemiology - Atrial Fibrillation (CHARGE-AF) score effectively predicts atrial fibrillation (AF) risk. Repeated CHARGE-AF scores offer minimal additional benefit for AF prediction in clinical settings.
Area of Science:
- Cardiology
- Epidemiology
- Genomics
Background:
- The Cohorts for Heart and Aging Research in Genomic Epidemiology - Atrial Fibrillation (CHARGE-AF) score is a validated tool for estimating 5-year atrial fibrillation (AF) risk.
- Previous studies have established the utility of the CHARGE-AF score in predicting AF risk.
Purpose of the Study:
- To evaluate the utility of repeated CHARGE-AF scores for improving AF risk prediction.
- To determine if longitudinal CHARGE-AF score assessments enhance the accuracy of predicting incident AF.
Main Methods:
- Analysis of 10,188 participants from the Atherosclerosis Risk in Communities (ARIC) study with complete data from four clinic visits.
- Calculation of CHARGE-AF scores at each visit, with incident AF determined over a median follow-up of 19.5 years.
- Comparison of Cox regression models incorporating single vs. multiple CHARGE-AF scores to assess predictive discrimination and calibration.
Main Results:
- A single visit 4 CHARGE-AF score demonstrated strong association with incident AF (HR=1.14 per 1% increase).
- Addition of prior CHARGE-AF scores did not significantly improve model discrimination (C-statistic 0.702-0.703).
- Sex modified the association, with a stronger risk prediction for females.
Conclusions:
- A single, most recent CHARGE-AF score measurement provides robust predictive value for incident AF.
- Repeated CHARGE-AF score assessments offer limited incremental benefit for AF risk prediction.
- The most recent CHARGE-AF score is sufficient for AF risk assessment in clinical practice with longitudinal data.

