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, suggesting that the most recent assessment is sufficient for AF risk prediction.
Abstract
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.
The EHR-based machine learning model, FIND-AF 2.0, identifies a high-risk subpopulation for AF diagnosis among patients at elevated risk of stroke and could enable scalable, EHR-driven, risk-guided AF screening.
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A supervised machine learning algorithm for AF in a Western Pacific population was derived and demonstrated that higher risk was associated with hospitalisation for other cardio-renal diseases and death.
J. Hsu, C. Hayward, Tobin Joseph et al.· Heart, Lung and Circulation· 0 citations
External validation of the GLORIA-AF Stroke Weighted Risk Score supports its transportability and potential adjunctive role in guideline-directed thromboembolic risk assessment and generally greater net benefit than CHA2DS2-VA.
Prediabetes was associated with a modestly increased risk of new-onset AF, particularly persistent AF, and was associated with HF development among individuals without established arrhythmia.
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Diffuse LV interstitial fibrosis quantified by ECV independently predicts incident AF and is associated with adverse atrial remodeling, supporting ventricular fibrosis as an early substrate for AF development.
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BACKGROUND
Accurate prediction of ischemic stroke or systemic embolism is central to atrial fibrillation (AF) management, yet the transportability of available risk scores to Chinese populations remains uncertain.
OBJECTIVE
Externally validate and compare published stroke risk scores in Chinese patients with non-valvular AF.
METHODS
We systematically retrieved and externally validated 17 published models in a retrospective cohort of 1,283 adults hospitalized with non-valvular AF at a Chinese tertiary-care center. Discrimination was evaluated using the 4-year Uno C-index and time-dependent areas under the curve (AUC). Models providing absolute risks also underwent calibration, Brier score, and reclassification analyses. Sensitivity analyses accounted for death as a competing event and separately evaluated patients not receiving OAC at baseline.
RESULTS
During 4 years of follow-up, 103 primary outcomes occurred, corresponding to 2.85 events per 100 person-years. Across models, 4-year Uno C-indices ranged from 0.572 to 0.767, compared with 0.690 for CHA2DS2-VASc. GARFIELD-AF 2017, GARFIELD-AF 2021, and ATRIA had higher Uno C-indices than CHA2DS2-VASc. However, no model had significantly higher time-dependent AUC at any time point. For 1-year calibration, CHA2DS2-VASc had predicted and observed risks of 3.39% and 3.54%, respectively, with an observed-to-expected ratio of 1.04, whereas several other models underestimated risk. Brier scores and reclassification measures showed no consistent advantage Competing-risk analyses lowered cumulative incidence without changing comparative patterns. Among patients not receiving OAC at baseline, no model outperformed CHA2DS2-VASc.
CONCLUSION
Candidate models did not consistently outperform CHA2DS2-VASc in this external validation; CHA2DS2-VASc remains practical, while other models may require population-specific validation and recalibration.
Unknown authors· Heart Rhythm· 0 citations
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