Left atrial assessment improves prediction of incident atrial fibrillation beyond CHARGE-AF risk score

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Abstract Background Atrial fibrillation (AF) is associated with substantial mortality and morbidity, even when it is asymptomatic and/or remains undetected. Clinical predictive models such as Charge-AF are not robust enough to be used in the clinical practice. We investigated the benefits of adding echocardiographic risk variables to Charge-AF risk model for its potential use in targeted screening for AF. Methods This study included 1108 consecutive patients aged 60±14 years old without a previous diagnosis of AF referred for echocardiographic assessment. Left atrial and ventricular functions were measured using strain analysis. Five-year predicted risk for AF was estimated, using Charge-AF risk model. The incidence of AF was ascertained from resting and exercise stress electrocardiograms and Holter recordings. Sequential Cox models were used to identify echocardiographic variables for predicting the incidence of new onset AF. Results The overall incident rate of AF was 3.1%, 5.5% and 7.2% at 1 year, 3 year and 5 year, respectively. Left atrial volume index (LAVi) predicted the incidence with a hazard ratio of 1.06 (p<0.001), that was independent of and increment to Charge-AF risk estimates (figure A). The addition of E/e’ (hazard ratio=1.064, p=0.001), left atrial reservoir strain (hazard ratio=0.98, p=0.034), left atrial booster strain (hazard ratio=0.97, p=0.048) to the baseline model including Charge-AF risk estimates also showed a significant, but less incremental benefit for AF prediction, compared with LAVi. When all these echocardiographic variables were added to a stepwise nested Cox model, only LAVi was significantly and independently predictive of AF incidence beyond Charge-AF risk estimates. This finding was confirmed by Kaplan-Meier curves showing that an increase in LAVi was progressively associated with increased risk for AF (figure B). When the cumulative AF incidence was analyzed separately between patients with higher (predicted risk >= 1.5%/year) and lower (predicted risk < 1.5%/year) Charge-AF risk estimates, LAVi was able to sub-stratify only patients with higher Charge-AF risk estimate (figures C and D). Conclusion LAVi provided incremental value in predicting incident AF in hospital patients. The inclusion of LAVi to the diagnostic algorithm may help guide screening and further monitoring for AF risk in this population.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.