Utility of risk prediction models to detect atrial fibrillation in screened participants. (29th November 2020)

• Record Type: Journal Article
• Authors: Poorthuis, Michiel H F; Jones, Nicholas R; Sherliker, Paul; Clack, Rachel; de Borst, Gert J; Clarke, Robert; Lewington, Sarah; Halliday, Alison; Bulbulia, Richard
• Is Part Of: European journal of preventive cardiology. Volume 28:Number 6(2021)
• Page Start: 586
• Page End: 595
• Publication Date: 2020-11-29
• Subjects: Atrial fibrillation -- Risk prediction models -- Stroke -- Selective screening -- External validation; Cardiovascular system -- Diseases -- Prevention -- Periodicals; Cardiac patients -- Rehabilitation -- Periodicals; 616.12
• DOI: 10.1093/eurjpc/zwaa082
• Languages: English
• ISSNs: 2047-4873

Abstract

Abstract: Aims: Atrial fibrillation (AF) is associated with higher risk of stroke. While the prevalence of AF is low in the general population, risk prediction models might identify individuals for selective screening of AF. We aimed to systematically identify and compare the utility of established models to predict prevalent AF. Methods and results: Systematic search of PubMed and EMBASE for risk prediction models for AF. We adapted established risk prediction models and assessed their predictive performance using data from 2.5M individuals who attended vascular screening clinics in the USA and the UK and in the subset of 1.2M individuals with CHA2 DS2 -VASc ≥2. We assessed discrimination using area under the receiver operating characteristic (AUROC) curves and agreement between observed and predicted cases using calibration plots. After screening 6959 studies, 14 risk prediction models were identified. In our cohort, 10 464 (0.41%) participants had AF. For discrimination, six prediction model had AUROC curves of 0.70 or above in all individuals and those with CHA2 DS2 -VASc ≥2. In these models, calibration plots showed very good concordance between predicted and observed risks of AF. The two models with the highest observed prevalence in the highest decile of predicted risk, CHARGE-AF and MHS, showed an observed prevalence of AF of 1.6% with a number needed to screen of 63. Selective screening of the 10% highest risk identified 39% of cases with AF. Conclusion: Prediction models can reliably identify individuals at high risk of AF. The best performing models showed an almost fourfold higher prevalence of AF by selective screening of individuals in the highest decile of risk compared with systematic screening of all cases. Registration: This systematic review was registered (PROSPERO CRD42019123847).