Background: Stroke mainly occurs in patients without atrial fibrillation (AF). This study explored risk prediction models for ischemic stroke and transient ischemic attack (TIA) in patients without AF.
Methods: Three US-based healthcare databases (Truven MarketScan Commercial Claims and Encounters [CCAE], Medicare Supplemental [MDCR], and Optum Clinformatics [Optum]) were used to establish patient cohorts without AF during the index period of 2008-2012. The performance of 2 existing models (CHADS2 and CHA2DS2-VASc) for predicting stroke and TIA was examined by fitting a logistic regression to a training dataset and evaluating predictive accuracy in a validation dataset (area under the curve, AUC) using patients with complete follow-up of 1 or 3 years, separately.
Results: The commercial populations were younger and had fewer comorbidities than Medicare-eligible population. The incidence proportions of ischemic stroke and TIA during 1 and 3 years of follow-up were .5% and 1.9% (CCAE), .6% and 2.2% (Optum), and 4.6% and 13.1% (MDCR), respectively. The models performed consistently across all 3 databases, with the AUC ranging from .69 to .77 and from .68 to .73 for 1- and 3-year prediction, respectively. Predictive accuracy was lower than the initial work of CHADS2 evaluation in patients with AF (AUC: .82), but consistent with a subsequent meta-analysis of CHADS2 (.60-.80) and CHA2DS2-VASc performance (.64-.79).
Conclusion: Although the existing schemes for predicting ischemic stroke and TIA in patients with AF can be applied to patients without AF with comparable predictive accuracy, the evidence suggests that there is room for improvement in these models' performance.
Keywords: Stroke; risk prediction; stroke prevention; transient ischemic attack.
Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.