Background No scores presently exist to predict bleeding in atrial fibrillation (AF) populations using direct oral anticoagulants (DOACs). We used data from two independent healthcare claims databases to develop and validate a predictive model of major bleeding in a contemporary AF population. Methods Patients with non-valvular AF initiating oral anticoagulation were identified in the MarketScan databases from 2007–2014. Using Cox regression models in 1000 bootstrapped samples, we developed a model that selected variables predicting major bleeding in the first year after anticoagulant initiation. The final model was validated in patients with non-valvular AF in the Optum Clinformatics database in the period 2009–2015. The discriminative ability of existing bleeding scores were individually evaluated and compared with the new bleeding model termed Anticoagulation-specific Bleeding Score (ABS) in both MarketScan and Optum. Results Among 119,083 patients with AF initiating oral anticoagulation in the derivation cohort, 4,030 experienced a bleeding event. The variable selection model identified 15 variables (including individual type of oral anticoagulant) associated with major bleeding. Discrimination of the model was modest [c-statistic 0.68, 95% confidence interval (CI) 0.67–0.69]. The model was subsequently applied to 81,285 AF patients in the validation data set (3,238 bleeding events), showing similar discrimination (c-statistic 0.68, 95% CI 0.67–0.69). In both cohorts, the predictive performance of the ABS was better than the existing models for bleeding prediction in AF. Conclusions We developed a model that uses administrative healthcare data for the identification of AF patients at higher risk of bleeding after initiation of oral anticoagulation, taking into account the lower bleeding risk in DOAC compared to warfarin users.
Bibliographical noteFunding Information:
Research reported in this publication was supported by the National Heart, Lung, and Blood Institute (https://www.nhlbi.nih.gov/grants-and-training/funding-opportunities-and-contacts) and the National Institute of Aging (https://www.nia.nih. gov/research/grants-funding) of the National Institutes of Health under awards number R01-HL122200 [AA], R01-HL131579 [PLL], and R21-AG058445 [AA], as well as support from the American Heart Association (http://professional. heart.org/professional/ResearchPrograms/UCM_ 316889_Research.jsp) grant 6EIA26410001 [AA]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Optum provided support in the form of salaries for authors [LGSB], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section.