Predicting major bleeding among hospitalized patients using oral anticoagulants for atrial fibrillation after discharge

• Published in: PloS one 2021-03, Vol.16 (3), p.e0246691-e0246691
• Main Authors: Qazi, Jakub Z, Schnitzer, Mireille E, Côté, Robert, Martel, Marie-Josée, Dorais, Marc, Perreault, Sylvie
• Format: Article
• Language: English
• Subjects: Anticoagulants (Medicine); Atrial fibrillation; Care and treatment; Complications and side effects; Hemorrhage; Medicine and Health Sciences; Patient outcomes; Physical Sciences; Research and Analysis Methods; Risk factors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0246691

Real-world predictors of major bleeding (MB) have been well-studied among warfarin users, but not among all direct oral anticoagulant (DOAC) users diagnosed with atrial fibrillation (AF). Thus, our goal was to build a predictive model of MB for new users of all oral anticoagulants (OAC) with AF. We identified patients hospitalized for any cause and discharged alive in the community from 2011 to 2017 with a primary or secondary diagnosis of AF in Quebec's RAMQ and Med-Echo administrative databases. Cohort entry occurred at the first OAC claim. Patients were categorized according to OAC type. Outcomes were incident MB, gastrointestinal bleeding (GIB), non-GI extracranial bleeding (NGIB) and intracranial bleeding within 1 year of follow-up. Covariates included age, sex, co-morbidities (within 3 years before cohort entry) and medication use (within 2 weeks before cohort entry). We used logistic-LASSO and adaptive logistic-LASSO regressions to identify MB predictors among OAC users. Discrimination and calibration were assessed for each model and a global model was selected. Subgroup analyses were performed for MB subtypes and OAC types. Our cohort consisted of 14,741 warfarin, 3,722 dabigatran, 6,722 rivaroxaban and 11,196 apixaban users aged 70-86 years old. The important MB predictors were age, prior MB and liver disease with ORs ranging from 1.37-1.64. The final model had a c-statistic of 0.63 (95% CI 0.60-0.65) with adequate calibration. The GIB and NGIB models had similar c-statistics of 0.65 (95% CI 0.63-0.66) and 0.67 (95% CI 0.64-0.70), respectively. MB and MB subtype predictors were similar among DOAC and warfarin users. The predictors selected by our models and their discriminative potential are concordant with published data. Thus, these models can be useful tools for future pharmacoepidemiologic studies involving older oral anticoagulant users with AF.