New score for predicting major bleeding in patients with atrial fibrillation using direct oral anticoagulants

Abstract

PurposeOur aim was to identify factors associated with major bleeding in patients with atrial fibrillation (AF) on direct oral anticoagulants (DOACs) and to construct and externally validate a predictive model that would provide a validated tool for clinical assessment of major bleeding. MethodsIn the development cohort, prediction model was built by logistic regression, the area under the curve (AUC), and Nomogram. External validation, analytical identification and calibration of the model using AUC, calibration curves and Hosmer-Lemeshow test. ResultsThe development cohort consisted of 4209 patients from 7 centers and the external validation cohort consisted of 1800 patients from 12 centers. Multifactorial analysis showed that age > 65 years, history of bleeding, anemia, vascular disease, antiplatelet therapy/non-steroidal anti-inflammatory drugs and rivaroxaban were independent risk factors for major bleeding, and gastrointestinal protective agents was a protective factor. The Alfalfa-MB model was constructed using these seven factors (AUC = 0.807), and in the external validation cohort, the model showed good discriminatory power (AUC = 0.743) and good calibration (Hosmer-Lemeshow test P value of 0.205). The predictive power of the six bleeding scores was ORBIT (AUC = 0.706), HAS-BLED (AUC = 0.648), ATRIA (AUC = 0.645), HEMORR2 HAGES (AUC = 0.632), ABC (AUC = 0.619) and Shireman (AUC = 0.599) in descending order. ConclusionBased on 7 factors, we derived and externally validated a predictive model for major bleeding with DOACs in patients with AF (Alfalfa-MB). The model has good predictive value and may be an effective tool to help reduce the occurrence of major bleeding in patients with DOACs.