Language
English
Publication Date
3-1-2023
Journal
Heart Rhythm O2
DOI
10.1016/j.hroo.2022.11.004
PMID
36993910
PMCID
PMC10041076
PubMedCentral® Posted Date
11-24-2022
PubMedCentral® Full Text Version
Post-print
Abstract
Background: Current risk scores that are solely based on clinical factors have shown modest predictive ability for understanding of factors associated with gaps in real-world prescription of oral anticoagulation (OAC) in patients with atrial fibrillation (AF).
Objective: In this study, we sought to identify the role of social and geographic determinants, beyond clinical factors associated with variation in OAC prescriptions using a large national registry of ambulatory patients with AF.
Methods: Between January 2017 and June 2018, we identified patients with AF from the American College of Cardiology PINNACLE (Practice Innovation and Clinical Excellence) Registry. We examined associations between patient and site-of-care factors and prescription of OAC across U.S. counties. Several machine learning (ML) methods were used to identify factors associated with OAC prescription.
Results: Among 864,339 patients with AF, 586,560 (68%) were prescribed OAC. County OAC prescription rates ranged from 26.8% to 93%, with higher OAC use in the Western United States. Supervised ML analysis in predicting likelihood of OAC prescriptions and identified a rank order of patient features associated with OAC prescription. In the ML models, in addition to clinical factors, medication use (aspirin, antihypertensives, antiarrhythmic agents, lipid modifying agents), and age, household income, clinic size, and U.S. region were among the most important predictors of an OAC prescription.
Conclusion: In a contemporary, national cohort of patients with AF underuse of OAC remains high, with notable geographic variation. Our results demonstrated the role of several important demographic and socioeconomic factors in underutilization of OAC in patients with AF.
Keywords
Anticoagulation, Atrial fibrillation, Care gaps, Guidelines, Machine learning
Published Open-Access
yes
Recommended Citation
Azizi, Zahra; Ward, Andrew T; Lee, Donghyun J; et al., "Sociodemographic Determinants of Oral Anticoagulant Prescription in Patients With Atrial Fibrillations: Findings From the Pinnacle Registry Using Machine Learning" (2023). Faculty and Staff Publications. 4325.
https://digitalcommons.library.tmc.edu/baylor_docs/4325