Safety and Effectiveness of Oral Anticoagulants in Atrial Fibrillation: Real-World Insights Using Natural Language Processing and Machine Learning.

We assessed the effectiveness and safety of vitamin K antagonists (VKAs) versus direct oral anticoagulants (DOACs) in patients with atrial fibrillation (AF) using artificial intelligence techniques. This is a retrospective study in 15 Spanish hospitals (2014-2020), including adult AF patients with no history of anticoagulation, thrombosis events, rheumatic mitral valvular heart disease, mitral valve stenosis, or pregnancy. We employed EHRead technology based on natural language processing (NLP) and machine learning (ML), along with SNOMED-CT terminology, to extract clinical data from electronic health records (EHRs). Using propensity score matching (PSM), the effectiveness, safety, and hospital mortality of VKAs versus DOACs were analyzed through Kaplan-Meier curves and Cox regression. Out of 138,773,332 EHRs from 4.6 million individuals evaluated, 44,292 patients were included, 79.6% on VKAs and 20.4% on DOACs. Most patients were elderly [VKA 78 (70, 84) and DOAC 75 (66, 83) years], with numerous comorbidities (75.5% and 70.2% hypertension, 47.2% and 39.9% diabetes, and 40.3% and 34.8% heart failure, respectively). Additionally, 60.4% of VKA and 48.7% of DOAC users had a CHA2DS2-VASc Score ≥4. After PSM, 8929 patients per subgroup were selected. DOAC users showed a lower risk of thrombotic events [HR 0.81 (95% CI 0.70-0.94)], minor bleeding [HR 0.89 (95% CI 0.83-0.96)], and mortality [HR 0.80 (95% CI 0.69-0.92)]. Applying NLP and ML, we generated valuable real-world evidence on anticoagulated AF patients in Spain. Even in complex populations, DOACs have demonstrated a better safety and effectiveness profile than VKAs.