Abstract

Introduction: Atrial fibrillation (AF) that is associated with acute precipitants frequently recurs and is associated with long-term morbidity and mortality. Leveraging a large electronic medical record (EMR) database, we previously reported the performance of an automated EMR-based algorithm to identify the acute precipitants of newly detected-AF, but its validation in other databases has not been performed. Hypothesis: Modified EMR-based algorithm would accurately identify acute precipitants of AF and oral anticoagulation (OAC) use after AF diagnosis Methods: Among all records (inpatient/outpatient/ER) in a single-institution EMR (10/1/17 - 12/31/19), we first identified 4493 records documenting newly-diagnosed AF (ICD 10: I48). We then applied the modified EMR-based algorithm to identify 13 acute AF precipitants (within 30 days of AF diagnosis, Figure 1) and determine OAC use after an AF diagnosis. We manually adjudicated a random subset of identified records (892 of 4493) to derive performance metrics of the EMR algorithm. Results: Of 4493 records with newly-diagnosed AF, the algorithm identified ≥1 acute precipitant in 831 records and ≥2 precipitants in 206 records. The most common precipitants were respiratory failure (38%) and pneumonia (35%). Among patients with CHADS2Vasc ≥2, 44% with an acute precipitant were prescribed an OAC vs. 63% free from a precipitant after initial AF diagnosis. EMR algorithm accurately identified the precipitants and OAC use after AF diagnosis (PPV 89% for both, Figure 1). Conclusions: In this validation study, our novel EMR-based automated algorithm was highly accurate at identifying acute precipitants of AF and OAC use after AF diagnosis. We also observed differential rates of OAC use between those with and without an acute precipitant. Application of the algorithm to multi-institutional datasets could help map the gaps in care (e.g., OAC use) AF patients with acute precipitants often experience.

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