Identifying Patients with Atrial Fibrillation in Administrative Data

Abstract Background Identifying patients with atrial fibrillation (AF) using administrative data is important for epidemiologic and outcomes research. Although administrative data covers large populations, it is necessary to assess their validity in identifying AF patients. Methods We used Ontario family physician electronic medical records from the E lectronic M edical R ecord A dministrative data L inked D atabase (EMRALD) as a reference standard to assess the accuracy of administrative data algorithms in identifying patients with AF. From a random sample of 7500 adult patients, patients with AF as recorded in family physician records were identified. Results The optimal algorithm consisted of ANY of: hospitalization or an emergency room code for AF OR prescription for an AF specific antiarrhythmic OR billing code for cardioversion, OR prescription for an anticoagulant that was accompanied by a physician billing code for arrhythmia. The algorithm sensitivity was 80.7% (95% CI: 75.1-86.3), specificity 99.1% (95% CI: 98.9-99.3), positive predictive value 71.1% (95% CI: 65.1-77.1), negative predictive value 99.5% (95% CI: 99.3-99.7). This algorithm, applied to the Ontario population, resulted in a calculated increase in AF prevalence from 1.68% to 2.36% over the years 2000-2014. Anticoagulation rates for AF patients increased from 53% in 2011 to 60% in 2014. Amongst AF patients on anticoagulants, novel oral anticoagulant (NOAC) utilization increased from