Accuracy and validation of an automated electronic algorithm to identify patients with atrial fibrillation at risk for stroke

Background There is no universally accepted algorithm for identifying atrial fibrillation (AF) patients and stroke risk using electronic data for use in performance measures. Methods Patients with AF seen in clinic were identified based on International Classification of Diseases, Ninth Revision (ICD-9) codes. CHADS2 and CHA2 DSs -Vasc scores were derived from a broad, 10-year algorithm using IICD-9 codes dating back 10 years and a restrictive, 1-year algorithm that required a diagnosis within the past year. Accuracy of claims-based AF diagnoses and of each stroke risk classification algorithm were evaluated using chart reviews for 300 patients. These algorithms were applied to assess system-wide anticoagulation rates. Results Between 6/1/2011, and 5/31/2012, we identified 6,397 patients with AF. Chart reviews confirmed AF or atrial flutter in 95.7%. A 1-year algorithm using CHA2 DS2 -Vasc score ≥2 to identify patients at risk for stroke maximized positive predictive value (97.5% [negative predictive value 65.1%]). The PPV of the 10-year algorithm using CHADS2 was 88.0%; 12% those identified as high-risk had CHADS2 scores