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

• Published in: The American heart journal Vol. 169; no. 1; pp. 39 - 44.e2
• Main Authors: Navar-Boggan, Ann Marie, Rymer, Jennifer A., Piccini, Jonathan P., Shatila, Wassim, Ring, Lauren, Stafford, Judith A., Al-Khatib, Sana M., Peterson, Eric D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2015; Elsevier Limited
• Subjects: Accuracy; Aged; Aged, 80 and over; Algorithms; Anticoagulants; Anticoagulants - therapeutic use; Atrial Fibrillation - drug therapy; Atrial Fibrillation - epidemiology; Cardiac arrhythmia; Cardiovascular; Cardiovascular disease; Codes; Comorbidity; Diabetes; Female; Heart failure; Hospitals; Humans; Hypertension; Male; Middle Aged; Mortality; Older people; Patients; Risk Assessment - methods; Sensitivity and Specificity; Stroke; Stroke - epidemiology
• ISSN: 0002-8703; 1097-6744; 1097-6744
• DOI: 10.1016/j.ahj.2014.09.014

There is no universally accepted algorithm for identifying atrial fibrillation (AF) patients and stroke risk using electronic data for use in performance measures. Patients with AF seen in clinic were identified based on International Classification of Diseases, Ninth Revision(ICD-9) codes. CHADS2 and CHA2DSs-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. 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 CHA2DS2-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 <2. Anticoagulation rates were identical using 1-year and 10-year algorithms for patients with CHADS2 scores ≥2 (58.5% on anticoagulation) and CHA2DS2-Vasc scores ≥2 (56.0% on anticoagulation). Automated methods can be used to identify patients with prevalent AF indicated for anticoagulation but may have misclassification up to 12%, which limits the utility of relying on administrative data alone for quality assessment. Misclassification is minimized by requiring comorbidity diagnoses within the prior year and using a CHA2DS2-Vasc based algorithm. Despite differences in accuracy between algorithms, system-wide anticoagulation rates assessed were similar regardless of algorithm used.