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

Published

Journal Article

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. CHADS(2) and CHA(2)DS(s)-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 CHA(2)DS(2)-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 CHADS(2) was 88.0%; 12% those identified as high-risk had CHADS(2) scores <2. Anticoagulation rates were identical using 1-year and 10-year algorithms for patients with CHADS(2) scores ≥2 (58.5% on anticoagulation) and CHA(2)DS(2)-Vasc scores ≥2 (56.0% on anticoagulation). CONCLUSIONS: 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 CHA(2)DS(2)-Vasc based algorithm. Despite differences in accuracy between algorithms, system-wide anticoagulation rates assessed were similar regardless of algorithm used.

Full Text

Duke Authors

Cited Authors

  • Navar-Boggan, AM; Rymer, JA; Piccini, JP; Shatila, W; Ring, L; Stafford, JA; Al-Khatib, SM; Peterson, ED

Published Date

  • January 2015

Published In

Volume / Issue

  • 169 / 1

Start / End Page

  • 39 - 44.e2

PubMed ID

  • 25497246

Pubmed Central ID

  • 25497246

Electronic International Standard Serial Number (EISSN)

  • 1097-6744

Digital Object Identifier (DOI)

  • 10.1016/j.ahj.2014.09.014

Language

  • eng

Conference Location

  • United States