title: Accuracy and validation of an automated electronic algorithm to identify patients with atrial fibrillation at risk for stroke.
abstract: 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.
authors.affiliation: Duke University Medical Center, Durham, NC. Electronic address: ann.navar@duke.edu.
authors.forename: Ann Marie
authors.initials: AM
authors.lastname: Navar-Boggan
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Rymer
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Jonathan P
authors.initials: JP
authors.lastname: Piccini
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Wassim
authors.initials: W
authors.lastname: Shatila
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Lauren
authors.initials: L
authors.lastname: Ring
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Judith A
authors.initials: JA
authors.lastname: Stafford
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Sana M
authors.initials: SM
authors.lastname: Al-Khatib
authors.affiliation: Duke University Medical Center, Durham, NC.
authors.forename: Eric D
authors.initials: ED
authors.lastname: Peterson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000925:Anticoagulants / Q000627:therapeutic use; D001281:Atrial Fibrillation / Q000188:drug therapy / Q000453:epidemiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D020521:Stroke / Q000453:epidemiology*
chemical_list: D000925:Anticoagulants