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(IC...
| Publicado en: | American Heart Journal Vol. 169; no. 1; pp. 39 - 40 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jan2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103863370&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103863370 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00028703 04K jtl: American Heart Journal issn: 00028703 maglogo: N pubinfo: dt: Jan2015 vid: 169 iid: 1 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 103863370 NLM25497246 2012837448 10.1016/j.ahj.2014.09.014 NLM25497246 103863370 ppf: 39 ppct: 1 formats: tig: atl: Accuracy and validation of an automated electronic algorithm to identify patients with atrial fibrillation at risk for stroke. aug: au: Navar-Boggan, Ann Marie Rymer, Jennifer A Piccini, Jonathan P Shatila, Wassim Ring, Lauren Stafford, Judith A Al-Khatib, Sana M Peterson, Eric D affil: Duke University Medical Center, Durham, NC. Electronic address: ann.navar@duke.edu. sug: subj: Algorithms Atrial Fibrillation Epidemiology Stroke Epidemiology Aged Aged, 80 and Over Anticoagulants Therapeutic Use Atrial Fibrillation Drug Therapy Female Human Male Middle Age Risk Assessment Methods Sensitivity and Specificity Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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