A Simple and Portable Algorithm for Identifying Atrial Fibrillation in the Electronic Medical Record

Abstract

Atrial fibrillation (AF) is common and increases stroke risk and mortality. Many knowledge gaps remain with respect to practice patterns and outcomes. Electronic medical records (EMRs) may serve as powerful research tools if AF status can be properly ascertained. We sought to develop an algorithm for identifying subjects with and without AF in the EMR and compare it to previous methods. Using a hospital network EMR (n = 5,737,846), we randomly selected 8,200 subjects seen at a large academic medical center in January 2014 to derive and validate 7 AF classification schemas (4 cases and 3 controls) to construct a composite AF algorithm. In an independent sample of 172,138 subjects, we compared this algorithm against published AF classification methods. In total, we performed manual adjudication of AF in 700 subjects. Three AF schemas (AF1, AF2, and AF4) achieved positive predictive value (PPV) >0.9. Two control schemas achieved PPV >0.9 (control 1 and control 3). A combination algorithm AF1, AF2, and AF4 (PPV 88%; 8.2% classified) outperformed published classification methods including >1 outpatient International Statistical Classification of Diseases, Ninth Revision code or 1 outpatient code with an electrocardiogram demonstrating AF (PPV 82%; 5.9% classified), ≥ 1 inpatient International Statistical Classification of Diseases, Ninth Revision code or electrocardiogram demonstrating AF (PPV 88%; 6.1% classified), or the intersection of these (PPV 84%; 7.4% classified). When applied simultaneously, the case and control algorithms classified 98.4% of the cohort with zero disagreement. In conclusion, we derived a parsimonious and portable algorithm to identify subjects with and without AF with high sensitivity. If broadly applied, this algorithm can provide optimal power for EMR-based AF research.

Copyright © 2016 Elsevier Inc. All rights reserved.

Figures

Figure 1

Patient Flow Patient flow through the study. AF classification categories were created a priori and internally validated in a cohort of 8,200 patients. High-performing categories were then combined and iteratively adjusted to develop an AF classification algorithm. This algorithm was compared to previously published methods in an independent sample of 172,138 individuals. AF – atrial fibrillation. EMR – electronic medical record.

Figure 2

Comparative Algorithm Performance Comparative algorithm performance on an independent comparison set. Our composite AF algorithms were compared against two previously published AF identification methods and two additional billing-code based methods in terms of both positive predictive value for AF and percent of the cohort captured. Definitions: comparator 1 (>1 outpatient ICD9 code or 1 outpatient ICD9 code + ECG with AF), comparator 2 (≥1 inpatient ICD9 code or ECG with AF), comparator 3 (comparator 1 or comparator 2), comparator 4 (≥1 inpatient ICD9 code or ≥1 outpatient ICD9 code), composite AF Algorithm (our primary AF algorithm), modified AF algorithm (our second AF algorithm). AF – atrial fibrillation. ARIC – Atherosclerosis Risk in Communities. ATRIA – Anticoagulation and Risk factors in Atrial Fibrillation. CHS – Cardiovascular Health Study.