Identification of Incident Atrial Fibrillation From Electronic Medical Records

Alanna M. Chamberlain, Véronique L. Roger, Peter A. Noseworthy, Lin Y. Chen, Susan A. Weston, Ruoxiang Jiang, Alvaro Alonso

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


BACKGROUND: Electronic medical records are increasingly used to identify disease cohorts; however, computable phenotypes using electronic medical record data are often unable to distinguish between prevalent and incident cases. METHODS AND RESULTS: We identified all Olmsted County, Minnesota residents aged ≥18 with a first-ever International Classification of Diseases, Ninth Revision (ICD-9) diagnostic code for atrial fibrillation or atrial flutter from 2000 to 2014 (N=6177), and a random sample with an International Classification of Diseases, Tenth Revision (ICD-10) code from 2016 to 2018 (N=200). Trained nurse abstractors reviewed all medical records to validate the events and ascertain the date of onset (incidence date). Various algorithms based on number and types of codes (inpatient/outpatient), medications, and procedures were evaluated. Positive predictive value (PPV) and sensitivity of the algorithms were calculated. The lowest PPV was observed for 1 code (64.4%), and the highest PPV was observed for 2 codes (any type) >7 days apart but within 1 year (71.6%). Requiring either 1 inpatient or 2 outpatient codes separated by >7 days but within 1 year had the best balance between PPV (69.9%) and sensitivity (95.5%). PPVs were slightly higher using ICD-10 codes. Requiring an anticoagulant or antiarrhythmic prescription or electrical cardioversion in addition to diagnostic code(s) modestly improved the PPVs at the expense of large reductions in sensitivity. CONCLUSIONS: We developed simple, exportable, computable phenotypes for atrial fibrillation using structured electronic medical record data. However, use of diagnostic codes to identify incident atrial fibrillation is prone to some misclassification. Further study is warranted to determine whether more complex phenotypes, including unstructured data sources or using machine learning techniques, may improve the accuracy of identifying incident atrial fibrillation.

Original languageEnglish (US)
Article numbere023237
JournalJournal of the American Heart Association
Issue number7
StatePublished - Apr 5 2022

Bibliographical note

Funding Information:
This work was supported by grants from the American Heart Association (11SDG7260039) and the National Institute on Aging (R21AG062580 and R01AG034676). Dr. Roger is an Established Investigator of the American

Funding Information:
P.A.N. receives research funding from National Institutes of Health, including the National Heart, Lung, and Blood Institute and the National Institute on Aging, Agency for Healthcare Research and Quality, Food and Drug Administration, and the American Heart Association. P.A.N. is a study investigator in an ablation trial sponsored by Medtronic. P.A.N. and Mayo Clinic are involved in potential equity/royalty relationship with AliveCor. P.A.N. has served on an expert advisory panel for Optum. P.A.N. and Mayo Clinic have filed patents related to the application of artificial intelligence to the ECG for diagnosis and risk stratification. The remaining authors have no disclosures to report.

Funding Information:
Heart Association. Additional support was provided by grant 16EIA26410001 from the American Heart Association (Alonso) and grant K24HL148521 from the National Heart, Lung, and Blood Institute (Alonso). The funding sources played no role in the design, conduct, or reporting of this study.

Publisher Copyright:
© 2022 The Authors and Mayo Clinic. Published on behalf of the American Heart Association, Inc., by Wiley.


  • atrial fibrillation
  • computable phenotype
  • electronic medical records


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