ArticleJournal of the American Heart Association2020
Impact of Different Electronic Cohort Definitions to Identify Patients With Atrial Fibrillation From the Electronic Medical Record.
Article in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Electrocardiographic Predictors of Atrial Fibrillation.Medical sciences (Basel, Switzerland) · 2023Pooled it
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- Validation of electronic health record-based International Society on Thrombosis and Haemostasis bleeding algorithms in a US health system.Research and practice in thrombosis and haemostasis · 2026Article
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Association between first anticoagulant prescription and embolic and hemorrhagic events among older adults with atrial fibrillation.Journal of internal medicine · 2026Observational
- Are Machine Learning methods effective in detecting undiagnosed atrial fibrillation in primary care settings using electronic health records? A systematic review.PLOS digital health · 2025Article
- Atrial Fibrillation and Retinal Stroke.JAMA network open · 2025Article
- Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records.Journal of comparative effectiveness research · 2024Article
- Identification of recurrent atrial fibrillation using natural language processing applied to electronic health records.European heart journal. Quality of care & clinical outcomes · 2024Article
- Complex Percutaneous Coronary Intervention Outcomes in Older Adults.Journal of the American Heart Association · 2023Article
- An introduction to ICD code development for pediatric neurology.Annals of the Child Neurology Society · 2023Review
- A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.PLOS digital health · 2023Article
- Artificial Intelligence Technologies in Cardiology.Journal of cardiovascular development and disease · 2023Review
- Article
- Atrial fibrillation as a novel risk factor for retinal stroke: A protocol for a population-based retrospective cohort study.PloS one · 2023Article
- Administrative Data in Cardiovascular Research-A Comparison of Polish National Health Fund and CRAFT Registry Data.International journal of environmental research and public health · 2022Article
- Comparison between the International Classification of Primary Care and the International Classification of Diseases Classifications in Primary Care in Korea.Korean journal of family medicine · 2022Article
- Area Deprivation Index and Cardiac Readmissions: Evaluating Risk-Prediction in an Electronic Health Record.Journal of the American Heart Association · 2021Observational
- Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record.Circulation. Cardiovascular quality and outcomes · 2020Article
- Comparison of International Classification of Diseases and Related Health Problems, Tenth Revision Codes With Electronic Medical Records Among Patients With Symptoms of Coronavirus Disease 2019.JAMA network open · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Background Electronic medical records (EMRs) allow identification of disease-specific patient populations, but varying electronic cohort definitions could result in different populations. We compared the characteristics of an electronic medical record-derived atrial fibrillation (AF) patient population using 5 different electronic cohort definitions. Methods and Results Adult patients with at least 1 AF billing code from January 1, 2010, to December 31, 2017, were included. Based on different electronic cohort definitions, we trained 5 different logistic regression models using a labeled training data set (n=786). Each model yielded a predicted probability; patients were classified as having AF if the probability was higher than a specified cut point. Test characteristics were calculated for each model. These models were then applied to the full cohort and resulting characteristics were compared. In the training set, the comprehensive model (including demographics, billing codes, and natural language processing results) performed best, with an area under the curve of 0.89, sensitivity of 0.90, and specificity of 0.87. Among a candidate population (n=22 000), the proportion of patients identified as having AF varied from 61% in the model using diagnosis or procedure
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