Combining Inpatient and Outpatient Data for Diagnosis of Non-Valvular Atrial Fibrillation Using Electronic Health Records: A Validation Study
Received 18 September 2019
Accepted for publication 29 March 2020
Published 20 May 2020 Volume 2020:12 Pages 477—483
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 4
Editor who approved publication: Professor Irene Petersen
Orna Reges,1,2,* Hagay Weinberg,3,4,* Moshe Hoshen,1,5 Philip Greenland,2 Hana’a Rayyan-Assi,1 Meytal Avgil Tsadok,1 Asaf Bachrach,1 Ran Balicer,1,6 Morton Leibowitz,1 Moti Haim7,8
1Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel; 2Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; 3Internal Medicine Department, Meir Medical Center, Kfar-Saba, Israel; 4Department of Medicine, MidCentral District Health Board, Palmerston-North, New Zealand; 5National Information Systems, Computational Authority, Ministry of Health, Jerusalem, Isarel; 6Department of Epidemiology, Ben-Gurion University of the Negev, Beer-Sheva, Israel; 7Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel; 8Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
*These authors contributed equally to this work
Correspondence: Orna Reges
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 680 North Lake Shore Drive, Suite 1400, Chicago, IL 60611, USA
Purpose: Previous studies have demonstrated differences in atrial fibrillation (AF) detection based on data from hospital sources without data from outpatient sources. We investigated the detection of documented diagnoses of non-valvular AF in a large Israeli health-care organization using electronic health record data from multiple sources.
Patients and Methods: This was an open-chart validation study. Three distinct algorithms for identifying AF in electronic health records, differing in the source of their International Classification of Diseases, Ninth Revision code and use of the associated free text, were defined. Algorithm 1 incorporated inpatient data with outpatient data and the associated free text. Algorithm 2 incorporated inpatient and outpatient data regardless of the free text associated with AF diagnosis. Algorithm 3 used only inpatient data source. These algorithms were compared to a gold standard and their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. To establish the gold standard (documentation of arrhythmia based on electrocardiography interpretation or a cardiologist’s written diagnosis), 200 patients at highest risk for having non-valvular AF were randomly selected for open-chart validation by two physicians.
Results: The algorithm that included hospital settings, outpatient settings, and incorporated associated free text in the outpatient records had the optimal balance between all validation measures, with a high level of sensitivity (85.4%), specificity (95.0%), PPV (81.4%), and NPV (96.2%). The alternative algorithm that combined inpatient and outpatient data without free text also performed better than the algorithm that included only hospital data (82.9%, 95.0%, 81.0%, and 95.6%, compared to 70.7%, 96.9%, 85.3%, and 92.8%, sensitivity, specificity, PPV, and NPV, respectively).
Conclusion: In this study, involving a comprehensive data collection from inpatient and outpatient sources, incorporating outpatient data with inpatient data improved the diagnosis of non-valvular AF compared to inpatient data alone.
Keywords: atrial fibrillation, validation, electronic health records
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