A common data model to assess cardiovascular hospitalization and mortality in atrial fibrillation patients using administrative claims and medical records
Authors Panaccio M, Cummins G, Wentworth C, Lanes S, Reynolds S, Reynolds M, Miao R, Koren A
Received 27 March 2014
Accepted for publication 31 July 2014
Published 12 January 2015 Volume 2015:7 Pages 77—90
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Vera Ehrenstein
Mary P Panaccio,1 Gordon Cummins,2 Charles Wentworth,3 Stephan Lanes,4 Shannon L Reynolds,5 Matthew W Reynolds,3 Raymond Miao,1 Andrew Koren1
1US Medical Affairs, Sanofi, Bridgewater, NJ, USA; 2Health Engagement and Communications, Quintiles, Durham, NC, USA; 3Evidera, Lexington, MA, USA; 4HealthCore Inc., Andover, MA, USA; 5Comprehensive Health Insights, Louisville, KY, USA
Purpose: Atrial fibrillation/flutter (AF) is frequently associated with cardiovascular comorbidities. Observational health care databases are commonly used for research purposes in studies of quality of care, health economics, outcomes research, drug safety, and epidemiology. This retrospective cohort study applied a common data model to administrative claims data (Truven Health Analytics MarketScan® claims databases [MS-Claims]) and electronic medical records data (Geisinger Health System's MedMining electronic medical record database [MG-EMR]) to examine the risk of cardiovascular hospitalization and all-cause mortality in relation to clinical risk factors in recent-onset AF and to assess the consistency of analyses for each data source.
Methods: Cohorts of patients with newly diagnosed AF (n=105,262 [MS-Claims] and n=3,919 [MG-EMR]) and demographically similar patients without AF (n=105,262 [MS-Claims] and n=3,872 [MG-EMR]) were followed from the qualifying AF diagnosis until cardiovascular hospitalization, death, database disenrollment, or study completion. A common data model standardized the data in structure, format, content, and nomenclature to allow for systematic assessment and comparison of outcomes from two disparate data sets.
Results: In both databases, AF patients had greater overall baseline comorbidity and higher incidence rates of cardiovascular hospitalization (threefold higher) and all-cause mortality (46% higher) than non-AF patients. For AF patients, incidence rates of cardiovascular hospitalization and all-cause mortality were increased by the concomitant presence of coronary disease, chronic obstructive pulmonary disease, and stroke at baseline. Overall, the pattern of cardiovascular hospitalization in the MS-Claims database was similar to that in the MG-EMR database. Compared with the MS-Claims database, the use of cardiovascular medications and the capture of certain comorbidities among AF patients appeared to be higher in the MG-EMR data set.
Conclusion: Similar standardized analyses across EMR and Claims databases were consistent in the association of AF with acute morbidity and an increased risk of all-cause mortality. Areas of inconsistency were due to differences in underlying population demographics and cardiovascular risks and completeness of certain data fields.
Keywords: atrial fibrillation, cardiovascular hospitalization, common data models, epidemiology, observational databases
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