| Title |
A common data model to assess cardiovascular hospitalization and mortality in atrial fibrillation patients using administrative claims and medical records
|
|---|---|
| Published in |
Clinical Epidemiology, January 2015
|
| DOI | 10.2147/clep.s64936 |
| Pubmed ID | |
| Authors |
Mary P Panaccio, Gordon Cummins, Charles Wentworth, Stephan Lanes, Shannon L Reynolds, Matthew W Reynolds, Raymond Miao, Andrew Koren |
| Abstract |
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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 59 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 22% |
| Student > Ph. D. Student | 10 | 17% |
| Student > Master | 7 | 12% |
| Student > Bachelor | 4 | 7% |
| Student > Postgraduate | 3 | 5% |
| Other | 8 | 13% |
| Unknown | 15 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 24 | 40% |
| Computer Science | 4 | 7% |
| Nursing and Health Professions | 3 | 5% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Psychology | 2 | 3% |
| Other | 4 | 7% |
| Unknown | 21 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 January 2015.
All research outputs
#20,251,039
of 22,780,165 outputs
Outputs from Clinical Epidemiology
#656
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Outputs of similar age
#295,669
of 352,944 outputs
Outputs of similar age from Clinical Epidemiology
#13
of 13 outputs
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