| Autor: |
Mei‐Sing Ong, Jeffrey G. Klann, Kueiyu Joshua Lin, Bradley A. Maron, Shawn N. Murphy, Marc D. Natter, Kenneth D. Mandl |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, Vol 9, Iss 19 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2047-9980 |
| DOI: |
10.1161/JAHA.120.016648 |
| Popis: |
Background Real‐world healthcare data are an important resource for epidemiologic research. However, accurate identification of patient cohorts—a crucial first step underpinning the validity of research results—remains a challenge. We developed and evaluated claims‐based case ascertainment algorithms for pulmonary hypertension (PH), comparing conventional decision rules with state‐of‐the‐art machine‐learning approaches. Methods and Results We analyzed an electronic health record‐Medicare linked database from two large academic tertiary care hospitals (years 2007–2013). Electronic health record charts were reviewed to form a gold standard cohort of patients with (n=386) and without PH (n=164). Using health encounter data captured in Medicare claims (including patients’ demographics, diagnoses, medications, and procedures), we developed and compared 2 approaches for identifying patients with PH: decision rules and machine‐learning algorithms using penalized lasso regression, random forest, and gradient boosting machine. The most optimal rule‐based algorithm—having ≥3 PH‐related healthcare encounters and having undergone right heart catheterization—attained an area under the receiver operating characteristic curve of 0.64 (sensitivity, 0.75; specificity, 0.48). All 3 machine‐learning algorithms outperformed the most optimal rule‐based algorithm (P |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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