Development of a computable phenotype using electronic health records for venous thromboembolism in medical inpatients: the Medical Inpatient Thrombosis and Hemostasis study

Ryan M Thomas; Katherine Wilkinson; Insu Koh; Ang Li; Janine S A Warren; Nicholas S Roetker; Nicholas L Smith; Chris E Holmes; Timothy B Plante; Allen B Repp; Mary Cushman; Neil A Zakai

doi:10.1016/j.rpth.2023.100162

Development of a computable phenotype using electronic health records for venous thromboembolism in medical inpatients: the Medical Inpatient Thrombosis and Hemostasis study

Res Pract Thromb Haemost. 2023 Apr 24;7(4):100162. doi: 10.1016/j.rpth.2023.100162. eCollection 2023 May.

Authors

Ryan M Thomas^{1

2}, Katherine Wilkinson³, Insu Koh³, Ang Li⁴, Janine S A Warren¹, Nicholas S Roetker⁵, Nicholas L Smith^{6

7

8}, Chris E Holmes^{1

2}, Timothy B Plante^{1

2}, Allen B Repp^{1

2}, Mary Cushman^{1

2

3}, Neil A Zakai^{1

2

3}

Affiliations

¹ Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
² University of Vermont Medical Center, Burlington, Vermont, USA.
³ Department of Pathology & Laboratory Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
⁴ Department of Medicine, Baylor University Medical Center, Houston, Texas, USA.
⁵ Chronic Disease Research Group, Hennepin Healthcare Research Institute, Minneapolis, Minnesota, USA.
⁶ Department of Epidemiology, University of Washington, Seattle, Washington, USA.
⁷ Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle Washington, USA.
⁸ Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, Washington, USA.

Abstract

Background: Accurate and efficient methods to identify venous thromboembolism (VTE) events in hospitalized people are needed to support large-scale studies. Validated computable phenotypes using a specific combination of discrete, searchable elements in electronic health records to identify VTE and distinguish between hospital-acquired (HA)-VTE and present-on-admission (POA)-VTE would greatly facilitate the study of VTE, obviating the need for chart review.

Objectives: To develop and validate computable phenotypes for POA- and HA-VTE in adults hospitalized for medical reasons.

Methods: The population included admissions to medical services from 2010 to 2019 at an academic medical center. POA-VTE was defined as VTE diagnosed within 24 hours of admission, and HA-VTE as VTE identified more than 24 hours after admission. Using discharge diagnosis codes, present-on-admission flags, imaging procedures, and medication administration records, we iteratively developed computable phenotypes for POA-VTE and HA-VTE. We assessed the performance of the phenotypes using manual chart review and survey methodology.

Results: Among 62,468 admissions, 2693 had any VTE diagnosis code. Using survey methodology, 230 records were reviewed to validate the computable phenotypes. Based on the computable phenotypes, the incidence of POA-VTE was 29.4 per 1000 admissions and that of HA-VTE was 3.6 per 1000 admissions. The POA-VTE computable phenotype had positive predictive value and sensitivity of 88.8% (95% CI, 79.8%-94.0%) and 99.1% (95% CI, 94.0%- 99.8%), respectively. Corresponding values for the HA-VTE computable phenotype were 84.2% (95% CI, 60.8%-94.8%) and 72.3% (95% CI, 40.9%-90.8%).

Conclusion: We developed computable phenotypes for HA-VTE and POA-VTE with adequate positive predictive value and sensitivity. This phenotype can be used in electronic health record data-based research.

Keywords: International Classification of Diseases; electronic health records; inpatients; predictive value of tests; venous thromboembolism.