Thomas Ryan M, Wilkinson Katherine, Koh Insu, Li Ang, Warren Janine S A, Roetker Nicholas S, Smith Nicholas L, Holmes Chris E, Plante Timothy B, Repp Allen B, Cushman Mary, Zakai Neil A
Department of Medicine, Larner College of Medicine at the University of Vermont, Burlington, Vermont, USA.
University of Vermont Medical Center, Burlington, Vermont, USA.
Res Pract Thromb Haemost. 2023 Apr 24;7(4):100162. doi: 10.1016/j.rpth.2023.100162. eCollection 2023 May.
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.
To develop and validate computable phenotypes for POA- and HA-VTE in adults hospitalized for medical reasons.
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.
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%).
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.
为支持大规模研究，需要准确、高效的方法来识别住院患者的静脉血栓栓塞（VTE）事件。使用电子健康记录中离散、可搜索元素的特定组合来识别VTE并区分医院获得性（HA）-VTE和入院时存在（POA）-VTE的经过验证的可计算表型将极大地促进VTE的研究，无需进行病历审查。
为因医疗原因住院的成年人开发并验证POA-VTE和HA-VTE的可计算表型。
研究人群包括2010年至2019年在一家学术医疗中心接受医疗服务的住院患者。POA-VTE定义为入院后24小时内诊断出的VTE，HA-VTE定义为入院后24小时以上发现的VTE。利用出院诊断代码、入院时存在标志、影像检查程序和用药记录，我们迭代开发了POA-VTE和HA-VTE的可计算表型。我们使用人工病历审查和调查方法评估了这些表型的性能。
在62468例住院患者中，2693例有任何VTE诊断代码。使用调查方法，审查了230份记录以验证可计算表型。根据可计算表型，POA-VTE的发生率为每1000例住院患者中有29.4例，HA-VTE的发生率为每1000例住院患者中有3.6例。POA-VTE可计算表型的阳性预测值和敏感性分别为88.8%（95%CI，79.8%-94.0%）和99.1%（95%CI，94.0%-99.8%）。HA-VTE可计算表型的相应值分别为84.2%（95%CI，60.8%-94.8%）和72.3%（95%CI，40.9%-90.8%）。
我们开发了具有足够阳性预测值和敏感性的HA-VTE和POA-VTE可计算表型。这种表型可用于基于电子健康记录数据的研究。
