Accurately identifying incident cases of venous thromboembolism in the electronic health record: Performance of a novel phenotyping algorithm

IF 3.7 3区医学 Q1 HEMATOLOGY

Thrombosis research Pub Date : 2024-09-07 DOI:10.1016/j.thromres.2024.109143

Ania Syrowatka , Avery Pullman , Elizabeth Pajares , Kyra White , Michael Sainlaire , Jin Chen , Frank Chang , Krissy Gray , John Laurentiev , Wenyu Song , Tien Thai , Li Zhou , Stuart R. Lipsitz , David W. Bates , Lipika Samal , Patricia C. Dykes

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引用次数: 0

Abstract

Background

Accurate identification of incident venous thromboembolism (VTE) for quality improvement and health services research is challenging. The purpose of this study was to evaluate the performance of a novel incident VTE phenotyping algorithm defined using standard terminologies, requiring three key indicators documented in the electronic health record (EHR): VTE diagnostic code, VTE-related imaging procedure code, and anticoagulant medication code.

Methods

Retrospective chart reviews were conducted to assess the performance of the algorithm using a random sample of phenotype(+) and phenotype(−) diagnostic encounters from primary care practices and acute care sites affiliated with five hospitals across a large integrated care delivery system in Massachusetts. The performance of the algorithm was evaluated by calculating the positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity, using the phenotype(+) and phenotype(−) diagnostic encounters sample and target population data.

Results

Based on gold-standard manual chart review, the algorithm had a PPV of 95.2 % (95 % CI: 93.1–96.8 %), NPV of 97.1 % (95 % CI: 95.3–98.4 %), sensitivity of 91.7 % (95 % CI: 90.8–92.6 %), and specificity of 98.4 % (95 % CI: 98.1–98.6 %). The algorithm systematically misclassified a low number of specific types of encounters, highlighting potential areas for improvement.

Conclusions

This novel phenotyping algorithm offers an accurate approach for identifying incident VTE in general populations using EHR data and standard terminologies, and accurately identifies the specific encounter and date of diagnosis of the incident VTE. This approach can be used for measurement of incident VTE to drive quality improvement, research to expand the evidence, and development of quality metrics and clinical decision support to improve the diagnostic process.

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在电子病历中准确识别静脉血栓栓塞症病例：新型表型算法的性能

背景准确识别静脉血栓栓塞症（VTE）事件以用于质量改进和医疗服务研究具有挑战性。本研究旨在评估使用标准术语定义的新型事件 VTE 表型算法的性能，该算法需要电子健康记录（EHR）中记录的三个关键指标：方法采用随机抽样的表型（+）和表型（-）诊断病例，对马萨诸塞州大型综合医疗服务系统的初级医疗实践和五家医院附属急症医疗点进行回顾性病历审查，以评估该算法的性能。通过计算阳性预测值 (PPV)、阴性预测值 (NPV)、灵敏度和特异性，利用表型（+）和表型（-）诊断案例样本和目标人群数据，对算法的性能进行了评估。结果基于金标准人工病历审查，该算法的 PPV 为 95.2 %（95 % CI：93.1-96.8 %），NPV 为 97.1 %（95 % CI：95.3-98.4 %），灵敏度为 91.7 %（95 % CI：90.8-92.6 %），特异性为 98.4 %（95 % CI：98.1-98.6 %）。结论这种新型表型算法提供了一种使用电子病历数据和标准术语在普通人群中识别VTE事件的准确方法，并能准确识别VTE事件的具体病例和诊断日期。这种方法可用于对偶发 VTE 的测量，以推动质量改进、扩大证据的研究以及质量指标和临床决策支持的开发，从而改进诊断过程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Thrombosis research 医学-外周血管病

CiteScore

14.60

自引率

4.00%

发文量

364

审稿时长

31 days

期刊介绍： Thrombosis Research is an international journal dedicated to the swift dissemination of new information on thrombosis, hemostasis, and vascular biology, aimed at advancing both science and clinical care. The journal publishes peer-reviewed original research, reviews, editorials, opinions, and critiques, covering both basic and clinical studies. Priority is given to research that promises novel approaches in the diagnosis, therapy, prognosis, and prevention of thrombotic and hemorrhagic diseases.