Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.
Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.
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DOI：
10.1007/s10877-021-00664-6
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发表时间：
2022-04
影响因子：
2.2
通讯作者：
Hravnak M
中科院分区：
文献类型：
--
作者：
Pellathy T;Saul M;Clermont G;Dubrawski AW;Pinsky MR;Hravnak M
使用异类电子健康记录(EHR)数据的大数据分析研究需要准确识别疾病表型病例和对照。过度依赖基于行政数据的实地真相确定可能导致有偏见和不准确的调查结果。医院获得性静脉血栓栓塞症(HA-VTE)的诊断具有挑战性，因为它的时间演变和可变的EHR文献。为了确定机器学习建模的基本事实，我们比较了管理编码做出的HA-VTE诊断的准确性与人工审查黄金标准诊断测试结果的准确性。我们对3680例确诊为HA-VTE的成年降压病房患者的EHR数据进行了回顾性分析。确定了VTE的国际疾病分类第九版(ICD-9-CM)编码。使用术语提取筛选与VTE诊断测试相关的4544份放射学报告，然后由临床专家手动审查以确认诊断。在415例具有ICD-9-CM编码的VTE患者中，有219例具有急性发作类型编码。检测报告审查发现了158例新发的HA-VTE病例。只有40%的ICD-9-CM编码病例(n=87)通过阳性诊断测试报告得到确认，使得大多数管理编码病例无法通过确认性诊断测试得到证实。此外，45%的诊断测试确认的HA-VTE病例缺乏相应的ICD代码。ICD-9-CM编码遗漏了诊断试验确认的HA-VTE病例和未确认VTE的不准确分配病例，提示对行政编码的依赖导致不准确的HA-VTE表型。需要其他方法来开发可跨电子病历供应商数据移植的更敏感和更具体的VTE表型解决方案，以支持大数据分析中的病例查找。
Big data analytics research using heterogeneous electronic health record (EHR) data requires accurate identification of disease phenotype cases and controls. Overreliance on ground truth determination based on administrative data can lead to biased and inaccurate findings. Hospital-acquired venous thromboembolism (HA-VTE) is challenging to identify due to its temporal evolution and variable EHR documentation. To establish ground truth for machine learning modeling, we compared accuracy of HA-VTE diagnoses made by administrative coding to manual review of gold standard diagnostic test results. We performed retrospective analysis of EHR data on 3680 adult stepdown unit patients identifying HA-VTE. International Classification of Diseases, Ninth Revision (ICD-9-CM) codes for VTE were identified. 4544 radiology reports associated with VTE diagnostic tests were screened using terminology extraction and then manually reviewed by a clinical expert to confirm diagnosis. Of 415 cases with ICD-9-CM codes for VTE, 219 were identified with acute onset type codes. Test report review identified 158 new-onset HA-VTE cases. Only 40% of ICD-9-CM coded cases (n = 87) were confirmed by a positive diagnostic test report, leaving the majority of administratively coded cases unsubstantiated by confirmatory diagnostic test. Additionally, 45% of diagnostic test confirmed HA-VTE cases lacked corresponding ICD codes. ICD-9-CM coding missed diagnostic test-confirmed HA-VTE cases and inaccurately assigned cases without confirmed VTE, suggesting dependence on administrative coding leads to inaccurate HA-VTE phenotyping. Alternative methods to develop more sensitive and specific VTE phenotype solutions portable across EHR vendor data are needed to support case-finding in big-data analytics.
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