Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database.
Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database.
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从大型多站点数据库的电子医疗记录中识别艾滋病毒感染者的算法比较。
DOI：
10.1093/jamiaopen/ooac033
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发表时间：
2022-07
期刊：
影响因子：
2.1
通讯作者：
Schneider, John
中科院分区：
文献类型：
--
作者：
Ridgway, Jessica P.;Mason, Joseph A.;Friedman, Eleanor E.;Devlin, Samantha;Zhou, Junlan;Meltzer, David;Schneider, John
随着电子病历（EMR）数据越来越多地用于HIV临床和流行病学研究，从EMR数据中准确识别HIV感染者（PWH）至关重要。我们试图评估EMR数据类型，并比较EMR算法在多中心EMR数据库中识别PWH。我们收集了来自芝加哥地区以患者为中心的结果研究网络（CAPriCORN）的7个医疗保健系统的EMR数据，包括诊断代码、抗逆转录病毒治疗（ART）和实验室检查结果。总共有13935例患者的HIV实验室检测结果呈阳性; 33412例患者的诊断代码为HIV; 17725例患者接受ART治疗。只有8576例患者的所有3种数据类型（实验室结果、诊断代码和ART）均显示HIV阳性状态。一个先前验证的组合算法确定了22411例PWH患者。结合联合收割机实验室结果、管理数据和ART的EMR算法可应用于多中心EMR数据以识别PWH。电子病历（EMR）数据越来越多地用于艾滋病毒相关研究。因此，从电子病历中的数据中准确识别艾滋病毒阳性者非常重要。我们评估了不同类型的EMR数据，并在多中心EMR数据库中比较了用于识别HIV感染者（PWH）的EMR算法。我们的数据来源是芝加哥地区以患者为中心的结果研究网络（CAPriCORN），其中包含来自芝加哥不同医疗保健系统的EMR数据。我们从CAPriCORN收集了不同的EMR数据类型，包括诊断代码，HIV药物数据和实验室检测结果，以确定哪些数据类型最有助于确定患者是否为HIV阳性。在该数据库中，13935名患者的艾滋病毒实验室检测结果呈阳性; 33412名患者的诊断代码为艾滋病毒; 17725名患者接受了艾滋病毒特异性药物治疗。在所有3种数据类型（实验室结果、诊断代码和药物）中，仅8576例患者被确定为HIV阳性。我们应用了一种利用不同数据类型组合的算法，并将22411例患者确定为PWH。总之，我们发现结合联合收割机实验室结果、诊断代码和药物的EMR算法可以应用于多中心EMR数据以识别PWH。
As electronic medical record (EMR) data are increasingly used in HIV clinical and epidemiologic research, accurately identifying people with HIV (PWH) from EMR data is paramount. We sought to evaluate EMR data types and compare EMR algorithms for identifying PWH in a multicenter EMR database. We collected EMR data from 7 healthcare systems in the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) including diagnosis codes, anti-retroviral therapy (ART), and laboratory test results. In total, 13 935 patients had a positive laboratory test for HIV; 33 412 patients had a diagnosis code for HIV; and 17 725 patients were on ART. Only 8576 patients had evidence of HIV-positive status for all 3 data types (laboratory results, diagnosis code, and ART). A previously validated combination algorithm identified 22 411 patients as PWH. EMR algorithms that combine laboratory results, administrative data, and ART can be applied to multicenter EMR data to identify PWH. Electronic medical record (EMR) data are increasingly utilized for HIV-related research. Therefore, it is important to accurately identify people who are HIV-positive from data present in EMRs. We evaluated different types of EMR data and compared EMR algorithms for identifying people with HIV (PWH) in a multicenter EMR database. Our data source was the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), which contains EMR data from diverse healthcare systems in Chicago. We collected different EMR data types from CAPriCORN, including diagnosis codes, HIV medication data, and laboratory test results, to determine which data types were most helpful for determining if patients were HIV-positive. In the database, 13 935 patients had a positive laboratory test for HIV; 33 412 patients had a diagnosis code for HIV; and 17 725 patients were prescribed HIV-specific medication. Only 8576 patients were identified as HIV-positive in all 3 data types (laboratory results, diagnosis code, and medications). We applied an algorithm that utilized combinations of different data types, and it identified 22 411 patients as PWH. In conclusion, we found that EMR algorithms that combine laboratory results, diagnosis codes, and medications can be applied to multicenter EMR data to identify PWH.
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DOI：
10.1080/09540121.2014.911813
发表时间：
2014-01-01
影响因子：
1.7
作者：
Felsen, Uriel R.;Bellin, Eran Y.;Zingman, Barry S.
通讯作者：
Zingman, Barry S.
影响因子：
2.9
作者：
Levison, J.;Triant, V.;Regan, S.
通讯作者：
Regan, S.
DOI：
10.1097/qai.0000000000001970
发表时间：
2019-09-01
影响因子：
3.6
作者：
Arey, Alyssa L.;Cassidy-Stewart, Hope;Flynn, Colin P.
通讯作者：
Flynn, Colin P.
影响因子：
3.4
作者：
O'Malley, KJ;Cook, KF;Ashton, CM
通讯作者：
Ashton, CM
影响因子：
4.6
作者：
Ridgway JP;Lee A;Devlin S;Kerman J;Mayampurath A
通讯作者：
Mayampurath A