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Development and validation of an algorithm to identify patients newly diagnosed with hiv infection from electronic health records

机译:开发和验证从电子健康记录中识别新近被诊断出患有HIV感染的患者的算法

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An algorithm was developed that identifies patients with new diagnoses of HIV infection by the use of electronic health records. It was based on the sequence of HIV diagnostic tests, entry of ICD-9-CM diagnostic codes, and measurement of HIV-1 plasma RNA levels in persons undergoing HIV testing from 2006 to 2012 at four large urban Veterans Health Administration (VHA) facilities. Source data were obtained from the VHA National Corporate Data Warehouse. Chart review was done by a single trained abstractor to validate site-level data regarding new diagnoses. We identified 1,153 patients as having a positive HIV diagnostic test within the VHA. Of these, 57% were determined to have prior knowledge of their HIV status from testing at non-VHA facilities. An algorithm based on the sequence and results of available laboratory tests and ICD-9-CM entries identified new HIV diagnoses with a sensitivity of 83%, specificity of 86%, positive predictive value of 85%, and negative predictive value of 90%. There were no meaningful demographic or clinical differences between newly diagnosed patients who were correctly or incorrectly classified by the algorithm. We have validated a method to identify cases of new diagnosis of HIV infection in large administrative datasets. This method, which has a sensitivity of 83%, specificity of 86%, positive predictive value of 85%, and negative predictive value of 90% can be used in analyses of the epidemiology of newly diagnosed HIV infection.
机译:开发了一种算法,该算法通过使用电子健康记录来识别具有新的HIV感染诊断的患者。它基于2006年至2012年在四个大型城市退伍军人卫生管理局(VHA)进行HIV检测的人群中HIV诊断检测的顺序,ICD-9-CM诊断代码的输入以及HIV-1血浆RNA水平的测量。 。源数据是从VHA国家企业数据仓库中获得的。图表审查由一名受过训练的抽象人员完成,以验证有关新诊断的站点级别数据。我们确定了1,153名患者在VHA中的HIV诊断测试呈阳性。在这些人中,有57%的人被确定具有非VHA设施测试中的艾滋病毒感染状况的先验知识。一种基于可用实验室测试的序列和结果以及ICD-9-CM条目的算法,以83%的敏感性,86%的特异性,85%的阳性预测值和90%的阴性预测值确定了新的HIV诊断。在通过算法正确或错误分类的新诊断患者之间,没有有意义的人口统计学或临床差异。我们已经验证了一种在大型行政数据集中识别HIV感染新诊断病例的方法。该方法灵敏度为83%,特异性为86%,阳性预测值为85%,阴性预测值为90%,可用于新诊断HIV感染的流行病学分析。

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