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利用电子健康记录数据在大型城市学术医疗中心识别新发子宫肌瘤和子宫内膜异位症:一项验证研究。

Using electronic health record data to identify incident uterine fibroids and endometriosis within a large, urban academic medical center: a validation study.

作者信息

Charifson Mia, Beaton-Mata Geidily, Lipschultz Robyn, Robinson India, Sasse Simone A, Hur Hye-Chun, Lee Shilpi-Mehta S, Hade Erinn M, Kahn Linda G

机构信息

Department of Population Health, New York University Grossman School of Medicine, NY, USA.

Department of Obstetrics and Gynecology, New York University Langone Health, NY, USA.

出版信息

Am J Epidemiol. 2025 Mar 18. doi: 10.1093/aje/kwaf058.

DOI:10.1093/aje/kwaf058
PMID:40102190
Abstract

Electronic health records (EHRs) present opportunities to study uterine fibroids uterine fibroids and endometriosis within diverse populations. When using EHR data, it is important to validate outcome classification via diagnosis codes. We performed a validation study of three approaches (1: ICD-10 code alone, 2: ICD-10 code + diagnostic procedure, and 3: ICD-10 code + all diagnostic information) to identify incident uterine fibroids and endometriosis patients among n=750 NYU Langone Health 2016-2023. Chart review was used to determine the true diagnosis status. When using a binary classification system (incident vs. non-incident patient), Approaches 2 and 3 had higher positive predictive values (PPVs) for uterine fibroids (0.86 and 0.87 vs. 0.78) and for endometriosis (0.70 and 0.73 vs. 0.66), but Approach 1 outperformed the other two in negative predictive values (NPVs) for both outcomes. When using a three-level classification system (incident vs. prevalent vs. disease free patients), PPV for prevalent patients was low for all approaches, while PPV/NPV of disease-free patients was generally above 0.8. Using ICD-10 codes alone yielded higher NPVs but resulted in lower PPVs compared with the other approaches. Continued validation of uterine fibroids/endometriosis EHR studies is warranted to increase research into these understudied gynecologic conditions.

摘要

电子健康记录(EHRs)为研究不同人群中的子宫肌瘤和子宫内膜异位症提供了机会。在使用EHR数据时,通过诊断代码验证结局分类非常重要。我们对三种方法(1:仅使用ICD - 10代码,2:ICD - 10代码 + 诊断程序,3:ICD - 10代码 + 所有诊断信息)进行了一项验证研究,以在n = 750名纽约大学朗格尼健康中心2016 - 2023年的患者中识别出子宫肌瘤和子宫内膜异位症的新发患者。通过病历审查来确定真正的诊断状态。当使用二元分类系统(新发患者与非新发患者)时,方法2和方法3对子宫肌瘤(分别为0.86和0.87,而方法1为0.78)和子宫内膜异位症(分别为0.70和0.73,而方法1为0.66)具有更高的阳性预测值(PPV),但在两种结局的阴性预测值(NPV)方面,方法1优于其他两种方法。当使用三级分类系统(新发患者与患病患者与无疾病患者)时,所有方法中患病患者的PPV都较低,而无疾病患者的PPV/ NPV通常高于0.8。与其他方法相比,仅使用ICD - 10代码产生更高的NPV,但导致更低的PPV。有必要对子宫肌瘤/子宫内膜异位症的EHR研究进行持续验证,以增加对这些研究不足的妇科疾病的研究。

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