开发电子健康记录算法，以准确识别患有幼年特发性关节炎的患者。
Developing electronic health record algorithms that accurately identify patients with juvenile idiopathic arthritis.
机构信息
Lipscomb University College of Pharmacy and Health Sciences, Nashville, TN, United States.
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
出版信息
Semin Arthritis Rheum. 2023 Apr;59:152167. doi: 10.1016/j.semarthrit.2023.152167. Epub 2023 Jan 18.
BACKGROUND
The objective of this study was to develop an algorithm that accurately identifies juvenile idiopathic arthritis (JIA) patients in the electronic health record (EHR).
METHODS
Algorithms were developed in a de-identified EHR by searching for a priori JIA ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes and JIA-related keywords. Exclusion criteria were selected to remove other autoimmune diseases. A training set of 200 patients was randomly selected from patients containing ≥1 occurrence of a JIA ICD-9 or ICD-10-CM code. Case status was determined by a rheumatology clinic note documenting a JIA diagnosis before age 20. For each algorithm, positive predictive value (PPV), sensitivity, and F-measure were determined using the training set.
RESULTS
We developed 103 algorithms using combinations of ICD codes, keywords, and exclusion criteria. The algorithm requiring 4 or more counts of JIA ICD-9 or ICD-10-CM codes, keywords "enthesitis" and "uveitis", and exclusion of ICD-9 or ICD-10-CM codes for systemic lupus erythematosus, dermatomyositis, polymyositis, and dermatopolymyositis had the highest PPV of 97% in the training set with an F-measure of 87%. There were 1,131 JIA cases returned by this algorithm. We validated the highest performing algorithm in a separate cohort from the training set with a PPV of 92% and an F-measure of 75%.
CONCLUSION
We developed and validated JIA EHR algorithms with ICD-9 and ICD-10-CM codes to accurately identify a JIA cohort. Three algorithms achieved PPVs of 97%, each with different algorithm criteria, allowing for users to select an algorithm to best fit their research needs.
背景
本研究旨在开发一种能够准确识别电子病历（EHR）中幼年特发性关节炎（JIA）患者的算法。
方法
通过搜索预先确定的 JIA ICD-9（国际疾病分类，第 9 版）和 ICD-10-CM（国际疾病分类，第 10 版，临床修订版）代码以及 JIA 相关关键字，在去识别的 EHR 中开发算法。排除标准用于去除其他自身免疫性疾病。从包含≥1 次 JIA ICD-9 或 ICD-10-CM 代码的患者中随机选择 200 名患者作为训练集。病例状态通过记录 20 岁前 JIA 诊断的风湿病临床记录来确定。对于每种算法，使用训练集确定阳性预测值（PPV）、灵敏度和 F 度量。
结果
我们使用 ICD 代码、关键字和排除标准的组合开发了 103 种算法。需要 4 次或更多次 JIA ICD-9 或 ICD-10-CM 代码、关键字“附着点炎”和“虹膜炎”以及排除 ICD-9 或 ICD-10-CM 代码用于系统性红斑狼疮、皮肌炎、多发性肌炎和皮肌多肌炎的算法在训练集中的 PPV 最高为 97%，F 度量为 87%。该算法返回了 1,131 例 JIA 病例。我们在与训练集分开的队列中验证了性能最高的算法，其 PPV 为 92%，F 度量为 75%。
结论
我们开发并验证了使用 ICD-9 和 ICD-10-CM 代码的 JIA EHR 算法，以准确识别 JIA 队列。三种算法的 PPV 均达到 97%，每种算法的标准均不同，允许用户选择最适合其研究需求的算法。
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