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在行政索赔数据中用于识别骨质疏松性髋部骨折患者的操作定义的验证

Validation of Operational Definition to Identify Patients with Osteoporotic Hip Fractures in Administrative Claims Data.

作者信息

Lee Young-Kyun, Yoo Jun-Il, Kim Tae-Young, Ha Yong-Chan, Koo Kyung-Hoi, Choi Hangseok, Lee Seung-Mi, Suh Dong-Churl

机构信息

Department of Orthopedic Surgery, Seoul National University Bundang Hospital, Seongnam 13620, Korea.

Department of Orthopedic Surgery, Gyeongsang National University Hospital, Jinju 52727, Korea.

出版信息

Healthcare (Basel). 2022 Sep 8;10(9):1724. doi: 10.3390/healthcare10091724.

Abstract

As incidences of osteoporotic hip fractures (OHFs) have increased, identifying OHFs has become important to establishing the medical guidelines for their management. This study was conducted to develop an operational definition to identify patients with OHFs using two diagnosis codes and eight procedure codes from health insurance claims data and to assess the operational definition's validity through a chart review. The study extracted data on OHFs from 522 patients who underwent hip surgeries based on diagnosis codes. Orthopedic surgeons then reviewed these patients' medical records and radiographs to identify those with true OHFs. The validities of nine different algorithms of operational definitions, developed using a combination of three levels of diagnosis codes and eight procedure codes, were assessed using various statistics. The developed operational definition showed an accuracy above 0.97 and an area under the receiver operating characteristic curve above 0.97, indicating excellent discriminative power. This study demonstrated that the operational definition that combines diagnosis and procedure codes shows a high validity in detecting OHFs and can be used as a valid tool to detect OHFs from big health claims data.

摘要

随着骨质疏松性髋部骨折(OHF)的发病率不断上升,识别OHF对于制定其治疗的医学指南变得至关重要。本研究旨在制定一个操作定义,以利用健康保险理赔数据中的两个诊断代码和八个程序代码来识别OHF患者,并通过图表审查评估该操作定义的有效性。该研究基于诊断代码从522例接受髋关节手术的患者中提取了OHF数据。然后,骨科医生审查了这些患者的病历和X光片,以识别真正患有OHF的患者。使用三种诊断代码级别和八个程序代码的组合开发了九种不同的操作定义算法,并使用各种统计方法评估了其有效性。所开发的操作定义显示出高于0.97的准确率和高于0.97的受试者工作特征曲线下面积,表明具有出色的判别能力。本研究表明,结合诊断和程序代码的操作定义在检测OHF方面具有很高的有效性,可作为从大型健康理赔数据中检测OHF的有效工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4005/9498336/ea464b04ecfa/healthcare-10-01724-g001.jpg

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