Sing Chor-Wing, Woo Yu-Cho, Lee Alan C H, Lam Joanne K Y, Chu Jody K P, Wong Ian C K, Cheung Ching-Lung
Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong.
Department of Medicine, Queen Mary Hospital, Pokfulam, Hong Kong.
Pharmacoepidemiol Drug Saf. 2017 Aug;26(8):973-976. doi: 10.1002/pds.4208. Epub 2017 Apr 3.
Large medical record databases facilitate epidemiology research in fracture. However, the validity of fracture in the databases is needed to ensure the reliability of data. We aimed to assess the validity of International Classification of Diseases, 9th Revision (ICD-9) code algorithms for identifying major osteoporotic fracture in the Clinical Data Analysis and Reporting System (CDARS) in Hong Kong.
The CDARS is a database developed by the Hong Kong Hospital Authority for research purpose. We used ICD-9 code algorithm for identifying major osteoporotic fracture, including vertebral fracture, humerus fracture, forearm/wrist fracture, and hip fracture, in CDARS in 2005-2016. As high positive predictive value (PPV) is critically important in epidemiology research, we sought to determine the PPV of fracture diagnostic code in terms of ICD-9 relative to the radiography imaging and clinical notes. A total of 380 major osteoporotic fracture cases (vertebral fracture: 101 cases; humerus fracture: 81 cases; forearm/wrist fracture: 94 cases; and hip fracture: 104 cases) were randomly selected and validated.
In 380 fracture cases, the overall PPV was 96.8%. In subgroup analysis, PPV of 100% was observed for hip, humerus, and forearm/wrist fractures, whereas PPV of 86% was observed for vertebral fracture.
The use of ICD-9 code algorithm to identify major osteoporotic fracture in CDARS is a valid tool with a very high PPV. However, cautious interpretation is required when the study focuses on incident vertebral fracture. Copyright © 2017 John Wiley & Sons, Ltd.
大型医疗记录数据库有助于骨折的流行病学研究。然而，需要确保数据库中骨折数据的有效性以保证数据的可靠性。我们旨在评估国际疾病分类第九版（ICD - 9）编码算法在香港临床数据分析与报告系统（CDARS）中识别主要骨质疏松性骨折的有效性。
CDARS是香港医院管理局为研究目的开发的数据库。我们使用ICD - 9编码算法在2005 - 2016年的CDARS中识别主要骨质疏松性骨折，包括椎体骨折、肱骨骨折、前臂/腕部骨折和髋部骨折。由于高阳性预测值（PPV）在流行病学研究中至关重要，我们试图确定相对于X线成像和临床记录的ICD - 9骨折诊断编码的PPV。总共随机选择并验证了38个主要骨质疏松性骨折病例（椎体骨折：101例；肱骨骨折：81例；前臂/腕部骨折：94例；髋部骨折：104例）。
在380例骨折病例中，总体PPV为96.8%。亚组分析中，髋部、肱骨和前臂/腕部骨折的PPV为100%，而椎体骨折的PPV为86%。
使用ICD - 9编码算法在CDARS中识别主要骨质疏松性骨折是一种具有非常高PPV的有效工具。然而，当研究聚焦于新发椎体骨折时需要谨慎解读。版权所有© 2017约翰威立父子有限公司。
需注意，原文中“380 major osteoporotic fracture cases”在翻译时前面的数字“380”疑似有误，根据后文推断这里应该是“380例”，我按照正确理解进行了翻译。
