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开发和验证用于对国际医院数据中的诊断编码进行分类的工具。

Development and external validation of tools for categorizing diagnosis codes in international hospital data.

机构信息

Department of Medicine, University of Toronto, Toronto, ON, Canada.

St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.

出版信息

Int J Med Inform. 2024 Sep;189:105508. doi: 10.1016/j.ijmedinf.2024.105508. Epub 2024 May 29.

Abstract

BACKGROUND

The Clinical Classification Software Refined (CCSR) is a tool that groups many thousands of International Classification of Diseases 10th Revision (ICD-10) diagnosis codes into approximately 500 clinically meaningful categories, simplifying analyses. However, CCSR was developed for use in the United States and may not work well with other country-specific ICD-10 coding systems.

METHOD

We developed an algorithm for semi-automated matching of Canadian ICD-10 codes (ICD-10-CA) to CCSR categories using discharge diagnoses from adult admissions at 7 hospitals between Apr 1, 2010 and Dec 31, 2020, and manually validated the results. We then externally validated our approach using inpatient hospital encounters in Denmark from 2017 to 2018.

KEY RESULTS

There were 383,972 Canadian hospital admissions with 5,186 distinct ICD-10-CA diagnosis codes and 1,855,837 Danish encounters with 4,612 ICD-10 diagnosis codes. Only 46.6% of Canadian codes and 49.4% of Danish codes could be directly categorized using the official CCSR tool. Our algorithm facilitated the mapping of 98.5% of all Canadian codes and 97.7% of Danish codes. Validation of our algorithm by clinicians demonstrated excellent accuracy (97.1% and 97.0% in Canadian and Danish data, respectively). Without our algorithm, many common conditions did not match directly to a CCSR category, such as 96.6% of hospital admissions for heart failure.

CONCLUSION

The GEMINI CCSR matching algorithm (available as an open-source package at https://github.com/GEMINI-Medicine/gemini-ccsr) improves the categorization of Canadian and Danish ICD-10 codes into clinically coherent categories compared to the original CCSR tool. We expect this approach to generalize well to other countries and enable a wide range of research and quality measurement applications.

摘要

背景

临床分类软件精炼版(CCSR)是一种工具,可将成千上万的国际疾病分类第 10 版(ICD-10)诊断代码分组为大约 500 个具有临床意义的类别,从而简化分析。然而,CCSR 是为美国开发的,可能与其他特定于国家的 ICD-10 编码系统不兼容。

方法

我们开发了一种算法,用于使用 2010 年 4 月 1 日至 2020 年 12 月 31 日期间在 7 家医院成人入院的出院诊断,对加拿大 ICD-10 代码(ICD-10-CA)与 CCSR 类别进行半自动匹配,并手动验证结果。然后,我们使用 2017 年至 2018 年丹麦住院患者的住院就诊数据对我们的方法进行了外部验证。

主要结果

有 383972 例加拿大住院患者,共有 5186 个独特的 ICD-10-CA 诊断代码,1855837 例丹麦患者,有 4612 个 ICD-10 诊断代码。只有 46.6%的加拿大代码和 49.4%的丹麦代码可以使用官方 CCSR 工具直接进行分类。我们的算法实现了 98.5%的加拿大代码和 97.7%的丹麦代码的映射。临床医生对我们算法的验证表明其准确性非常高(加拿大和丹麦数据的准确率分别为 97.1%和 97.0%)。如果没有我们的算法,许多常见疾病就无法直接与 CCSR 类别匹配,例如心力衰竭住院患者的 96.6%。

结论

GEMINI CCSR 匹配算法(可在 https://github.com/GEMINI-Medicine/gemini-ccsr 上作为开源软件包获得)与原始 CCSR 工具相比,可将加拿大和丹麦 ICD-10 代码更准确地分类为具有临床意义的类别。我们预计这种方法将很好地推广到其他国家,并能够实现广泛的研究和质量测量应用。

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