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.
METHODS: 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.
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.
CONCLUSIONS: 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.
METHODS: 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.
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.
CONCLUSIONS: 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（CCSR）是一种工具，它将成千上万的国际疾病分类第10版（ICD-10）诊断代码分为大约500个有临床意义的类别，简化分析。然而,CCSR是为在美国使用而开发的，可能无法与其他特定国家的ICD-10编码系统良好合作。
方法：我们在2010年4月1日至2020年12月31日期间，使用7家医院成人入院的出院诊断，开发了加拿大ICD-10代码（ICD-10-CA）与CCSR类别的半自动匹配算法，并手动验证了结果。然后，我们从2017年到2018年在丹麦的住院医院中对我们的方法进行了外部验证。
结果：有383,972名加拿大住院患者，有5,186个不同的ICD-10-CA诊断代码，1,855,837名丹麦患者有4,612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工具直接分类。我们的算法促进了所有加拿大代码的98.5％和丹麦代码的97.7％的映射。临床医生对我们算法的验证证明了出色的准确性（在加拿大和丹麦数据中为97.1％和97.0％，分别)。没有我们的算法，许多常见条件与CCSR类别不直接匹配，如96.6%的住院患者为心力衰竭。
结论：GEMINICCSR匹配算法（在https://github.com/GEMINI-Medicine/gemini-ccsr上作为开源软件包提供）与原始CCSR工具相比，改进了将加拿大和丹麦ICD-10代码分类为临床连贯类别。我们希望这种方法能够很好地推广到其他国家，并实现广泛的研究和质量测量应用。
方法：我们在2010年4月1日至2020年12月31日期间，使用7家医院成人入院的出院诊断，开发了加拿大ICD-10代码（ICD-10-CA）与CCSR类别的半自动匹配算法，并手动验证了结果。然后，我们从2017年到2018年在丹麦的住院医院中对我们的方法进行了外部验证。
结果：有383,972名加拿大住院患者，有5,186个不同的ICD-10-CA诊断代码，1,855,837名丹麦患者有4,612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工具直接分类。我们的算法促进了所有加拿大代码的98.5％和丹麦代码的97.7％的映射。临床医生对我们算法的验证证明了出色的准确性（在加拿大和丹麦数据中为97.1％和97.0％，分别)。没有我们的算法，许多常见条件与CCSR类别不直接匹配，如96.6%的住院患者为心力衰竭。
结论：GEMINICCSR匹配算法（在https://github.com/GEMINI-Medicine/gemini-ccsr上作为开源软件包提供）与原始CCSR工具相比，改进了将加拿大和丹麦ICD-10代码分类为临床连贯类别。我们希望这种方法能够很好地推广到其他国家，并实现广泛的研究和质量测量应用。
