Validation of ICD-10-CM Diagnostic Codes for Identifying Patients with ST-Elevation and Non-ST-Elevation Myocardial Infarction in a National Health Insurance Claims Database.

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2023-10-17 eCollection Date: 2023-01-01 DOI:10.2147/CLEP.S431231

Tou-Yuan Tsai, Jen-Feng Lin, Yu-Kang Tu, Jian-Heng Lee, Yu-Ting Hsiao, Sheng-Feng Sung, Ming-Jen Tsai

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引用次数: 0

Abstract

Purpose: Distinguishing ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) is crucial in acute myocardial infarction (AMI) research due to their distinct characteristics. However, the accuracy of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for STEMI and NSTEMI in Taiwan's National Health Insurance (NHI) database remains unvalidated. Therefore, we developed and validated case definition algorithms for STEMI and NSTEMI using ICD-10-CM and NHI billing codes.

Patients and methods: We obtained claims data and medical records of inpatient visits from 2016 to 2021 from the hospital's research-based database. Potential STEMI and NSTEMI cases were identified using diagnostic codes, keywords, and procedure codes associated with AMI. Chart reviews were then conducted to confirm the cases. The performance of the developed algorithms for STEMI and NSTEMI was assessed and subsequently externally validated.

Results: The algorithm that defined STEMI as any STEMI ICD code in the first three diagnosis fields had the highest performance, with a sensitivity of 93.6% (95% confidence interval [CI], 91.7-95.2%), a positive predictive value (PPV) of 89.4% (95% CI, 87.1-91.4%), and a kappa of 0.914 (95% CI, 0.900-0.928). The algorithm that used the NSTEMI ICD code listed in any diagnosis field performed best in identifying NSTEMI, with a sensitivity of 82.6% (95% CI, 80.7-84.4%), a PPV of 96.5% (95% CI, 95.4-97.4), and a kappa of 0.889 (95% CI, 0.878-0.901). The algorithm that included either STEMI or NSTEMI ICD codes listed in any diagnosis field showed excellent performance in defining AMI, with a sensitivity of 89.4% (95% CI, 88.2-90.6%), a PPV of 95.6% (95% CI, 94.7-96.4%), and a kappa of 0.923 (95% CI, 0.915-0.931). External validation confirmed these algorithms' efficacy.

Conclusion: Our results provide valuable reference algorithms for identifying STEMI and NSTEMI cases in Taiwan's NHI database.

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国家健康保险索赔数据库中用于识别ST段抬高和非ST段抬高心肌梗死患者的ICD-10-CM诊断代码的验证。

目的：区分ST段抬高型心肌梗死（STEMI）和非ST段抬高性心肌梗死（NSTEMI）由于其独特的特点，在急性心肌梗死（AMI）研究中至关重要。然而，台湾国家健康保险（NHI）数据库中STEMI和NSTEMI的国际疾病分类，第十次修订，临床改良（ICD-10-CM）代码的准确性仍然没有得到验证。因此，我们使用ICD-10-CM和NHI计费代码开发并验证了STEMI和NSTEMI的病例定义算法。患者和方法：我们从医院基于研究的数据库中获得了2016年至2021年住院就诊的索赔数据和医疗记录。使用与AMI相关的诊断代码、关键词和程序代码来识别潜在的STEMI和NSTEMI病例。随后进行了图表审查，以确认这些案例。对所开发的STEMI和NSTEMI算法的性能进行了评估，随后进行了外部验证。结果：在前三个诊断领域中，将STEMI定义为任何STEMI ICD代码的算法具有最高的性能，灵敏度为93.6%（95%置信区间[CI]，91.7-95.2%），阳性预测值（PPV）为89.4%（95%CI，87.1-91.4%），kappa为0.914（95%CI，0.900-0.928）。使用任何诊断领域中列出的NSTEMI ICD代码的算法在识别NSTEMI方面表现最好，灵敏度为82.6%（95%可信区间，80.7-84.4%），PPV为96.5%（95%置信区间，95.4-97.4），kappa为0.889（95%CI，0.878-0.901）。该算法包括任何诊断领域列出的STEMI或NSTEMI ICD代码，在定义AMI方面表现出优异的性能，灵敏度为89.4%（95%CI为88.2-90.6%），PPV为95.6%（95%CI：94.7-96.4%），kappa为0.923（95%CI；0.915-0.931）。外部验证证实了这些算法的有效性。结论：我们的结果为识别台湾NHI数据库中的STEMI和NSTEMI病例提供了有价值的参考算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.