{"title":"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。","authors":"Tou-Yuan Tsai, Jen-Feng Lin, Yu-Kang Tu, Jian-Heng Lee, Yu-Ting Hsiao, Sheng-Feng Sung, Ming-Jen Tsai","doi":"10.2147/CLEP.S431231","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Patients and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>Our results provide valuable reference algorithms for identifying STEMI and NSTEMI cases in Taiwan's NHI database.</p>","PeriodicalId":10362,"journal":{"name":"Clinical Epidemiology","volume":"15 ","pages":"1027-1039"},"PeriodicalIF":3.4000,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/eb/80/clep-15-1027.PMC10590151.pdf","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"Tou-Yuan Tsai, Jen-Feng Lin, Yu-Kang Tu, Jian-Heng Lee, Yu-Ting Hsiao, Sheng-Feng Sung, Ming-Jen Tsai\",\"doi\":\"10.2147/CLEP.S431231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Patients and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>Our results provide valuable reference algorithms for identifying STEMI and NSTEMI cases in Taiwan's NHI database.</p>\",\"PeriodicalId\":10362,\"journal\":{\"name\":\"Clinical Epidemiology\",\"volume\":\"15 \",\"pages\":\"1027-1039\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/eb/80/clep-15-1027.PMC10590151.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Epidemiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/CLEP.S431231\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/CLEP.S431231","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
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.
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.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
