Chang Kyun Lee1, Hyo Jung Ha1, Shin Ju Oh1, Jung-Wook Kim1, Jung Kuk Lee2, Hyun-Soo Kim3, Soon Man Yoon4, Sang-Bum Kang5, Eun Soo Kim6, Tae Oh Kim7, Soo-Young Na8, Jun Lee9, Sang-Wook Kim10, Hoon Sup Koo11, Byung Kyu Park12, Han Hee Lee13, Eun Sun Kim14, Jae Jun Park15, Min Seob Kwak16, Jae Myung Cha16, Byong Duk Ye17, Chang Hwan Choi18, Hyo Jong Kim1. 1. Center for Crohn's and Colitis, Department of Gastroenterology, Kyung Hee University College of Medicine, Seoul, South Korea. 2. Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea. 3. Department of Internal Medicine, Yonsei University Wonju College of Medicine, Wonju, Republic of Korea. 4. Department of Internal Medicine, Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, South Korea. 5. Department of Internal Medicine, College of Medicine, Daejeon St. Mary's Hospital, The Catholic University of Korea, Daejeon, South Korea. 6. Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea. 7. Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea. 8. Department of Internal Medicine, Jeju National University School of Medicine, Jeju, South Korea. 9. Department of Internal Medicine, Chosun University College of Medicine, Gwangju, South Korea. 10. Department of Internal Medicine, Chonbuk National University Medical School, Jeonju, South Korea. 11. Department of Internal Medicine, Konyang University College of Medicine, Daejeon, South Korea. 12. Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Goyang, South Korea. 13. Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea. 14. Department of Internal Medicine, Korea University College of Medicine, Seoul, South Korea. 15. Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea. 16. Department of Internal Medicine, Kyung Hee University Hospital at Gang Dong, Kyung Hee University College of Medicine, Seoul, South Korea. 17. Department of Gastroenterology and Inflammatory Bowel Disease Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. 18. Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, South Korea.
Abstract
BACKGROUND AND AIM: We conducted a nationwide validation study of diagnostic algorithms to identify cases of inflammatory bowel disease (IBD) within the Korea National Health Insurance System (NHIS) database. METHOD: Using the NHIS dataset, we developed 44 algorithms combining the International Classification of Diseases (ICD)-10 codes, codes for Rare and Intractable Diseases (RID) registration and claims data for health care encounters, and pharmaceutical prescriptions for IBD-specific drugs. For each algorithm, we compared the case identification results from electronic medical records data with the gold standard (chart-based diagnosis). A multiple sampling test verified the validation results from the entire study population. RESULTS: A random nationwide sample of 1697 patients (848 potential cases and 849 negative control cases) from 17 hospitals were included for validation. A combination of the ICD-10 code, ≥ 1 claims for health care encounters, and ≥ 1 prescription claims (reference algorithm) achieved excellent performance (sensitivity, 93.1% [95% confidence interval 91-94.7]; specificity, 98.1% [96.9-98.8]; positive predictive value, 97.5% [96.1-98.5]; negative predictive value, 94.5% [92.8-95.8]) with the lowest error rate (4.2% [3.3-5.3]). The multiple sampling test confirmed that the reference algorithm achieves the best performance regarding IBD diagnosis. Algorithms including the RID registration codes exhibited poorer performance compared with that of the reference algorithm, particularly for the diagnosis of patients affiliated with secondary hospitals. The performance of the reference algorithm showed no statistical difference depending on the hospital volume or IBD type, with P-value < 0.05. CONCLUSIONS: We strongly recommend the reference algorithm as a uniform standard operational definition for future studies using the NHIS database.
BACKGROUND AND AIM: We conducted a nationwide validation study of diagnostic algorithms to identify cases of inflammatory bowel disease (IBD) within the Korea National Health Insurance System (NHIS) database. METHOD: Using the NHIS dataset, we developed 44 algorithms combining the International Classification of Diseases (ICD)-10 codes, codes for Rare and Intractable Diseases (RID) registration and claims data for health care encounters, and pharmaceutical prescriptions for IBD-specific drugs. For each algorithm, we compared the case identification results from electronic medical records data with the gold standard (chart-based diagnosis). A multiple sampling test verified the validation results from the entire study population. RESULTS: A random nationwide sample of 1697 patients (848 potential cases and 849 negative control cases) from 17 hospitals were included for validation. A combination of the ICD-10 code, ≥ 1 claims for health care encounters, and ≥ 1 prescription claims (reference algorithm) achieved excellent performance (sensitivity, 93.1% [95% confidence interval 91-94.7]; specificity, 98.1% [96.9-98.8]; positive predictive value, 97.5% [96.1-98.5]; negative predictive value, 94.5% [92.8-95.8]) with the lowest error rate (4.2% [3.3-5.3]). The multiple sampling test confirmed that the reference algorithm achieves the best performance regarding IBD diagnosis. Algorithms including the RID registration codes exhibited poorer performance compared with that of the reference algorithm, particularly for the diagnosis of patients affiliated with secondary hospitals. The performance of the reference algorithm showed no statistical difference depending on the hospital volume or IBD type, with P-value < 0.05. CONCLUSIONS: We strongly recommend the reference algorithm as a uniform standard operational definition for future studies using the NHIS database.
Authors: Su Young Kim; Yeon Seo Cho; Hyun-Soo Kim; Jung Kuk Lee; Hee Man Kim; Hong Jun Park; Hyunil Kim; Jihoon Kim; Dae Ryong Kang Journal: Gut Liver Date: 2021-11-18 Impact factor: 4.321
Authors: Kwang-Sig Lee; Eun Sun Kim; In-Seok Song; Hae-In Kim; Ki Hoon Ahn Journal: Int J Environ Res Public Health Date: 2022-03-05 Impact factor: 3.390