Nationwide validation study of diagnostic algorithms for inflammatory bowel disease in Korean National Health Insurance Service database. Issue 5 (27th October 2019)

• Record Type: Journal Article
• Authors: Lee, Chang Kyun; Ha, Hyo Jung; Oh, Shin Ju; Kim, Jung‐Wook; Lee, Jung Kuk; Kim, Hyun‐Soo; Yoon, Soon Man; Kang, Sang‐Bum; Kim, Eun Soo; Kim, Tae Oh; Na, Soo‐Young; Lee, Jun; Kim, Sang‐Wook; Koo, Hoon Sup; Park, Byung Kyu; Lee, Han Hee; Kim, Eun Sun; Park, Jae Jun; Kwak, Min Seob; Cha, Jae Myung; Ye, Byong Duk; Choi, Chang Hwan; Kim, Hyo Jong
• Is Part Of: Journal of gastroenterology and hepatology. Volume 35:Issue 5(2020)
• Page Start: 760
• Page End: 768
• Publication Date: 2019-10-27
• Subjects: administrative claims, health care -- diagnostic algorithm -- inflammatory bowel disease -- operational definition; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Gastroenterology -- Periodicals; Liver Diseases -- Periodicals; 616.33
• DOI: 10.1111/jgh.14855
• Languages: English
• ISSNs: 0815-9319

Abstract

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.