Detection Algorithms for Gastrointestinal Perforation Cases in the Medical Information Database Network (MID-NET®) in Japan

Masatoshi Tanigawa; Mei Kohama; Kaori Hirata; Rieko Izukura; Tadashi Kandabashi; Yoko Kataoka; Naoki Nakashima; Michio Kimura; Yoshiaki Uyama; Hideto Yokoi

doi:10.1007/s43441-024-00619-4

Detection Algorithms for Gastrointestinal Perforation Cases in the Medical Information Database Network (MID-NET^®) in Japan

Ther Innov Regul Sci. 2024 Apr 21. doi: 10.1007/s43441-024-00619-4. Online ahead of print.

Authors

Affiliations

¹ Clinical Research Support Center, Kagawa University Hospital, 1750-1 Ikenobe, Miki-Cho, Kita-Gun, Kagawa, 761-0793, Japan. tanigawa.masatoshi.k6@kagawa-u.ac.jp.
² Office of Medical Informatics and Epidemiology, Pharmaceutical and Medical Devices Agency, Shin-Kasumigaseki Building, 3-3-2 Kasumigaseki, Chiyoda-ku, Tokyo, 100-0013, Japan.
³ Social Medicine, Department of Basic Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
⁴ Medical Information Center, Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
⁵ Clinical Research Support Center, Kagawa University Hospital, 1750-1 Ikenobe, Miki-Cho, Kita-Gun, Kagawa, 761-0793, Japan.
⁶ Department of Medical Informatics, Hamamatsu University Hospital, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka, 431-3192, Japan.
⁷ Department of Medical Informatics, Kagawa University Hospital, 1750-1 Ikenobe, Miki-Cho, Kita-Gun, Kagawa, 761-0793, Japan.

PMID: 38644459
DOI: 10.1007/s43441-024-00619-4

Abstract

Background: The Medical Information Database Network (MID-NET^®) in Japan is a vast repository providing an essential pharmacovigilance tool. Gastrointestinal perforation (GIP) is a critical adverse drug event, yet no well-established GIP identification algorithm exists in MID-NET^®.

Methods: This study evaluated 12 identification algorithms by combining ICD-10 codes with GIP therapeutic procedures. Two sites contributed 200 inpatients with GIP-suggestive ICD-10 codes (100 inpatients each), while a third site contributed 165 inpatients with GIP-suggestive ICD-10 codes and antimicrobial prescriptions. The positive predictive values (PPVs) of the algorithms were determined, and the relative sensitivity (rSn) among the 165 inpatients at the third institution was evaluated.

Results: A trade-off between PPV and rSn was observed. For instance, ICD-10 code-based definitions yielded PPVs of 59.5%, whereas ICD-10 codes with CT scan and antimicrobial information gave PPVs of 56.0% and an rSn of 97.0%, and ICD-10 codes with CT scan and antimicrobial information as well as three types of operation codes produced PPVs of 84.2% and an rSn of 24.2%. The same algorithms produced statistically significant differences in PPVs among the three institutions. Combining diagnostic and procedure codes improved the PPVs. The algorithm combining ICD-10 codes with CT scan and antimicrobial information and 80 different operation codes offered the optimal balance (PPV: 61.6%, rSn: 92.4%).

Conclusion: This study developed valuable GIP identification algorithms for MID-NET^®, revealing the trade-offs between accuracy and sensitivity. The algorithm with the most reasonable balance was determined. These findings enhance pharmacovigilance efforts and facilitate further research to optimize adverse event detection algorithms.

Keywords: Gastrointestinal perforation; ICD-10; Identification algorithm; Medical information database; Pharmacovigilance; Positive predictive value.

Abstract

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