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

• Published in: Therapeutic innovation & regulatory science Vol. 58; no. 4; pp. 746 - 755
• Main Authors: Tanigawa, Masatoshi, Kohama, Mei, Hirata, Kaori, Izukura, Rieko, Kandabashi, Tadashi, Kataoka, Yoko, Nakashima, Naoki, Kimura, Michio, Uyama, Yoshiaki, Yokoi, Hideto
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.07.2024; Springer Nature B.V
• Subjects: Accuracy; Adult; Adverse Drug Reaction Reporting Systems; Aged; Aged, 80 and over; Algorithms; Codes; Computed tomography; Computer programs; Consent; Databases, Factual; Decision making; Disease; Drug Safety and Pharmacovigilance; Esophagus; Female; Humans; Identification; Insurance claims; International Classification of Diseases; Intestinal Perforation; Intubation; Japan; Male; Medicine; Middle Aged; Optimization; Original Research; Patients; Pharmacotherapy; Pharmacovigilance; Pharmacy; Statistical analysis; Surgery; Tradeoffs; Validation studies; Validity; Japan; Identification algorithm; Positive predictive value; Gastrointestinal perforation; ICD-10; Pharmacovigilance; Medical information database
• ISSN: 2168-4790; 2168-4804; 2168-4804
• DOI: 10.1007/s43441-024-00619-4

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.