Publications by authors named "M Kohama"
Article Synopsis
- The Medical Information Database Network (MID-NET) in Japan is crucial for tracking adverse drug events like gastrointestinal perforation, but lacks effective detection algorithms.
- This study tested 12 algorithms that combine ICD-10 codes with treatment information from 200 inpatients across multiple sites, assessing their positive predictive values (PPVs) and sensitivities.
- Results showed a trade-off between PPV and sensitivity, with the best algorithm achieving a PPV of 61.6% and sensitivity of 92.4%, improving the accuracy of identifying gastrointestinal perforations and aiding future pharmacovigilance research.
Intestinal perforation and obstruction are known to be one of the adverse events caused by antipsychotics; however, warning information on package inserts varies among antipsychotics. To investigate the risks of gastrointestinal perforation and intestinal obstruction in patients prescribed atypical antipsychotics compared with those in patients prescribed typical antipsychotics, a nested case-control study was conducted utilizing real-world data from the MID-NET medical information database in Japan. The study period spanned from January 1, 2009, to December 31, 2018.
View Article and Find Full Text PDFBackground: Japanese pharmaceutical authorities have conducted regulatory renovations of pharmacovigilance planning (PVP) since implementing new procedures for developing post-marketing study plans in 2018 in order to promote more focused and scientific approaches. This study aimed to descriptively assess the effects of those regulatory renovations on PVP for new drugs in Japan.
Methods: We identified PVP information (drug characteristics, efficacy and safety issues, and additional activities) from the first version of risk management plans for new drugs approved between 2016 and 2019.
Purpose: We aimed to develop a reliable identification algorithm combining diagnostic codes with several treatment factors for inpatients with acute ischemic stroke (AIS) to conduct pharmacoepidemiological studies using the administrative database MID-NET® in Japan.
Methods: We validated 11 identification algorithms based on 56 different diagnostic codes (International Classification of Diseases, Tenth Revision; ICD-10) using Diagnosis Procedure Combination (DPC) data combined with information on AIS therapeutic procedures added as "AND" condition or "OR" condition. The target population for this study was 366 randomly selected hospitalized patients with possible cases of AIS, defined as relevant ICD-10 codes and diagnostic imaging and prescription or surgical procedure, in three institutions between April 1, 2015 and March 31, 2017.