MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan －Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data

渡邊, 浩

doi:https://doi.org/10.1371/journal.pone.0255863

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MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan －Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data

http://hdl.handle.net/10271/00004263

名前 / ファイル	ライセンス	アクション
論文本文 (1.3 MB)

Item type

学位論文 / Thesis or Dissertation(1)

公開日

2023-02-08

タイトル

MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan －Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data

言語

eng

キーワード

主題

electronic medical records (EMR)

キーワード

主題

post-marketing safety surveillance of drugs

キーワード

主題

SS-MIX2 Standardized Storage

資源タイプ

資源タイプ識別子

http://purl.org/coar/resource_type/c_db06

資源タイプ

doctoral thesis

アクセス権

open access

アクセス権URI

http://purl.org/coar/access_right/c_abf2

その他のタイトル

厚生労働省の有害事象検出データベースであるMID-NETの先行研究であるMIHARIプロジェクト－電子カルテからのデータと医療請求データを使用した薬物の有害事象の信号検出の検証研究

著者

渡邊, 浩

書誌情報

en : PLOS ONE

巻 16, 号 9, p. e0255863, 発行日 2021-09-08

出版者

PLOS (Public Library of Science)

言語

権利

言語

権利情報

Copyright: 2021 Watanabe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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内容記述タイプ

Abstract

内容記述

We studied the effectiveness of the direct data collection from electronic medical records (EMR) when it is used for monitoring adverse drug events and also detection of already known adverse events. In this study, medical claim data and SS-MIX2 standardized storage data were used to identify four diseases (diabetes, dyslipidemia, hyperthyroidism, and acute renal failure) and the validity of the outcome definitions was evaluated by calculating positive predictive values (PPV). The maximum positive predictive value (PPV) for diabetes based on medical claim data was 40.7% and that based on prescription data from SS-MIX2 Standardized Storage was 44.7%. The PPV for dyslipidemia was 50% or higher under either of the conditions. The PPV for hyperthyroidism based on disease name data alone was 20?30%, but exceeded 60% when prescription data was included in the evaluation. Acute renal failure was evaluated using information from medical records in addition to the data. The PPV for acute renal failure based on the data of disease names and laboratory examination results was slightly higher at 53.7% and increased to 80?90% when patients who previously had a high serum creatinine (Cre) level were excluded. When defining a disease, it is important to include the condition specific to the disease; furthermore, it is very useful if laboratory examination results are also included. Therefore, the inclusion of laboratory examination results in the definitions, as in the present study, was considered very useful for the analysis of multi-center SS-MIX2 standardized storage data.

言語

学位名

博士（医学）

学位の区分

内容記述

doctoral

学位の分野

内容記述

医学系研究科

学位授与機関

学位授与機関識別子Scheme

kakenhi

学位授与機関識別子

13802

学位授与機関名

浜松医科大学

学位授与年月日

2022-05-20

学位授与番号

乙第588号

EISSN

収録物識別子タイプ

EISSN

収録物識別子

1932-6203

PubMed番号

識別子タイプ

PMID

Versions

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2023-06-20 16:16:32.888278

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MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan －Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data

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