Exploring predictors of severe adverse effects in patients with acute drug poisoning

DOI Web Site
  • MORINAGA Mutsuko
    Faculty of Health Science and Technology, Kawasaki University of Medical Welfare
  • KATAOKA Hiromi
    Faculty of Health Science and Technology, Kawasaki University of Medical Welfare
  • TOHYAMA Kaoru
    Faculty of Health Science and Technology, Kawasaki University of Medical Welfare

Bibliographic Information

Other Title
  • 急性薬物中毒患者の重症化予測因子の探索
  • キュウセイ ヤクブツ チュウドク カンジャ ノ ジュウショウカ ヨソク インシ ノ タンサク

Search this article

Abstract

<p>Some drugs have severe adverse effects such as impaired consciousness and shock. Drug testing is effective for determining the causes of severe adverse effects, but the analyses of drugs require precision instruments that are complicated and expensive to operate and need time and expertise to achieve proficiency and obtain results. Therefore, analytical methods are not easy to establish in any laboratory. Given this background, we have calculated the factors related to the severity of adverse effects by univariate analysis using the data from 197 drug-addicted patients who were brought to the Kawasaki Medical School Emergency Medical Center, for the purpose of developing a universal method using pathologic parameters for laboratories to test, such as clinical laboratory results, parameters, vital signs, medication, and treatment histories. We have derived the equation that can be used to predict the necessity of treatments of severe cases by multivariate logistic regression analysis using these parameters. Furthermore, we have calculated the validity by ROC analysis with multiple patterns from the obtained factors. For the prediction of severity, CK-MB level, APTT, monocytes %, overdose, and GCS score were the important factors. As suggested by ROC analysis, although 0.701 was only based on patient background and 0.700 on the clinical laboratory results, 0.789 on the combined analyses was considered to indicate a high validity of discrimination level.</p>

Journal

Keywords

Details 詳細情報について

Report a problem

Back to top