Literature DB >> 32145905

Risk Factors for Acetaminophen-induced Liver Injury: A Single-center Study from Japan.

Noriaki Hidaka1, Yuichi Kaji2, Shingo Takatori3, Akihiro Tanaka4, Ichiro Matsuoka2, Mamoru Tanaka4.   

Abstract

PURPOSE: Acetaminophen has been increasingly used for the treatment of cancer-related pain in Japan since the revision of the package insert on January 21, 2011. However, high-dose acetaminophen may cause liver injury. The objectives of this study were to investigate the prevalence of liver injury in patients receiving acetaminophen and to identify the risk factors.
METHODS: The subjects were patients who were treated with acetaminophen ≥1500 mg/d for ≥4 weeks at Ehime University Hospital between April 2011 and December 2014. Drug-induced liver injury was evaluated by alanine aminotransferase and alkaline phosphatase levels, Naranjo score, and Child-Pugh classification.
FINDINGS: A total of 287 of 562 patients were treated for 4 weeks with acetaminophen ≥1500 mg/d. Twenty of 102 patients analyzed had drug-induced liver injury. Multivariate analysis was performed with variables from the results of univariate analysis (sex, age ≥70 years, abnormal alanine aminotransferase and alkaline phosphatase levels, and serious liver disease), and age ≥70 years and serious liver disease were significant risk factors. IMPLICATIONS: The findings from the present observational, single-center study suggest that serious liver disease before administration is an independent risk factor for acetaminophen-induced liver injury.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  acetaminophen; cancer pain; child-pugh classification; liver injury; risk factor

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Year:  2020        PMID: 32145905     DOI: 10.1016/j.clinthera.2020.02.003

Source DB:  PubMed          Journal:  Clin Ther        ISSN: 0149-2918            Impact factor:   3.393


  1 in total

1.  Detection of Synergistic Interaction on an Additive Scale Between Two Drugs on Abnormal Elevation of Serum Alanine Aminotransferase Using Machine-Learning Algorithms.

Authors:  Hayato Akimoto; Takuya Nagashima; Kimino Minagawa; Takashi Hayakawa; Yasuo Takahashi; Satoshi Asai
Journal:  Front Pharmacol       Date:  2022-07-06       Impact factor: 5.988

  1 in total

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