論文

国際誌
2021年1月6日

Antidepressant prescriptions have not fully reflected evolving evidence from cumulative network meta-analyses and guideline recommendations.

Journal of clinical epidemiology
  • Yan Luo
  • ,
  • Edoardo G Ostinelli
  • ,
  • Ethan Sahker
  • ,
  • Anna Chaimani
  • ,
  • Yuki Kataoka
  • ,
  • Yusuke Ogawa
  • ,
  • Andrea Cipriani
  • ,
  • Georgia Salanti
  • ,
  • Toshi A Furukawa

133
開始ページ
14
終了ページ
23
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.jclinepi.2020.12.023

OBJECTIVES: This study compares three major elements of evidence-based medicine (EBM) practices, namely evidence synthesis, clinical practice guidelines (CPGs), and real-world prescriptions in the United States, regarding antidepressant treatments of major depression over the past 3 decades. STUDY DESIGN AND SETTING: We conducted network meta-analyses (NMAs) of antidepressants every 5 years up to 2016 based on a comprehensive data set of double-blind randomized controlled trials. We identified CPGs and extracted their recommendations. We surveyed the prescriptions in the United States at 5-year intervals up to 2015. RESULTS: Most drugs recommended by CPGs presented favorable performance in efficacy and acceptability in NMAs. However, CPG recommendations were often in terms of drug classes rather than individual drugs, whereas NMAs suggested distinctive difference between drugs within the same class. The update intervals of all CPGs were longer than 5 years. All the antidepressants prescribed frequently in the United States were recommended by CPGs. However, changes in prescriptions did not correspond to alterations in CPGs or to apparent changes in the effects indicated by NMAs. Many factors including marketing efforts, regulations, or patient values may have played a role. CONCLUSION: Enhancements including accelerating CPG updates and monitoring the impact of marketing on prescriptions should be considered in future EBM implementation.

リンク情報
DOI
https://doi.org/10.1016/j.jclinepi.2020.12.023
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/33359320
ID情報
  • DOI : 10.1016/j.jclinepi.2020.12.023
  • PubMed ID : 33359320

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