論文

査読有り 筆頭著者 責任著者 国際誌
2021年12月

Conventional risk prediction models fail to accurately predict mortality risk among patients with coronavirus disease 2019 in intensive care units: a difficult time to assess clinical severity and quality of care

Journal of Intensive Care
  • Hideki Endo
  • Hiroyuki Ohbe
  • Junji Kumasawa
  • Shigehiko Uchino
  • Satoru Hashimoto
  • Yoshitaka Aoki
  • Takehiko Asaga
  • Eiji Hashiba
  • Junji Hatakeyama
  • Katsura Hayakawa
  • Nao Ichihara
  • Hiromasa Irie
  • Tatsuya Kawasaki
  • Hiroshi Kurosawa
  • Tomoyuki Nakamura
  • Hiroshi Okamoto
  • Hidenobu Shigemitsu
  • Shunsuke Takaki
  • Kohei Takimoto
  • Masatoshi Uchida
  • Ryo Uchimido
  • Hiroaki Miyata
  • 全て表示

9
1
開始ページ
42
終了ページ
42
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1186/s40560-021-00557-5

Since the start of the coronavirus disease 2019 (COVID-19) pandemic, it has remained unknown whether conventional risk prediction tools used in intensive care units are applicable to patients with COVID-19. Therefore, we assessed the performance of established risk prediction models using the Japanese Intensive Care database. Discrimination and calibration of the models were poor. Revised risk prediction models are needed to assess the clinical severity of COVID-19 patients and monitor healthcare quality in ICUs overwhelmed by patients with COVID-19.

リンク情報
DOI
https://doi.org/10.1186/s40560-021-00557-5
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/34074343
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169380
ID情報
  • DOI : 10.1186/s40560-021-00557-5
  • ORCIDのPut Code : 94789310
  • PubMed ID : 34074343
  • PubMed Central 記事ID : PMC8169380

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