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

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 UchidaRyo Uchimido, Hiroaki Miyata

研究成果: Letter査読

抄録

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.

本文言語English
論文番号42
ジャーナルJournal of Intensive Care
9
1
DOI
出版ステータスPublished - 12-2021

All Science Journal Classification (ASJC) codes

  • 集中医療医学

フィンガープリント

「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」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル