tetano
Editor, Senior Moderator
J Intensive Care
. 2021 Jun 1;9(1):42.
doi: 10.1186/s40560-021-00557-5.
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[SUP] 1 2 [/SUP], Hiroyuki Ohbe[SUP] 3 [/SUP], Junji Kumasawa[SUP] 4 [/SUP], Shigehiko Uchino[SUP] 5 [/SUP], Satoru Hashimoto[SUP] 6 [/SUP], Yoshitaka Aoki[SUP] 7 [/SUP], Takehiko Asaga[SUP] 8 [/SUP], Eiji Hashiba[SUP] 9 [/SUP], Junji Hatakeyama[SUP] 10 [/SUP], Katsura Hayakawa[SUP] 11 [/SUP], Nao Ichihara[SUP] 12 [/SUP], Hiromasa Irie[SUP] 13 [/SUP], Tatsuya Kawasaki[SUP] 14 [/SUP], Hiroshi Kurosawa[SUP] 15 [/SUP], Tomoyuki Nakamura[SUP] 16 [/SUP], Hiroshi Okamoto[SUP] 17 [/SUP], Hidenobu Shigemitsu[SUP] 18 [/SUP], Shunsuke Takaki[SUP] 19 [/SUP], Kohei Takimoto[SUP] 20 [/SUP], Masatoshi Uchida[SUP] 21 [/SUP], Ryo Uchimido[SUP] 18 [/SUP], Hiroaki Miyata[SUP] 22 [/SUP]
Affiliations
Abstract
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.
Keywords: Coronavirus disease 2019; Intensive care unit; Quality improvement; Risk of death; Risk prediction model.
. 2021 Jun 1;9(1):42.
doi: 10.1186/s40560-021-00557-5.
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[SUP] 1 2 [/SUP], Hiroyuki Ohbe[SUP] 3 [/SUP], Junji Kumasawa[SUP] 4 [/SUP], Shigehiko Uchino[SUP] 5 [/SUP], Satoru Hashimoto[SUP] 6 [/SUP], Yoshitaka Aoki[SUP] 7 [/SUP], Takehiko Asaga[SUP] 8 [/SUP], Eiji Hashiba[SUP] 9 [/SUP], Junji Hatakeyama[SUP] 10 [/SUP], Katsura Hayakawa[SUP] 11 [/SUP], Nao Ichihara[SUP] 12 [/SUP], Hiromasa Irie[SUP] 13 [/SUP], Tatsuya Kawasaki[SUP] 14 [/SUP], Hiroshi Kurosawa[SUP] 15 [/SUP], Tomoyuki Nakamura[SUP] 16 [/SUP], Hiroshi Okamoto[SUP] 17 [/SUP], Hidenobu Shigemitsu[SUP] 18 [/SUP], Shunsuke Takaki[SUP] 19 [/SUP], Kohei Takimoto[SUP] 20 [/SUP], Masatoshi Uchida[SUP] 21 [/SUP], Ryo Uchimido[SUP] 18 [/SUP], Hiroaki Miyata[SUP] 22 [/SUP]
Affiliations
- PMID: 34074343
- DOI: 10.1186/s40560-021-00557-5
Abstract
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.
Keywords: Coronavirus disease 2019; Intensive care unit; Quality improvement; Risk of death; Risk prediction model.