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

• Published in: Journal of intensive care Vol. 9; no. 1; pp. 1 - 42
• Main Authors: Endo, Hideki, Ohbe, Hiroyuki, Kumasawa, Junji, Uchino, Shigehiko, Hashimoto, Satoru, Aoki, Yoshitaka, Asaga, Takehiko, Hashiba, Eiji, Hatakeyama, Junji, Hayakawa, Katsura, Ichihara, Nao, Irie, Hiromasa, Kawasaki, Tatsuya, Kurosawa, Hiroshi, Nakamura, Tomoyuki, Okamoto, Hiroshi, Shigemitsu, Hidenobu, Takaki, Shunsuke, Takimoto, Kohei, Uchida, Masatoshi, Uchimido, Ryo, Miyata, Hiroaki
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 01-06-2021; BioMed Central; BMC
• Subjects: Coronavirus disease 2019; Coronaviruses; COVID-19; Intensive care; Intensive care unit; Japan; Letter to the Editor; Medical care; Medical research; Medicine, Experimental; Mortality; Quality improvement; Quality management; Risk of death; Risk prediction model; Japan; United Kingdom > UK
• ISSN: 2052-0492; 2052-0492
• 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. Keywords: Coronavirus disease 2019, Risk of death, Intensive care unit, Risk prediction model, Quality improvement