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

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 Japane...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Letter to the Editor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169380/
https://www.ncbi.nlm.nih.gov/pubmed/34074343
http://dx.doi.org/10.1186/s40560-021-00557-5
Descripción
Sumario: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.

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