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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...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2021
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| 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 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Endo, Hideki |
| collection | PubMed |
| description | 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. |
| format | Online Article Text |
| id | pubmed-8169380 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2021 |
| publisher | BioMed Central |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-81693802021-06-02 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 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 J Intensive Care Letter to the Editor 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. BioMed Central 2021-06-01 /pmc/articles/PMC8169380/ /pubmed/34074343 http://dx.doi.org/10.1186/s40560-021-00557-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| spellingShingle | Letter to the Editor 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 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Letter to the Editor |
| url | 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 |
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