WHO Ordinal Scale and Inflammation Risk Categories in COVID-19. Comparative Study of the Severity Scales
© 2022. The Author(s), under exclusive licence to Society of General Internal Medicine..
BACKGROUND: The WHO ordinal severity scale has been used to predict mortality and guide trials in COVID-19. However, it has its limitations.
OBJECTIVE: The present study aims to compare three classificatory and predictive models: the WHO ordinal severity scale, the model based on inflammation grades, and the hybrid model.
DESIGN: Retrospective cohort study with patient data collected and followed up from March 1, 2020, to May 1, 2021, from the nationwide SEMI-COVID-19 Registry. The primary study outcome was in-hospital mortality. As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Categories 6 and 7 were grouped in the same category.
KEY RESULTS: A total of 17,225 patients were included in the study. Patients classified as high risk in each of the WHO categories according to the degree of inflammation were as follows: 63.8% vs. 79.9% vs. 90.2% vs. 95.1% (p<0.001). In-hospital mortality for WHO ordinal scale categories 3 to 6/7 was as follows: 0.8% vs. 24.3% vs. 45.3% vs. 34% (p<0.001). In-hospital mortality for the combined categories of ordinal scale 3a to 5b was as follows: 0.4% vs. 1.1% vs. 11.2% vs. 27.5% vs. 35.5% vs. 41.1% (p<0.001). The predictive regression model for in-hospital mortality with our proposed combined ordinal scale reached an AUC=0.871, superior to the two models separately.
CONCLUSIONS: The present study proposes a new severity grading scale for COVID-19 hospitalized patients. In our opinion, it is the most informative, representative, and predictive scale in COVID-19 patients to date.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2022 |
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Erschienen: |
2022 |
Enthalten in: |
Zur Gesamtaufnahme - volume:37 |
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Enthalten in: |
Journal of general internal medicine - 37(2022), 8 vom: 08. Juni, Seite 1980-1987 |
Sprache: |
Englisch |
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Links: |
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Themen: |
COVID-19 |
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Anmerkungen: |
Date Completed 16.06.2022 Date Revised 08.08.2022 published: Print-Electronic Citation Status MEDLINE |
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doi: |
10.1007/s11606-022-07511-7 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM339263008 |
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520 | |a © 2022. The Author(s), under exclusive licence to Society of General Internal Medicine. | ||
520 | |a BACKGROUND: The WHO ordinal severity scale has been used to predict mortality and guide trials in COVID-19. However, it has its limitations | ||
520 | |a OBJECTIVE: The present study aims to compare three classificatory and predictive models: the WHO ordinal severity scale, the model based on inflammation grades, and the hybrid model | ||
520 | |a DESIGN: Retrospective cohort study with patient data collected and followed up from March 1, 2020, to May 1, 2021, from the nationwide SEMI-COVID-19 Registry. The primary study outcome was in-hospital mortality. As this was a hospital-based study, the patients included corresponded to categories 3 to 7 of the WHO ordinal scale. Categories 6 and 7 were grouped in the same category | ||
520 | |a KEY RESULTS: A total of 17,225 patients were included in the study. Patients classified as high risk in each of the WHO categories according to the degree of inflammation were as follows: 63.8% vs. 79.9% vs. 90.2% vs. 95.1% (p<0.001). In-hospital mortality for WHO ordinal scale categories 3 to 6/7 was as follows: 0.8% vs. 24.3% vs. 45.3% vs. 34% (p<0.001). In-hospital mortality for the combined categories of ordinal scale 3a to 5b was as follows: 0.4% vs. 1.1% vs. 11.2% vs. 27.5% vs. 35.5% vs. 41.1% (p<0.001). The predictive regression model for in-hospital mortality with our proposed combined ordinal scale reached an AUC=0.871, superior to the two models separately | ||
520 | |a CONCLUSIONS: The present study proposes a new severity grading scale for COVID-19 hospitalized patients. In our opinion, it is the most informative, representative, and predictive scale in COVID-19 patients to date | ||
650 | 4 | |a Journal Article | |
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