Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation - 02/10/20
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Abstract |
Study objective |
The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).
Methods |
This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.
Results |
During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.
Conclusion |
A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.
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| Please see page 443 for the Editor’s Capsule Summary of this article. |
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| Supervising editor: Gregory J. Moran, MD. Specific detailed information about possible conflict of interest for individual editors is available at editors. |
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| Author contributions: AH, NGR, and RAT designed the project. NGR, HPY, WLS, and RAT extracted and processed the data. AH, NGR, FPW, DvD, and RAT created the models. SS designed the Web interface. All authors contributed substantially to article revisions. RAT takes responsibility for the paper as a whole. |
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| All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. |
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| Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). Dr. Wilson acknowledges funding from the National Institutes of Health R01DK113191 and P30DK079310. Dr. Schulz was an investigator for a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; is a technical consultant to HugoHealth, a personal health information platform; is cofounder of Refactor Health, an artificial intelligence–augmented data mapping platform for health care; and is a consultant for Interpace Diagnostics Group, a molecular diagnostics company. |
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| Trial registration number: XXXXXXXX |
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| Funding sources had no involvement in the design of the study. Researchers are independent from funders. |
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Vol 76 - N° 4
P. 442-453 - octobre 2020 Retour au numéro
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