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Research Article
Aaron Jones
McMaster University
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Objectives: Risk prediction scores are important tools to support clinical decision-making for patients with coronavirus disease (COVID-19). The objective of this paper was to validate the 4C mortality score, originally developed in the United Kingdom, for a Canadian population.
Methods: We conducted an external validation study within a registry of COVID-19 positive emergency department visits and hospital admissions in the Kitchener-Waterloo and Hamilton regions of southern Ontario between March 4 and January 9, 2020. We examined the validity of the 4C score to prognosticate in-hospital mortality using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals calculated via bootstrapping.
Results: The study included 560 individuals, of whom 115 (20.5%) died in-hospital. Median age was 69 years and 281 individuals (51%) were male. The AUC of the 4C score was 0.83, 95% confidence interval 0.79-0.87. Mortality rates across the pre-defined risk groups were 0% (Low), 3.2% (Intermediate), 25.9% (High), and 59.5% (Very High). The AUC was 0.80 (0.76-0.85) among hospital inpatients.
Interpretation: The 4C score is a valid tool to prognosticate mortality from COVID-19 in Canadian emergency departments and hospitals.
Keywords
COVID-19, Clinical risk prediction models, External validation
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Subject Areas
Health Economics & Outcomes Research
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Aaron Jones
McMaster University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 10 Mar, 2021
No community comments so far
Editor invited
On 11 Mar, 2021
Submission checks complete
On 08 Mar, 2021
First submitted
On 22 Feb, 2021
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Objectives: Risk prediction scores are important tools to support clinical decision-making for patients with coronavirus disease (COVID-19). The objective of this paper was to validate the 4C mortality score, originally developed in the United Kingdom, for a Canadian population.
Methods: We conducted an external validation study within a registry of COVID-19 positive emergency department visits and hospital admissions in the Kitchener-Waterloo and Hamilton regions of southern Ontario between March 4 and January 9, 2020. We examined the validity of the 4C score to prognosticate in-hospital mortality using the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals calculated via bootstrapping.
Results: The study included 560 individuals, of whom 115 (20.5%) died in-hospital. Median age was 69 years and 281 individuals (51%) were male. The AUC of the 4C score was 0.83, 95% confidence interval 0.79-0.87. Mortality rates across the pre-defined risk groups were 0% (Low), 3.2% (Intermediate), 25.9% (High), and 59.5% (Very High). The AUC was 0.80 (0.76-0.85) among hospital inpatients.
Interpretation: The 4C score is a valid tool to prognosticate mortality from COVID-19 in Canadian emergency departments and hospitals.
Figure 1
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