Derivation and validation of a clinical decision rule to risk‐stratify COVID‐19 patients discharged from the emergency department: The CCEDRRN COVID discharge score

Brooks, Steven C.; Rosychuk, Rhonda J.; Perry, Jeffrey J.; Morrison, Laurie J.; Wiemer, Hana; Fok, Patrick; Rowe, Brian H.; Daoust, Raoul; Vatanpour, Shabnam; Turner, Joel; Landes, Megan; Ohle, Robert; Hayward, Jake; Scheuermeyer, Frank; Welsford, Michelle; Hohl, Corinne M.

doi:10.1002/emp2.12868

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…As of Oct. 31, 2023, the main results of 12 of the 16 RCTs 20-28 (75%) and 4 of the 7 cohort studies [29][30][31][32][33] (57.1%) had been published. References and citation metrics appear in Appendix 1, Table S4.…”

Section: Resultsmentioning

confidence: 99%

The response of Canada’s clinical health research ecosystem to the COVID-19 pandemic

Lamontagne,

Masse,

Yarnell

et al. 2024

CMAJ

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Section: Resultsmentioning

confidence: 99%

The response of Canada’s clinical health research ecosystem to the COVID-19 pandemic

Lamontagne,

Masse,

Yarnell

et al. 2024

CMAJ

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“…We developed multivariable logistic regression models for the outcomes of mechanical ventilation, ICU admission and in-hospital mortality, and reported adjusted odds ratios (ORs) and their 95% CIs. Predictor variables were chosen a priori based on their clinical importance and prior literature: age, sex, obesity, secondary immunodeficiency (malignant neoplasm, transplant recipient, or moderate/severe liver disease), admission primarily for COVID-19 (versus with incidental SARS-CoV-2 infection), subvariant dominance, illicit substance use, and vaccination status [ 6 , 10 , 12 , 20 ]. We also included presenting hospital site as a fixed effect in all multivariable models to adjust for clustering.…”

Section: Methodsmentioning

confidence: 99%

Comparing methods to classify admitted patients with SARS-CoV-2 as admitted for COVID-19 versus with incidental SARS-CoV-2: A cohort study

Hohl,

Cragg,

Purssel

et al. 2023

PLoS ONE

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Introduction Not all patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection develop symptomatic coronavirus disease 2019 (COVID-19), making it challenging to assess the burden of COVID-19-related hospitalizations and mortality. We aimed to determine the proportion, resource utilization, and outcomes of SARS-CoV-2 positive patients admitted for COVID-19, and assess the impact of using the Center for Disease Control’s (CDC) discharge diagnosis-based algorithm and the Massachusetts state department’s drug administration-based classification system on identifying admissions for COVID-19. Methods In this retrospective cohort study, we enrolled consecutive SARS-CoV-2 positive patients admitted to one of five hospitals in British Columbia between December 19, 2021 and May 31,2022. We completed medical record reviews, and classified hospitalizations as being primarily for COVID-19 or with incidental SARS-CoV-2 infection. We applied the CDC algorithm and the Massachusetts classification to estimate the difference in hospital days, intensive care unit (ICU) days and in-hospital mortality and calculated sensitivity and specificity. Results Of 42,505 Emergency Department patients, 1,651 were admitted and tested positive for SARS-CoV-2, with 858 (52.0%, 95% CI 49.6–54.4) admitted for COVID-19. Patients hospitalized for COVID-19 required ICU admission (14.0% versus 8.2%, p<0.001) and died (12.6% versus 6.4%, p<0.001) more frequently compared with patients with incidental SARS-CoV-2. Compared to case classification by clinicians, the CDC algorithm had a sensitivity of 82.9% (711/858, 95% CI 80.3%, 85.4%) and specificity of 98.1% (778/793, 95% CI 97.2%, 99.1%) for COVID-19-related admissions and underestimated COVID-19 attributable hospital days. The Massachusetts classification had a sensitivity of 60.5% (519/858, 95% CI 57.2%, 63.8%) and specificity of 78.6% (623/793, 95% CI 75.7%, 81.4%) for COVID-19-related admissions, underestimating total number of hospital and ICU bed days while overestimating COVID-19-related intubations, ICU admissions, and deaths. Conclusion Half of SARS-CoV-2 hospitalizations were for COVID-19 during the Omicron wave. The CDC algorithm was more specific and sensitive than the Massachusetts classification, but underestimated the burden of COVID-19 admissions. Trial registration Clinicaltrials.gov, NCT04702945.

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Leveraging a clinical emergency department dataset to estimate two-dose COVID-19 vaccine effectiveness and duration of protection in Canada

Ting,

Rosychuk,

Hau

et al. 2024

Vaccine

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Derivation and validation of a clinical decision rule to risk‐stratify COVID‐19 patients discharged from the emergency department: The CCEDRRN COVID discharge score

Abstract: Objective: To risk-stratify COVID-19 patients being considered for discharge from the emergency department (ED). Methods:We conducted an observational study to derive and validate a clinical decision rule to identify COVID-19 patients at risk for hospital admission or death within

Cited by 5 publications

References 38 publications

The response of Canada’s clinical health research ecosystem to the COVID-19 pandemic

The response of Canada’s clinical health research ecosystem to the COVID-19 pandemic

Comparing methods to classify admitted patients with SARS-CoV-2 as admitted for COVID-19 versus with incidental SARS-CoV-2: A cohort study

Leveraging a clinical emergency department dataset to estimate two-dose COVID-19 vaccine effectiveness and duration of protection in Canada

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