Derivation and Validation of a Clinical Score to Predict Death Among Non-Palliative Covid-19 Patients Presenting to Emergency Departments: The Ccedrrn Covid Mortality Score

Hohl, Corinne M.; Rosychuk, Rhonda J.; Archambault, Patrick; O’Sullivan, Fiona; Leeies, Murdoch; Mercier, Éric; Clark, Gordon M.; Innes, Grant; Brooks, Steven C.; Hayward, Jake; Ho, Vivian; Jelić, Tomislav; Welsford, Michelle; Sivilotti, Marco L.A.; Morrison, Laurie J.; Perry, Jeffrey J.

doi:10.1101/2021.07.28.21261283

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Since the inception of our study to validate this score, many other systems have been proposed. These have been developed in a variety of different patient populations using a wide range of methods 27–35. Some models have been independently assessed and performance varies 36.…”

Section: Discussionmentioning

confidence: 99%

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network

et al. 2022

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ObjectivesEstimating mortality risk in hospitalised SARS-CoV-2+ patients may help with choosing level of care and discussions with patients. The Coronavirus Clinical Characterisation Consortium Mortality Score (4C Score) is a promising COVID-19 mortality risk model. We examined the association of risk factors with 30-day mortality in hospitalised, full-code SARS-CoV-2+ patients and investigated the discrimination and calibration of the 4C Score. This was a retrospective cohort study of SARS-CoV-2+ hospitalised patients within the RECOVER (REgistry of suspected COVID-19 in EmeRgency care) network.Setting99 emergency departments (EDs) across the USA.ParticipantsPatients ≥18 years old, positive for SARS-CoV-2 in the ED, and hospitalised.Primary outcomeDeath within 30 days of the index visit. We performed logistic regression analysis, reporting multivariable risk ratios (MVRRs) and calculated the area under the ROC curve (AUROC) and mean prediction error for the original 4C Score and after dropping the C reactive protein (CRP) component.ResultsOf 6802 hospitalised patients with COVID-19, 1149 (16.9%) died within 30 days. The 30-day mortality was increased with age 80+ years (MVRR=5.79, 95% CI 4.23 to 7.34); male sex (MVRR=1.17, 1.05 to 1.28); and nursing home/assisted living facility residence (MVRR=1.29, 1.1 to 1.48). The 4C Score had comparable discrimination in the RECOVER dataset compared with the original 4C validation dataset (AUROC: RECOVER 0.786 (95% CI 0.773 to 0.799), 4C validation 0.763 (95% CI 0.757 to 0.769). Score-specific mortalities in our sample were lower than in the 4C validation sample (mean prediction error 6.0%). Dropping the CRP component from the 4C Score did not substantially affect discrimination and 4C risk estimates were now close (mean prediction error 0.7%).ConclusionsWe independently validated 4C Score as predicting risk of 30-day mortality in hospitalised SARS-CoV-2+ patients. We recommend dropping the CRP component of the score and using our recalibrated mortality risk estimates.

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Section: Discussionmentioning

confidence: 99%

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network

et al. 2022

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“… 16 The network conducts observational studies and develops clinical decision rules to inform emergency department care. 17 – 19 The CCEDRRN patient engagement committee reviewed and provided input into the development of the research question, the choice of outcomes and the study protocol, and reviewed the submitted manuscript. Patient partners were involved in developing CCEDRRN’s website and knowledge translation tools to disseminate study results ( https://www.ccedrrn.com/ ).…”

Section: Methodsmentioning

confidence: 99%

The CCEDRRN COVID-19 Mortality Score to predict death among nonpalliative patients with COVID-19 presenting to emergency departments: a derivation and validation study

Hohl

Rosychuk

Archambault

et al. 2022

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T hroughout the COVID-19 pandemic, health care systems around the world have been confronted with, and at times overwhelmed by, high numbers of critically ill patients. 1,2 For every critically ill patient in the emergency department, several less severely ill patients present for care; in some cases, their condition deteriorates later, which places additional pressure on resources. Accurate, disease-specific mortality prediction is needed to inform shared decision-making with patients and their families around the patients' goals of care. Disease-specific mortality prediction can also allow health care systems to allocate resources in the most transparent, objective and fair manner possible to save as many lives as possible and facilitate timely access to palliative care, if needed. [3][4][5]

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Treatments, resource utilization, and outcomes of COVID-19 patients presenting to emergency departments across pandemic waves: an observational study by the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN)

et al. 2022

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Background Treatment for coronavirus disease 2019 (COVID-19) evolved between pandemic waves. Our objective was to compare treatments, acute care utilization, and outcomes of COVID-19 patients presenting to emergency departments (ED) across pandemic waves. Methods This observational study enrolled consecutive eligible COVID-19 patients presenting to 46 EDs participating in the Canadian COVID-19 ED Rapid Response Network (CCEDRRN) between March 1 and December 31, 2020. We collected data by retrospective chart review. Our primary outcome was in-hospital mortality. Secondary outcomes included treatments, hospital and ICU admissions, ED revisits and readmissions. Logistic regression modeling assessed the impact of pandemic wave on outcomes. Results We enrolled 9,967 patients in 8 provinces, 3,336 from the first and 6,631 from the second wave. Patients in the second wave were younger, fewer met criteria for severe COVID-19, and more were discharged from the ED. Adjusted for patient characteristics and disease severity, steroid use increased (odds ratio [OR] 7.4; 95% confidence interval [CI] 6.2–8.9), and invasive mechanical ventilation decreased (OR 0.5; 95% CI 0.4–0.7) in the second wave compared to the first. After adjusting for differences in patient characteristics and disease severity, the odds of hospitalization (OR 0.7; 95% CI 0.6–0.8) and critical care admission (OR 0.7; 95% CI 0.6–0.9) decreased, while mortality remained unchanged (OR 0.7; 95% CI 0.5–1.1). Interpretation In patients presenting to cute care facilities, we observed rapid uptake of evidence-based therapies and less use of experimental therapies in the second wave. We observed increased rates of ED discharges and lower hospital and critical care resource use over time. Substantial reductions in mechanical ventilation were not associated with increasing mortality. Advances in treatment strategies created health system efficiencies without compromising patient outcomes. Trial registration Clinicaltrials.gov, NCT04702945.

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Derivation and Validation of a Clinical Score to Predict Death Among Non-Palliative Covid-19 Patients Presenting to Emergency Departments: The Ccedrrn Covid Mortality Score

Cited by 3 publications

References 28 publications

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network

External validation of the 4C Mortality Score for hospitalised patients with COVID-19 in the RECOVER network

The CCEDRRN COVID-19 Mortality Score to predict death among nonpalliative patients with COVID-19 presenting to emergency departments: a derivation and validation study

Treatments, resource utilization, and outcomes of COVID-19 patients presenting to emergency departments across pandemic waves: an observational study by the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN)

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