External Validation of the 4C Mortality Score among COVID-19 Patients Visiting the Emergency Department or admitted to Hospital in Ontario, Canada

Abstract

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.

Prospective validation of the 4C prognostic models for adults hospitalised with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol

“…This may represent clinical differences across each region, including patient population and the medical management of hospitalised patients with COVID-19. Nevertheless, external validation in Brazil, 6 Canada, 7 France, 8 Netherlands 9 and Pakistan 10 has demonstrated consistent performance for the 4C Mortality Score.…”

Comparison of different prognostic scores in estimating short- and long-term mortality in COVID-19 patients above 60 years old in a university hospital in Belgium

“…But for the 1-year mortality, CCI had the highest AUROC curve (Table 2 and Figure 1d). These results were also reflected in the way in which these scores are normally used: 4CMS is used to estimate intrahospital mortality [10], and CCI is used to estimate mortality after 10 years [8]. CCI had also already shown a good predictivity in mortality at 1 year in elderly patients experiencing a first acute heart failure hospitalization [29], as well as at 1 year after having had a hip fracture [30].…”

“…It focuses on the number of comorbidities. Once two comorbidities were included, you get the maximum score [10]. The CCI takes into account more specific consideration of the impact on the gravity of each comorbidity on the total score [8].…”

“…This score considered comorbidities as well as physiological parameters and laboratory values at admission. Literature confirms its good predictive value for intrahospital mortality in critically ill COVID-19 patients [10]. Since then, many more specific mortality risk assessment scales have been developed for the COVID-19 infection.…”

Severity of Illness Scores and Biomarkers for Prognosis of Patients with Coronavirus Disease 2019

“…32 33 Comparable results have been found in studies that performed external validation in Brazil, Canada, France, the Netherlands, and Pakistan. 34 35 36 While this model has a high potential for its generalizability to patients around the world, most of the studies that have externally validated this model have only included patients admitted to the hospital, suggesting that this model is best used for predicting the mortality of hospitalized patients. Recently, a small study in Saudi Arabia performed a retrospective analysis of 506 patients diagnosed with COVID-19, which included hospitalized and outpatients, and found that the 4C Mortality score correctly estimated 95.3% of the deaths.…”