Abstract
BackgroundAlready at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis.MethodsWe used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead.ResultsCause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC = 0.872 [CI 95%: 0.835–0.910]).ConclusionsThis study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.
Highlights
IntroductionClinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality
Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality
This paper introduces an absolute cause-specific risk regression approach to perform risk assessment [19,20,21] for patients hospitalized with COVID-19
Summary
Clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. 20% of such confirmed cases were severe, among which most were admitted in the intensive care unit (ICU) and required early intubation and mechanical ventilation. Hospitals seek effective methods for managing such severe patients. Present guidelines on COVID-19 treatment identify ICU admission, ventilation, and mortality risk as typical outcomes in high-risk patients and consider these patients as potential candidates for medical treatment [3, 4]. Personal risk profiles can help physicians make the correct decision for optimal patient treatment and hospital capacity management
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