COVID-19: The Development and Validation of a New Mortality Risk Score

Area of Science:

• Infectious Diseases
• Epidemiology
• Clinical Medicine

Background:

• The COVID-19 pandemic overwhelmed healthcare systems globally, particularly in Italy.
• Hospital overcrowding led to suboptimal care, with many patients potentially treatable at home.
• A reliable scoring system was needed to predict mortality risk in COVID-19 patients upon admission.

Purpose of the Study:

• To develop and validate a novel scoring system for predicting in-hospital mortality in COVID-19 patients.
• To assist clinicians in making timely and informed decisions regarding patient management and resource allocation.
• To identify patients at high risk of death for enhanced therapeutic interventions.

Main Methods:

• Retrospective analysis of two Italian University Hospital databases.
• Development of a multivariable logistic regression model to identify predictors of in-hospital death.
• External validation of the developed scoring system.

Main Results:

• Seven variables (age, oxygen saturation, hemoglobin, white blood cell count, neutrophil percentage, platelets, creatinine) were associated with in-hospital death.
• The CZ-COVID-19 Score demonstrated strong predictive performance (AUC 0.924 in derivation, 0.808 in validation).
• The CZ-COVID-19 Score outperformed existing COVID-19 mortality prediction scores in sensitivity, specificity, and AUC.

Conclusions:

• The CZ-COVID-19 Score is a simple, validated tool for identifying COVID-19 patients at high risk of mortality.
• This score can guide therapeutic decisions, optimize hospital resource allocation, and prevent unnecessary hospitalizations.
• Implementation of the CZ-COVID-19 Score can improve patient care and alleviate healthcare system strain during pandemics.