Performance of Risk Scores in SARS-CoV-2 Infection: A Retrospective Study

Alessandro Geremia, Arturo Montineri, Alessandra Sorce, Anastasia Xourafa, Enrico Buccheri (), Antonino Catalano, Pietro Castellino, Agostino Gaudio and D.O.CoV Research
Additional contact information
Alessandro Geremia: Unit of Infectious Diseases, San Marco Hospital, 95121 Catania, Italy
Arturo Montineri: Unit of Infectious Diseases, San Marco Hospital, 95121 Catania, Italy
Alessandra Sorce: Department of Health Promotion, Mother and Child Care, Unit of Nephrology and Dialysis, Hypertension Excellence Centre, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90133 Palermo, Italy
Anastasia Xourafa: Unit of Thalassemia, University Policlinic “G. Rodolico”, 95123 Catania, Italy
Enrico Buccheri: Unit of Internal Medicine, University Policlinic “G. Rodolico”, 95123 Catania, Italy
Antonino Catalano: Department of Clinical and Experimental Medicine, University of Messina, 98124 Messina, Italy
Pietro Castellino: Unit of Internal Medicine, University Policlinic “G. Rodolico”, 95123 Catania, Italy
Agostino Gaudio: Unit of Internal Medicine, University Policlinic “G. Rodolico”, 95123 Catania, Italy
D.O.CoV Research: The list of other members of the D.O.CoV Research Group is shown in Acknowledgments.

IJERPH, 2025, vol. 22, issue 8, 1-14

Abstract: Prognostic scores that help allocate resources and time to the most critical patients could have potentially improved the response to the SARS-CoV-2 pandemic. We assessed the performance of five risk scores in predicting death or transfer to the intensive care unit (ICU) or sub-intensive care unit (SICU) in hospitalised patients with SARS-CoV-2 infection, with the three aims of retrospectively analysing the effectiveness of these tools, identifying frail patients at risk of death or complications due to infection, and applying these tools in the event of future pandemics. A retrospective observational study was conducted by evaluating data from patients hospitalised with SARS-CoV-2 infection. Among 134 patients considered, 119 were enrolled. All patients were adults, with a mean age of 64 years, and were hospitalised in the Infectious Diseases Division. We compared the five scores using receiver operating characteristic curves and calculation of the areas under the curve (AUCs) to determine their predictive performance. Four of the five scores demonstrated a high accuracy in predicting mortality among COVID-19-positive patients, with AUCs between 0.749 and 0.885. However, only two of the five scores showed good performance in predicting transfer to the ICU or SICU, with AUCs ranging from 0.740 to 0.802. The 4C Mortality Score and COVID-GRAM presented the highest performance for both outcomes. These two scores are easy to apply and low cost. They could still be used in clinical practice as predictive tools for frail and elderly patients with SARS-CoV-2 infection, as well as in the event of future pandemics.

Keywords: COVID-19; mortality risk; predictive tools; 4C Mortality Score; COVID-GRAM (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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