COV-Score predicts mortality in SARS-CoV-2 infection

Abstract Purpose to present the importance of quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate the lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 include only qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Methods We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). Results We found associated with higher mortality a lower percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin, respectively a higher percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine. The most accurate regression model included as predictors: age, lymphocytes, PaO2/FiO2 ratio, and percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score).Conclusion COV-Score represents a viable alternative to current prediction scores, demonstrating a better sensitivity and specificity in predicting a poor outcome at the time of admission. Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall accuracy prediction.