Aloisio E, et al. Arch Pathol Lab Med 2020.
Context: A relevant portion of COVID-19 patients develop severe disease with negative outcomes. Several biomarkers have been proposed to predict COVID-19 severity, but no definite interpretative criteria have been established to date for stratifying risk. Objective: To evaluate six serum biomarkers (C-reactive protein, lactate dehydrogenase, D-dimer, albumin, ferritin and cardiac troponin T) for predicting COVID-19 severity and to define related cut-offs able to aid clinicians in risk stratification of hospitalized patients. Design: A retrospective study of 427 COVID-19 patients was performed. Patients were divided into groups based on their clinical outcome: non-survivors vs. survivors and patients admitted to intensive care unit vs. others. ROC curves and likelihood ratios were employed to define predictive cut-offs for evaluated markers. Results: Marker concentrations at peak were significantly different between groups for both selected outcomes. At univariate logistic regression analysis, all parameters were significantly associated with higher odds of death and intensive care. At the multivariate analysis, high concentrations of lactate dehydrogenase and low concentrations of albumin in serum remained significantly associated with higher odds of death, while only low lactate dehydrogenase activities remained associated with lower odds of intensive care admission. The best cut-offs for death prediction were >731 U/L for lactate dehydrogenase and ≤18 g/L for albumin, while a lactate dehydrogenase activity <425 U/L was associated with a negative likelihood ratio of 0.10 for intensive treatment. Conclusions: Our study identifies which biochemistry tests represent major predictors of COVID-19 severity and defines the best cut-offs for their use.