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ObjectiveWe previously showed that angiotensin type-1 receptor and ACE2 autoantibodies (AT1-AA, ACE2-AA) are associated with COVID-19 severity. Our aim is to find correlations of these autoantibodies with routine biochemical parameters that allow an initial classification of patients.MethodsIn an initial cohort of 119 COVID-19 patients, serum AT1-AA and ACE2-AA concentrations were obtained within 24 h after diagnosis. In 50 patients with a complete set of routine biochemical parameters, clinical data and disease outcome information, a Random Forest algorithm was used to select prognostic indicators, and the Spearman coefficient was used to analyze correlations with AT1-AA, ACE2-AA.ResultsHemoglobin, lactate dehydrogenase and procalcitonin were selected. A decrease in one unit of hemoglobin, an increase in 0.25 units of procalcitonin, or an increase in 100 units of lactate dehydrogenase increased the severity of the disease by 35.27, 69.25, and 3.2%, respectively. Our binary logistic regression model had a predictive capability to differentiate between mild and moderate/severe disease of 84%, and between mild/moderate and severe disease of 76%. Furthermore, the selected parameters showed strong correlations with AT1-AA or ACE2-AA, particularly in men.ConclusionHemoglobin, lactate dehydrogenase and procalcitonin can be used for initial classification of COVID-19 patients in the admission day. Subsequent determination of more complex or late arrival biomarkers may provide further data on severity, mechanisms, and therapeutic options. |
