Early and accurate prediction of COVID-19 hospitalization risk and symptomatic course of SARS-CoV-2 infection

BackgroundWhilst SARS-CoV-2 infection has become endemic, COVID-19 related hospitalization and mortality are still considerably high. Both anti-viral and immune modulating therapies against COVID-19 are available, but they must be initiated early after infection and given only to patients of need. Currently, patients demographics and clinical pre-conditions factors are used to determine treatment eligibility. However, the latter do not provide accurate prediction and there are no useful biomarkers for early accurate prediction of COVID-19 related hospitalization risk and disease progression. MethodsNon-vaccinated patients (N=185) were recruited early after the first positive SARS-CoV-2 test. Biochemistry, hematology and 8 serum cytokine levels were longitudinally measured within the first month. FindingsEarly levels of LDH, IL-6 or CRP, each alone or their combinations, were identified as accurate predictors for the risk of hospitalization (sensitivity=93.6-100%, specificity=93.4-96.7%, p<0.0001). Moreover, the combination of 4 cytokines (IFN-, IFN-{gamma}, IL-6, IL-17A) was the only accurate predictor for symptoms risk (sensitivity=97.5%, specificity=92.3%, p<0.0001). In comparison, age and BMI showed significantly lower predictive values than above biomarkers. Prediction with above biomarkers was independent of sampling time (0-11 days post symptoms onset), age, gender, BMI, clinical pre-conditions or SARS-CoV-2 variant. Furthermore, the early higher levels of LDH, CRP and inflammatory cytokines in hospitalized, as compared to non-hospitalized, patients, stayed consistently higher for at least 4 weeks. InterpretationThe risk for COVID-19 hospitalization or symptoms can be accurately predicted as early as the time of the first positive SARS-CoV-2 test, with biomarkers that are feasibly measurable at point-of-testing. These findings could allow for better early personalized treatment and optimization of clinical management of COVID-19 patients.