Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases (preprint)

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimers Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

Development and Validation of a Simplified Risk Score for the Prediction of Critical COVID-19 Illness in Newly Diagnosed Patients (preprint)

Scores for identifying patients at high risk of progression of the coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), are discussed as key instruments for clinical decision-making and patient management during the current pandemic. Here we used the patient data from the multicenter Lean European Open Survey on SARS-CoV-2 - Infected Patients (LEOSS) and applied a technique of variable selection in order to develop a simplified score to identify patients at increased risk of critical illness or death. A total of 1,946 patients, who were tested positive for SARS-CoV-2 were included in the initial analysis. They were split into a derivation and a validation cohort (n=1,297 and 649, respectively). A stability selection among a total of 105 baseline predictors for the combined endpoint of progression to critical phase or COVID-19-related death allowed us to develop a simplified score consisting of five predictors: CRP, Age, clinical disease phase (uncomplicated vs. complicated), serum urea and D-dimer (abbreviated as CAPS-D score). This score showed an AUC of 0.81 (CI95%: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (CI95%: 0.77-0.85) during the full follow-up. Finally, we used an additional prospective cohort of 682 patients, who were diagnosed largely after the “first wave” of the pandemic to validate predictive accuracy of the score, observing similar results (AUC for an event within 7 days: 0.83, CI95%, 0.78-0.87; for full follow-up: 0.82, CI95%, 0.78-0.86). We thus successfully establish and validate an easily applicable score to calculate the risk of disease progression of COVID-19 to critical illness or death.