A new collaboration to combat a novel virus
Through a collaboration with a number of leading healthcare institutions across the globe, we initiated a year-long project to explore the validity of this concept and to develop a predictive model based upon machine learning to test the concept. Armed with deidentified COVID-19 patient data from more than 14,500 COVID-19 patients from our research partners at Houston Methodist Hospital (TX, US), Emory University Healthcare (GA, US), and La Paz University Hospital (Spain), we conducted a retrospective analysis which included various clinical, demographic and laboratory data. Drawing from our expertise in molecular, hemostasis, hematology, chemistry, and immunoassay testing, we began to combine and review data from selected lab parameters and explored their potential interdependent relationships in developing a model capable of predicting the likely progression to severe disease and life-threatening multiorgan dysfunction in COVID-19 patients. Nine clinically significant lab parameters were identified and selected for inclusion in the algorithm. In addition to patient age: D-dimer, Lactate dehydrogenase (LDH), Lymphocyte %, Eosinophil %, Creatinine, C-reactive protein (CRP), Ferritin, PT-INR and Cardiac Troponin-I.