Georgia Charkoftaki, Reza Aalizadeh, Alvaro Santos-Neto, Wan Ying Tan, Emily A Davidson, Varvara Nikolopoulou, Yewei Wang, Brian Thompson, Tristan Furnary, Ying Chen, Elsio A Wunder, Andreas Coppi, Wade Schulz, Akiko Iwasaki, Richard W Pierce, Charles S Dela Cruz, Gary V Desir, Naftali Kaminski, Shelli Farhadian, Kirill Veselkov, Rupak Datta, Melissa Campbell, Nikolaos S Thomaidis, Albert I Ko, David C Thompson, Vasilis Vasiliou
Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0...
August 29, 2023: Human Genomics