Early prediction of COVID-19 patient survival by targeted plasma multi-omics and machine learning

Richard, V.R.; Gaither, C.; Popp, R.; Chaplygina, D.; Brzhozovskiy, A.; Kononikhin, A.; ... ; Borchers, C.H. (2022)

Article / Letter to editor

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reli-able tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and con-trols and, strikingly, a significant difference between sur-vivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a ma-chine learning multi-omic model that considers the con-centrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospi-talized COVID-19 patients.

• All authors: Richard, V.R.; Gaither, C.; Popp, R.; Chaplygina, D.; Brzhozovskiy, A.; Kononikhin, A.; Mohammed, Y.; Zahedi, R.P.; Nikolaev, E.N.; Borchers, C.H.
• Date: 2022-10-01
• Journal: Molecular & Cellular Proteomics
• Volume: 21
• Issue: 10
• DOI: doi:10.1016/j.mcpro.2022.100277