{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["21(10)"],"submitter":["Richard VR"],"pubmed_abstract":["The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable 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 controls and, strikingly, a significant difference between survivors 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 machine learning multi-omic model that considers the concentrations 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 hospitalized COVID-19 patients."],"journal":["Molecular & cellular proteomics : MCP"],"pagination":["100277"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9345792"],"repository":["biostudies-literature"],"pubmed_title":["Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning."],"pmcid":["PMC9345792"],"pubmed_authors":["Chaplygina D","Borchers CH","Kononikhin A","Nikolaev EN","Gaither C","Popp R","Zahedi RP","Brzhozovskiy A","Mohammed Y","Richard VR"],"additional_accession":[]},"is_claimable":false,"name":"Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning.","description":"The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable 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 controls and, strikingly, a significant difference between survivors 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 machine learning multi-omic model that considers the concentrations 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 hospitalized COVID-19 patients.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2026-05-31T15:50:04.684Z","creation":"2024-11-14T03:41:59.434Z"},"accession":"S-EPMC9345792","cross_references":{"pubmed":["35931319"],"doi":["10.1016/j.mcpro.2022.100277"]}}