An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.

Autor: Charkoftaki G; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA., Aalizadeh R; Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, Zografou, 15771, Greece., Santos-Neto A; São Carlos Institute of Chemistry, University of São Paulo, São Carlos, SP, 13566-590, Brazil., Tan WY; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.; Internal Medicine Residency Program, Department of Internal Medicine, Norwalk Hospital, Norwalk, CT, USA., Davidson EA; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.; Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, USA., Nikolopoulou V; Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, Zografou, 15771, Greece., Wang Y; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA., Thompson B; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA., Furnary T; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.; Harvard Medical School, Harvard University, Boston, MA, USA., Chen Y; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA., Wunder EA; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, USA.; Institute Gonçalo Moniz, Fundação Oswaldo Cruz, Brazilian Ministry of Health, Salvador, Brazil., Coppi A; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA., Schulz W; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.; Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA., Iwasaki A; Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.; Howard Hughes Medical Institute, MD, Chevy Chase, USA., Pierce RW; Department of Pediatrics , Yale School of Medicine, New Haven, CT, USA., Cruz CSD; Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA., Desir GV; Department of Internal Medicine, Section of Nephrology, Yale University School of Medicine, New Haven, CT, USA., Kaminski N; Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA., Farhadian S; Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA.; Department of Neurology, Yale School of Medicine, New Haven, CT, USA.; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, USA., Veselkov K; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.; Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK., Datta R; Veterans Affairs Connecticut Healthcare System, CT, West Haven, USA.; Department of Internal Medicine, Yale School of Medicine, CT, New Haven, USA., Campbell M; Department of Pediatrics, Division of Pediatric Infectious Diseases, School of Medicine, Duke University, NC, Durham, USA., Thomaidis NS; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA.; Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, Zografou, 15771, Greece., Ko AI; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, USA.; Institute Gonçalo Moniz, Fundação Oswaldo Cruz, Brazilian Ministry of Health, Salvador, Brazil.; Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, CT, USA., Thompson DC; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA., Vasiliou V; Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT, USA. Vasilis.vasiliou@yale.edu.
Jazyk: angličtina
Zdroj: Human genomics [Hum Genomics] 2023 Aug 29; Vol. 17 (1), pp. 80. Date of Electronic Publication: 2023 Aug 29.
DOI: 10.1186/s40246-023-00521-4
Abstrakt: 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 = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R 2  = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.
(© 2023. BioMed Central Ltd., part of Springer Nature.)
Databáze: MEDLINE
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