Browsing by Author "Edmonds, Brian T."

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    An integrated clinico-metabolomic model improves prediction of death in sepsis
    (American Association for the Advancement of Science, 2013) Langley, Raymond J.; Tsalik, Ephraim L.; van Velkinburgh, Jennifer C.; Glickman, Seth W.; Rice, Brandon J.; Wang, Chunping; Chen, Bo; Carin, Lawrence; Suarez, Arturo; Mohney, Robert P.; Freeman, Debra H.; Wang, Mu; You, Jinsam; Wulff, Jacob; Thompson, J. Will; Moseley, M. Arthur; Reisinger, Stephanie; Edmonds, Brian T.; Grinnell, Brian; Nelson, David R.; Dinwiddie, Darrell L.; Miller, Neil A.; Saunders, Carol J.; Soden, Sarah S.; Rogers, Angela J.; Gazourian, Lee; Fredenburgh, Laura E.; Massaro, Anthony F.; Baron, Rebecca M.; Choi, Augustine M. K.; Corey, G. Ralph; Ginsburg, Geoffrey S.; Cairns, Charles B.; Otero, Ronny M.; Fowler, Vance G., Jr.; Rivers, Emanuel P.; Woods, Christopher W.; Kingsmore, Stephen F.; Medicine, School of Medicine
    Sepsis is a common cause of death, but outcomes in individual patients are difficult to predict. Elucidating the molecular processes that differ between sepsis patients who survive and those who die may permit more appropriate treatments to be deployed. We examined the clinical features and the plasma metabolome and proteome of patients with and without community-acquired sepsis, upon their arrival at hospital emergency departments and 24 hours later. The metabolomes and proteomes of patients at hospital admittance who would ultimately die differed markedly from those of patients who would survive. The different profiles of proteins and metabolites clustered into the following groups: fatty acid transport and β-oxidation, gluconeogenesis, and the citric acid cycle. They differed consistently among several sets of patients, and diverged more as death approached. In contrast, the metabolomes and proteomes of surviving patients with mild sepsis did not differ from survivors with severe sepsis or septic shock. An algorithm derived from clinical features together with measurements of five metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.