An integrated clinico-metabolomic model improves prediction of death in sepsis.

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 ß-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.

Multiplex proteomic approaches to sepsis research: case studies employing new technologies.

Sepsis is a multifactorial disease that provides unique challenges to the critical care physician. Diagnosis is hampered by the lack of a quantitative in vitro diagnostic test, instead, it relies on a series of clinical measures. The complex nature of the disease, with involvement of several physiologic systems, suggests a need to simultaneously monitor many clinical parameters. Novel proteomic technologies now exist that enable the multiplex measurement of multiple protein analytes from the same sample. Integration of these analytical measures with patient clinical data may provide the foundation for a better understanding of disease diagnosis, disease progression and the selection of optimal therapeutic regimen. The future challenge is the translation of these multiplex approaches from investigative research to clinical diagnostics for the greatest impact on patient treatment decisions.