No abstract

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 die differed markedly from those who would survive. The different profiles of proteins and metabolites clustered into 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 seven metabolites predicted patient survival. This algorithm may help to guide the treatment of individual patients with sepsis.

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational, cohort study determined whether host gene expression patterns discriminate non-infectious from infectious illness, and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or non-infectious illness as well as 44 healthy controls was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a non-infectious cause of illness (26 probes). Overall accuracy was 87% (238/273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, p<0.03) and three published classifiers of bacterial vs. viral infection (78-83%). The classifiers developed here externally validated in five publicly available datasets (AUC 0.90-0.99). A sixth publically available dataset included twenty-five patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI; viral ARI; co-infection; and neither a bacterial nor viral response. These findings create an opportunity to develop and utilize host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.