Host gene expression classifiers diagnose acute respiratory illness etiology.

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 noninfectious 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 noninfectious 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 noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 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, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.

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.

Challenges in enrollment of minority, pediatric, and geriatric patients in emergency and acute care clinical research.

STUDY OBJECTIVE: Emergency department (ED) -based clinical research has the potential to include patient populations that are typically underrepresented in clinical research. The objective of this study is to assess how emergency clinical care and research processes, informed consent, and patient demographic factors (age, sex, and ethnicity/race) affect enrollment and consent in clinical research in the ED. METHODS: This was an analysis of prospectively collected data of all patients (aged 2 to 101 years) eligible for one of 7 clinical research studies from February 2005 to April 2007 in an academic ED. We measured rates of enrollment and consent in the clinical studies. RESULTS: One thousand two hundred two of the 4418 patients screened for participation in 7 clinical studies were clinically eligible for enrollment. Of the 868 patients who were able to provide a voluntary decision regarding consent, 639 (73.6%) agreed to participate; an overall enrollment rate of 53.2%. The mean age of patients enrolled was 51.8 years (range 3 to 98 years). Black patients (49.2% enrollment) and Latino patients (18.4% enrollment) were less likely to be enrolled in comparison with white patients (58.3% enrollment) (adjusted odds ratio [OR] of enrollment for blacks=0.64; 95% confidence interval [CI] 0.50 to 0.82; adjusted OR of enrollment for Latinos=0.16; 95% CI 0.08 to 0.33). Enrollment rates were lower among pediatric (40.0%) and geriatric patients (49.1%) in comparison with adult patients ages 18 to 64 years (55.5%) (adjusted OR of enrollment for pediatric patients=0.70, 95% CI 0.34 to 1.43; adjusted OR of enrollment for geriatric patients=0.69, 95% CI 0.53 to 0.90). Unique issues contributing to underenrollment included challenges in consent among pediatric and elderly patients, language issues in Latino patients, reduced voluntary consent rates among black patients, and perhaps underuse of minimal risk waivers. CONCLUSION: In a large academic ED, minority, pediatric, and geriatric patients were less likely to be enrolled in acute care clinical research studies than middle-aged whites. Enrollment and consent strategies designed to enhance research participation in these important patient populations may be necessary to address disparities in the development and application of evidence-based emergency and acute care.