D. Freeman

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

A molecular signature, derived from integrated analysis of clinical data, the metabolome, and the proteome in prospective human studies, improved the prediction of death in patients with sepsis, potentially identifying a subset of patients who merit intensive treatment. Understanding Survival of the Fittest in Sepsis Differentiating mild infections from life-threatening ones is a complex decision that is made millions of times a year in U.S. emergency rooms. Should a patient be sent home with antibiotics and chicken soup? Or should he or she be hospitalized for intensive treatment? Sepsis—a serious infection that is associated with a generalized inflammatory response—is one of the leading causes of death. In two prospective clinical studies reported by Langley et al., patients arriving at four urban emergency departments with symptoms of sepsis were evaluated clinically and by analysis of their plasma proteome and metabolome. Survivors and nonsurvivors at 28 days were compared, and a molecular signature was detected that appeared to differentiate these outcomes—even as early as the time of hospital arrival. The signature was part of a large set of differences between these groups, showing that better energy-producing fatty acid catabolism was associated with survival of the fittest in sepsis. A test developed from the signature was able to predict sepsis survival and nonsurvival reproducibly and better than current methods. This test could help to make all important decisions in the emergency room more accurate. 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.

Host gene expression classifiers diagnose acute respiratory illness etiology.

Pathogen-specific host gene expression changes may combat inappropriate antibiotic use and emerging antibiotic resistance. Resisting antibiotics No matter the cause, acute respiratory infections can be miserable. Indeed, these infections are one of the most common reasons for seeking medical care. A clear diagnostic can help medical practitioners resist the patient-induced pressure to prescribe antibiotics as a catch-all therapy, which increases the risk of bacteria developing antibiotic resistance. Now, Tsalik et al. report clear differences in host gene expression induced by bacterial and viral infection as well as by noninfectious illness. These differences can be used to discriminate between these groups, and a host gene expression classifier may be a helpful diagnostic platform to curb unnecessary antibiotic use. 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.

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.

Thrombolysis in myocardial infarction risk score in an observation unit setting.

OBJECTIVE The Thrombolysis in Myocardial Infarction (TIMI) score is a validated tool for risk stratification of acute coronary syndrome. We hypothesized that the TIMI risk score would be able to risk stratify patients in observation unit for acute coronary syndrome. METHODS STUDY DESIGN Retrospective cohort study of consecutive adult patients placed in an urban academic hospital emergency department observation unit with an average annual census of 65,000 between 2004 and 2007. Exclusion criteria included elevated initial cardiac biomarkers, ST segment changes on ECG, unstable vital signs, or unstable arrhythmias. A composite of significant coronary artery disease (CAD) indicators, including diagnosis of myocardial infarction, percutaneous coronary intervention, coronary artery bypass surgery, or death within 30 days and 1 year, were abstracted via chart review and financial record query. The entire cohort was stratified by TIMI risk scores (0-7) and composite event rates with 95% confidence interval were calculated. RESULTS In total 2228 patients were analyzed. Average age was 54.5 years, 42.0% were male. The overall median TIMI risk score was 1. Eighty (3.6%) patients had 30-day and 119 (5.3%) had 1-year CAD indicators. There was a trend toward increasing rate of composite CAD indicators at 30 days and 1 year with increasing TIMI score, ranging from a 1.2% event rate at 30 days and 1.9% at 1 year for TIMI score of 0 and 12.5% at 30 days and 21.4% at 1 year for TIMI ≥ 4. CONCLUSIONS In an observation unit cohort, the TIMI risk score is able to risk stratify patients into low-, moderate-, and high-risk groups.

BNP-mediated vasodilatation for dialysis-dependent patient with acute heart failure syndrome in the emergency department

Acutely decompensated heart failure syndrome is a common emergency department presentation in patients with renal failure. B-type natriuretic peptide-mediated vasodilatation may provide a unique bridge in renal failure patients with acutely decompensated heart failure syndrome to treatment with dialysis. We evaluated the efficacy of B-type natriuretic peptide-mediated vasodilatation in acutely decompensated heart failure syndrome emergency department patients with hemodialysis dependent renal failure. This was a prospective, interventional trial. All patients received nesiritide infusion in addition to usual care. Outcome measures included hemodynamic parameters and dyspnea visual analog scale. Eight patients were enrolled, and all demonstrated significant improvement in their dyspnea visual analog scale (Δ 50.1 mm; p < .001 vs. pre-infusion) and APEX score (Δ 48.4%; p < .001 vs. pre-infusion). Three patients improved enough to be discharged from the emergency department for outpatient dialysis. In this hypothesis-generating initial trial, B-type natriuretic peptide-mediated vasodilatation with nesiritide improved symptoms in heart failure patients with hemodialysis-dependent renal failure and appears additive to standard treatment. Further trials are required to test this hypothesis.