Metabolomics is a relatively new technology that involves the measurement of an organism’s global metabolic response to some physiologic stress. The study of this technology is gaining momentum, as measurement of a person’s metabolic profile in easily accessible biological fluids can help distinguish disease states from non-disease states earlier. Thus, patients could receive appropriate therapies earlier, which can improve outcomes in cases such as pneumonia and sepsis. In the May 2013 issue of Respiratory and Critical Care Medicine, the authors used 1H proton nuclear magnetic resonance spectroscopy to measure the concentrations of 58 different metabolites in serum samples taken from 140 pediatric subjects from 11 different institutions.

The subjects were divided into healthy controls, children with septic shock, and children with systemic inflammatory response syndrome (SIRS) who were admitted to a pediatric intensive care unit. They were also further divided into age groups (neonates, infants, toddlers, and school age children) to determine if the metabolic profiles changed with age. Once the samples were obtained and metabolites measured by spectroscopy, multivariate statistical analyses were applied to separate metabolic variation in the subjects to detect patterns in metabolic profiles. For example, principal component analysis (PCA) was used to reduce the number of variables by removing redundancies. Other statistical methods that were then used included partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA).

By first applying PCA and then PLS-DA methods to the data, the authors were able to delineate successfully the metabolic scores of healthy control subjects from SIRS and septic groups on a 3-D score scatter plot. Although there was some overlap between the SIRS and septic groups, overall, the studied groups were well clustered. Using this statistical method, no differences in metabolic patterns developed because of age differences. Additionally, using the OPLS-DA model, distinct metabolic profiles could also be used to delineate the SIRS group versus the control group and the septic group versus the control group. Again, however, the comparison between the septic group and the SIRS group was less defined. Of note, the use of the OPLS-DA model demonstrated differences in metabolic profiles between the school-age children compared to the younger patient groups.

Another important aspect of this work involves the ability of metabolomics to prognosticate. The authors discovered that by measuring these metabolites and applying these statistical methods, they could more accurately predict mortality rates compared to the conventionally used physiologically-based Pediatric Risk of Mortality III-Acute Physiology Score model or procalcitonin levels.

This is the first study to examine the use of metabolic profiling to evaluate pediatric patients with septic shock, and it is an important first step that promises a new way to diagnose pediatric patients with septic shock and aid with prognosis. Further study with a larger cohort of patients is needed, as it may lead to even better delineation of metabolic profiles and diagnoses. The hope is that one day septic pediatric patients may be treated earlier and have improved outcomes.

This Concise Critical Appraisal is authored by SCCM member Daniel E. Sloniewsky, MD. Each installment highlights journal articles most relevant to the critical care practitioner.

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