Plasticity of the systemic inflammatory response to acute infection during critical illness: development of the riboleukogram

Prognostic and predictive enrichment in sepsis

Sepsis is a heterogeneous disease state that is both common and consequential in critically ill patients. Unfortunately, the heterogeneity of sepsis at the individual patient level has hindered advances in the field beyond the current therapeutic standards, which consist of supportive care and antibiotics. This complexity has prompted attempts to develop a precision medicine approach, with research aimed towards stratifying patients into more homogeneous cohorts with shared biological features, potentially facilitating the identification of new therapies. Several investigators have successfully utilized leukocyte-derived mRNA and discovery-based approaches to subgroup patients on the basis of biological similarities defined by transcriptomic signatures. A critical next step is to develop a consensus sepsis subclassification system, which includes transcriptomic signatures as well as other biological and clinical data. This goal will require collaboration among various investigative groups, and validation in both existing data sets and prospective studies. Such studies are required to bring precision medicine to the bedside of critically ill patients with sepsis.

Investigating PIK3R3 and ATp2A1 Genes Expressions in Ventilator-Associated Pneumonia Patients Admitted to the Intensive Care Unit of Masih Daneshvari Hospital in 2016

BACKGROUND

Infectious diseases such as ventilator- associated pneumonia (VAP) are one of the serious problems in intensive care units (ICU) of hospitals. To date, there has been no appropriate clinical and diagnostic marker for early detection of this disease. In this study, expression of PIK₃R₃ and ATp₂A₁ genes in patients with VAP were assessed to be used as biomarkers to identify and confirm the disease.

METHODS

This study was conducted by using peripheral blood samples of 60 individuals, including 30 patients with VAP and 30 healthy volunteers. First, the peripheral blood samples were taken and then RNA was extracted and converted into cDNA. Finally, the assessment of genes was performed by Real-time PCR.

RESULTS

In peripheral blood samples, 46.6% and 30% were positive for PIK₃R₃ expression in patients and healthy groups, respectively. The ATp₂A₁ expression in patients and healthy controls were found 40% and 23.3%, respectively. Comparing the ΔCT obtained for the PIK₃R₃ and ATp₂A₁ genes showed statistically significant differences between the two groups of patients and healthy subjects (p=0.042, p=0.036).

CONCLUSION

ATp₂A₁ and PIK₃R₃ may be used as biomarkers for early detection of VAP disease. However, further studies are required.

Mining significant high utility gene regulation sequential patterns

BACKGROUND

Mining frequent gene regulation sequential patterns in time course microarray datasets is an important mining task in bioinformatics. Although finding such patterns are of paramount important for studying a disease, most existing work do not consider gene-disease association during gene regulation sequential pattern discovery. Moreover, they consider more absent/existence effects of genes during the mining process than taking the degrees of genes expression into account. Consequently, such techniques discover too many patterns which may not represent important information to biologists to investigate the relationships between the disease and underlying reasons hidden in gene regulation sequences.

RESULTS

We propose a utility model by considering both the gene-disease association score and their degrees of expression levels under a biological investigation. We propose an efficient method called Top-HUGS, for discoverying significant high utility gene regulation sequential patterns from a time-course microarray dataset.

CONCLUSIONS

In this study, the proposed methods were evaluated on a publicly available time course microarray dataset. The experimental results show higher accuracies compared to the baseline methods. Our proposed methods found that several new gene regulation sequential patterns involved in such patterns were useful for biologists and provided further insights into the mechanisms underpinning biological processes. To effectively work with the proposed method, a web interface is developed to our system using Java. To the best of our knowledge, this is the first demonstration for significant high utility gene regulation sequential pattern discovery.

Critical cardiac care in children: looking backward and looking forward

The growth of Pediatric Cardiovascular Intensive Care as a subspecialty has been incredible. Outcomes have improved, care delivery has matured, and research has made advances. Within this review, we take the opportunity to examine the subspecialty's past accomplishments with pride, take stock in its current state, and look forward with excitement to its future. While outcomes in general have improved dramatically, we must always be aware of the outcomes that matter to families and patients. Additionally, we must constantly ask ourselves to improve. Research into neuroprotection and individual therapeutic strategies based in genomic medicine provide the next opportunity for the subspecialty to improve.

Risk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays?

Sepsis mortality rates have decreased in recent years but remain unacceptably high. Risk stratification and prognostication is of particular importance because high-risk patients may benefit from earlier clinical interventions, whereas low-risk patients may benefit from not undergoing unnecessary procedures. Prognostication is currently done mostly via clinical criteria and blood lactate levels. This article summarizes the literature on the complexity of changes at the molecular level for the casual reader.

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

What was old is new again: using the host response to diagnose infectious disease

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, focusing on both pathogen discovery and the host response. Here, we review how 'omics' technologies improve sepsis diagnosis, early pathogen identification and personalize therapy. Such host response diagnostics are possible due to the confluence of advanced laboratory techniques (e.g., transcriptomics, metabolomics, proteomics) along with advanced mathematical modeling such as machine learning techniques. The road ahead is promising, but obstacles remain before the impact of such advanced diagnostic modalities is felt at the bedside.

A Chemical Engineer's Perspective on Health and Disease

Chemical process systems engineering considers complex supply chains which are coupled networks of dynamically interacting systems. The quest to optimize the supply chain while meeting robustness and flexibility constraints in the face of ever changing environments necessitated the development of theoretical and computational tools for the analysis, synthesis and design of such complex engineered architectures. However, it was realized early on that optimality is a complex characteristic required to achieve proper balance between multiple, often competing, objectives. As we begin to unravel life's intricate complexities, we realize that that living systems share similar structural and dynamic characteristics; hence much can be learned about biological complexity from engineered systems. In this article, we draw analogies between concepts in process systems engineering and conceptual models of health and disease; establish connections between these concepts and physiologic modeling; and describe how these mirror onto the physiological counterparts of engineered systems.

Differential network analyses of Alzheimer's disease identify early events in Alzheimer's disease pathology

In late-onset Alzheimer's disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with low topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early events occurring in AD pathology. Cellular defense mechanisms try to intervene but fail, finally resulting in AD pathology as the disease progresses. Furthermore, disease annotation of the low TO genes in two independent protein interaction networks has resulted in association between cancer, diabetes, renal diseases, and cardiovascular diseases.

Systemic inflammatory response to smoking in chronic obstructive pulmonary disease: evidence of a gender effect

BACKGROUND

Tobacco smoking is the main risk factor of chronic obstructive pulmonary disease (COPD) but not all smokers develop the disease. An abnormal pulmonary and systemic inflammatory response to smoking is thought to play a major pathogenic role in COPD, but this has never been tested directly.

METHODS

We studied the systemic biomarker and leukocyte transcriptomic response (Affymetrix microarrays) to smoking exposure in 10 smokers with COPD and 10 smokers with normal spirometry. We also studied 10 healthy never smokers (not exposed to smoking) as controls. Because some aspects of COPD may differ in males and females, and the inflammatory response to other stressors (infection) might be different in man and women, we stratified participant recruitment by sex. Differentially expressed genes were validated by q-PCR. Ontology enrichment was evaluated and interaction networks inferred.

RESULTS

Principal component analysis identified sex differences in the leukocyte transcriptomic response to acute smoking. In both genders, we identified genes that were differentially expressed in response to smoking exclusively in COPD patients (COPD related signature) or smokers with normal spirometry (Smoking related signature), their ontologies and interaction networks.

CONCLUSIONS

The use of an experimental intervention (smoking exposure) to investigate the transcriptomic response of peripheral leukocytes in COPD is a step beyond the standard case-control transcriptomic profiling carried out so far, and has facilitated the identification of novel COPD and Smoking expression related signatures which differ in males and females.

Identifying key regulatory genes in the whole blood of septic patients to monitor underlying immune dysfunctions

There is currently no reliable tool available to measure immune dysfunction in septic patients in the clinical setting. This proof-of-concept study assesses the potential of gene expression profiling of whole blood as a tool to monitor immune dysfunction in critically ill septic patients. Whole-blood samples were collected daily for up to 5 days from patients admitted to the intensive care unit with sepsis. RNA isolated from whole-blood samples was assayed on Illumina HT-12 gene expression microarrays consisting of 48,804 probes. Microarray analysis identified 3,677 genes as differentially expressed across 5 days between septic patients and healthy controls. Of the 3,677 genes, biological pathway analysis identified 86 genes significantly downregulated in the sepsis patients were present in pathways relating to immune response. These 86 genes correspond to known immune pathways implicated in sepsis, including lymphocyte depletion, reduced T-lymphocyte activation, and deficient antigen presentation. Furthermore, expression levels of these genes correlated with clinical severity, with a significantly greater degree of downregulation found in nonsurvivors compared with survivors. The results show that whole-blood gene expression analysis can capture systemic immune dysfunctions in septic patients. Our study provides an experimental basis to support further study on the use of a gene expression-based assay, to assess immunosuppression, and to guide immunotherapy in future clinical trials.

From data patterns to mechanistic models in acute critical illness

The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.

Gene expression profiling in sepsis: timing, tissue, and translational considerations

Sepsis is a complex inflammatory response to infection. Microarray-based gene expression studies of sepsis have illuminated the complex pathogen recognition and inflammatory signaling pathways that characterize sepsis. More recently, gene expression profiling has been used to identify diagnostic and prognostic gene signatures, as well as novel therapeutic targets. Studies in pediatric cohorts suggest that transcriptionally distinct subclasses might account for some of the heterogeneity seen in sepsis. Time series analyses have pointed to rapid and dynamic shifts in transcription patterns associated with various phases of sepsis. These findings highlight current challenges in sepsis knowledge translation, including the need to adapt complex and time-consuming whole-genome methods for use in the intensive care unit environment, where rapid diagnosis and treatment are essential.

An efficient method for mining cross-timepoint gene regulation sequential patterns from time course gene expression datasets

BACKGROUND

Observation of gene expression changes implying gene regulations using a repetitive experiment in time course has become more and more important. However, there is no effective method which can handle such kind of data. For instance, in a clinical/biological progression like inflammatory response or cancer formation, a great number of differentially expressed genes at different time points could be identified through a large-scale microarray approach. For each repetitive experiment with different samples, converting the microarray datasets into transactional databases with significant singleton genes at each time point would allow sequential patterns implying gene regulations to be identified. Although traditional sequential pattern mining methods have been successfully proposed and widely used in different interesting topics, like mining customer purchasing sequences from a transactional database, to our knowledge, the methods are not suitable for such biological dataset because every transaction in the converted database may contain too many items/genes.

RESULTS

In this paper, we propose a new algorithm called CTGR-Span (Cross-Timepoint Gene Regulation Sequential pattern) to efficiently mine CTGR-SPs (Cross-Timepoint Gene Regulation Sequential Patterns) even on larger datasets where traditional algorithms are infeasible. The CTGR-Span includes several biologically designed parameters based on the characteristics of gene regulation. We perform an optimal parameter tuning process using a GO enrichment analysis to yield CTGR-SPs more meaningful biologically. The proposed method was evaluated with two publicly available human time course microarray datasets and it was shown that it outperformed the traditional methods in terms of execution efficiency. After evaluating with previous literature, the resulting patterns also strongly correlated with the experimental backgrounds of the datasets used in this study.

CONCLUSIONS

We propose an efficient CTGR-Span to mine several biologically meaningful CTGR-SPs. We postulate that the biologist can benefit from our new algorithm since the patterns implying gene regulations could provide further insights into the mechanisms of novel gene regulations during a biological or clinical progression. The Java source code, program tutorial and other related materials used in this program are available at http://websystem.csie.ncku.edu.tw/CTGR-Span.rar.

Effects of coupled dose and rhythm manipulation of plasma cortisol levels on leukocyte transcriptional response to endotoxin challenge in humans

Severe traumas are associated with hypercortisolemia due to both disruption of cortisol secretion rhythm and increase in its total concentration. Understanding the effects of altered cortisol levels and rhythms on immune function is of great clinical interest, to prevent conditions such as sepsis from complicating the recovery. This in vivo study assesses the responses of circulating leukocytes to coupled dose and rhythm manipulation of cortisol, preceding an immune challenge induced by endotoxin administration. Through continuous infusion, plasma cortisol concentration was increased to and kept constant at a level associated with major physiologic stress. In response, transcriptional programming of leukocytes was altered to display a priming response before endotoxin exposure. Enhanced expression of a number of receptors and signaling proteins, as well as lowered protein translation and mitochondrial function indicated a sensitization against potential infectious threats. Despite these changes, response to endotoxin followed very similar patterns in both cortisol and saline pre-treated groups except one cluster including probe sets associated with major players regulating inflammatory response. In sum, altered dose and rhythm of plasma cortisol levels engendered priming of circulating leukocytes when preceded an immune challenge. This transcriptional program change associated with stimulated surveillance function and suppressed energy-intensive processes, emphasized permissive actions of cortisol on immune function.

Sepsis: multiple abnormalities, heterogeneous responses, and evolving understanding

Sepsis represents the host's systemic inflammatory response to a severe infection. It causes substantial human morbidity resulting in hundreds of thousands of deaths each year. Despite decades of intense research, the basic mechanisms still remain elusive. In either experimental animal models of sepsis or human patients, there are substantial physiological changes, many of which may result in subsequent organ injury. Variations in age, gender, and medical comorbidities including diabetes and renal failure create additional complexity that influence the outcomes in septic patients. Specific system-based alterations, such as the coagulopathy observed in sepsis, offer both potential insight and possible therapeutic targets. Intracellular stress induces changes in the endoplasmic reticulum yielding misfolded proteins that contribute to the underlying pathophysiological changes. With these multiple changes it is difficult to precisely classify an individual's response in sepsis as proinflammatory or immunosuppressed. This heterogeneity also may explain why most therapeutic interventions have not improved survival. Given the complexity of sepsis, biomarkers and mathematical models offer potential guidance once they have been carefully validated. This review discusses each of these important factors to provide a framework for understanding the complex and current challenges of managing the septic patient. Clinical trial failures and the therapeutic interventions that have proven successful are also discussed.

In silico modeling: methods and applications to trauma and sepsis

OBJECTIVES

To familiarize clinicians with advances in computational disease modeling applied to trauma and sepsis.

DATA SOURCES

PubMed search and review of relevant medical literature.

SUMMARY

Definitions, key methods, and applications of computational modeling to trauma and sepsis are reviewed.

CONCLUSIONS

Computational modeling of inflammation and organ dysfunction at the cellular, organ, whole-organism, and population levels has suggested a positive feedback cycle of inflammation → damage → inflammation that manifests via organ-specific inflammatory switching networks. This structure may manifest as multicompartment "tipping points" that drive multiple organ dysfunction. This process may be amenable to rational inflammation reprogramming.

Empirical Antibiotic Therapy for Ventilator-Associated Pneumonia

Ventilator-associated pneumonia (VAP) is the most common infectious complication in the intensive care unit. It can increase duration of mechanical ventilation, length of stay, costs, and mortality. Improvements in the administration of empirical antibiotic therapy have potential to reduce the complications of VAP. This review will discuss the current data addressing empirical antibiotic therapy and the effect on mortality in patients with VAP. It will also address factors that could improve the administration of empirical antibiotics and directions for future research.

New approaches to sepsis: molecular diagnostics and biomarkers

Sepsis is among the most common causes of death in hospitals. It arises from the host response to infection. Currently, diagnosis relies on nonspecific physiological criteria and culture-based pathogen detection. This results in diagnostic uncertainty, therapeutic delays, the mis- and overuse of antibiotics, and the failure to identify patients who might benefit from immunomodulatory therapies. There is a need for new sepsis biomarkers that can aid in therapeutic decision making and add information about screening, diagnosis, risk stratification, and monitoring of the response to therapy. The host response involves hundreds of mediators and single molecules, many of which have been proposed as biomarkers. It is, however, unlikely that one single biomarker is able to satisfy all the needs and expectations for sepsis research and management. Among biomarkers that are measurable by assays approved for clinical use, procalcitonin (PCT) has shown some usefulness as an infection marker and for antibiotic stewardship. Other possible new approaches consist of molecular strategies to improve pathogen detection and molecular diagnostics and prognostics based on transcriptomic, proteomic, or metabolic profiling. Novel approaches to sepsis promise to transform sepsis from a physiologic syndrome into a group of distinct biochemical disorders and help in the development of better diagnostic tools and effective adjunctive sepsis therapies.

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Developing a gene expression model for predicting ventilator-associated pneumonia in trauma patients: a pilot study

Background

Ventilator-associated pneumonia (VAP) carries significant mortality and morbidity. Predicting which patients will become infected could lead to measures to reduce the incidence of VAP.

Methodology/Principal Findings

The goal was to begin constructing a model for VAP prediction in critically-injured trauma patients, and to identify differentially expressed genes in patients who go on to develop VAP compared to similar patients who do not. Gene expression profiles of lipopolysaccharide stimulated blood cells in critically injured trauma patients that went on to develop ventilator-associated pneumonia (n = 10) was compared to those that never developed the infection (n = 10). Eight hundred and ten genes were differentially expressed between the two groups (ANOVA, P<0.05) and further analyzed by hierarchical clustering and principal component analysis. Functional analysis using Gene Ontology and KEGG classifications revealed enrichment in multiple categories including regulation of protein translation, regulation of protease activity, and response to bacterial infection. A logistic regression model was developed that accurately predicted critically-injured trauma patients that went on to develop VAP (VAP+) and those that did not (VAP−). Five genes (PIK3R3, ATP2A1, PI3, ADAM8, and HCN4) were common to all top 20 significant genes that were identified from all independent training sets in the cross validation. Hierarchical clustering using these five genes accurately categorized 95% of patients and PCA visualization demonstrated two discernable groups (VAP+ and VAP−).

Conclusions/Significance

A logistic regression model using cross-validation accurately predicted patients that developed ventilator-associated pneumonia and should now be tested on a larger cohort of trauma patients.

Comparison of low and high dose ionising radiation using topological analysis of gene coexpression networks

BACKGROUND

The growing use of imaging procedures in medicine has raised concerns about exposure to low-dose ionising radiation (LDIR). While the disastrous effects of high dose ionising radiation (HDIR) is well documented, the detrimental effects of LDIR is not well understood and has been a topic of much debate. Since little is known about the effects of LDIR, various kinds of wet-lab and computational analyses are required to advance knowledge in this domain. In this paper we carry out an "upside-down pyramid" form of systems biology analysis of microarray data. We characterised the global genomic response following 10 cGy (low dose) and 100 cGy (high dose) doses of X-ray ionising radiation at four time points by analysing the topology of gene coexpression networks. This study includes a rich experimental design and state-of-the-art computational systems biology methods of analysis to study the differences in the transcriptional response of skin cells exposed to low and high doses of radiation.

RESULTS

Using this method we found important genes that have been linked to immune response, cell survival and apoptosis. Furthermore, we also were able to identify genes such as BRCA1, ABCA1, TNFRSF1B, MLLT11 that have been associated with various types of cancers. We were also able to detect many genes known to be associated with various medical conditions.

CONCLUSIONS

Our method of applying network topological differences can aid in identifying the differences among similar (eg: radiation effect) yet very different biological conditions (eg: different dose and time) to generate testable hypotheses. This is the first study where a network level analysis was performed across two different radiation doses at various time points, thereby illustrating changes in the cellular response over time.

Clinical review: sepsis and septic shock--the potential of gene arrays

Over the past decade several investigators have applied microarray technology and related bioinformatic approaches to clinical sepsis and septic shock, thus allowing for an assessment of how, or if, this branch of genomic medicine has meaningfully impacted the field of sepsis research. The ability to simultaneously and efficiently measure the steady-state mRNA abundance of thousands of transcripts from a given tissue source (that is, 'transcriptomics') has provided an unprecedented opportunity to gain a broader, genome-level 'picture' of complex and heterogeneous clinical syndromes such as sepsis. A trancriptomic approach to sepsis and septic shock is technically challenging on multiple levels, but nonetheless modest, tangible advances are being realized. These include a genome-level understanding of the complexity of sepsis and septic shock, identification of novel candidate pathways and targets for potential intervention, discovery of novel, candidate diagnostic and stratification biomarkers, and the ability to stratify patients into clinically relevant, expression-based subclasses. The challenges moving forward include robust validation studies, standardization of technical approaches, standardization and further development of analytical algorithms, and large-scale collaborations.

Computational and systems biology in trauma and sepsis: current state and future perspectives

Trauma, often accompanied by hemorrhage, is a leading cause of death worldwide, often leading to inflammation-related late complications that include sepsis and multiple organ failure. These secondary complications are a manifestation of the complexity of biological responses elicited by trauma/hemorrhage, responses that span most, if not all, cell types, tissues, and organ systems. This daunting complexity at the patient level is manifest by the near total dearth of available therapeutics, and we suggest that this dire condition is due in large part to the lack of a rational, systems-oriented framework for drug development, clinical trial design, in-hospital diagnostics, and post-hospital care. We have further suggested that mechanistic computational modeling can form the basis of such a rational framework, given the maturity of systems biology/computational biology. Herein, we briefly summarize the state of the art of these approaches, and highlight the biological insights and novel hypotheses derived from these approaches. We propose a rational framework for transitioning through the currently fragmented process from identification of biological networks that are potential therapeutic targets, through clinical trial design, to personalized diagnosis and care. Insights derived from systems and computational biology in trauma and sepsis include the centrality of Damage-Associated Molecular Pattern molecules as drivers of both beneficial and detrimental inflammation, along with a novel view of multiple organ dysfunction as a cascade of containment failures with distinct implications for therapy. Finally, we suggest how these insights might be best implemented to drive transformational change in the fields of trauma and sepsis.

Sepsis: from pattern to mechanism and back

Sepsis is a clinical entity in which complex inflammatory and physiological processes are mobilized, not only across a range of cellular and molecular interactions, but also in clinically relevant physiological signals accessible at the bedside. There is a need for a mechanistic understanding that links the clinical phenomenon of physiologic variability with the underlying patterns of the biology of inflammation, and we assert that this can be facilitated through the use of dynamic mathematical and computational modeling. An iterative approach of laboratory experimentation and mathematical/computational modeling has the potential to integrate cellular biology, physiology, control theory, and systems engineering across biological scales, yielding insights into the control structures that govern mechanisms by which phenomena, detected as biological patterns, are produced. This approach can represent hypotheses in the formal language of mathematics and computation, and link behaviors that cross scales and domains, thereby offering the opportunity to better explain, diagnose, and intervene in the care of the septic patient.

Using "off-the-shelf" tools for terabyte-scale waveform recording in intensive care: computer system design, database description and lessons learned

Until now, the creation of massive (long-term and multichannel) waveform databases in intensive care required an interdisciplinary team of clinicians, engineers and informaticians and, in most cases, also design-specific software and hardware development. Recently, several commercial software tools for waveform acquisition became available. Although commercial products and even turnkey systems are now being marketed as simple and effective, the performance of those solutions is not known. The additional expense upfront may be worthwhile if commercial software can eliminate the need for custom software and hardware systems and the associated investment in teams and development. We report the development of a computer system for long-term large-scale recording and storage of multichannel physiologic signals that was built using commercial solutions (software and hardware) and existing hospital IT infrastructure. Both numeric (1 Hz) and waveform (62.5-500 Hz) data were captured from 24 SICU bedside monitors simultaneously and stored in a file-based vital sign data bank (VSDB) during one-year period (total DB size is 4.21TB). In total, vital signs were recorded from 1,175 critically ill patients. Up to six ECG leads, all other monitored waveforms, and all monitored numeric data were recorded in most of the cases. We describe the details of building blocks of our system, provide description of three datasets exported from our VSDB and compare the contents of our VSDB with other available waveform databases. Finally, we summarize lessons learned during recording, storage, and pre-processing of physiologic signals.

Validation of the riboleukogram to detect ventilator-associated pneumonia after severe injury

OBJECTIVE

We hypothesized that circulating leukocyte RNA profiles or “riboleukograms” detect ventilator-associated pneumonia after blunt trauma.

SUMMARY BACKGROUND DATA

A pilot microarray study of 11 ventilator-associated pneumonia (VAP) patients suggested that 85 leukocyte genes can be used to diagnose VAP. Validation of this gene set to detect VAP was tested using data from an independent patient cohort.

METHODS

A total of 158 intubated blunt trauma patients were enrolled at 5 centers, where 57 (36%) developed VAP. Patient age was 34.2 ± 11.1 years; 65% were male. Circulating leukocyte GeneChip U133 2.0 expression values were measured at time 0.5, 1, 4, 7, 14, 21, and 28 days after injury. DChip normalized leukocyte transcriptional profiles were analyzed using repeated measures logistic regression. A compound covariate model based on leukocyte gene transcriptional profiles in a training subset of patients was tested to determine predictive accuracy for VAP 4 days prior to clinical diagnosis in the test subset.

RESULTS

Using gene expression values measured on each study day at an FDR <0.05, 27 (32%) of the 85 genes were associated with the diagnosis of VAP 1 to 4 days before diagnosis. However, the compound covariate model based on these 85-genes did not predict VAP in the test cohort better than chance (P = 0.27). In contrast, a compound covariate model based upon de novo transcriptional analysis of the 158 patients predicted VAP better than chance 4 days before diagnosis with a sensitivity of 57% and a specificity of 69%.

CONCLUSION

Our results validate those described in a pilot study, confirming that riboleukograms are associated with the development of VAP days prior to clinical diagnosis. Similarly, a riboleukogram predictive model tested on a larger cohort of 158 patients was better than chance at predicting VAP days prior to clinical diagnosis.

Personalized medicine: genetic variation and loss of physiologic complexity are associated with mortality in 644 trauma patients

OBJECTIVE

Personalized medicine merges genetics, physiology, and patient outcome. Loss of physiologic complexity (heart rate [HR] variability) is a bedside biomarker for autonomic nervous system (ANS) dysfunction. We hypothesized that variability in ANS receptor proteins (genetics) and loss of complexity (physiology) are independently associated with mortality in critical illness.

SUMMARY BACKGROUND DATA

Decreased HR complexity has been associated with increased mortality and morbidity in trauma and other critically ill populations. Genetic variations in alpha-1A and beta-2 adrenergic receptors (ADRA1A, ADRB2) have been associated with changes in smooth muscle tone in various tissues, and implicated in bronchial hyper-responsiveness, metabolic syndrome, and other disorders.

METHODS

A cohort of 644 trauma intensive care unit (ICU) admissions had complexity data and genetic samples. Two ANS receptor polymorphisms (rs1048101, Alpha ADRA1A and rs1042714, Beta ADRB2) were genotyped. Physiologic complexity at various points in the ICU stay was measured using previously-studied integer HR multiscale entropy (MSE) over 6-hour intervals (~21,600 HR data points/interval/patient). Logistic regression assessed the concurrent relationship of genotypes, complexity, and probability of survival, an acuity score incorporating age, injury mechanism/severity, and admission vitals, to risk of death.

RESULTS

Of total, 96 patients (15%) died. Nonsurvivors had lower complexity at early, middle, and late portions of ICU stay (median MSE at least 25% less in nonsurvivors, P < 0.001) and lower incidence of the GG ADRB2 genotype (7.5% vs. 18.3%, P < 0.001). In multivariable logistic regression, the GG ADRB2 genotype carried ~3-fold decrease in mortality odds (odd ratio [OR] = 0.33, P = 0.01), independent of significant effects in HR MSE (OR = 0.93, P < 0.001), and probability of survival (OR = 0.22, P < 0.001).

CONCLUSIONS

This first study to simultaneously examine ANS genetics, the biomarker complexity, and mortality concludes: (1) ANS genetics and physiologic complexity are independently related to mortality; (2) Genetics and complexity add information over traditional acuity scoring (probability of survival); and (3) Simultaneous assessment of ANS physiology and genetics may yield novel research, diagnostic, and therapeutic opportunities in critical illness.

Preliminary evidence for leukocyte transcriptional signatures for pediatric ventilator-associated pneumonia

OBJECTIVE

Ventilator-associated pneumonia (VAP) is a significant contributor to intensive care unit (ICU) morbidity and mortality and presents a significant diagnostic challenge. Our hypothesis was that blood RNA expression profiles can be used to track the response to VAP in children, using the same methods that proved informational in adults.

DESIGN

A pilot, nonrandomized, repeated measures case-control study of changes in the abundance of total RNA in buffy coat and clinical scores for VAP.

SETTING

A large, multispecialty university-based pediatric ICU and cardiac ICU.

PATIENTS

Seven children requiring intubation and mechanical ventilation.

INTERVENTIONS

Blood samples were drawn at time of enrollment and every 48 hours for a maximum of 11 samples (21 days). Patients ranged in age from 1 to 18 months (mean 8 months). All patients survived to the end of the study. Of the 7 patients studied, 4 developed VAP.

MEASUREMENTS AND MAIN RESULTS

Statistical analysis of the Affymetrix Human Genome Focus GeneChip signal was conducted on normalized expression values of 8793 probe sets using analysis of variance (ANOVA) with a false discovery rate of 0.10. The expression patterns of 48 genes appeared to discriminate between the 2 classes of ventilated children: those with and those without pneumonia. Gene expression network analysis revealed several gene ontologies of interest, including cell proliferation, differentiation, growth, and apoptosis, as well as genes not previously implicated in sepsis.

CONCLUSIONS

These preliminary data are the first in critically ill children supporting the hypothesis that there is a detectable VAP signal in gene expression profiles. Larger studies are needed to validate these preliminary findings and test the diagnostic value of longitudinal changes in leukocyte RNA signatures.

Diagnosis, management and prevention of ventilator-associated pneumonia: an update

Ventilator-associated pneumonia (VAP) affects 10-20% of mechanically ventilated patients and is associated with increased morbidity and mortality and high costs. Early diagnosis is crucial for rapid appropriate antimicrobial therapy to be instituted, but debate remains as to the optimal diagnostic strategy. Noninvasive clinical-based diagnosis is rapid but may not be as accurate as invasive techniques. Increased use of biomarkers and advances in genomics and proteomics may help speed up diagnosis. Management of VAP relies principally on appropriate antimicrobial therapy, which should be selected according to individual patient factors, such as previous antibacterial therapy and length of hospitalization or mechanical ventilation, and local infection and resistance patterns. In addition, once bacterial culture and sensitivity results are available, broad-spectrum therapy should be de-escalated to provide a more specific, narrower-spectrum cover. Optimum duration of antibacterial therapy is difficult to define and should be tailored to clinical response. Biomarker levels may be useful to monitor response to therapy. With the high morbidity and mortality, prevention of VAP is important and several strategies have been shown to reduce the rates of VAP in mechanically ventilated patients, including using noninvasive ventilation where possible, and semi-recumbent positioning. Other potentially beneficial preventive techniques include subglottal suctioning, oral decontamination strategies and antimicrobial-coated endotracheal tubes, although further study is needed to confirm the cost effectiveness of these strategies.

Genome-wide transcription profiling of human sepsis: a systematic review

Introduction

Sepsis is thought to be an abnormal inflammatory response to infection. However, most clinical trials of drugs that modulate the inflammatory response of sepsis have been unsuccessful. Emerging genomic evidence shows that the host response in sepsis does not conform to a simple hyper-inflammatory/hypo-inflammatory model. We, therefore, synthesized current genomic studies that examined the host response of circulating leukocytes to human sepsis.

Methods

Electronic searches were performed in Medline and Embase (1987 to October 2010), supplemented by additional searches in multiple microarray data repositories. We included studies that (1) used microarray, (2) were performed in humans and (3) investigated the host response mediated by circulating leukocytes.

Results

We identified 12 cohorts consisting of 784 individuals providing genome-wide expression data in early and late sepsis. Sepsis elicited an immediate activation of pathogen recognition receptors, accompanied by an increase in the activities of signal transduction cascades. These changes were consistent across most cohorts. However, changes in inflammation related genes were highly variable. Established inflammatory markers, such as tumour necrosis factor-α (TNF-α), interleukin (IL)-1 or interleukin-10, did not show any consistent pattern in their gene-expression across cohorts. The finding remains the same even after the cohorts were stratified by timing (early vs. late sepsis), patient groups (paediatric vs. adult patients) or settings (clinical sepsis vs. endotoxemia model). Neither a distinctive pro/anti-inflammatory phase nor a clear transition from a pro-inflammatory to anti-inflammatory phase could be observed during sepsis.

Conclusions

Sepsis related inflammatory changes are highly variable on a transcriptional level. We did not find strong genomic evidence that supports the classic two phase model of sepsis.

Addressing the complexity of chronic obstructive pulmonary disease: from phenotypes and biomarkers to scale-free networks, systems biology, and P4 medicine

Chronic obstructive pulmonary disease (COPD) is a complex disease at the clinical, cellular, and molecular levels. However, its diagnosis, assessment, and therapeutic management are based almost exclusively on the severity of airflow limitation. A better understanding of the multiple dimensions of COPD and its relationship to other diseases is very relevant and of high current interest. Recent theoretical (scale-free networks), technological (high-throughput technology, biocomputing), and analytical improvements (systems biology) provide tools capable of addressing the complexity of COPD. The information obtained from the integrated use of those techniques will be eventually incorporated into routine clinical practice. This review summarizes our current knowledge in this area and offers an insight into the elements needed to progress toward an integrated, multilevel view of COPD based on the novel scientific strategy of systems biology and its potential clinical derivative, P4 medicine (Personalized, Predictive, Preventive, and Participatory).

MORE for multiple organ dysfunction syndrome: Multiple Organ REanimation, REgeneration, and REprogramming

Those who care for the critically ill and injured rightfully celebrate the advances made by our field over its first 50 yrs. Advances in systems, tissue, and molecular engineering, together defined as "health engineering," will provide unprecedented opportunities to treat multiple organ dysfunction syndrome in the 21st century. In the future, Multiple Organ REanimation, REgeneration, and REprogramming will be responsible for new treatment approaches for those with multiple organ dysfunction syndrome; several examples are presented here. Thus, as we spent the first 50 yrs of care for the critical ill and injured learning how best to hook humans up to machines, we will spend the next 50 yrs understanding better how to liberate patients from mechanical support. It is difficult to know when these advances will be realized given that the rate of change continues to increase and the seemingly impossible goal of reprogramming fully differentiated cells was accomplished recently by manipulating a few transcription factors. It is not unrealistic to expect that in the next couple of decades that it will be possible to dedifferentiate dysfunctional somatic cells in vivo to a more robust, resistant cell phenotype. Our future should be aimed in part at refining our skill sets and refocusing (even rebranding) critical care as health engineering aimed at Multiple Organ REanimation, REgeneration, and REprogramming.

Estimating sparse gene regulatory networks using a bayesian linear regression

In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. We represent the regulatory relationships between the genes using weights, with the "net" regulation influence on a gene's expression being the summation of the independent regulatory inputs. We estimate the weights using a Bayesian linear regression method for sparse parameter vectors. We apply our proposed method to the extraction of differential gene expression software selected genes of a human buffy-coat microarray expression profile dataset of ventilator-associated pneumonia (VAP), and compare the estimation result with the GRNs estimated using both a correlation coefficient method and a database-based method ingenuity pathway analysis. A biological analysis of the resulting consensus network that is derived using the GRNs, estimated with both our and the correlation-coefficient methods results in four biologically meaningful subnetworks. Also, our method performs either better than or competitively with the existing well-established GRN estimation methods. Moreover, it performs comparatively with respect to: 1) the ground-truth GRNs for the in silico 50- and 100-gene datasets reported recently in the DREAM3 challenge and 2) the GRN estimated using a mutual information-based method for the top-ranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset.

Host lung gene expression patterns predict infectious etiology in a mouse model of pneumonia

Background

Lower respiratory tract infections continue to exact unacceptable worldwide mortality, often because the infecting pathogen cannot be identified. The respiratory epithelia provide protection from pneumonias through organism-specific generation of antimicrobial products, offering potential insight into the identity of infecting pathogens. This study assesses the capacity of the host gene expression response to infection to predict the presence and identity of lower respiratory pathogens without reliance on culture data.

Methods

Mice were inhalationally challenged with S. pneumoniae, P. aeruginosa, A. fumigatus or saline prior to whole genome gene expression microarray analysis of their pulmonary parenchyma. Characteristic gene expression patterns for each condition were identified, allowing the derivation of prediction rules for each pathogen. After confirming the predictive capacity of gene expression data in blinded challenges, a computerized algorithm was devised to predict the infectious conditions of subsequent subjects.

Results

We observed robust, pathogen-specific gene expression patterns as early as 2 h after infection. Use of an algorithmic decision tree revealed 94.4% diagnostic accuracy when discerning the presence of bacterial infection. The model subsequently differentiated between bacterial pathogens with 71.4% accuracy and between non-bacterial conditions with 70.0% accuracy, both far exceeding the expected diagnostic yield of standard culture-based bronchoscopy with bronchoalveolar lavage.

Conclusions

These data substantiate the specificity of the pulmonary innate immune response and support the feasibility of a gene expression-based clinical tool for pneumonia diagnosis.

Evidence for a novel blood RNA diagnostic for pediatric appendicitis: the riboleukogram

OBJECTIVE

To test the hypothesis that gene expression analysis of circulating white blood cells and/or plasma cytokines could be used to improve diagnostic accuracy in children being evaluated for appendicitis.

METHODS

We recruited 28 children being evaluated for abdominal pain from a tertiary pediatric emergency department. Twenty patients were used as a training cohort and 8 patients as a validation cohort. After consent was obtained, blood was processed for plasma cytokine analysis and RNA gene expression. Alvarado and pediatric appendicitis scores were obtained. Principal components analysis was used to explore global differences in gene expression. The random forest method was used to classify patients into those with and without appendicitis in the prospective cohort. Comparisons were made evaluating clinical scoring systems, cytokine analysis, and gene expression analysis to accurately predict appendicitis.

RESULTS

The random forest method accurately predicted appendicitis in 4 of 5 patients in the prospective cohort. Cytokine analysis was not as accurate as gene expression analysis; however, it did accurately rule out all 3 patients in the prospective cohort. Pediatric appendicitis scores and Alvarado scores were not useful for predicting appendicitis.

CONCLUSIONS

Our findings provide proof of technical feasibility and support the diagnostic potential of plasma cytokines to rule out and riboleukograms to rule in the diagnosis of appendicitis.

Association between lymphotoxin-alpha (tumor necrosis factor-beta) intron polymorphism and predisposition to severe sepsis is modified by gender and age

OBJECTIVE

To investigate the significance of functional polymorphisms of inflammatory response genes by analysis of a large population of patients, both with and without severe sepsis, and representative of the diverse populations (geographic diversity, physician diversity, clinical treatment diversity) that would be encountered in critical care clinical practice.

DESIGN

: Collaborative case-control study conducted from July 2001 to December 2005.

SETTING

A heterogeneous population of patients from 12 U.S. intensive care units represented by the Genetic Predisposition to Severe Sepsis archive.

PATIENTS

A total of 854 patients with severe sepsis and an equal number of mortality, age, gender, and race-matched patients also admitted to the intensive care unit without evidence of any infection (matched nonseptic controls).

MEASUREMENTS AND MAIN RESULTS

We developed assays for six functional single nucleotide polymorphisms present before the first codon of tumor necrosis factor at -308, IL1B at -511, IL6 at -174, IL10 at -819, and CD14 at -159, and in the first intron of LTA (also known as tumor necrosis factor-B) at +252 (LTA[+252]). The Project IMPACT critical care clinical database information management system developed by the Society of Critical Care Medicine and managed by Tri-Analytics and Cerner Corporation was utilized. Template-directed dye-terminator incorporation assay with fluorescence polarization detection was used as a high-throughput genotyping strategy. Fifty-three percent of the patients were male with 87.3% and 6.4% of Caucasian and African American racial types, respectively. Overall mortality was 35.1% in both severe sepsis and matched nonseptic control patients group. Average ages (standard deviation) of the severe sepsis and matched nonseptic control patients were 63.0 (16.05) and 65.0 (15.58) yrs old, respectively. Among the six single nucleotide polymorphisms, LTA (+252) was most overrepresented in the septic patient group (% severe sepsis; AA 45.6: AG 51.1: GG 56.7, p = .005). Furthermore, the genetic risk effect was most pronounced in males, age >60 yrs (p = .005).

CONCLUSIONS

LTA(+252) may influence predisposition to severe sepsis, a predisposition that is modulated by gender and age. Although the genetic influences can be overwhelmed by both comorbid factors and acute illness in individual cases, population studies suggest that this is an influential biological pathway modulating risk of critical illnesses.

Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia induce distinct host responses

OBJECTIVE

Pathogens that cause pneumonia may be treated in a targeted fashion by antibiotics, but if this therapy fails, then treatment involves only nonspecific supportive measures, independent of the inciting infection. The purpose of this study was to determine whether host response is similar after disparate infections with similar mortalities.

DESIGN

Prospective, randomized controlled study.

SETTING

Animal laboratory in a university medical center.

INTERVENTIONS

Pneumonia was induced in FVB/N mice by either Streptococcus pneumoniae or two different concentrations of Pseudomonas aeruginosa. Plasma and bronchoalveolar lavage fluid from septic animals was assayed by a microarray immunoassay measuring 18 inflammatory mediators at multiple time points.

MEASUREMENTS AND MAIN RESULTS

The host response was dependent on the causative organism as well as kinetics of mortality, but the pro-inflammatory and anti-inflammatory responses were independent of inoculum concentration or degree of bacteremia. Pneumonia caused by different concentrations of the same bacteria, Pseudomonas aeruginosa, also yielded distinct inflammatory responses; however, inflammatory mediator expression did not directly track the severity of infection. For all infections, the host response was compartmentalized, with markedly different concentrations of inflammatory mediators in the systemic circulation and the lungs. Hierarchical clustering analysis resulted in the identification of five distinct clusters of the host response to bacterial infection. Principal components analysis correlated pulmonary macrophage inflammatory peptide-2 and interleukin-10 with progression of infection, whereas elevated plasma tumor necrosis factor sr2 and macrophage chemotactic peptide-1 were indicative of fulminant disease with >90% mortality within 48 hrs.

CONCLUSIONS

Septic mice have distinct local and systemic responses to Streptococcus pneumoniae and Pseudomonas aeruginosa pneumonia. Targeting specific host inflammatory responses induced by distinct bacterial infections could represent a potential therapeutic approach in the treatment of sepsis.

Bridging the gap between systems biology and medicine

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

Using systems biology to simplify complex disease: immune cartography

What if there was a rapid, inexpensive, and accurate blood diagnostic that could determine which patients were infected, identify the organism(s) responsible, and identify patients who were not responding to therapy? We hypothesized that systems analysis of the transcriptional activity of circulating immune effector cells could be used to identify conserved elements in the host response to systemic inflammation, and furthermore, to discriminate between sterile and infectious etiologies. We review herein a validated, systems biology approach demonstrating that 1) abdominal and pulmonary sepsis diagnoses can be made in mouse models using microarray (RNA) data from circulating blood, 2) blood microarray data can be used to differentiate between the host response to Gram-negative and Gram-positive pneumonia, 3) the endotoxin response of normal human volunteers can be mapped at the level of gene expression, and 4) a similar strategy can be used in the critically ill to follow septic patients and quantitatively determine immune recovery. These findings provide the foundation of immune cartography and demonstrate the potential of this approach for rapidly diagnosing sepsis and identifying pathogens. Further, our data suggest a new approach to determine how specific pathogens perturb the physiology of circulating leukocytes in a cell-specific manner. Large, prospective clinical trails are needed to validate the clinical utility of leukocyte RNA diagnostics (e.g., the riboleukogram).

Antimicrobial stewardship in the intensive care unit: advances and obstacles

Antimicrobial stewardship involves a multifaceted approach that strives to combat the emergence of resistance, improve clinical outcomes, and control costs by improving antimicrobial use. Therefore, stewardship is of great importance and relevance in the intensive care unit. Clinical decision support systems, biomarker-derived treatment algorithms, and improved knowledge regarding the different components of antimicrobial therapy represent some of the advances that have been made in stewardship. Yet, significant obstacles have prevented the full achievement of stewardship's goals, and approaches to confronting these obstacles should be appreciated. Clinicians should realize that antimicrobials are important therapeutic agents and strive to use them wisely.

Rethinking Sepsis: New Insights from Gene Expression Profiling Studies

Critically ill patients encompass an enormously heterogeneous population and, as such, therapeutic interventions, including drug therapy, can produce multiple outcomes in different patient subgroups. For example, researchers not only look for an ‘average effect’ of a drug on a typical patient, but also seek to understand individual variability. The presence of variability impacts significantly on the success of clinical trials and failure to identify this variability can result in the clinical trial being under-powdered to detect a treatment effect. For clinicians, failure to recognize variability can result in unintended toxicity or excessive harm in certain patients. Hence, understanding variability is critically important in both research and clinical practice.