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

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Leukocyte Transcriptional Response in Sepsis

BACKGROUND

The complex host response to sepsis is incompletely understood. The aim of this investigation is to use leukocyte RNA sequencing to characterize biological functions, cellular pathways, and key regulatory molecules driving sepsis pathophysiology.

METHODS

This was a prospective, observational study of emergency department patients with sepsis, at an urban, academic, tertiary care center. In the derivation cohort, we collected blood at enrollment and 90 days after hospital discharge allowing each patient to serve as an internal control. We performed RNA sequencing to quantify transcriptional expression changes during sepsis and non-sepsis states. We then performed unsupervised and supervised analyses, as well as functional and pathway analyses. We selected the top down and upregulated genes and key regulatory molecules for validation. Validation occurred in a cohort of septic and non-septic using real-time PCR.

RESULTS

The derivation cohort included 5 patients, and RNA sequencing revealed 916 unique mRNA transcripts differentially expressed during sepsis. Among these, 673 (73%) genes were upregulated, and 243 (27%) were downregulated. Functional enrichment analysis revealed a highly dynamic downstream effect of the transcriptional activity during sepsis. Of the 43 functional cellular pathways activated during sepsis, the top pathways were closely associated with inflammation and response to infection. Validation occurred in 18 septic and 25 non-septic control patients, with 34/45 (76%) of identified genes validated. The regulatory analysis identified several key regulators of sepsis.

CONCLUSIONS

Highly dynamic transcriptional activity occurs in leukocytes during sepsis, activating key cellular pathways and master regulatory molecules that drive the sepsis process.

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.

LPS-induced modules of co-expressed genes in equine peripheral blood mononuclear cells

BACKGROUND

Lipopolysaccharide (endotoxin, LPS) is a strong inducer of the innate immune response. It is widespread in our environment, e.g. in house dust and contributes to asthma. Compared to humans, horses are even more sensitive to LPS. However, data on LPS effects on the equine transcriptome are very limited. Using RNA-seq we analysed LPS-induced differences in the gene expression in equine peripheral blood mononuclear cells at the gene and gene-network level in two half-sib families and one group of unrelated horses.

RESULTS

24 h-LPS challenge of equine immune cells resulted in substantial changes in the transcriptomic profile (1,265 differentially expressed genes) showing partial overlap with human data. One of the half-sib families showed a specific response different from the other two groups of horses. We also identified co-expressed gene modules that clearly differentiated 24 h-LPS- from non-stimulated samples. These modules consisted of 934 highly interconnected genes and included genes involved in the immune response (e.g. IL6, CCL22, CXCL6, CXCL2), however, none of the top ten hub genes of the modules have been annotated as responsive to LPS in gene ontology.

CONCLUSIONS

Using weighted gene co-expression network analysis we identified ten co-expressed gene modules significantly regulated by in vitro stimulation with LPS. Apart from 47 genes (5%) all other genes highly interconnected within the most up- and down-regulated modules were also significantly differentially expressed (FDR < 0.05). The LPS-regulated module hub genes have not yet been described as having a role in the immune response to LPS (e.g. VAT1 and TTC25).

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.

Potential Cost-effectiveness of Early Identification of Hospital-acquired Infection in Critically Ill Patients

RATIONALE

Limitations in methods for the rapid diagnosis of hospital-acquired infections often delay initiation of effective antimicrobial therapy. New diagnostic approaches offer potential clinical and cost-related improvements in the management of these infections.

OBJECTIVES

We developed a decision modeling framework to assess the potential cost-effectiveness of a rapid biomarker assay to identify hospital-acquired infection in high-risk patients earlier than standard diagnostic testing.

METHODS

The framework includes parameters representing rates of infection, rates of delayed appropriate therapy, and impact of delayed therapy on mortality, along with assumptions about diagnostic test characteristics and their impact on delayed therapy and length of stay. Parameter estimates were based on contemporary, published studies and supplemented with data from a four-site, observational, clinical study. Extensive sensitivity analyses were performed. The base-case analysis assumed 17.6% of ventilated patients and 11.2% of nonventilated patients develop hospital-acquired infection and that 28.7% of patients with hospital-acquired infection experience delays in appropriate antibiotic therapy with standard care. We assumed this percentage decreased by 50% (to 14.4%) among patients with true-positive results and increased by 50% (to 43.1%) among patients with false-negative results using a hypothetical biomarker assay. Cost of testing was set at $110/d.

MEASUREMENTS AND MAIN RESULTS

In the base-case analysis, among ventilated patients, daily diagnostic testing starting on admission reduced inpatient mortality from 12.3 to 11.9% and increased mean costs by $1,640 per patient, resulting in an incremental cost-effectiveness ratio of $21,389 per life-year saved. Among nonventilated patients, inpatient mortality decreased from 7.3 to 7.1% and costs increased by $1,381 with diagnostic testing. The resulting incremental cost-effectiveness ratio was $42,325 per life-year saved. Threshold analyses revealed the probabilities of developing hospital-acquired infection in ventilated and nonventilated patients could be as low as 8.4 and 9.8%, respectively, to maintain incremental cost-effectiveness ratios less than $50,000 per life-year saved.

CONCLUSIONS

Development and use of serial diagnostic testing that reduces the proportion of patients with delays in appropriate antibiotic therapy for hospital-acquired infections could reduce inpatient mortality. The model presented here offers a cost-effectiveness framework for future test development.

A molecular biomarker to diagnose community-acquired pneumonia on intensive care unit admission

RATIONALE

Community-acquired pneumonia (CAP) accounts for a major proportion of intensive care unit (ICU) admissions for respiratory failure and sepsis. Diagnostic uncertainty complicates case management, which may delay appropriate cause-specific treatment.

OBJECTIVES

To characterize the blood genomic response in patients with suspected CAP and identify a candidate biomarker for the rapid diagnosis of CAP on ICU admission.

METHODS

The study comprised two cohorts of consecutively enrolled patients treated for suspected CAP on ICU admission. Patients were designated CAP (cases) and no-CAP patients (control subjects) by post hoc assessment. The first (discovery) cohort (101 CAP and 33 no-CAP patients) was enrolled between January 2011 and July 2012; the second (validation) cohort (70 CAP and 30 no-CAP patients) between July 2012 and June 2013. Blood was collected within 24 hours of ICU admission.

MEASUREMENTS AND MAIN RESULTS

Blood microarray analysis of CAP and no-CAP patients revealed shared and distinct gene expression patterns. A 78-gene signature was defined for CAP, from which a FAIM3:PLAC8 gene expression ratio was derived with area under curve of 0.845 (95% confidence interval, 0.764-0.917) and positive and negative predictive values of 83% and 81%, respectively. Robustness of the FAIM3:PLAC8 ratio was ascertained by quantitative polymerase chain reaction in the validation cohort. The FAIM3:PLAC8 ratio outperformed plasma procalcitonin and IL-8 and IL-6 in discriminating between CAP and no-CAP patients.

CONCLUSIONS

CAP and no-CAP patients presented shared and distinct blood genomic responses. We propose the FAIM3:PLAC8 ratio as a candidate biomarker to assist in the rapid diagnosis of CAP on ICU admission. Clinical trial registered with www.clinicaltrials.gov (NCT 01905033).

Diagnostic accuracy of pulmonary host inflammatory mediators in the exclusion of ventilator-acquired pneumonia

BACKGROUND

Excessive use of empirical antibiotics is common in critically ill patients. Rapid biomarker-based exclusion of infection may improve antibiotic stewardship in ventilator-acquired pneumonia (VAP). However, successful validation of the usefulness of potential markers in this setting is exceptionally rare.

OBJECTIVES

We sought to validate the capacity for specific host inflammatory mediators to exclude pneumonia in patients with suspected VAP.

METHODS

A prospective, multicentre, validation study of patients with suspected VAP was conducted in 12 intensive care units. VAP was confirmed following bronchoscopy by culture of a potential pathogen in bronchoalveolar lavage fluid (BALF) at >10(4) colony forming units per millilitre (cfu/mL). Interleukin-1 beta (IL-1β), IL-8, matrix metalloproteinase-8 (MMP-8), MMP-9 and human neutrophil elastase (HNE) were quantified in BALF. Diagnostic utility was determined for biomarkers individually and in combination.

RESULTS

Paired BALF culture and biomarker results were available for 150 patients. 53 patients (35%) had VAP and 97 (65%) patients formed the non-VAP group. All biomarkers were significantly higher in the VAP group (p<0.001). The area under the receiver operator characteristic curve for IL-1β was 0.81; IL-8, 0.74; MMP-8, 0.76; MMP-9, 0.79 and HNE, 0.78. A combination of IL-1β and IL-8, at the optimal cut-point, excluded VAP with a sensitivity of 100%, a specificity of 44.3% and a post-test probability of 0% (95% CI 0% to 9.2%).

CONCLUSIONS

Low BALF IL-1β in combination with IL-8 confidently excludes VAP and could form a rapid biomarker-based rule-out test, with the potential to improve antibiotic stewardship.

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.

Predicting critical transitions in a model of systemic inflammation

The human body can be viewed as a dynamical system, with physiological states such as health and disease broadly representing steady states. From this perspective, and given inter- and intra-individual heterogeneity, an important task is identifying the propensity to transition from one steady state to another, which in practice can occur abruptly. Detecting impending transitions between steady states is of significant importance in many fields, and thus a variety of methods have been developed for this purpose, but lack of data has limited applications in physiology. Here, we propose a model-based approach towards identifying critical transitions in systemic inflammation based on a minimal amount of assumptions about the availability of data and the structure of the system. We derived a warning signal metric to identify forthcoming abrupt transitions occurring in a mathematical model of systemic inflammation with a gradually increasing bacterial load. Intervention to remove the inflammatory stimulus was successful in restoring homeostasis if undertaken when the warning signal was elevated rather than waiting for the state variables of the system themselves to begin moving to a new steady state. The proposed combination of data and model-based analysis for predicting physiological transitions represents a step forward towards the quantitative study of complex biological systems.

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.

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.

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.

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.

Microfluidics-based capture of human neutrophils for expression analysis in blood and bronchoalveolar lavage

Gene expression analysis can be a powerful tool in predicting patient outcomes and identifying patients who may benefit from targeted therapies. However, isolating human blood polymorphonuclear cells (PMNs) for genomic analysis has been challenging. We used a novel microfluidic technique that isolates PMNs by capturing CD66b(+) cells and compared it with dextran-Ficoll gradient isolation. We also used microfluidic isolation techniques for blood and bronchoalveolar lavage (BAL) samples of patients with acute respiratory distress syndrome (ARDS) to evaluate PMN genomic alterations secondary to pulmonary sequestration. PMNs obtained from ex vivo lipopolysaccharide (LPS)-stimulated or -unstimulated whole blood from five healthy volunteers were isolated by either dextran-Ficoll gradient, microfluidics capture, or a combination of the two techniques. Blood and BAL fluid PMNs were also isolated using microfluidics from seven hospitalized patients with ARDS. Gene expression was inferred from extracted RNA using Affymetrix U133 Plus 2.0 GeneChips. All methods of PMN isolation produced similar quantities of high-quality RNA, when adjusted for recovered cell number. Unsupervised analysis and hierarchical clustering indicated that LPS stimulation was the primary factor affecting gene expression patterns among all ex vivo samples. Patterns of gene expression from blood and BAL PMNs differed significantly from each other in the patients with ARDS. Isolation of PMNs by microfluidics can be applied to both blood and BAL specimens from critically ill, hospitalized patients. Unique genomic expression patterns are obtained from the blood and BAL fluid of critically ill patients with ARDS, and these differ significantly from genomic patterns seen after ex vivo LPS stimulation.

A dynamic view of trauma/hemorrhage-induced inflammation in mice: principal drivers and networks

BACKGROUND

Complex biological processes such as acute inflammation induced by trauma/hemorrhagic shock/ (T/HS) are dynamic and multi-dimensional. We utilized multiplexing cytokine analysis coupled with data-driven modeling to gain a systems perspective into T/HS.

METHODOLOGY/PRINCIPAL FINDINGS

Mice were subjected to surgical cannulation trauma (ST) ± hemorrhagic shock (HS; 25 mmHg), and followed for 1, 2, 3, or 4 h in each case. Serum was assayed for 20 cytokines and NO(2) (-)/NO(3) (-). These data were analyzed using four data-driven methods (Hierarchical Clustering Analysis [HCA], multivariate analysis [MA], Principal Component Analysis [PCA], and Dynamic Network Analysis [DyNA]). Using HCA, animals subjected to ST vs. ST + HS could be partially segregated based on inflammatory mediator profiles, despite a large overlap. Based on MA, interleukin [IL]-12p40/p70 (IL-12.total), monokine induced by interferon-γ (CXCL-9) [MIG], and IP-10 were the best discriminators between ST and ST/HS. PCA suggested that the inflammatory mediators found in the three main principal components in animals subjected to ST were IL-6, IL-10, and IL-13, while the three principal components in ST + HS included a large number of cytokines including IL-6, IL-10, keratinocyte-derived cytokine (CXCL-1) [KC], and tumor necrosis factor-α [TNF-α]. DyNA suggested that the circulating mediators produced in response to ST were characterized by a high degree of interconnection/complexity at all time points; the response to ST + HS consisted of different central nodes, and exhibited zero network density over the first 2 h with lesser connectivity vs. ST at all time points. DyNA also helped link the conclusions from MA and PCA, in that central nodes consisting of IP-10 and IL-12 were seen in ST, while MIG and IL-6 were central nodes in ST + HS.

CONCLUSIONS/SIGNIFICANCE

These studies help elucidate the dynamics of T/HS-induced inflammation, complementing other forms of dynamic mechanistic modeling. These methods should be applicable to the analysis of other complex biological processes.

Gene expression in human sepsis: what have we learned?

In the previous issue of Critical Care, Tang and colleagues offer a very novel systematic review of 12 studies of gene expression in blood of human sepsis. The review concludes that there is no discernable transition from a pro- to an anti-inflammatory expression phenotype in blood in human sepsis. The authors found that upregulation of pathogen recognition receptors and signal transduction pathways was a consistent theme in expression studies. The review by Tang and colleagues has strengths, including defined screening criteria, broad literature review, strict inclusion criteria, and transparent methods for assessing strengths and weaknesses of studies. There are other issues to consider. First, one source of variation in gene expression studies in sepsis is variability in time from onset of sepsis to time of blood draw. Another source of variation is differences between tissues in gene expression at the same time in sepsis. Whole blood is a mélange of tissues (a variety of leukocytes); therefore, one assesses a weighted sum of all leukocyte classes. About half of the studies assessed peripheral mononuclear cells. A third great source of variable gene expression in sepsis is heterogeneity of causes and microbiology of sepsis. Only one study compared Gram-positive with Gram-negative sepsis. Only three studies confirmed microarray data with an independent measurement of expression. One interpretation is that two of three studies of early sepsis found increased expression of pro-inflammatory genes. In late sepsis, three of six studies found increased expression of pro-inflammatory genes whereas three of six studies found decreased expression of pro-inflammatory genes. The balance of pro- to anti-inflammatory gene expression is difficult to quantify. Sample size is highly variable in studies (n = 12 to 176). These limitations require a leap of faith to suggest that the paradigm of sepsis as a pro-inflammatory phenotype that shifts to an anti-inflammatory phenotype is flawed: the absence of evidence in expression studies is not the same as having well-conducted studies with clear negative evidence.

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.

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.