Validation of SeptiCyte RAPID to Discriminate Sepsis from Non-Infectious Systemic Inflammation.

(1) Background: SeptiCyte RAPID is a molecular test for discriminating sepsis from non-infectious systemic inflammation, and for estimating sepsis probabilities. The objective of this study was the clinical validation of SeptiCyte RAPID, based on testing retrospectively banked and prospectively collected patient samples. (2) Methods: The cartridge-based SeptiCyte RAPID test accepts a PAXgene blood RNA sample and provides sample-to-answer processing in ~1 h. The test output (SeptiScore, range 0-15) falls into four interpretation bands, with higher scores indicating higher probabilities of sepsis. Retrospective (N = 356) and prospective (N = 63) samples were tested from adult patients in ICU who either had the systemic inflammatory response syndrome (SIRS), or were suspected of having/diagnosed with sepsis. Patients were clinically evaluated by a panel of three expert physicians blinded to the SeptiCyte test results. Results were interpreted under either the Sepsis-2 or Sepsis-3 framework. (3) Results: Under the Sepsis-2 framework, SeptiCyte RAPID performance for the combined retrospective and prospective cohorts had Areas Under the ROC Curve (AUCs) ranging from 0.82 to 0.85, a negative predictive value of 0.91 (sensitivity 0.94) for SeptiScore Band 1 (score range 0.1-5.0; lowest risk of sepsis), and a positive predictive value of 0.81 (specificity 0.90) for SeptiScore Band 4 (score range 7.4-15; highest risk of sepsis). Performance estimates for the prospective cohort ranged from AUC 0.86-0.95. For physician-adjudicated sepsis cases that were blood culture (+) or blood, urine culture (+)(+), 43/48 (90%) of SeptiCyte scores fell in Bands 3 or 4. In multivariable analysis with up to 14 additional clinical variables, SeptiScore was the most important variable for sepsis diagnosis. A comparable performance was obtained for the majority of patients reanalyzed under the Sepsis-3 definition, although a subgroup of 16 patients was identified that was called septic under Sepsis-2 but not under Sepsis-3. (4) Conclusions: This study validates SeptiCyte RAPID for estimating sepsis probability, under both the Sepsis-2 and Sepsis-3 frameworks, for hospitalized patients on their first day of ICU admission.

Correction to: Blood transcriptomic discrimination of bacterial and viral infections in the emergency department: a multi-cohort observational validation study.

An amendment to this paper has been published and can be accessed via the original article.

Blood transcriptomic discrimination of bacterial and viral infections in the emergency department: a multi-cohort observational validation study.

BACKGROUND: There is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions. METHODS: We used computational machine learning to derive a rule-out blood transcriptomic signature of bacterial infection (SeptiCyte™ TRIAGE) from eight published case-control studies. We then validated this signature by itself in independent case-control data from more than 1500 samples in total, and in combination with our previously published signature for viral infections (SeptiCyte™ VIRUS) using pooled data from a further 1088 samples. Finally, we tested the performance of these signatures in a prospective observational cohort of emergency department (ED) patients with fever, and we used the combined SeptiCyte™ signature in a mixture modelling approach to estimate the prevalence of bacterial and viral infections in febrile ED patients without microbiological diagnoses. RESULTS: The combination of SeptiCyte™ TRIAGE with our published signature for viral infections (SeptiCyte™ VIRUS) discriminated bacterial and viral infections in febrile ED patients, with a receiver operating characteristic area under the curve of 0.95 (95% confidence interval 0.90-1), compared to 0.79 (0.68-0.91) for WCC and 0.73 (0.61-0.86) for CRP. At pre-test probabilities 0.35 and 0.72, the combined SeptiCyte™ score achieved a negative predictive value for bacterial infection of 0.97 (0.90-0.99) and 0.86 (0.64-0.96), compared to 0.90 (0.80-0.94) and 0.66 (0.48-0.79) for WCC and 0.88 (0.69-0.95) and 0.60 (0.31-0.72) for CRP. In a mixture modelling approach, the combined SeptiCyte™ score estimated that 24% of febrile ED cases receiving antibacterials without a microbiological diagnosis were due to viral infections. Our analysis also suggested that a proportion of patients with bacterial infection recovered without antibacterials. CONCLUSIONS: Blood transcriptional biomarkers offer exciting opportunities to support precision antibacterial prescribing in ED and improve diagnostic classification of patients without microbiologically confirmed infections.

Serum Protein Changes in Pediatric Sepsis Patients Identified With an Aptamer-Based Multiplexed Proteomic Approach.

OBJECTIVES: Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, is a leading cause of death and disability among children worldwide. Identifying sepsis in pediatric patients is difficult and can lead to treatment delay. Our objective was to assess the host proteomic response to infection utilizing an aptamer-based multiplexed proteomics approach to identify novel serum protein changes that might help distinguish between pediatric sepsis and infection-negative systemic inflammation and hence can potentially improve sensitivity and specificity of the diagnosis of sepsis over current clinical criteria approaches. DESIGN: Retrospective, observational cohort study. SETTING: PICU and cardiac ICU, Seattle Children's Hospital, Seattle, WA. PATIENTS: A cohort of 40 children with clinically overt sepsis and 30 children immediately postcardiopulmonary bypass surgery (infection-negative systemic inflammation control subjects) was recruited. Children with sepsis had a confirmed or suspected infection, two or more systemic inflammatory response syndrome criteria, and at least cardiovascular and/or pulmonary organ dysfunction. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Serum samples from 35 of the sepsis and 28 of the bypass surgery subjects were available for screening with an aptamer-based proteomic platform that measures 1,305 proteins to search for large-scale serum protein expression pattern changes in sepsis. A total of 111 proteins were significantly differentially expressed between the sepsis and control groups, using the linear models for microarray data (linear modeling) and Boruta (decision trees) R packages, with 55 being previously identified in sepsis patients. Weighted gene correlation network analysis helped identify 76 proteins that correlated highly with clinical sepsis traits, 27 of which had not been previously reported in sepsis. CONCLUSIONS: The serum protein changes identified with the aptamer-based multiplexed proteomics approach used in this study can be useful to distinguish between sepsis and noninfectious systemic inflammation.

Physician agreement on the diagnosis of sepsis in the intensive care unit: estimation of concordance and analysis of underlying factors in a multicenter cohort.

BACKGROUND: Differentiating sepsis from the systemic inflammatory response syndrome (SIRS) in critical care patients is challenging, especially before serious organ damage is evident, and with variable clinical presentations of patients and variable training and experience of attending physicians. Our objective was to describe and quantify physician agreement in diagnosing SIRS or sepsis in critical care patients as a function of available clinical information, infection site, and hospital setting. METHODS: We conducted a post hoc analysis of previously collected data from a prospective, observational trial (N = 249 subjects) in intensive care units at seven US hospitals, in which physicians at different stages of patient care were asked to make diagnostic calls of either SIRS, sepsis, or indeterminate, based on varying amounts of available clinical information (clinicaltrials.gov identifier: NCT02127502). The overall percent agreement and the free-marginal, inter-observer agreement statistic kappa (κ free) were used to quantify agreement between evaluators (attending physicians, site investigators, external expert panelists). Logistic regression and machine learning techniques were used to search for significant variables that could explain heterogeneity within the indeterminate and SIRS patient subgroups. RESULTS: Free-marginal kappa decreased between the initial impression of the attending physician and (1) the initial impression of the site investigator (κ free 0.68), (2) the consensus discharge diagnosis of the site investigators (κ free 0.62), and (3) the consensus diagnosis of the external expert panel (κ free 0.58). In contrast, agreement was greatest between the consensus discharge impression of site investigators and the consensus diagnosis of the external expert panel (κ free 0.79). When stratified by infection site, κ free for agreement between initial and later diagnoses had a mean value + 0.24 (range - 0.29 to + 0.39) for respiratory infections, compared to + 0.70 (range + 0.42 to + 0.88) for abdominal + urinary + other infections. Bioinformatics analysis failed to clearly resolve the indeterminate diagnoses and also failed to explain why 60% of SIRS patients were treated with antibiotics. CONCLUSIONS: Considerable uncertainty surrounds the differential clinical diagnosis of sepsis vs. SIRS, especially before organ damage has become highly evident, and for patients presenting with respiratory clinical signs. Our findings underscore the need to provide physicians with accurate, timely diagnostic information in evaluating possible sepsis.

Validation of a Host Response Assay, SeptiCyte LAB, for Discriminating Sepsis from Systemic Inflammatory Response Syndrome in the ICU.

RATIONALE: A molecular test to distinguish between sepsis and systemic inflammation of noninfectious etiology could potentially have clinical utility. OBJECTIVES: This study evaluated the diagnostic performance of a molecular host response assay (SeptiCyte LAB) designed to distinguish between sepsis and noninfectious systemic inflammation in critically ill adults. METHODS: The study employed a prospective, observational, noninterventional design and recruited a heterogeneous cohort of adult critical care patients from seven sites in the United States (n = 249). An additional group of 198 patients, recruited in the large MARS (Molecular Diagnosis and Risk Stratification of Sepsis) consortium trial in the Netherlands ( www.clinicaltrials.gov identifier NCT01905033), was also tested and analyzed, making a grand total of 447 patients in our study. The performance of SeptiCyte LAB was compared with retrospective physician diagnosis by a panel of three experts. MEASUREMENTS AND MAIN RESULTS: In receiver operating characteristic curve analysis, SeptiCyte LAB had an estimated area under the curve of 0.82-0.89 for discriminating sepsis from noninfectious systemic inflammation. The relative likelihood of sepsis versus noninfectious systemic inflammation was found to increase with increasing test score (range, 0-10). In a forward logistic regression analysis, the diagnostic performance of the assay was improved only marginally when used in combination with other clinical and laboratory variables, including procalcitonin. The performance of the assay was not significantly affected by demographic variables, including age, sex, or race/ethnicity. CONCLUSIONS: SeptiCyte LAB appears to be a promising diagnostic tool to complement physician assessment of infection likelihood in critically ill adult patients with systemic inflammation. Clinical trial registered with www.clinicaltrials.gov (NCT01905033 and NCT02127502).

Gene networks in skeletal muscle following endurance exercise are coexpressed in blood neutrophils and linked with blood inflammation markers.

It remains incompletely understood whether there is an association between the transcriptome profiles of skeletal muscle and blood leukocytes in response to exercise or other physiological stressors. We have previously analyzed the changes in the muscle and blood neutrophil transcriptome in eight trained men before and 3, 48, and 96 h after 2 h cycling and running. Because we collected muscle and blood in the same individuals and under the same conditions, we were able to directly compare gene expression between the muscle and blood neutrophils. Applying weighted gene coexpression network analysis (WGCNA) as an advanced network-driven method to these original data sets enabled us to compare the muscle and neutrophil transcriptomes in a rigorous and systematic manner. Two gene networks were identified that were preserved between skeletal muscle and blood neutrophils, functionally related to mitochondria and posttranslational processes. Strong preservation measures (Zsummary > 10) for both muscle-neutrophil gene networks were evident within the postexercise recovery period. Muscle and neutrophil gene coexpression was strongly correlated in the mitochondria-related network (r = 0.97; P = 3.17E-2). We also identified multiple correlations between muscular gene subnetworks and exercise-induced changes in blood leukocyte counts, inflammation, and muscle damage markers. These data reveal previously unidentified gene coexpression between skeletal muscle and blood neutrophils following exercise, showing the value of WGCNA to understand exercise physiology. Furthermore, these findings provide preliminary evidence in support of the notion that blood neutrophil gene networks may potentially help us to track physiological and pathophysiological changes in the muscle.NEW & NOTEWORTHY By using weighted gene coexpression network analysis, an advanced bioinformatics method, we have identified previously unknown, functional gene networks that are preserved between skeletal muscle and blood neutrophils during recovery from exercise. These novel preliminary data suggest that muscular gene networks are coexpressed in blood leukocytes following physiological stress. This is a step forward toward the development of blood neutrophil gene subnetworks as part of blood biomarker panels to assess muscle health and disease.

Diagnostic Accuracy of a Host Gene Expression Signature That Discriminates Clinical Severe Sepsis Syndrome and Infection-Negative Systemic Inflammation Among Critically Ill Children.

OBJECTIVES: SeptiCyte Lab (Immunexpress, Seattle, WA), a molecular signature measuring the relative expression levels of four host messenger RNAs, was developed to discriminate critically ill adults with infection-positive versus infection-negative systemic inflammation. The objective was to assess the performance of Septicyte Lab in critically ill pediatric patients. DESIGN: Prospective observational study. SETTING: Pediatric and Cardiac ICUs, Seattle Children's Hospital, Seattle, WA. PATIENTS: A cohort of 40 children with clinically overt severe sepsis syndrome and 30 children immediately postcardiopulmonary bypass surgery was recruited. The clinically overt severe sepsis syndrome children had confirmed or highly suspected infection (microbial culture orders, antimicrobial prescription), two or more systemic inflammatory response syndrome criteria (including temperature and leukocyte criteria), and at least cardiovascular ± pulmonary organ dysfunction. INTERVENTIONS: None (observational study only). MEASUREMENTS AND MAIN RESULTS: Next-generation RNA sequencing was conducted on PAXgene blood RNA samples, successfully for 35 of 40 (87.5%) of the clinically overt severe sepsis syndrome patients and 29 of 30 (96.7%) of the postcardiopulmonary bypass patients. Forty patient samples (~ 60% of cohort) were reanalyzed by reverse transcription-quantitative polymerase chain reaction, to check for concordance with next-generation sequencing results. Postcardiopulmonary bypass versus clinically overt severe sepsis syndrome descriptors included the following: age, 7.3 ± 5.5 versus 9.0 ± 6.6 years; gender, 41% versus 49% male; Pediatric Risk of Mortality, version III, 7.0 ± 4.6 versus 8.7 ± 6.4; Pediatric Logistic Organ Dysfunction, version II, 5.1 ± 2.2 versus 4.8 ± 2.8. SeptiCyte Lab strongly differentiated postcardiopulmonary bypass and clinically overt severe sepsis syndrome patients by receiver operating characteristic curve analysis, with an area-under-curve value of 0.99 (95% CI, 0.96-1.00). Equivalent performance was found using reverse transcription-quantitative polymerase chain reaction. There was no significant correlation between the score produced by the SeptiCyte Lab test and measures of illness severity, immune compromise, or microbial culture status. CONCLUSIONS: SeptiCyte Lab is able to discriminate clearly between clinically well-defined and homogeneous postcardiopulmonary bypass and clinically overt severe sepsis syndrome groups in children. A broader investigation among children with more heterogeneous inflammation-associated diagnoses and care settings is warranted.

Evaluation of haemoglobin in blister fluid as an indicator of paediatric burn wound depth.

The early and accurate assessment of burns is essential to inform patient treatment regimens; however, this first critical step in clinical practice remains a challenge for specialist burns clinicians worldwide. In this regard, protein biomarkers are a potential adjunct diagnostic tool to assist experienced clinical judgement. Free circulating haemoglobin has previously shown some promise as an indicator of burn depth in a murine animal model. Using blister fluid collected from paediatric burn patients, haemoglobin abundance was measured using semi-quantitative Western blot and immunoassays. Although a trend was observed in which haemoglobin abundance increased with burn wound severity, several patient samples deviated significantly from this trend. Further, it was found that haemoglobin concentration decreased significantly when whole cells, cell debris and fibrinous matrix was removed from the blister fluid by centrifugation; although the relationship to depth was still present. Statistical analyses showed that haemoglobin abundance in the fluid was more strongly related to the time between injury and sample collection and the time taken for spontaneous re-epithelialisation. We hypothesise that prolonged exposure to the blister fluid microenvironment may result in an increased haemoglobin abundance due to erythrocyte lysis, and delayed wound healing.

Urinary biomarkers of physical activity: candidates and clinical utility.

Chronic physical inactivity is a major risk factor for a number of important lifestyle diseases, while inappropriate exposure to high physical demands is a risk factor for musculoskeletal injury and fatigue. Proteomic and metabolomic investigations of the physical activity continuum - extreme sedentariness to extremes in physical performance - offer increasing insight into the biological impacts of physical activity. Moreover, biomarkers, revealed in such studies, may have utility in the monitoring of metabolic and musculoskeletal health or recovery following injury. As a diagnostic matrix, urine is non-invasive to collect and it contains many biomolecules, which reflect both positive and negative adaptations to physical activity exposure. This review examines the utility and landscape of biomarkers of physical activity with particular reference to those found in urine.

A fragment of the LG3 peptide of endorepellin is present in the urine of physically active mining workers: a potential marker of physical activity.

Biomarker analysis has been implemented in sports research in an attempt to monitor the effects of exertion and fatigue in athletes. This study proposed that while such biomarkers may be useful for monitoring injury risk in workers, proteomic approaches might also be utilised to identify novel exertion or injury markers. We found that urinary urea and cortisol levels were significantly elevated in mining workers following a 12 hour overnight shift. These levels failed to return to baseline over 24 h in the more active maintenance crew compared to truck drivers (operators) suggesting a lack of recovery between shifts. Use of a SELDI-TOF MS approach to detect novel exertion or injury markers revealed a spectral feature which was associated with workers in both work categories who were engaged in higher levels of physical activity. This feature was identified as the LG3 peptide, a C-terminal fragment of the anti-angiogenic/anti-tumourigenic protein endorepellin. This finding suggests that urinary LG3 peptide may be a biomarker of physical activity. It is also possible that the activity mediated release of LG3/endorepellin into the circulation may represent a biological mechanism for the known inverse association between physical activity and cancer risk/survival.

A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches.

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called "omics" disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.