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

PAGE-based transfer learning from single-cell to bulk sequencing enhances model generalization for sepsis diagnosis

Sepsis, caused by infections, sparks a dangerous bodily response. The transcriptional expression patterns of host responses aid in the diagnosis of sepsis, but the challenge lies in their limited generalization capabilities. To facilitate sepsis diagnosis, we present an updated version of single-cell Pair-wise Analysis of Gene Expression (scPAGE) using transfer learning method, scPAGE2, dedicated to data fusion between single-cell and bulk transcriptome. Compared to scPAGE, the upgrade to scPAGE2 featured ameliorated Differentially Expressed Gene Pairs (DEPs) for pretraining a model in single-cell transcriptome and retrained it using bulk transcriptome data to construct a sepsis diagnostic model, which effectively transferred cell-layer information from single-cell to bulk transcriptome. Seven datasets across three transcriptome platforms and fluorescence-activated cell sorting (FACS) were used for performance validation. The model involved four DEPs, showing robust performance across next-generation sequencing and microarray platforms, surpassing state-of-the-art models with an average AUROC of 0.947 and an average AUPRC of 0.987. Analysis of scRNA-seq data reveals higher cell proportions with JAM3-PIK3AP1 expression in sepsis monocytes, decreased ARG1-CCR7 in B and T cells. Elevated IRF6-HP in sepsis monocytes confirmed by both scRNA-seq and an independent cohort using FACS. Both the superior performance of the model and the in vitro validation of IRF6-HP in monocytes emphasize that scPAGE2 is effective and robust in the construction of sepsis diagnostic model. We additionally applied scPAGE2 to acute myeloid leukemia and demonstrated its superior classification performance. Overall, we provided a strategy to improve the generalizability of classification model that can be adapted to a broad range of clinical prediction scenarios.

Transcriptional pathways of terminal differentiation in high- and low-density blood granulocytes in sepsis

BACKGROUND

Trauma and infection induce emergency granulopoiesis. Counts of immature granulocytes and transcriptional pathways of terminal granulocytic differentiation in blood are elevated in sepsis but correlate with disease severity. This limits their performance as sepsis biomarkers in critically ill patients. We hypothesized that activation of these pathways in sepsis is attributable to immature low-density (LD) rather than mature high-density (HD) granulocytes.

METHODS

We included patients with sepsis and systemic inflammatory response syndrome (SIRS) of comparable disease severity, and additionally septic shock, on intensive or intermediate care unit admission. Blood granulocyte isolation by CD15 MicroBeads was followed by density-gradient centrifugation. Flow cytometry was used to determine counts of developmental stages (precursors) and their relative abundancies in total, HD, and LD granulocytes. Five degranulation markers were quantified in plasma by multiplex immunoassays. A set of 135 genes mapping granulocyte differentiation was assayed by QuantiGene™ Plex. CEACAM4, PLAC8, and CD63 were analyzed by qRT-PCR. Nonparametric statistical tests were applied.

RESULTS

Precursor counts appeared higher in sepsis than SIRS but did not correlate with disease severity for early immature and mature granulocytes. Precursor subpopulations were enriched at least ten-fold in LD over HD granulocytes without sepsis-SIRS differences. Degranulation markers in blood were comparable in sepsis and SIRS. Higher expression of early developmental genes in sepsis than SIRS was more pronounced in LD and less in HD than total granulocytes. Only the cell membrane protein encoding genes CXCR2 and CEACAM4 were more highly expressed in SIRS than sepsis. By qRT-PCR, the azurophilic granule genes CD63 and PLAC8 showed higher sepsis than SIRS levels in LD granulocytes and PLAC8 also in total granulocytes where its discriminatory performance resembled C-reactive protein (CRP).

CONCLUSIONS

Transcriptional programs of early terminal granulocytic differentiation distinguish sepsis from SIRS due to both higher counts of immature granulocytes and elevated expression of early developmental genes in sepsis. The sustained expression of PLAC8 in mature granulocytes likely accounts for its selection in the whole blood SeptiCyte™ LAB test. Total granulocyte PLAC8 rivals CRP as sepsis biomarker. However, infection-specific transcriptional pathways, that differentiate sepsis from sterile stress-induced granulocytosis more reliably than CRP, remain to be identified.

Rapid and Robust Identification of Sepsis Using SeptiCyte RAPID in a Heterogeneous Patient Population

Background/Objective: SeptiCyte RAPID is a transcriptional host response assay that discriminates between sepsis and non-infectious systemic inflammation (SIRS) with a one-hour turnaround time. The overall performance of this test in a cohort of 419 patients has recently been described [Balk et al., J Clin Med 2024, 13, 1194]. In this study, we present the results from a detailed stratification analysis in which SeptiCyte RAPID performance was evaluated in the same cohort across patient groups and subgroups encompassing different demographics, comorbidities and disease, sources and types of pathogens, interventional treatments, and clinically defined phenotypes. The aims were to identify variables that might affect the ability of SeptiCyte RAPID to discriminate between sepsis and SIRS and to determine if any patient subgroups appeared to present a diagnostic challenge for the test. Methods: (1) Subgroup analysis, with subgroups defined by individual demographic or clinical variables, using conventional statistical comparison tests. (2) Principal component analysis and k-means clustering analysis to investigate phenotypic subgroups defined by unique combinations of demographic and clinical variables. Results: No significant differences in SeptiCyte RAPID performance were observed between most groups and subgroups. One notable exception involved an enhanced SeptiCyte RAPID performance for a phenotypic subgroup defined by a combination of clinical variables suggesting a septic shock response. Conclusions: We conclude that for this patient cohort, SeptiCyte RAPID performance was largely unaffected by key variables associated with heterogeneity in patients suspected of sepsis.

Assessment of MeMed BV assays for differentiating between bacterial and viral respiratory infections

INTRODUCTION

Distinguishing bacterial from viral infections remains a challenge due to clinically indistinguishable presentations. Non-infectious conditions such as malignancy, pulmonary emboli and rheumatological conditions may also present with fever. Consequently, patients are often over-treated with antimicrobial agents or may not receive adequate therapy.

AREAS COVERED

This article provides a comprehensive review of a novel protein host-signature assay, the MeMed BV assay, that distinguishes bacterial from viral infections. The focus is on the use of the test in respiratory tract infections including assay performance characteristics, clinical profiles and data on cost-effectiveness. The changing landscape from the use of single inflammatory biomarkers, such as C-reactive protein, to alternative and diverse host signature biomarkers, is also discussed.

EXPERT OPINION

The MeMed BV assay is one of several novel host biomarkers that provide rapid results and demonstrate enhanced performance compared to single test biomarkers. This assay has been validated by a large number of carefully controlled clinical trials that demonstrate improved performance characteristics for distinguishing bacterial infections or combined bacterial/viral infections from viral or noninfectious causes of fever compared to C-reactive protein and procalcitonin. However, these trials may over-state assay performance as samples with equivocal band results are often not included in the statistical analysis. More real-world studies addressing clinical implementation of the MeMed BV assay or other biomarkers into ambulatory settings are needed.

Cellular host response sepsis test for risk stratification of patients in the emergency department: A pooled analysis

OBJECTIVES

Sepsis is one of the most common, costly, and misdiagnosed conditions in U.S. emergency departments (EDs). ED providers often treat on nonspecific signs, subjective suspicion, or presumption of infection, resulting in over- and undertreatment. An increased understanding of host response has opened a new direction for sepsis diagnostics. The IntelliSep test is a U.S. Food and Drug Administration-cleared cellular host response diagnostic that could help distinguish sepsis in ED settings. Our objective was to evaluate the potential of the cellular host response test to expedite appropriate care for patients who present with signs of infection.

METHODS

We performed a pooled analysis of five adult (≥18 years) cohorts enrolled at seven geographically diverse U.S. sites in separate studies. Structured blinded adjudication was used to classify presence or absence of sepsis, and only patients with high confidence in the adjudicated label were included (n = 1002), defined as patients for whom there was consensus in the determination of sepsis per the Sepsis-3 and severe sepsis per the Sepsis-2 definitions between both the independent adjudication panel and the site-level physician.

RESULTS

Among patients with signs or suspicion of infection, the test achieved similar or better performance compared to other indicators in identifying patients at high risk for sepsis (specificity > 83%) and significantly superior performance in identifying those at low risk (sensitivity > 92%; 0% sepsis-associated mortality). The test also stratified severity of illness, as shown by 30-day in-hospital mortality (p < 0.001), hospital length of stay (p < 0.01), and use of hospital resources (p < 0.001).

CONCLUSIONS

Our data suggest that the cellular host response test provides clinically actionable results for patients at both high and low risk for sepsis and provides a rapid, objective means for risk stratification of patients with signs of infection. If integrated into standard of care, the test may help improve outcomes and reduce unnecessary antibiotic use.

Enhancing sepsis biomarker development: key considerations from public and private perspectives

Implementation of biomarkers in sepsis and septic shock in emergency situations, remains highly challenging. This viewpoint arose from a public-private 3-day workshop aiming to facilitate the transition of sepsis biomarkers into clinical practice. The authors consist of international academic researchers and clinician-scientists and industry experts who gathered (i) to identify current obstacles impeding biomarker research in sepsis, (ii) to outline the important milestones of the critical path of biomarker development and (iii) to discuss novel avenues in biomarker discovery and implementation. To define more appropriately the potential place of biomarkers in sepsis, a better understanding of sepsis pathophysiology is mandatory, in particular the sepsis patient's trajectory from the early inflammatory onset to the late persisting immunosuppression phase. This time-varying host response urges to develop time-resolved test to characterize persistence of immunological dysfunctions. Furthermore, age-related difference has to be considered between adult and paediatric septic patients. In this context, numerous barriers to biomarker adoption in practice, such as lack of consensus about diagnostic performances, the absence of strict recommendations for sepsis biomarker development, cost and resources implications, methodological validation challenges or limited awareness and education have been identified. Biomarker-guided interventions for sepsis to identify patients that would benefit more from therapy, such as sTREM-1-guided Nangibotide treatment or Adrenomedullin-guided Enibarcimab treatment, appear promising but require further evaluation. Artificial intelligence also has great potential in the sepsis biomarker discovery field through capability to analyse high volume complex data and identify complex multiparametric patient endotypes or trajectories. To conclude, biomarker development in sepsis requires (i) a comprehensive and multidisciplinary approach employing the most advanced analytical tools, (ii) the creation of a platform that collaboratively merges scientific and commercial needs and (iii) the support of an expedited regulatory approval process.

Putative mRNA Biomarkers for the Eradication of Infection in an Equine Experimental Model of Septic Arthritis

Septic arthritis (SA) in horses has long-term health implications. The success of its resolution hinges on the implementation of early, aggressive treatment, which is often sustained over a prolonged period. Common diagnostic methods do not allow for the reliable detection of the eradication of joint infection. A potential alternative is the discovery and characterization of mRNA biomarkers. The purpose of this study was to identify potential mRNA biomarkers for the eradication of joint infection in equine SA and to compare their expression with our previously published proteomics data. In addition, the transcriptomics data were compared to the mRNA biomarker panel, SeptiCyte Lab, used to distinguish sepsis from non-septic shock in humans. A comparative transcriptomics analysis of synovial fluid from the SA joints of five horses with active infection and subsequent post-treatment eradicated infection in the same joints and five horses with non-septic synovitis was performed. Eight novel mRNA transcripts were identified that were significantly upregulated (>3-fold) in horses with active SA compared to horses post-eradication of infection after treatment and horses with non-septic synovitis. Two proteins in our proteomics data corresponded to these mRNA transcripts, but were not statistically different. The transcripts used in the SeptiCyte test were not differentially expressed in our study. Our results suggest that mRNA may be a useful source of biomarkers for the eradication of joint infection in horses and warrants further investigation.

The molecular landscape of sepsis severity in infants: enhanced coagulation, innate immunity, and T cell repression

Introduction

Sepsis remains a major cause of mortality and morbidity in infants. In recent years, several gene marker strategies for the early identification of sepsis have been proposed but only a few have been independently validated for adult cohorts and applicability to infant sepsis remains unclear. Biomarkers to assess disease severity and risks of shock also represent an important unmet need.

Methods

To elucidate characteristics driving sepsis in infants, we assembled a multi-transcriptomic dataset from public microarray datasets originating from five independent studies pertaining to bacterial sepsis in infant < 6-months of age (total n=335). We utilized a COmbat co-normalization strategy to enable comparative evaluation across multiple studies while preserving the relationship between cases and controls.

Results

We found good concordance with only two out of seven of the published adult sepsis gene signatures (accuracy > 80%), highlighting the narrow utility of adult-derived signatures for infant diagnosis. Pseudotime analysis of individual subjects' gene expression profiles showed a continuum of molecular changes forming tight clusters concurrent with disease progression between healthy controls and septic shock cases. In depth gene expression analyses between bacteremia, septic shock, and healthy controls characterized lymphocyte activity, hemostatic processes, and heightened innate immunity during the molecular transition toward a state of shock.

Discussion

Our analysis revealed the presence of multiple significant transcriptomic perturbations that occur during the progression to septic shock in infants that are characterized by late-stage induction of clotting factors, in parallel with a heightened innate immune response and a suppression of adaptive cell functionality.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

Host response signature trends in persistent bacteraemia and metastatic infection due to Staphylococcus aureus and Gram-negative bacilli: a prospective multicentre observational study

BACKGROUND

A prompt diagnosis of bacteraemia and sepsis is essential. Markers to predict the risk of persistent bacteraemia and metastatic infection are lacking. SeptiCyte RAPID is a host response assay stratifying patients according to the risk of infectious vs sterile inflammation through a scoring system (SeptiScore). In this study we explore the association between SeptiScore and persistent bacteraemia as well as metastatic and persistent infection in the context of a proven bacteraemia episode.

METHODS

This is a prospective multicentre observational 14-month study on patients with proven bacteraemia caused by Staphylococcus aureus or Gram-negative bacilli. Samples for assessment by SeptiCyte were collected with paired blood cultures for 4 consecutive days after the index blood culture.

RESULTS

We included 86 patients in the study, 40 with S. aureus and 46 with Gram-negative bacilli bacteraemia. SeptiScores over the follow-up were higher in patients with Gram-negative compared to S. aureus bacteraemia (median 6.4, IQR 5.5-7.4 vs 5.6 IQR 5.1-6.2, p = 0.002). Higher SeptiScores were found to be associated with positive blood cultures at follow-up (AUC = 0.86, 95%CI 0.68-1.00) and with a diagnosis of metastatic infection at day 1 and 2 of follow-up (AUC = 0.79, 95%CI 0.57-1.00 and AUC = 0.82, 95%CI 0.63-1.00 respectively) in the context of Gram-negative bacteraemia while no association between SeptiScore and the outcomes of interest was observed in S. aureus bacteraemia. Mixed models confirmed the association of SeptiScore with positive blood cultures at follow-up (p = 0.04) and metastatic infection (p = 0.03) in the context of Gram-negative bacteraemia but not S. aureus bacteraemia after adjusting for confounders.

CONCLUSIONS

SeptiScores differ in the follow-up of S. aureus and Gram-negative bacteraemia. In the setting of Gram-negative bacteraemia SeptiScore demonstrated a good negative predictive value for the outcomes of interest and might help rule out the persistence of infection defined as metastatic spread, lack of source control or persistent bacteraemia.

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.

Advances in sepsis biomarkers

Sepsis, a dysregulated host immune response to an infectious agent, significantly increases morbidity and mortality for hospitalized patients worldwide. This chapter reviews (1) the basic principles of infectious diseases, pathophysiology and current definition of sepsis, (2) established sepsis biomarkers such lactate, procalcitonin and C-reactive protein, (3) novel, newly regulatory-cleared/approved biomarkers, such as assays that evaluate white blood cell properties and immune response molecules, and (4) emerging biomarkers and biomarker panels to highlight future directions and opportunities in the diagnosis and management of sepsis.

The 'analysis of gene expression and biomarkers for point-of-care decision support in Sepsis' study; temporal clinical parameter analysis and validation of early diagnostic biomarker signatures for severe inflammation andsepsis-SIRS discrimination

Introduction

Early diagnosis of sepsis and discrimination from SIRS is crucial for clinicians to provide appropriate care, management and treatment to critically ill patients. We describe identification of mRNA biomarkers from peripheral blood leukocytes, able to identify severe, systemic inflammation (irrespective of origin) and differentiate Sepsis from SIRS, in adult patients within a multi-center clinical study.

Methods

Participants were recruited in Intensive Care Units (ICUs) from multiple UK hospitals, including fifty-nine patients with abdominal sepsis, eighty-four patients with pulmonary sepsis, forty-two SIRS patients with Out-of-Hospital Cardiac Arrest (OOHCA), sampled at four time points, in addition to thirty healthy control donors. Multiple clinical parameters were measured, including SOFA score, with many differences observed between SIRS and sepsis groups. Differential gene expression analyses were performed using microarray hybridization and data analyzed using a combination of parametric and non-parametric statistical tools.

Results

Nineteen high-performance, differentially expressed mRNA biomarkers were identified between control and combined SIRS/Sepsis groups (FC>20.0, p<0.05), termed 'indicators of inflammation' (I°I), including CD177, FAM20A and OLAH. Best-performing minimal signatures e.g. FAM20A/OLAH showed good accuracy for determination of severe, systemic inflammation (AUC>0.99). Twenty entities, termed 'SIRS or Sepsis' (S°S) biomarkers, were differentially expressed between sepsis and SIRS (FC>2·0, p-value<0.05).

Discussion

The best performing signature for discriminating sepsis from SIRS was CMTM5/CETP/PLA2G7/MIA/MPP3 (AUC=0.9758). The I°I and S°S signatures performed variably in other independent gene expression datasets, this may be due to technical variation in the study/assay platform.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Bioinformatic Analysis and Machine Learning Methods in Neonatal Sepsis: Identification of Biomarkers and Immune Infiltration

The disease neonatal sepsis (NS) poses a serious threat to life, and its pathogenesis remains unclear. Using the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified and functional enrichment analyses were conducted. Three machine learning algorithms containing the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) were applied to identify the optimal feature genes (OFGs). This study conducted CIBERSORT to present the abundance of immune infiltrates between septic and control neonates and assessed the relationship between OFGs and immune cells. In total, 44 DEGs were discovered between the septic and control newborns. Throughout the enrichment analysis, DEGs were primarily related to inflammatory signaling pathways and immune responses. The OFGs derived from machine learning algorithms were intersected to yield four biomarkers, namely Hexokinase 3 (HK3), Cystatin 7 (CST7), Resistin (RETN), and Glycogenin 1 (GYG1). The potential biomarkers were validated in other datasets and LPS-stimulated HEUVCs. Septic infants showed a higher proportion of neutrophils (p < 0.001), M0 macrophages (p < 0.001), and regulatory T cells (p = 0.004). HK3, CST7, RETN, and GYG1 showed significant correlations with immune cells. Overall, the biomarkers offered promising insights into the molecular mechanisms of immune regulation for the prediction and treatment of NS.

Rapid identification of sepsis in the emergency department

Objectives

Recent research has helped define the complex pathways in sepsis, affording new opportunities for advancing diagnostics tests. Given significant advances in the field, a group of academic investigators from emergency medicine, intensive care, pathology, and pharmacology assembled to develop consensus around key gaps and potential future use for emerging rapid host response diagnostics assays in the emergency department (ED) setting.

Methods

A modified Delphi study was conducted that included 26 panelists (expert consensus panel) from multiple specialties. A smaller steering committee first defined a list of Delphi statements related to the need for and future potential use of a hypothetical sepsis diagnostic test in the ED. Likert scoring was used to assess panelists agreement or disagreement with statements. Two successive rounds of surveys were conducted and consensus for statements was operationally defined as achieving agreement or disagreement of 75% or greater.

Results

Significant gaps were identified related to current tools for assessing risk of sepsis in the ED. Strong consensus indicated the need for a test providing an indication of the severity of dysregulated host immune response, which would be helpful even if it did not identify the specific pathogen. Although there was a relatively high degree of uncertainty regarding which patients would most benefit from the test, the panel agreed that an ideal host response sepsis test should aim to be integrated into ED triage and thus should produce results in less than 30 minutes. The panel also agreed that such a test would be most valuable for improving sepsis outcomes and reducing rates of unnecessary antibiotic use.

Conclusion

The expert consensus panel expressed strong consensus regarding gaps in sepsis diagnostics in the ED and the potential for new rapid host response tests to help fill these gaps. These finding provide a baseline framework for assessing key attributes of evolving host response diagnostic tests for sepsis in the ED.

The Role of Transcriptomics in Redefining Critical Illness

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at  https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from  https://link.springer.com/bookseries/8901 .

Potent anti-inflammatory responses: Role of hydrogen in IL-1α dominated early phase systemic inflammation

Introduction: It has been proven that hydrogen has obvious anti-inflammatory effects in animal experiments and clinical practice. However, the early dynamic process of the inflammatory response caused by lipopolysaccharide (LPS) and the anti-inflammatory effect of hydrogen has not been definitively reported.

Methods: Inflammation in male C57/BL6J mice or RAW264.7 cells was induced with LPS, for which hydrogen was immediately administered until samples were taken. Pathological changes in lung tissue were assessed using hematoxylin and eosin (HE) staining. Levels of inflammatory factors in serum were determined using liquid protein chip. The mRNA levels of chemotactic factors in lung tissues, leukocytes, and peritoneal macrophages were measured by qRT-PCR. The expression levels of IL-1α and HIF-1α were measured by immunocytochemistry.

Results: Hydrogen alleviated LPS-induced inflammatory infiltration in the lung tissues of mice. Among the 23 inflammatory factors screened, LPS-induced upregulation of IL-1α etc. was significantly inhibited by hydrogen within 1 hour. The mRNA expression of MCP-1, MIP-1α, G-CSF, and RANTES was inhibited obviously by hydrogen at 0.5 and 1 h in mouse peritoneal macrophages. In addition, hydrogen significantly blocked LPS or H2O2-induced upregulation of HIF-1α, and IL-1α in 0.5 h in RAW264.7 cells.

Discussion: The results suggested that hydrogen is potentially inhibitive against inflammation by inhibiting HIF-1α and IL-1α release at early occurrence. The target of the inhibitive LPS-induced-inflammatory action of hydrogen is chemokines in macrophages in the peritoneal cavity. This study provides direct experimental evidence for quickly controlling inflammation with the translational application of a hydrogen-assisted protocol.

Clinically Adjudicated Reference Standards for Evaluation of Infectious Diseases Diagnostics

Lack of a gold standard can present a challenge for evaluation of diagnostic test accuracy of some infectious diseases tests, particularly when the test's accuracy potentially exceeds that of its predecessors. This approach may measure agreement with an imperfect reference, rather than correctness, because the right answer is unknown. Solutions consist of multitest comparators, including those that involve a test under evaluation if multiple new tests are being evaluated together, using latent class modeling, and clinically adjudicated reference standards. Clinically adjudicated reference standards may be considered as comparator methods when no predefined test or composite of tests is sufficiently accurate; they emulate clinical practice in that multiple data pieces are clinically assessed together.

Trial of antibiotic restraint in presumed pneumonia: A Surgical Infection Society multicenter pilot

BACKGROUND

Pneumonia is the most common intensive care unit-acquired infection in the trauma and emergency general surgery population. Despite guidelines urging rapid antibiotic use, data supporting immediate antibiotic initiation in cases of suspected infection are limited. Our hypothesis was that a protocol of specimen-initiated antibiotic initiation would have similar compliance and outcomes to an immediate initiation protocol.

METHODS

We devised a pragmatic cluster-randomized crossover pilot trial. Four surgical and trauma intensive care units were randomized to either an immediate initiation or specimen-initiated antibiotic protocol for intubated patients with suspected pneumonia and bronchoscopically obtained cultures who did not require vasopressors. In the immediate initiation arm, antibiotics were started immediately after the culture regardless of patient status. In the specimen-initiated arm, antibiotics were delayed until objective Gram stain or culture results suggested infection. Each site participated in both arms after a washout period and crossover. Outcomes were protocol compliance, all-cause 30-day mortality, and ventilator-free alive days at 30 days. Standard statistical techniques were applied.

RESULTS

A total of 186 patients had 244 total cultures, of which only the first was analyzed. Ninety-three patients (50%) were enrolled in each arm, and 94.6% were trauma patients (84.4% blunt trauma). The median age was 50.5 years, and 21% of the cohort was female. There were no differences in demographics, comorbidities, sequential organ failure assessment, Acute Physiology and Chronic Health Evaluation II, or Injury Severity Scores. Antibiotics were started significantly later in the specimen-initiated arm (0 vs. 9.3 hours; p < 0.0001) with 19.4% avoiding antibiotics completely for that episode. There were no differences in the rate of protocol adherence, 30-day mortality, or ventilator-free alive days at 30 days.

CONCLUSION

In this cluster-randomized crossover trial, we found similar compliance rates between immediate and specimen-initiated antibiotic strategies. Specimen-initiated antibiotic protocol in patients with a suspected hospital-acquired pneumonia did not result in worse clinical outcomes compared with immediate initiation.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level II.

Evaluation of the host immune response assay SeptiCyte RAPID for potential triage of COVID-19 patients

Tools for the evaluation of COVID-19 severity would help clinicians with triage decisions, especially the decision whether to admit to ICU. The aim of this study was to evaluate SeptiCyte RAPID, a host immune response assay (Immunexpress, Seattle USA) as a triaging tool for COVID-19 patients requiring hospitalization and potentially ICU care. SeptiCyte RAPID employs a host gene expression signature consisting of the ratio of expression levels of two immune related mRNAs, PLA2G7 and PLAC8, measured from whole blood samples. Blood samples from 146 adult SARS-CoV-2 (+) patients were collected within 48 h of hospital admission in PAXgene blood RNA tubes at Hospital del Mar, Barcelona, Spain, between July 28th and December 1st, 2020. Data on demographics, vital signs, clinical chemistry parameters, radiology, interventions, and SeptiCyte RAPID were collected and analyzed with bioinformatics methods. The performance of SeptiCyte RAPID for COVID-19 severity assessment and ICU admission was evaluated, relative to the comparator of retrospective clinical assessment by the Hospital del Mar clinical care team. In conclusion, SeptiCyte RAPID was able to stratify COVID-19 cases according to clinical severity: critical vs. mild (AUC = 0.93, p < 0.0001), critical vs. moderate (AUC = 0.77, p = 0.002), severe vs. mild (AUC = 0.85, p = 0.0003), severe vs. moderate (AUC = 0.63, p = 0.05). This discrimination was significantly better (by AUC or p-value) than could be achieved by CRP, lactate, creatine, IL-6, or D-dimer. Some of the critical or severe cases had "early" blood draws (before ICU admission; n = 33). For these cases, when compared to moderate and mild cases not in ICU (n = 37), SeptiCyte RAPID had AUC = 0.78 (p = 0.00012). In conclusion, SeptiCyte RAPID was able to stratify COVID-19 cases according to clinical severity as defined by the WHO COVID-19 Clinical Management Living Guidance of January 25th, 2021. Measurements taken early (before a patient is considered for ICU admission) suggest that high SeptiScores could aid in predicting the need for later ICU admission.

Clustering ICU patients with sepsis based on the patterns of their circulating biomarkers: A secondary analysis of the CAPTAIN prospective multicenter cohort study

BACKGROUND

Although sepsis is a life-threatening condition, its heterogeneous presentation likely explains the negative results of most trials on adjunctive therapy. This study in patients with sepsis aimed to identify subgroups with similar immune profiles and their clinical and outcome correlates.

METHODS

A secondary analysis used data of a prospective multicenter cohort that included patients with early assessment of sepsis. They were described using Predisposition, Insult, Response, Organ failure sepsis (PIRO) staging system. Thirty-eight circulating biomarkers (27 proteins, 11 mRNAs) were assessed at sepsis diagnosis, and their patterns were determined through principal component analysis (PCA). Hierarchical clustering was used to group the patients and k-means algorithm was applied to assess the internal validity of the clusters.

RESULTS

Two hundred and three patients were assessed, of median age 64.5 [52.0-77.0] years and SAPS2 score 55 [49-61] points. Five main patterns of biomarkers and six clusters of patients (including 42%, 21%, 17%, 9%, 5% and 5% of the patients) were evidenced. Clusters were distinguished according to the certainty of the causal infection, inflammation, use of organ support, pro- and anti-inflammatory activity, and adaptive profile markers.

CONCLUSIONS

In this cohort of patients with suspected sepsis, we individualized clusters which may be described with criteria used to stage sepsis. As these clusters are based on the patterns of circulating biomarkers, whether they might help to predict treatment responsiveness should be addressed in further studies.

TRIAL REGISTRATION

The CAPTAIN study was registered on clinicaltrials.gov on June 22, 2011, # NCT01378169.

Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and sepsis in the emergency department

BACKGROUND AND IMPORTANCE

mRNA-based host response signatures have been reported to improve sepsis diagnostics. Meanwhile, prognostic markers for the rapid and accurate prediction of severity in patients with suspected acute infections and sepsis remain an unmet need. IMX-SEV-2 is a 29-host-mRNA classifier designed to predict disease severity in patients with acute infection or sepsis.

OBJECTIVE

Validation of the host-mRNA infection severity classifier IMX-SEV-2.

DESIGN, SETTINGS AND PARTICIPANTS

Prospective, observational, convenience cohort of emergency department (ED) patients with suspected acute infections.

OUTCOME MEASURES AND ANALYSIS

Whole blood RNA tubes were analyzed using independently trained and validated composite target genes (IMX-SEV-2). IMX-SEV-2-generated risk scores for severity were compared to the patient outcomes in-hospital mortality and 72-h multiorgan failure.

MAIN RESULTS

Of the 312 eligible patients, 22 (7.1%) died in hospital and 58 (18.6%) experienced multiorgan failure within 72 h of presentation. For predicting in-hospital mortality, IMX-SEV-2 had a significantly higher area under the receiver operating characteristic (AUROC) of 0.84 [95% confidence intervals (CI), 0.76-0.93] compared to 0.76 (0.64-0.87) for lactate, 0.68 (0.57-0.79) for quick Sequential Organ Failure Assessment (qSOFA) and 0.75 (0.65-0.85) for National Early Warning Score 2 (NEWS2), ( P  = 0.015, 0.001 and 0.013, respectively). For identifying and predicting 72-h multiorgan failure, the AUROC of IMX-SEV-2 was 0.76 (0.68-0.83), not significantly different from lactate (0.73, 0.65-0.81), qSOFA (0.77, 0.70-0.83) or NEWS2 (0.81, 0.75-0.86).

CONCLUSION

The IMX-SEV-2 classifier showed a superior prediction of in-hospital mortality compared to biomarkers and clinical scores among ED patients with suspected infections. No improvement for predicting multiorgan failure was found compared to established scores or biomarkers. Identifying patients with a high risk of mortality or multiorgan failure may improve patient outcomes, resource utilization and guide therapy decision-making.

Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation

Purpose

Sepsis is a serious life-threatening condition characterised by multi-organ failure due to a disturbed immune response caused by severe infection. The pathogenesis of sepsis is unclear. The aim of this article is to identify potential diagnostic and prognostic biomarkers of sepsis to improve the survival of patients with sepsis.

Methods

We downloaded 7 datasets from Gene Expression Omnibus database and screened the immune-related differential genes (IRDEGs). The related functions of IRDEGs were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). CIBERSORT was used to evaluate the infiltration of the immune cells, and Pearson algorithm of R software was used to calculate the correlation between the immune cell content and gene expression to screen the genes most related to immune cells in sepsis group, which were intersected with IRDEGs to obtain common genes. Key genes were identified from common genes based on the area under the receiver operating characteristic curve (AUC) greater than 0.8 in the 6 datasets. We then analyzed the predictive value of key genes in sepsis survival. Finally, we verified the expression of key genes in patients with sepsis by PCR analysis.

Results

A total of 164 IRDEGs were obtained, which were associated mainly with inflammatory and immunometabolic responses. Ten key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) were identified with high diagnostic efficacy. Logistic regression analysis revealed that six of the key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) may affect the survival prognosis of sepsis. PCR analysis confirmed that the expression of seven key genes (IL1R2, S100A12, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) was consistent with microarray outcome.

Conclusion

This study explored the immune and metabolic response mechanisms associated with sepsis, and identified ten potential diagnostic and six prognostic genes.

Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures

Purpose

Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis.

Methods

Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148).

Results

Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703).

Conclusion

Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06769-z.

Key Signature Genes of Early Terminal Granulocytic Differentiation Distinguish Sepsis From Systemic Inflammatory Response Syndrome on Intensive Care Unit Admission

Infection can induce granulopoiesis. This process potentially contributes to blood gene classifiers of sepsis in systemic inflammatory response syndrome (SIRS) patients. This study aimed to identify signature genes of blood granulocytes from patients with sepsis and SIRS on intensive care unit (ICU) admission. CD15+ cells encompassing all stages of terminal granulocytic differentiation were analyzed. CD15 transcriptomes from patients with sepsis and SIRS on ICU admission and presurgical controls (discovery cohort) were subjected to differential gene expression and pathway enrichment analyses. Differential gene expression was validated by bead array in independent sepsis and SIRS patients (validation cohort). Blood counts of granulocyte precursors were determined by flow cytometry in an extension of the validation cohort. Despite similar transcriptional CD15 responses in sepsis and SIRS, enrichment of canonical pathways known to decline at the metamyelocyte stage (mitochondrial, lysosome, cell cycle, and proteasome) was associated with sepsis but not SIRS. Twelve of 30 validated genes, from 100 selected for changes in response to sepsis rather than SIRS, were endo-lysosomal. Revisiting the discovery transcriptomes revealed an elevated expression of promyelocyte-restricted azurophilic granule genes in sepsis and myelocyte-restricted specific granule genes in sepsis followed by SIRS. Blood counts of promyelocytes and myelocytes were higher in sepsis than in SIRS. Sepsis-induced granulopoiesis and signature genes of early terminal granulocytic differentiation thus provide a rationale for classifiers of sepsis in patients with SIRS on ICU admission. Yet, the distinction of this process from noninfectious tissue injury-induced granulopoiesis remains to be investigated.

Prospective Validation of a Rapid Host Gene Expression Test to Discriminate Bacterial From Viral Respiratory Infection

Importance

Bacterial and viral causes of acute respiratory illness (ARI) are difficult to clinically distinguish, resulting in the inappropriate use of antibacterial therapy. The use of a host gene expression-based test that is able to discriminate bacterial from viral infection in less than 1 hour may improve care and antimicrobial stewardship.

Objective

To validate the host response bacterial/viral (HR-B/V) test and assess its ability to accurately differentiate bacterial from viral infection among patients with ARI.

Design, Setting, and Participants

This prospective multicenter diagnostic study enrolled 755 children and adults with febrile ARI of 7 or fewer days' duration from 10 US emergency departments. Participants were enrolled from October 3, 2014, to September 1, 2019, followed by additional enrollment of patients with COVID-19 from March 20 to December 3, 2020. Clinical adjudication of enrolled participants identified 616 individuals as having bacterial or viral infection. The primary analysis cohort included 334 participants with high-confidence reference adjudications (based on adjudicator concordance and the presence of an identified pathogen confirmed by microbiological testing). A secondary analysis of the entire cohort of 616 participants included cases with low-confidence reference adjudications (based on adjudicator discordance or the absence of an identified pathogen in microbiological testing). Thirty-three participants with COVID-19 were included post hoc.

Interventions

The HR-B/V test quantified the expression of 45 host messenger RNAs in approximately 45 minutes to derive a probability of bacterial infection.

Main Outcomes and Measures

Performance characteristics for the HR-B/V test compared with clinical adjudication were reported as either bacterial or viral infection or categorized into 4 likelihood groups (viral very likely [probability score <0.19], viral likely [probability score of 0.19-0.40], bacterial likely [probability score of 0.41-0.73], and bacterial very likely [probability score >0.73]) and compared with procalcitonin measurement.

Results

Among 755 enrolled participants, the median age was 26 years (IQR, 16-52 years); 360 participants (47.7%) were female, and 395 (52.3%) were male. A total of 13 participants (1.7%) were American Indian, 13 (1.7%) were Asian, 368 (48.7%) were Black, 131 (17.4%) were Hispanic, 3 (0.4%) were Native Hawaiian or Pacific Islander, 297 (39.3%) were White, and 60 (7.9%) were of unspecified race and/or ethnicity. In the primary analysis involving 334 participants, the HR-B/V test had sensitivity of 89.8% (95% CI, 77.8%-96.2%), specificity of 82.1% (95% CI, 77.4%-86.6%), and a negative predictive value (NPV) of 97.9% (95% CI, 95.3%-99.1%) for bacterial infection. In comparison, the sensitivity of procalcitonin measurement was 28.6% (95% CI, 16.2%-40.9%; P < .001), the specificity was 87.0% (95% CI, 82.7%-90.7%; P = .006), and the NPV was 87.6% (95% CI, 85.5%-89.5%; P < .001). When stratified into likelihood groups, the HR-B/V test had an NPV of 98.9% (95% CI, 96.1%-100%) for bacterial infection in the viral very likely group and a positive predictive value of 63.4% (95% CI, 47.2%-77.9%) for bacterial infection in the bacterial very likely group. The HR-B/V test correctly identified 30 of 33 participants (90.9%) with acute COVID-19 as having a viral infection.

Conclusions and Relevance

In this study, the HR-B/V test accurately discriminated bacterial from viral infection among patients with febrile ARI and was superior to procalcitonin measurement. The findings suggest that an accurate point-of-need host response test with high NPV may offer an opportunity to improve antibiotic stewardship and patient outcomes.

Infection and Burn Injury

Burn injury is debilitating and among one of the most frequently occurring traumas. Critical care improvements have allowed for increasingly positive outcomes. However, infection, whether it be localized to the site of the wound or systemic in nature, remains a serious cause of morbidity and mortality. Immune suppression predisposes the burn population to the development of invasive infections; and this along with the possibility of inhalation injury puts them at a significant risk for mortality. Emerging multi-drug-resistant pathogens, including Staphylococcus aureus, Enterococcus, Pseudomonas, Acinetobacter, Enterobacter, and yeast spp., continue to complicate clinical care measures, requiring innovative therapies and antimicrobial treatment. Close monitoring of antimicrobial regimens, strict decontamination procedures, early burn eschar removal, adequate wound closure, proper nutritional maintenance, and management of shock and resuscitation all play a significant role in mitigating infection. Novel antimicrobial therapies such as ultraviolet light, cold plasma and topical antiseptics must continue to evolve in order to lower the burden of infection in burn.

Faster infection diagnostics for intensive care unit (ICU) patients

INTRODUCTION

: The patient admitted to intensive care units (ICU) is critically ill, to some extent immunosuppressed, with a high risk of infection, sometimes by multidrug-resistant microorganisms. In this context, the intensivist expects from the microbiology service quick and understandable information so that appropriate antimicrobial treatment for that particular patient and infection can be initiated.

AREAS COVERED

: In this review of recent literature (2015-2021), we identified diagnostic methods for the most prevalent infections in these patients through a search of the databases Pubmed, evidence-based medicine online, York University reviewers group, Cochrane, MBE-Trip, and Sumsearch using the terms: adult, clinical laboratory techniques, critical care, early diagnosis, microbiology, molecular diagnostic techniques, spectrometry and metagenomics.

EXPERT OPINION

: There has been an exponential surge in diagnostic systems used directly on blood and other samples to expedite microbial identification and antimicrobial susceptibility testing of pathogens. Few studies have thus far assessed their clinical impact; final outcomes will also depend on preanalytical and post-analytical factors. Besides, many of the resistance mechanisms cannot yet be detected with molecular techniques, which impairs the prediction of the actual resistance phenotype.

Nonetheless, this is an exciting field with much yet to explore.

Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures

BACKGROUND

Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.

METHODS

Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.

FINDINGS

Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77-80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89-97%) accurately predicted endotype status in both ER and ICU validation cohorts.

INTERPRETATION

The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

The pathophysiology of sepsis - 2021 update: Part 2, organ dysfunction and assessment

DISCLAIMER

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

PURPOSE

This is the second article in a 2-part series discussing the pathophysiology of sepsis. Part 1 of the series reviewed the immunologic response and overlapping pathways of inflammation and coagulation that contribute to the widespread organ dysfunction. In this article (part 2), major organ systems and their dysfunction in sepsis are reviewed, with discussion of scoring systems used to identify patterns and abnormal vital signs and laboratory values associated with sepsis.

SUMMARY

Sepsis is a dysregulated host response to infection that produces significant morbidity, and patients with shock due to sepsis have circulatory and cellular and metabolic abnormalities that lead to a higher mortality. Cardiovascular dysfunction produces vasodilation, reduced cardiac output and hypotension/shock requiring fluids, vasopressors, and advanced hemodynamic monitoring. Respiratory dysfunction may require mechanical ventilation and attention to volume status. Renal dysfunction is a frequent manifestation of sepsis. Hematologic dysfunction produces low platelets and either elevation or reduction of leucocytes, so consideration of the neutrophil:lymphocyte ratio may be useful. Procoagulant and antifibrinolytic activity leads to coagulation that is stimulated by inflammation. Hepatic dysfunction manifest as elevated bilirubin is often a late finding in sepsis and may cause reductions in production of essential proteins. Neurologic dysfunction may result from local endothelial injury and systemic inflammation through activity of the vagus nerve.

CONCLUSION

Timely recognition and team response with efficient use of therapies can improve patient outcome, and pharmacists with a complete understanding of the pathophysiologic mechanisms and treatments are valuable members of that team.

Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study

BACKGROUND

We hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.

METHODS

We did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew's Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for "viral infection", "transcriptome", "biomarker", and "blood". We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity.

FINDINGS

We identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27-47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for IFI27 provided the highest accuracy for discriminating between test-negative controls and test-positive individuals at the time of their first positive SARS-CoV-2 PCR result, with AUROC of 0·95 (95% CI 0·91-0·99), sensitivity 0·84 (0·70-0·93), and specificity 0·95 (0·85-0·98) at a predefined threshold (Z score >2). The transcript performed equally well in individuals with and without symptoms. Three other candidate signatures (including two to 48 transcripts) had statistically equivalent discrimination to IFI27 (AUROCs 0·91-0·95).

INTERPRETATION

Our findings support further urgent evaluation and development of blood IFI27 transcripts as a biomarker for early phase SARS-CoV-2 infection for screening individuals at high risk of infection, such as contacts of index cases, to facilitate early case isolation and early use of antiviral treatments as they emerge.

FUNDING

Barts Charity, Wellcome Trust, and National Institute of Health Research.

A new paradigm for leprosy diagnosis based on host gene expression

Transcriptional profiling is a powerful tool to investigate and detect human diseases. In this study, we used bulk RNA-sequencing (RNA-Seq) to compare the transcriptomes in skin lesions of leprosy patients or controls affected by other dermal conditions such as granuloma annulare, a confounder for paucibacillary leprosy. We identified five genes capable of accurately distinguishing multibacillary and paucibacillary leprosy from other skin conditions. Indoleamine 2,3-dioxygenase 1 (IDO1) expression alone was highly discriminatory, followed by TLR10, BLK, CD38, and SLAMF7, whereas the HS3ST2 and CD40LG mRNA separated multi- and paucibacillary leprosy. Finally, from the main differentially expressed genes (DEG) and enriched pathways, we conclude that paucibacillary disease is characterized by epithelioid transformation and granuloma formation, with an exacerbated cellular immune response, while multibacillary leprosy features epithelial-mesenchymal transition with phagocytic and lipid biogenesis patterns in the skin. These findings will help catalyze the development of better diagnostic tools and potential host-based therapeutic interventions. Finally, our data may help elucidate host-pathogen interplay driving disease clinical manifestations.

Development of a 3-transcript host expression assay to differentiate between viral and bacterial infections in pigs

Indiscriminate use of antibiotics to treat infections that are of viral origin contributes to unnecessary use which potentially may induce resistance in commensal bacteria. To counteract this a number of host gene transcriptional studies have been conducted to identify genes that are differently expressed during bacterial and viral infections in humans, and thus could be used as a tool to base decisions on the use of antibiotics. In this paper, we aimed to evaluate the potential of a selection of genes that have been considered biomarkers in humans, to differentially diagnose bacterial from viral infections in the pig. First porcine PBMC were induced with six toll-like receptor (TLR) agonists (FliC, LPS, ODN 2216, Pam3CSK4, poly I:C, R848) to mimic host gene expression induced by bacterial or viral pathogens, or exposed to heat-killed Actinobacillus pleuropneumoniae or a split influenza virus. Genes that were differentially expressed between bacterial and viral inducers were further evaluated on clinical material comprising eleven healthy pigs, and six pigs infected with A. pleuropneumoniae. This comprised three virally upregulated genes (IFI44L, MxA, RSAD2) and four bacterially upregulated genes (IL-1β, IL-8, FAM89A, S100PBP). All six infected pigs could be differentially diagnosed to healthy pigs using a host gene transcription assay based on the geometric average of the bacterially induced genes IL-8 and S100PBP over that of the virally induced gene MxA.

Initial antimicrobial management of sepsis

Sepsis is a common consequence of infection, associated with a mortality rate > 25%. Although community-acquired sepsis is more common, hospital-acquired infection is more lethal. The most common site of infection is the lung, followed by abdominal infection, catheter-associated blood steam infection and urinary tract infection. Gram-negative sepsis is more common than gram-positive infection, but sepsis can also be due to fungal and viral pathogens. To reduce mortality, it is necessary to give immediate, empiric, broad-spectrum therapy to those with severe sepsis and/or shock, but this approach can drive antimicrobial overuse and resistance and should be accompanied by a commitment to de-escalation and antimicrobial stewardship. Biomarkers such a procalcitonin can provide decision support for antibiotic use, and may identify patients with a low likelihood of infection, and in some settings, can guide duration of antibiotic therapy. Sepsis can involve drug-resistant pathogens, and this often necessitates consideration of newer antimicrobial agents.

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via:  https://drinchai.shinyapps.io/BloodGen3Module/ .

Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.

Assessment of a Cellular Host Response Test as a Sepsis Diagnostic for Those With Suspected Infection in the Emergency Department

Supplemental Digital Content is available in the text.

Objectives:

Sepsis is a common cause of morbidity and mortality. A reliable, rapid, and early indicator can help improve efficiency of care and outcomes. To assess the IntelliSep test, a novel in vitro diagnostic that quantifies the state of immune activation by measuring the biophysical properties of leukocytes, as a rapid diagnostic for sepsis and a measure of severity of illness, as defined by Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation-II scores and the need for hospitalization.

Design, Setting, SUBJECTS:

Adult patients presenting to two emergency departments in Baton Rouge, LA, with signs of infection (two of four systemic inflammatory response syndrome criteria, with at least one being aberration of temperature or WBC count) or suspicion of infection (a clinician order for culture of a body fluid), were prospectively enrolled. Sepsis status, per Sepsis-3 criteria, was determined through a 3-tiered retrospective and blinded adjudication process consisting of objective review, site-level clinician review, and final determination by independent physician adjudicators.

MEASUREMENTS AND MAIN RESULTS:

Of 266 patients in the final analysis, those with sepsis had higher IntelliSep Index (median = 6.9; interquartile range, 6.1–7.6) than those adjudicated as not septic (median = 4.7; interquartile range, 3.7–5.9; p < 0.001), with an area under the receiver operating characteristic curve of 0.89 and 0.83 when compared with unanimous and forced adjudication standards, respectively. Patients with higher IntelliSep Index had higher Sequential Organ Failure Assessment (3 [interquartile range, 1–5] vs 1 [interquartile range, 0–2]; p < 0.001) and Acute Physiology and Chronic Health Evaluation-II (7 [interquartile range, 3.5–11.5] vs 5 [interquartile range, 2–9]; p < 0.05) and were more likely to be admitted to the hospital (83.6% vs 48.3%; p < 0.001) compared with those with lower IntelliSep Index.

CONCLUSIONS:

In patients presenting to the emergency department with signs or suspicion of infection, the IntelliSep Index is a promising tool for the rapid diagnosis and risk stratification for sepsis.

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

Optimal combination of early biomarkers for infection and sepsis diagnosis in the emergency department: The BIPS study

OBJECTIVE

To define the best combination of biomarkers for the diagnosis of infection and sepsis in the emergency room.

METHODS

In this prospective study, consecutive patients with a suspicion of infection in the emergency room were included. Eighteen different biomarkers measured in plasma, and twelve biomarkers measured on monocytes, neutrophils, B and T-lymphocytes were studied and the best combinations determined by a gradient tree boosting approach.

RESULTS

Overall, 291 patients were included and analysed, 148 with bacterial infection, and 47 with viral infection. The best biomarker combination which first allowed the diagnosis of bacterial infection, included HLA-DR (human leukocyte antigen DR) on monocytes, MerTk (Myeloid-epithelial-reproductive tyrosine kinase) on neutrophils and plasma metaloproteinase-8 (MMP8) with an area under the curve (AUC) = 0.94 [95% confidence interval (IC95): 0.91;0.97]. Among patients in whom a bacterial infection was excluded, the combination of CD64 expression, and CD24 on neutrophils and CX3CR1 on monocytes ended to an AUC = 0.98 [0.96;1] to define those with a viral infection.

CONCLUSION

In a convenient cohort of patients admitted with a suspicion of infection, two different combinations of plasma and cell surface biomarkers were performant to identify bacterial and viral infection.

Monitoring immunomodulation in patients with sepsis

Introduction: This review aims to summarize current progress of the last ten years in the development of biomarkers used for classifying the immune response of the septic host and for monitoring the efficacy of the applied adjunctive immunotherapy.Areas covered: An extensive search of the literature was performed. In this review the authors discuss available biomarkers of host immune response in sepsis toward two directions; immunosuppression and hyperinflammation. Ferritin, sCD163, sIL-2 ra, and IL-18 may help in the diagnosis of macrophage activation syndrome (MAS) complicating sepsis whereas lymphopenia, decreased HLA-DR expression on monocytes, overexpression of Programmed cell death protein-1 (PD-1)/Programmed death-ligand 1 (PD-L1) and IL-10 are indicators of sepsis-induced immunosuppression. Novel approaches in the classification of immune state in sepsis include Myeloid-Derived Suppressor Cells (MDSC) and specific endotypes, defined by gene expression and molecular techniques.Expert opinion: HLA-DR and ferritin are the most commonly used biomarkers to monitor immunomodulation in clinical practice whereas developing specific sepsis endotypes is the future target. New immunotherapy trials in sepsis need to incorporate biomarkers for a personalized treatment.

Nomogram prediction of surgical site infection of HIV-infected patients following orthopedic surgery: a retrospective study

BACKGROUND

Surgical site infection (SSI) is a devastating complication of orthopedic surgery, related with increased morbidity and mortality. This study was performed with the aim to compare the SSI rate in human immunodeficiency virus HIV-positive patients, to identify other risk factors for SSI and to establish a nomogram model to predict the risk of SSI.

METHODS

A total of 101 HIV-positive individuals following orthopedic surgery patients admitted to Beijing Ditan Hospital. Their characteristics were gathered. The univariate and multiple logistic regression analysis were performed to explore the risk factors of SSI. And the Nomogram prediction model was constructed and verified.

RESULTS

The independent predictive factors of SSI included CD4 (Odds ratio [OR], 0.041; P = 0.040), erythrocyte sedimentation rate (ESR) (OR, 89.773; P = 0.030), and procalcitonin (PCT) (OR, 220.746; P = 0.006). The scoring nomogram model was as follows: Logit (SSI) = - 2.63589-0.00314*CD4 < 430.75 = 1) + 0.04695*(ESR < 17.46 = 1) + 2.93694*(PCT < 0.22 = 1). The area under the Receiver Operating Characteristic (ROC) curve was 0.946. The cutoff score was - 2.1026 with a sensitivity of 93.33% and a specificity of 84.88%.

CONCLUSIONS

CD4, ESR, PCT might affect the occurrence of SSI after orthopedic surgery. The nomogram model constructed in this study is helpful for predicting the probability of SSI.

Machine learning based refined differential gene expression analysis of pediatric sepsis

Background

Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches.

Methods

In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure.

Results

Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89.

Conclusions

Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.