Transcriptomic Signatures in Sepsis and a Differential Response to Steroids. From the VANISH Randomized Trial

Molecular endotypes in sepsis: integration of multicohort transcriptomics based on RNA sequencing

BACKGROUND

The heterogeneity of host responses in sepsis has hindered efforts to develop targeted therapies for this large patient population. Although growing evidence has identified sepsis endotypes based on the microarray data, studies using RNA-seq data-which offers higher sensitivity and a broader dynamic range-remain limited. We hypothesized that integrating RNA-seq data from patients with sepsis would reveal molecular endotypes with distinct biological and clinical signatures.

METHODS

In this meta-analysis, we systematically searched for publicly available RNA-seq datasets of sepsis. Using identified datasets, we applied a consensus clustering algorithm to identify distinct endotypes. To characterize the biological differences between these endotypes, we performed gene-set enrichment analysis and immune cell deconvolution. Next, we investigated the association between these endotypes and mortality risks. We finally developed gene classifiers for endotype stratification and validated our endotype classification by applying these classifiers to an external cohort.

RESULTS

A total of 280 adults with sepsis from four datasets were included in this analysis. Using an unsupervised approach, we identified three distinct endotypes: coagulopathic (n = 83, 30%), inflammatory (n = 118, 42%), and adaptive endotype (n = 79, 28%). The coagulopathic endotype exhibited upregulated coagulation signaling, along with an increased monocyte and neutrophil composition, although the adaptive endotype demonstrated enhanced adaptive immune cell responses, marked by elevated T and B cell compositions. The inflammatory endotype was characterized by upregulated TNF-α/NF-κB signaling and IL-6/JAK/STAT3 pathways with an increased neutrophil composition. Patients with the coagulopathic endotype had a significantly higher risk of mortality than those with the adaptive endotype (30% vs. 16%, odds ratio 2.19, 95% confidence interval 1.04-4.78, p = 0.04). To enable the practical application of these findings, we developed endotype classification models and identified 14 gene classifiers. In a validation cohort of 123 patients, we consistently identified these three endotypes. Furthermore, the mortality risk pattern was reproduced, with the coagulopathic endotype showing greater mortality risk than the adaptive endotype (34% vs 18%, p = 0.10).

CONCLUSIONS

This multicohort RNA-seq meta-analysis identified three biologically and clinically distinct sepsis endotypes characterized by coagulopathic, adaptive, and inflammatory responses. This endotype-based approach to patient stratification may facilitate the development of more precise therapeutic strategies for sepsis.

Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefit of corticosteroids are not fully defined using the construct of organ dysfunction duration. This retrospective multicenter, proof-of-concept study aimed to evaluate the association between usage of corticosteroids and mortality of patients with sepsis, pneumonia and ARDS by emulating a target trial framework stratified by predicted organ dysfunction trajectory. The study employed a two staged machine learning (ML) methodology to first subphenotype based on organ dysfunction trajectory then predict this defined trajectory. Once patients were classified by predicted trajectory we conducted a target trial emulation. Our analysis revealed that the association between corticosteroid use and 28-day mortality varied by predicted trajectory and between cohorts.Our findings suggest that matching treatment strategies to empirically observed pathobiology may offer a more nuanced understanding of corticosteroid utility.

Adjunctive Corticosteroids for Hypotension in the Pediatric Cardiac ICU: Single-Center Retrospective Study, 2020-2021

OBJECTIVES

To characterize adjunctive corticosteroid use and associations between any exposure or cumulative dose and outcomes in pediatric cardiac surgical cases.

DESIGN

A retrospective cohort was admitted over 24 months (from January 2020 to December 2021).

SETTING

Single-center cardiac ICU (CICU) in a quaternary hospital.

PATIENTS

Descriptive analyses of all patients receiving hydrocortisone for hypotension. Further comparative analyses were restricted to postoperative infants (< 12 mo) exposed to corticosteroids vs. not, including propensity-score inverse weighted and matched analyses.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We examined associations between cumulative hydrocortisone dose and outcomes, including severe infection. Overall, 154 patients of all ages received steroids (91 post-surgical). Median (interquartile range [IQR]) cumulative hydrocortisone dose was 10.0 mg/kg (IQR, 6.0-21.2 mg/kg). Greater cumulative dose was associated with higher adjusted odds (95% CI) of severe infection (1.08 [95% CI, 1.03-1.12]). For comparative analyses, we identified 403 infants, including 68 with postoperative corticosteroid exposure. Propensity scores based on multiple factors, including peak modified Vasoactive-Inotropic Score (mVIS, excluding milrinone), compared outcomes and hemodynamic response with quantification of rate of mVIS fall from peak among 55 matched pairs. We failed to identify a difference in rate of mVIS fall between nonsteroid and steroid recipients (-0.162 [IQR, -0.228 to -0.053] vs. -0.160 [IQR, -0.300 to -0.046]; p = 0.674).

CONCLUSIONS

In our CICU cohort receiving adjunctive hydrocortisone for hypotension, we failed to identify a consistent signal regarding outcomes and rate of mVIS fall. Considering potential side effects, these data suggest prospective study is needed to better define the use of such treatment.

Sepsis subphenotypes, theragnostics and personalized sepsis care

Heterogeneity between critically ill patients with sepsis is a major barrier to the discovery of effective therapies. The use of machine learning techniques, coupled with improved understanding of sepsis biology, has led to the identification of patient subphenotypes. This exciting development may help overcome the problem of patient heterogeneity and lead to the identification of patient subgroups with treatable traits. Re-analyses of completed clinical trials have demonstrated that patients with different subphenotypes may respond differently to treatments. This suggests that future clinical trials that take a precision medicine approach will have a higher likelihood of identifying effective therapeutics for patients based on their subphenotype. In this review, we describe the emerging subphenotypes identified in the critically ill and outline the promising immune modulation therapies which could have a beneficial treatment effect within some of these subphenotypes. Furthermore, we will also highlight how bringing subphenotype identification to the bedside could enable a new generation of precision-medicine clinical trials.

Effect of methylprednisolone vs hydrocortisone on 30-day mortality in critically ill adults with septic shock: an analysis of the MIMIC-IV database

BACKGROUND

Methylprednisolone is still used to treat adults with septic shock in real-world clinical settings, despite current international guidelines recommending hydrocortisone. The aim of this study was to assess the effect of methylprednisolone vs hydrocortisone on 30-day mortality among critically ill patients with septic shock.

METHODS

We conducted a retrospective cohort study on adults with septic shock using the MIMIC-IV v3.0 database. Patients who received methylprednisolone after diagnosis were matched using propensity score matching (PSM) with those received hydrocortisone, to balance confounding factors between groups. The primary outcome was the 30-day mortality rate. Subgroup and sensitivity analyses were performed to assess the robustness of the conclusions.

RESULTS

A total of 1,607 septic shock patents were enrolled in this study, with an overall 30-day mortality rate of 42.1%. After 1:1 PSM, 376 pairs were successfully matched. The primary outcome occurred in 141 patients (37.5%) and in 131 patients (34.8%) in the methylprednisolone and hydrocortisone groups, respectively (HR = 1.105, 95% CI: 0.871-1.402, P = 0.410). In subgroup analyses based on age, sex, blood culture positivity, pneumonia, and invasive mechanical ventilation (IMV), along with sensitivity analyses for deletion of missing values, findings remained consistent. However, the methylprednisolone group exhibited a longer ICU stay, elevated blood glucose levels, and a shorter maintenance duration for vasopressin compared to the hydrocortisone group.

CONCLUSIONS

Among adults with septic shock, there was no significant difference in 30-day mortality between those administered methylprednisolone and hydrocortisone. It needs to be further verified in prospective, randomized controlled trials.

An international observational study validating gene-expression sepsis immune subgroups

BACKGROUND

Sepsis gene-expression sub-phenotypes with prognostic and theranostic potential have been discovered. These have been identified retrospectively and have not been translated to methods that could be deployed at the bedside. We aimed to identify subgroups of septic patients at high-risk of poor outcome, using a rapid, multiplex RNA-based test.

METHODS

Adults with sepsis, in the intensive care unit (ICU) were recruited from 17 sites in the United Kingdom, Sweden and France. Blood was collected at days 2-5 (S1), 6-8 (S2) and 13-15 (S3) after ICU admission and analyzed centrally. Patients were assigned into 'high' and 'low' risk groups using two models previously developed for the Immune-Profiling Panel prototype on the bioMérieux FilmArray® system.

RESULTS

357 patients were recruited (March 2021-November 2022). 69% were male with a median age of 67 years, APACHE II score of 21 and a 30% 90-day mortality rate. The proportions of high-risk patients decreased over the three sampling times (model 1: 53%, 40%, 15% and model 2: 81%, 74%, 37%). In model 1, 90-day mortality was higher in a high-risk group at each time (S1: 35% vs 24%, p = 0.04; S2: 43% vs 20%, p < 0.001; S3: 52% vs 24%, p = 0.007). In model 2, mortality was only significantly different at the second sampling time (S1: 30% vs 27%, p = 0.77; S2: 34% vs 14%, p = 0.002; S3: 35% vs 23%, p = 0.13).

CONCLUSIONS

Gene-expression diagnostics can identify patients with sepsis at high-risk of poor outcomes and could be used to identify patients for precision medicine trials.

REGISTRATION

ISRCTN11364482 Registered 24th September 2020.

Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome

PURPOSE

Machine learning has shown promise to detect useful subgroups of patients with sepsis from gene expression and protein data. This approach has rarely been deployed in metabolomic datasets. Metabolomic data are of interest as they capture effects from the genome, proteome, and environmental. We aimed to discover metabolic sub-phenotypes of septic shock, examine their temporal stability and association with clinical outcome.

METHODS

Analysis was performed in two double-blind randomized trials in septic shock (LeoPARDS (1402 samples from 470 patients) and VANISH (493 samples from 173 patients)). Patients were included soon after the onset of shock and had serum collected at up to four time points. Metabolic clusters were identified from 474 metabolites using k-means clustering in LeoPARDS and predicted in VANISH with an elastic net classifier.

RESULTS

Three sub-phenotypes were found. The main determinants of cluster membership were lipid species, especially lysophospholipids. Low lysophospholipid sub-phenotypes were associated with higher circulating cytokine levels. Persistence of low lysophospholipid sub-phenotypes was associated with higher mortality compared to the high lysophospholipid sub-phenotype (LeoPARDS: cluster 2 odds ratio 3.66 (95% CI 1.88-7.20), p = 0.0001, cluster 3 2.49 (1.29-4.81), p = 0.006; VANISH: cluster 2 4.13 (1.17-15.61), p = 0.03), cluster 3 3.22 (1.09-9.92), p = 0.04, vs cluster 1). We found no heterogeneity of treatment effect for any of the trial interventions by baseline metabolic sub-phenotype.

CONCLUSION

Three metabolic subgroups exist in septic shock which evolve over time. Persistence of low lysophospholipid sub-phenotypes is associated with mortality. Monitoring these subgroups could help identify patients at risk of poor outcome and direct novel therapies such as lysophospholipid supplementation.

REGISTRATION

Clinicaltirals.gov Identifiers, VANISH: ISRCTN 20769191, LeoPARDS: ISRCTN12776039.

Association of 91 Inflammatory Factors and 1400 Metabolites with Sepsis: A Mendelian Randomization Analysis

Objective: Observational studies suggest links between inflammatory factors, metabolites, and sepsis, yet their causality is uncertain. This study employs Mendelian Randomization (MR) to investigate the causality between these factors and sepsis, aiming to uncover the precise relationship and identify novel treatment approaches. Methods: We used summary data from genome-wide association studies (GWAS) involving 91 inflammatory factors, 1400 metabolites as exposure, and STREPTO SEPSIS as outcome. Inverse variance weighting (IVW) and MR-Egger were used to evaluate the causal effect between exposure and outcome. Sensitivity analyses were performed using Cochrane's Q test, MR-Egger intercept method, MR-PRESSO method and leave-one-out method. Results: Thymic stromal lymphopoietin levels (TSLP) (OR = 1.269; 95%CI = 1.016-1.585; P = .036) and Interleukin 15 receptor subunit alpha levels (IL-15Rα) (OR = 0.894; 95%CI = 0.801-0.998; P = .046) had a significant causal relationship with sepsis. Forty-four metabolites were associated with sepsis, including Spermidine to choline ratio (OR = 1.447; 95%CI = 1.104-1.977; P = .009), 4-hydroxyhippurate levels (OR = 1.448; 95%CI = 1.117-1.877; P = .005), and Sphingomyelin (d18:1/20:1, d18:2/20:0) levels (OR = 1.371; 95%CI = 1.139-1.651; P < .001). TSLP was associated with 19 metabolites, and IL-15Rα was associated with 30 metabolites. Conclusions: This study uncovers the causal link between sepsis and two inflammatory factors, TSLP and IL-15Rα, and suggests metabolites' potential in intervention. It also identifies 44 metabolites associated with sepsis, indicating possible biomarkers or therapeutic targets. The findings offer new perspectives on sepsis pathogenesis and could inform future treatment strategies.

Unsupervised Classification of the Host Response Identifies Dominant Pathobiological Signatures of Sepsis in Sub-Saharan Africa

Rationale: The global burden of sepsis is concentrated in sub-Saharan Africa, where inciting pathogens are diverse and HIV coinfection is a major driver of poor outcomes. Biological heterogeneity inherent to sepsis in this setting is poorly defined. Objectives: To identify dominant pathobiological signatures of sepsis in sub-Saharan Africa and their relationship to clinical phenotypes, patient outcomes, and biological classifications of sepsis identified in high-income countries (HICs). Methods: We analyzed two prospective cohorts of adults hospitalized with sepsis (severe infection with quick Sepsis-related Organ Failure Assessment score ⩾1) at disparate settings in Uganda (discovery cohort [Entebbe, urban], n = 242; validation cohort [Tororo, rural], n = 253). To identify pathobiological signatures in the discovery cohort, we applied unsupervised clustering to 173 soluble proteins reflecting key domains of the host response to severe infection. A random forest-derived classifier was used to predict signature assignment in the validation cohort. Measurements and Main Results: Two signatures (Uganda Sepsis Signature [USS]-1 and USS-2) were identified in the discovery cohort, distinguished by expression of proteins involved in myeloid cell and inflammasome activation, T-cell costimulation and exhaustion, and endothelial barrier dysfunction. A five-protein classifier (area under the receiver operating characteristic curve, 0.97) reproduced two signatures in the validation cohort with similar biological profiles. In both cohorts, USS-2 mapped to a more severe clinical phenotype associated with HIV and related immunosuppression, severe tuberculosis, and increased risk of 30-day mortality. Substantial biological overlap was observed between USS-2 and hyperinflammatory and reactive sepsis phenotypes identified in HICs. Conclusions: We identified prognostically enriched pathobiological signatures among patients with sepsis with diverse infections and high HIV prevalence in Uganda. Globally inclusive investigations are needed to define generalizable and context-specific mechanisms of sepsis pathobiology, with the goal of improving access to precision medicine treatment strategies.

Clinical subtypes in critically ill patients with sepsis: validation and parsimonious classifier model development

BACKGROUND

The application of sepsis subtypes to enhance personalized medicine in critically ill patients is hindered by the lack of validation across diverse cohorts and the absence of a simple classification model. We aimed to validate the previously identified SENECA clinical sepsis subtypes in multiple large ICU cohorts, and to develop parsimonious classifier models for δ-type adjudication in clinical practice.

METHODS

Data from four cohorts between 2008 and 2023 were used to assign α, β, γ and δ-type in patients fulfilling the Sepsis-3 criteria using clinical variables: (I) The Molecular diAgnosis and Risk stratification of Sepsis (MARS, n = 2449), (II) a contemporary continuation of the MARS study (MARS2, n = 2445) (III) the Dutch National Intensive Care Evaluation registry (NICE, n = 28,621) and (IV) the Medical Information Mart for Intensive Care including (MIMIC-IV, n = 18,661). K-means clustering using clinical variables was conducted to assess the optimal number of classes and compared to the SENECA subtypes. Parsimonious models were built in the SENECA derivation cohort to predict subtype membership using logistic regression, and validated in MARS and MIMIC-IV.

RESULTS

Among 52.226 patients with sepsis, the subtype distribution in MARS, MARS2 and NICE was 2-6% for the α-type, 1-5% for the β-type, 49-65% for the γ-type and 26-48% for the δ-type compared to 33%, 27%, 27% and 13% in the original SENECA derivation cohort, whereas subtype distribution in MIMIC-IV was more similar at 25%, 24%, 27% and 25%, respectively. In-hospital mortality rates were significantly different between the four cohorts for α, γ and δ-type (p < 0.001). Method-based validation showed moderate overlap with the original subtypes in both MARS and MIMIC-IV. A parsimonious model for all four subtypes had moderate to low accuracy (accuracy 62.2%), while a parsimonious classifier model with 3 variables (aspartate aminotransferase, serum lactate, and bicarbonate) had excellent accuracy in predicting the δ-type patients from all other types in the derivation cohort and moderate accuracy in the validation cohorts (MARS: area under the receiver operator characteristic curve (AUC) 0.93, 95% CI [0.92-0.94], accuracy 85.5% [84.0-86.8%]; MIMIC-IV: AUC 0.86 [0.85-0.87], accuracy 82.9% [82.4-83.4%]).

CONCLUSIONS

The distribution and mortality rates of clinical sepsis subtypes varied between US and European cohorts. A three-variable model could accurately identify the δ-type sepsis patients.

The Diagnostic Utility of Host RNA Biosignatures in Adult Patients With Sepsis: A Systematic Review and Meta-Analysis

OBJECTIVES

Sepsis is a life-threatening medical emergency, with a profound healthcare burden globally. Its pathophysiology is complex, heterogeneous and temporally dynamic, making diagnosis challenging. Medical management is predicated on early diagnosis and timely intervention. Transcriptomics is one of the novel "-omics" technologies being evaluated for recognition of sepsis. Our objective was to evaluate the performance of host gene expression biosignatures for the diagnosis of all-cause sepsis in adults.

DATA SOURCES

PubMed/Ovid Medline, Ovid Embase, and Cochrane databases from inception to June 2023.

STUDY SELECTION

We included studies evaluating the performance of host gene expression biosignatures in adults who were diagnosed with sepsis using existing clinical definitions. Controls where applicable were patients without clinical sepsis.

DATA EXTRACTION

Data including population demographics, sample size, study design, tissue specimen, type of transcriptome, health status of comparator group, and performance of transcriptomic biomarkers were independently extracted by at least two reviewers.

DATA SYNTHESIS

Meta-analysis to describe the performance of host gene expression biosignatures for the diagnosis of sepsis in adult patients was performed using the random-effects model. Risk of bias was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 117 studies (n = 17,469), comprising 132 separate patient datasets, were included in our final analysis. Performance of transcriptomics for the diagnosis of sepsis against pooled controls showed area under the receiver operating characteristic curve (AUC, 0.86; 95% CI, 0.84-0.88). Studies using healthy controls showed AUC 0.87 (95% CI, 0.84-0.89), while studies using controls with systemic inflammatory response syndrome (SIRS) had AUC 0.84 (95% CI, 0.78-0.90). Transcripts with excellent discrimination against SIRS controls include UrSepsisModel, a 210 differentially expressed genes biosignature, microRNA-143, and Septicyte laboratory.

CONCLUSIONS

Transcriptomics is a promising approach for the accurate diagnosis of sepsis in adults and demonstrates good discriminatory ability against both healthy and SIRS control subjects.

Next generation bioelectronic medicine: making the case for non-invasive closed-loop autonomic neuromodulation

The field of bioelectronic medicine has advanced rapidly from rudimentary electrical therapies to cutting-edge closed-loop systems that integrate real-time physiological monitoring with adaptive neuromodulation. Early innovations, such as cardiac pacemakers and deep brain stimulation, paved the way for these sophisticated technologies. This review traces the historical and technological progression of bioelectronic medicine, culminating in the emerging potential of closed-loop devices for multiple disorders of the brain and body. We emphasize both invasive techniques, such as implantable devices for brain, spinal cord and autonomic regulation, while we introduce new prospects for non-invasive neuromodulation, including focused ultrasound and newly developed autonomic neurography enabling precise detection and titration of inflammatory immune responses. The case for closed-loop non-invasive autonomic neuromodulation (incorporating autonomic neurography and splenic focused ultrasound stimulation) is presented through its applications in conditions such as sepsis and chronic inflammation, illustrating its capacity to revolutionize personalized healthcare. Today, invasive or non-invasive closed-loop systems have yet to be developed that dynamically modulate autonomic nervous system function by responding to real-time physiological and molecular signals; it represents a transformative approach to therapeutic interventions and major opportunity by which the bioelectronic field may advance. Knowledge gaps remain and likely contribute to the lack of available closed loop autonomic neuromodulation systems, namely, (1) significant exogenous and endogenous noise that must be filtered out, (2) potential drift in the signal due to temporal change in disease severity and/or therapy induced neuroplasticity, and (3) confounding effects of exogenous therapies (e.g., concurrent medications that dysregulate autonomic nervous system functions). Leveraging continuous feedback and real-time adjustments may overcome many of these barriers, and these next generation systems have the potential to stand at the forefront of precision medicine, offering new avenues for individualized and adaptive treatment.

Blood from septic patients with necrotising soft tissue infection treated with hyperbaric oxygen reveal different gene expression patterns compared to standard treatment

BACKGROUND

Sepsis and shock are common complications of necrotising soft tissue infections (NSTI). Sepsis encompasses different endotypes that are associated with specific immune responses. Hyperbaric oxygen (HBO2) treatment activates the cells oxygen sensing mechanisms that are interlinked with inflammatory pathways. We aimed to identify gene expression patterns associated with effects of HBO2 treatment in patients with sepsis caused by NSTI, and to explore sepsis-NSTI profiles that are more receptive to HBO2 treatment.

METHODS

An observational cohort study examining 83 NSTI patients treated with HBO2 in the acute phase of NSTI, fourteen of whom had received two sessions of HBO2 (HBOx2 group), and another ten patients (non-HBO group) who had not been exposed to HBO2. Whole blood RNA sequencing and clinical data were collected at baseline and after the intervention, and at equivalent time points in the non-HBO group. Gene expression profiles were analysed using machine learning techniques to identify sepsis endotypes, treatment response endotypes and clinically relevant transcriptomic signatures of response to treatment.

RESULTS

We identified differences in gene expression profiles at follow-up between HBO2-treated patients and patients not treated with HBO2. Moreover, we identified two patient endotypes before and after treatment that represented an immuno-suppressive and an immune-adaptive endotype respectively, and we characterized the genetic profile of the patients that transition from the immuno-suppressive to the immune-adaptive endotype after treatment. We discovered one gene MTCO2P12 that distinguished individuals who altered their endotype in response to treatment from non-responders.

CONCLUSION

The global gene expression pattern in blood changed in response to HBO2 treatment in a direction associated with clinical biochemistry improvement, and the study provides potential novel biomarkers and pathways for monitoring HBO2 treatment effects and predicting an HBO2 responsive NSTI-sepsis profile.

TRIAL REGISTRATION

Biological material was collected during the INFECT study, registered at ClinicalTrials.gov (NCT01790698) 04/02/2013.

Identifying subgroup of severe community-acquired pneumonia based on clinical metagenomics, a multicenter retrospective cohort study

Objective

Severe community-acquired pneumonia (sCAP) is one of the major diseases within the ICU. We hypothesize that subtyping sCAP based on simple inflammatory markers, organ dysfunction, and clinical metagenomics results is feasible.

Method

In this study, we retrospectively enrolled immunocompetent sCAP patients requiring invasive mechanical ventilation, who underwent clinical metagenomics from 17 medical centers. We collected data on potentially pathogenic species reported by clinical metagenomics and clinical information for all patients. Latent class analysis (LCA) was applied to routine clinical parameters such as gender, age, white blood cell (WBC), lymphocytes, C-reactive protein (CRP), and Procalcitonin (PCT), identifying two optimally fitting models.

Results

A total of 569 patients were enrolled. Compared to class B, class A was characterized by a younger age, higher CRP and PCT levels, and a higher incidence of coagulation dysfunction, liver failure, circulatory failure, and renal failure. However, the mortality rates were similar between the two groups. In class A, more cases of Streptococcus spp. and fewer cases of HSV-1 and Candida spp. were detected. Among the patients in the two phenotypes, 48.7% and 57.5% received corticosteroid treatment, respectively. In the class A, corticosteroid treatment was not associated with patient mortality (unadjusted hazard ratio (HR)=0.988; 95% confidence interval (CI), 0.634-1.541; p=0.959). In contrast, in the class B group, the use of corticosteroids was associated with a reduced mortality rate (adjusted HR=0.719; 95% CI, 0.525-0.986; p=0.04). Additional analysis showed that in class B, methylprednisolone was associated with reduced mortality (adjusted HR=0.61; 95% CI, 0.44-0.86; p=0.005), while dexamethasone was not associated with mortality (adjusted HR=1.4; 95% CI, 0.89-2.22; p=0.148). In addition, after dose conversion, the results showed that higher doses of corticosteroids in class B were associated with increased mortality (adjusted HR=1.01; 95% CI, 1.00-1.01; p=0.005).

Conclusion

We identified two classes based on clinical metagenomics and clinical features. Class B exhibited a better response to corticosteroid compared to class A. The rapid identification of these phenotypes could facilitate the screening of sCAP patients responsive to corticosteroid in future prospective clinical trials.

Aiming for precision: personalized medicine through sepsis subtyping

According to the latest definition, sepsis is characterized by life-threatening organ dysfunction caused by a dysregulated host response to an infection. However, this definition fails to grasp the heterogeneous nature and the underlying dynamic pathophysiology of the syndrome. In response to this heterogeneity, efforts have been made to stratify sepsis patients into subtypes, either based on their clinical presentation or pathophysiological characteristics. Subtyping introduces the possibility of the implementation of personalized medicine, whereby each patient receives treatment tailored to their individual disease manifestation. This review explores the currently known subtypes, categorized by subphenotypes and endotypes, as well as the treatments that have been researched thus far in the context of sepsis subtypes and personalized medicine.

Enhancing Infection Control in ICUS Through AI: A Literature Review

Introduction

Infection control in intensive care units (ICUs) is crucial due to the high risk of healthcare-associated infections (HAIs), which can increase patient morbidity, mortality, and costs. Effective measures such as hand hygiene, use of personal protective equipment (PPE), patient isolation, and environmental cleaning are vital to minimize these risks. The integration of artificial intelligence (AI) offers new opportunities to enhance infection control, from predicting outbreaks to optimizing antimicrobial use, ultimately improving patient safety and care in ICUs.

Objectives

The primary objectives are to explore AI's impact on predicting HAIs, real-time monitoring, automated sterilization, resource optimization, and personalized infection control plans.

Methodology

A comprehensive search of PubMed and Scopus was conducted for relevant articles up to January 2024, including case series, reports, and cohort studies. Animal studies and irrelevant articles were excluded, with a focus on those considered to have significant clinical relevance.

Discussion

The review highlights AI's prowess in predicting HAIs, surpassing conventional methods. Existing evidence demonstrates AI's efficacy in accurately predicting and mitigating HAIs. Real-time patient monitoring and alert systems powered by AI are shown to enhance infection detection and patient outcomes. The paper also addresses AI's role in automating sterilization and disinfection, with studies affirming its effectiveness in reducing infections. AI's resource optimization capabilities are exemplified in ICU settings, showcasing its potential to improve resource allocation efficiency. Furthermore, the review emphasizes AI's personalized approach to infection control post-procedures, elucidating its ability to analyze patient data and create tailored control plans.

Heterogeneity in the Effect of Early Goal-Directed Therapy for Septic Shock: A Secondary Analysis of Two Multicenter International Trials

OBJECTIVES

The optimal approach for resuscitation in septic shock remains unclear despite multiple randomized controlled trials (RCTs). Our objective was to investigate whether previously uncharacterized variation across individuals in their response to resuscitation strategies may contribute to conflicting average treatment effects in prior RCTs.

DESIGN

We randomly split study sites from the Australian Resuscitation of Sepsis Evaluation (ARISE) and Protocolized Care for Early Septic Shock (ProCESS) trials into derivation and validation cohorts. We trained machine learning models to predict individual absolute risk differences (iARDs) in 90-day mortality in derivation cohorts and tested for heterogeneity of treatment effect (HTE) in validation cohorts and swapped these cohorts in sensitivity analyses. We fit the best-performing model in a combined dataset to explore roles of patient characteristics and individual components of early goal-directed therapy (EGDT) to determine treatment responses.

SETTING

Eighty-one sites in Australia, New Zealand, Hong Kong, Finland, Republic of Ireland, and the United States.

PATIENTS

Adult patients presenting to the emergency department with severe sepsis or septic shock.

INTERVENTIONS

EGDT vs. usual care.

MEASUREMENTS AND MAIN RESULTS

A local-linear random forest model performed best in predicting iARDs. In the validation cohort, HTE was confirmed, evidenced by an interaction between iARD prediction and treatment ( p < 0.001). When patients were grouped based on predicted iARDs, treatment response increased from the lowest to the highest quintiles (absolute risk difference [95% CI], -8% [-19% to 4%] and relative risk reduction, 1.34 [0.89-2.01] in quintile 1 suggesting harm from EGDT, and 12% [1-23%] and 0.64 [0.42-0.96] in quintile 5 suggesting benefit). Sensitivity analyses showed similar findings. Pre-intervention albumin contributed the most to HTE. Analyses of individual EGDT components were inconclusive.

CONCLUSIONS

Treatment response to EGDT varied across patients in two multicenter RCTs with large benefits for some patients while others were harmed. Patient characteristics, including albumin, were most important in identifying HTE.

Immune Subtypes in Sepsis: A Retrospective Cohort Study Utilizing Clustering Methodology

Background

Sepsis is a heterogeneous clinical syndrome. Identifying distinct clinical phenotypes may enable more targeted therapeutic interventions and improve patient care.

Objective

This study aims to use clustering analysis techniques to identify different immune subtypes in sepsis patients and explore their clinical relevance and prognosis.

Methods

The study included 236 patients from the EICU at Shanghai Tenth People's Hospital, who met the Sepsis 3.0 diagnostic criteria. Blood samples were collected to measure lymphocyte subsets and cytokine levels, along with demographic and clinical data. K-means clustering analysis was used to categorize patients into three groups based on immune and inflammatory markers.

Results

Three immune subtypes were identified: the high immune activation subtype (Cluster 1), characterized by high levels of CRP and WBC, high levels of T cells, NK cells, and B cells, and low levels of IL-6, IL-8, and IL-10; the moderate immune activation subtype (Cluster 2), characterized by moderate levels of CRP, WBC, T cells, NK cells, B cells, IL-6, IL-8, and IL-10; and the high inflammation and immune suppression subtype (Cluster 3), characterized by very high levels of IL-6, IL-8, and IL-10, low levels of T cells, NK cells, and B cells, and relatively lower CRP levels. Patients in Cluster 3 had a significantly increased 28-day mortality risk compared to those in Cluster 1 (HR = 21.65, 95% CI: 7.46-62.87, p < 0.001). Kaplan-Meier survival curves showed the lowest survival rates for Cluster 3 and the highest for Cluster 1, with the differences among the three groups being highly statistically significant (p < 0.0001).

Conclusion

This study identified three immune subtypes of sepsis that are significantly associated with clinical outcomes. These findings provide evidence for personalized treatment strategies to improve patient outcomes.

A transcriptome-based risk model in sepsis enables prognostic prediction and drug repositioning

Septic management presented a tremendous challenge due to heterogeneous host responses. We aimed to develop a risk model for early septic stratification based on transcriptomic signature. Here, we combined genes OLAH, LY96, HPGD, and ABLIM1 into a prognostic risk score model, which demonstrated exceptional performance in septic diagnosis (AUC = 0.99-1.00) and prognosis (AUC = 0.61-0.70), outperforming that of Mars and SRS endotypes. Also, the model unveiled immunosuppressive status in high-risk patients and a poor response to hydrocortisone in low-risk individuals. Single-cell transcriptome analysis further elucidated expression patterns and effects of the four genes across immune cell types, illustrating integrated host responses reflected by this model. Upon distinct transcriptional profiles of risk subgroups, we identified fenretinide and meloxicam as therapeutic agents, which significantly improved survival in septic mice models. Our study introduced a risk model that optimized risk stratification and drug repurposing of sepsis, thereby offering a comprehensive management approach.

Defining the Differential Corticosteroid Response Basis from Multiple Omics Approaches

Since their discovery, corticosteroids have been widely used in the treatment of several diseases, including asthma, acute lymphoblastic leukemia, chronic obstructive pulmonary disease, and many other conditions. However, it has been noted that some patients develop undesired side effects or even fail to respond to treatment. The reasons behind this have not yet been fully elucidated. This poses a significant challenge to effective treatment that needs to be addressed urgently. Recent genomic, transcriptomic, and other omics-based approximations have begun to shed light into the genetic factors influencing interindividual variability in corticosteroid efficacy and its side effects. Here, we comprehensively revise the recent literature on corticosteroid response in various critical and chronic diseases, with a focus on omics approaches, and highlight existing knowledge gaps where further investigation is urgently needed.

A novel online calculator based on inflammation-related endotypes and clinical features to predict postoperative pulmonary infection in patients with cervical spinal cord injury

BACKGROUND

Postoperative pulmonary infection (POI) of patients with cervical spinal cord injury (CSCI) is highly heterogeneous, while the potential endotypes and related risk factors remain unclear.

METHODS

A retrospective collection of 290 CSCI patients was conducted from January 2010 to July 2024 using 1:1 propensity score matching to compare POI (n = 145) and non-POI (n = 145) groups. We generated laboratory examination data from admission patients and identified endotypes using unsupervised consensus clustering and machine learning. CSCI patients were randomly assigned to the training set (n = 203) and internal validation set (n = 87). A separate cohort comprising 245 CSCI patients were used for external validation. Independent predictors for POI were identified using univariate and multivariate logistic regression. A nomogram and an online calculator were developed and validated, both internally and externally.

RESULTS

Two inflammation-related endotypes were identified: high inflammation endotype (endotype C1) and low inflammation endotype (endotype C2). Eight predictors for POI were identified (including age, operation duration, number of surgical segments, time between injury and surgery, preoperative steroid pulse, American Spinal Injury Association (ASIA) grade, smoking history, and inflammation-related endotype). A nomogram integrating the risk factors showed excellent discrimination in the training set (AUC, 0.976; 95% CI 0.956-0.996), internal validation set (AUC, 0.993; 95% CI 0.981-1.000), and external validation set (AUC, 0.799; 95%CI 0.744-0.854). Calibration curves demonstrated excellent fit, and decision curves highlighted its favorable clinical value. An online calculator (https://tjspine.shinyapps.io/dynnomapp/) was constructed to improve the convenience and efficiency of our prediction model.

CONCLUSIONS

We identified inflammation-related endotype and constructed a web-based calculator for predicting POI in patients with CSCI, exhibiting excellent clinical utility.

Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches

Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data. By evaluating the overlap of existing immune endotypes in sepsis across over 6,000 samples, we developed cell-type specific signatures to quantify dysregulation in these immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. This dysregulation was not only observed in sepsis but also in ARDS, trauma, and burn patients, indicating a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation is linked to differential mortality in patients treated with anakinra or corticosteroids, underscoring its prognostic and therapeutic significance. In conclusion, this novel immunology-based framework for quantifying cellular compartment dysregulation offers a valuable tool for prognosis and therapeutic decision-making in critical illness.

Shadows and lights in sepsis immunotherapy

INTRODUCTION

Sepsis remains a major global public health challenge. The host's response in sepsis involves both an exaggerated inflammatory reaction and immunosuppressive mechanisms. A better understanding of this response has shed light on the failure of anti-inflammatory therapies administered under the 'one size fits all' approach during the last decades.

AREAS COVERED

To date, patients' management has moved toward a comprehensive precision medicine approach that aims to personalize immunotherapy, whether anti-inflammatory or immunostimulatory. Large Prospective interventional randomized controlled trials validating this approach are about to start. A crucial prerequisite for these studies is to stratify patients based on biomarkers that will help defining the patients' immuno-inflammatory trajectory.

EXPERT OPINION

Some biomarkers are already available in routine clinical care, while improvements are anticipated through the standardized use of transcriptomics and other multi-omics technologies in this field. With these precautions in mind, it is reasonable to anticipate improvement in outcomes in sepsis.

Targeting Sepsis: Disease Tolerance, Immune Resilience, and Compartmentalized Immunity

Introduction: Sepsis remains a major contributor to critical care mortality and morbidity worldwide. Despite advances in understanding its complex immunopathology, the compartmentalized nature of immune responses across different organs has yet to be fully translated into targeted therapies. This review explores the burden of sepsis on organ-specific immune dysregulation, immune resilience, and epigenetic reprogramming, emphasizing translational challenges and opportunities. Methods: We implemented a systematic literature search strategy, incorporating data from studies published between 2010 and 2024, to evaluate the role of molecular profiling techniques, including transcriptomics and epigenetic markers, in assessing the feasibility of targeted therapies. Results: Sepsis-induced immune dysregulation manifests differently in various organs, with lung, heart, liver, and kidney responses driven by unique local immune environments. Organ-specific biomarkers, such as the Spns2/S1P axis in lung macrophages, mitochondrial dysfunction in the heart, proenkephalin for early acute kidney injury (AKI), and adrenomedullin for predicting multi-organ failure, offer promising avenues for timely intervention. Furthermore, immune resilience, particularly through regulatory T-cell modulation and cytokine targeting (e.g., IL-18), is crucial for long-term recovery. Epigenetic mechanisms, including histone modification and trained immunity, present opportunities for reprogramming immune responses but require more precision to avoid unintended inflammatory sequelae. Conclusions: A deeper understanding of compartmentalized immune responses and the dynamic immune landscape in sepsis is critical for developing precision therapies. Real-time immune monitoring and organ-targeted interventions could revolutionize sepsis management, although significant barriers remain in clinical translation. Further research is required to establish biomarkers and treatment timing that optimize therapeutic efficacy while minimizing systemic risks.

Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64-0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752-0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

Heterogeneity, Bayesian thinking, and phenotyping in critical care: A primer

PURPOSE

To familiarize clinicians with the emerging concepts in critical care research of Bayesian thinking and personalized medicine through phenotyping and explain their clinical relevance by highlighting how they address the issues of frequent negative trials and heterogeneity of treatment effect.

SUMMARY

The past decades have seen many negative (effect-neutral) critical care trials of promising interventions, culminating in calls to improve the field's research through adopting Bayesian thinking and increasing personalization of critical care medicine through phenotyping. Bayesian analyses add interpretive power for clinicians as they summarize treatment effects based on probabilities of benefit or harm, contrasting with conventional frequentist statistics that either affirm or reject a null hypothesis. Critical care trials are beginning to include prospective Bayesian analyses, and many trials have undergone reanalysis with Bayesian methods. Phenotyping seeks to identify treatable traits to target interventions to patients expected to derive benefit. Phenotyping and subphenotyping have gained prominence in the most syndromic and heterogenous critical care disease states, acute respiratory distress syndrome and sepsis. Grouping of patients has been informative across a spectrum of clinically observable physiological parameters, biomarkers, and genomic data. Bayesian thinking and phenotyping are emerging as elements of adaptive clinical trials and predictive enrichment, paving the way for a new era of high-quality evidence. These concepts share a common goal, sifting through the noise of heterogeneity in critical care to increase the value of existing and future research.

CONCLUSION

The future of critical care medicine will inevitably involve modification of statistical methods through Bayesian analyses and targeted therapeutics via phenotyping. Clinicians must be familiar with these systems that support recommendations to improve decision-making in the gray areas of critical care practice.

Innate immune response in acute critical illness: a narrative review

BACKGROUND

Activation of innate immunity is a first line of host defense during acute critical illness (ACI) that aims to contain injury and avoid tissue damages. Aberrant activation of innate immunity may also participate in the occurrence of organ failures during critical illness. This review aims to provide a narrative overview of recent advances in the field of innate immunity in critical illness, and to consider future potential therapeutic strategies.

MAIN TEXT

Understanding the underlying biological concepts supporting therapeutic strategies modulating immune response is essential in decision-making. We will develop the multiple facets of innate immune response, especially its cellular aspects, and its interaction with other defense mechanisms. We will first describe the pathophysiological mechanisms of initiation of innate immune response and its implication during ACI. We will then develop the amplification of innate immunity mediated by multiple effectors. Our review will mainly focus on myeloid and lymphoid cellular effectors, the major actors involved in innate immune-mediated organ failure. We will third discuss the interaction and integration of innate immune response in a global view of host defense, thus considering interaction with non-immune cells through immunothrombosis, immunometabolism and long-term reprogramming via trained immunity. The last part of this review will focus on the specificities of the immune response in children and the older population.

CONCLUSIONS

Recent understanding of the innate immune response integrates immunity in a highly dynamic global vision of host response. A better knowledge of the implicated mechanisms and their tissue-compartmentalization allows to characterize the individual immune profile, and one day eventually, to develop individualized bench-to-bedside immunomodulation approaches as an adjuvant resuscitation strategy.

Platelet Metabolites as Candidate Biomarkers in Sepsis Diagnosis and Management Using the Proposed Explainable Artificial Intelligence Approach

Background: Sepsis is characterized by an atypical immune response to infection and is a dangerous health problem leading to significant mortality. Current diagnostic methods exhibit insufficient sensitivity and specificity and require the discovery of precise biomarkers for the early diagnosis and treatment of sepsis. Platelets, known for their hemostatic abilities, also play an important role in immunological responses. This study aims to develop a model integrating machine learning and explainable artificial intelligence (XAI) to identify novel platelet metabolomics markers of sepsis. Methods: A total of 39 participants, 25 diagnosed with sepsis and 14 control subjects, were included in the study. The profiles of platelet metabolites were analyzed using quantitative 1H-nuclear magnetic resonance (NMR) technology. Data were processed using the synthetic minority oversampling method (SMOTE)-Tomek to address the issue of class imbalance. In addition, missing data were filled using a technique based on random forests. Three machine learning models, namely extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and kernel tree boosting (KTBoost), were used for sepsis prediction. The models were validated using cross-validation. Clinical annotations of the optimal sepsis prediction model were analyzed using SHapley Additive exPlanations (SHAP), an XAI technique. Results: The results showed that the KTBoost model (0.900 accuracy and 0.943 AUC) achieved better performance than the other models in sepsis diagnosis. SHAP results revealed that metabolites such as carnitine, glutamate, and myo-inositol are important biomarkers in sepsis prediction and intuitively explained the prediction decisions of the model. Conclusion: Platelet metabolites identified by the KTBoost model and XAI have significant potential for the early diagnosis and monitoring of sepsis and improving patient outcomes.

Biomarkers in sepsis

Sepsis is a life-threatening condition characterized by dysregulated host response to infection leading to organ dysfunction. Despite advances in understanding its pathology, sepsis remains a global health concern and remains a major contributor to mortality. Timely identification is crucial for improving clinical outcomes, as delayed treatment significantly impacts survival. Accordingly, biomarkers play a pivotal role in diagnosis, risk stratification, and management. This review comprehensively discusses various biomarkers in sepsis and their potential application in antimicrobial stewardship and risk assessment. Biomarkers such as white blood cell count, neutrophil to lymphocyte ratio, erythrocyte sedimentation rate, C-reactive protein, interleukin-6, presepsin, and procalcitonin have been extensively studied for their diagnostic and prognostic value as well as in guiding antimicrobial therapy. Furthermore, this review explores the role of biomarkers in risk stratification, emphasizing the importance of identifying high-risk patients who may benefit from specific therapeutic interventions. Moreover, the review discusses the emerging field of transcriptional diagnostics and metagenomic sequencing. Advances in sequencing have enabled the identification of host response signatures and microbial genomes, offering insight into disease pathology and aiding species identification. In conclusion, this review provides a comprehensive overview of the current understanding and future directions of biomarker-based approaches in sepsis diagnosis, management, and personalized therapy.

High expression of L-GILZ transcript variant 1 (GILZ TV 1) is associated with increased 30-day sepsis mortality, and a high expression ratio possibly contraindicates hydrocortisone administration

BACKGROUND

Sepsis presents a challenge due to its complex immune responses, where balance between inflammation and anti-inflammation is critical for survival. Glucocorticoid-induced leucine zipper (GILZ) is key protein in achieving this balance, suppressing inflammation and mediating glucocorticoid response. This study aims to investigate GILZ transcript variants in sepsis patients and explore their potential for patient stratification and optimizing glucocorticoid therapy.

METHODS

Sepsis patients meeting the criteria outlined in Sepsis-3 were enrolled, and RNA was isolated from whole blood samples. Quantitative mRNA expression of GILZ transcript variants in both sepsis patient samples (n = 121) and the monocytic U937 cell line (n = 3), treated with hydrocortisone and lipopolysaccharides, was assessed using quantitative PCR (qPCR).

RESULTS

Elevated expression of GILZ transcript variant 1 (GILZ TV 1) serves as a marker for heightened 30-day mortality in septic patients. Increased levels of GILZ TV 1 within the initial day of sepsis onset are associated with a 2.2-[95% CI 1.2-4.3] fold rise in mortality, escalating to an 8.5-[95% CI 2.0-36.4] fold increase by day eight. GILZ TV1 expression is enhanced by glucocorticoids in cell culture but remains unaffected by inflammatory stimuli such as LPS. In septic patients, GILZ TV 1 expression increases over the course of sepsis and in response to hydrocortisone treatment. Furthermore, a high expression ratio of transcript variant 1 relative to all GILZ mRNA TVs correlates with a 2.3-fold higher mortality rate in patients receiving hydrocortisone treatment.

CONCLUSION

High expression of GILZ TV 1 is associated with a higher 30-day sepsis mortality rate. Moreover, a high expression ratio of GILZ TV 1 relative to all GILZ transcript variants is a parameter for identifying patient subgroups in which hydrocortisone may be contraindicated.

Dysregulation of Host-Pathogen Interactions in Sepsis: Host-Related Factors

Sepsis stands as a prominent contributor to sickness and death on a global scale. The most current consensus definition characterizes sepsis as a life-threatening organ dysfunction stemming from an imbalanced host response to infection. This definition does not capture the intricate array of immune processes at play in sepsis, marked by simultaneous states of heightened inflammation and immune suppression. This overview delves into the immune-related processes of sepsis, elaborating about mechanisms involved in hyperinflammation and immune suppression. Moreover, we discuss stratification of patients with sepsis based on their immune profiles and how this could impact future sepsis management.

Acute Management of Sepsis beyond 24 Hours

Sepsis manifests as a dysregulated immune response to an infection, leading to tissue damage, organ failure, and potentially death or long-term health issues. Sepsis remains a major health challenge globally, causing approximately 50 million cases and 11 million deaths annually. Early management of sepsis focuses on source control, antimicrobial treatment, and supporting vital organ function. Subsequent care includes metabolic, nutritional, and immune therapies to address the complex needs of septic patients. Metabolic management is based on obtaining moderate glucose targets. Nutritional support aims to mitigate hypercatabolism and muscle wasting, but aggressive early nutrition does not improve outcomes and could even be harmful. Immune modulation is crucial due to the dual nature of sepsis-induced immune responses. Corticosteroids have shown benefits in shock and organ dysfunction reversal and in mortality reduction with current guidelines recommending them in vasopressor therapy-dependent patients. In conclusion, sepsis management beyond the initial hours requires a multifaceted approach, focusing on metabolic, nutritional, and immune system support tailored to individual patient needs to enhance survival and recovery.

Informative Subtyping of Patients with Sepsis

Sepsis pathobiology is complex. Heterogeneity refers to the clinical and biological variation within sepsis cohorts. Sepsis subtypes refer to subpopulations within sepsis cohorts derived based on these observable variations and latent features. The overarching goal of such endeavors is to enable precision immunomodulation. However, we are yet to identify immune endotypes of sepsis to achieve this goal. The sepsis subtyping field is just starting to take shape. The current subtypes in the literature do not have a core set of shared features between studies. Thus, in this narrative review, we reason that there is a need to a priori state the purpose of sepsis subtyping and minimum set of features that would be required to achieve the goal of precision immunomodulation for future sepsis.

Identification and transcriptomic assessment of latent profile pediatric septic shock phenotypes

BACKGROUND

Sepsis poses a grave threat, especially among children, but treatments are limited owing to heterogeneity among patients. We sought to test the clinical and biological relevance of pediatric septic shock subclasses identified using reproducible approaches.

METHODS

We performed latent profile analyses using clinical, laboratory, and biomarker data from a prospective multi-center pediatric septic shock observational cohort to derive phenotypes and trained a support vector machine model to assign phenotypes in an internal validation set. We established the clinical relevance of phenotypes and tested for their interaction with common sepsis treatments on patient outcomes. We conducted transcriptomic analyses to delineate phenotype-specific biology and inferred underlying cell subpopulations. Finally, we compared whether latent profile phenotypes overlapped with established gene-expression endotypes and compared survival among patients based on an integrated subclassification scheme.

RESULTS

Among 1071 pediatric septic shock patients requiring vasoactive support on day 1 included, we identified two phenotypes which we designated as Phenotype 1 (19.5%) and Phenotype 2 (80.5%). Membership in Phenotype 1 was associated with ~ fourfold adjusted odds of complicated course relative to Phenotype 2. Patients belonging to Phenotype 1 were characterized by relatively higher Angiopoietin-2/Tie-2 ratio, Angiopoietin-2, soluble thrombomodulin (sTM), interleukin 8 (IL-8), and intercellular adhesion molecule 1 (ICAM-1) and lower Tie-2 and Angiopoietin-1 concentrations compared to Phenotype 2. We did not identify significant interactions between phenotypes, common treatments, and clinical outcomes. Transcriptomic analysis revealed overexpression of genes implicated in the innate immune response and driven primarily by developing neutrophils among patients designated as Phenotype 1. There was no statistically significant overlap between established gene-expression endotypes, reflective of the host adaptive response, and the newly derived phenotypes, reflective of the host innate response including microvascular endothelial dysfunction. However, an integrated subclassification scheme demonstrated varying survival probabilities when comparing patient endophenotypes.

CONCLUSIONS

Our research underscores the reproducibility of latent profile analyses to identify pediatric septic shock phenotypes with high prognostic relevance. Pending validation, an integrated subclassification scheme, reflective of the different facets of the host response, holds promise to inform targeted intervention among those critically ill.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations for Developing Precision Medicine in the ICU

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. To impact clinical care, identification of subpopulations must do more than differentiate prognosis. It must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway, but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry, and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields.

Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients

BACKGROUND

The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects.

METHODS

We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA.

RESULTS

We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time.

CONCLUSION

Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.

Sepsis endotypes identified by host gene expression across global cohorts

BACKGROUND

Sepsis from infection is a global health priority and clinical trials have failed to deliver effective therapeutic interventions. To address complicating heterogeneity in sepsis pathobiology, and improve outcomes, promising precision medicine approaches are helping identify disease endotypes, however, they require a more complete definition of sepsis subgroups.

METHODS

Here, we use RNA sequencing from peripheral blood to interrogate the host response to sepsis from participants in a global observational study carried out in West Africa, Southeast Asia, and North America (N = 494).

RESULTS

We identify four sepsis subtypes differentiated by 28-day mortality. A low mortality immunocompetent group is specified by features that describe the adaptive immune system. In contrast, the three high mortality groups show elevated clinical severity consistent with multiple organ dysfunction. The immunosuppressed group members show signs of a dysfunctional immune response, the acute-inflammation group is set apart by molecular features of the innate immune response, while the immunometabolic group is characterized by metabolic pathways such as heme biosynthesis.

CONCLUSIONS

Our analysis reveals details of molecular endotypes in sepsis that support immunotherapeutic interventions and identifies biomarkers that predict outcomes in these groups.

High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Hospital-Onset Sepsis Warrants Expanded Investigation and Consideration as a Unique Clinical Entity

Sepsis causes more than a quarter million deaths among hospitalized adults in the United States each year. Although most cases of sepsis are present on admission, up to one-quarter of patients with sepsis develop this highly morbid and mortal condition while hospitalized. Compared with patients with community-onset sepsis (COS), patients with hospital-onset sepsis (HOS) are twice as likely to require mechanical ventilation and ICU admission, have more than two times longer ICU and hospital length of stay, accrue five times higher hospital costs, and are twice as likely to die. Patients with HOS differ from those with COS with respect to underlying comorbidities, admitting diagnosis, clinical manifestations of infection, and severity of illness. Despite the differences between these patient populations, patients with HOS sepsis are understudied and warrant expanded investigation. Here, we outline important knowledge gaps in the recognition and management of HOS in adults and propose associated research priorities for investigators. Of particular importance are questions regarding standardization of research and clinical case identification, understanding of clinical heterogeneity among patients with HOS, development of tailored management recommendations, identification of impactful prevention strategies, optimization of care delivery and quality metrics, identification and correction of disparities in care and outcomes, and how to ensure goal-concordant care for patients with HOS.

Biological basis of critical illness subclasses: from the bedside to the bench and back again

Critical illness syndromes including sepsis, acute respiratory distress syndrome, and acute kidney injury (AKI) are associated with high in-hospital mortality and long-term adverse health outcomes among survivors. Despite advancements in care, clinical and biological heterogeneity among patients continues to hamper identification of efficacious therapies. Precision medicine offers hope by identifying patient subclasses based on clinical, laboratory, biomarker and 'omic' data and potentially facilitating better alignment of interventions. Within the previous two decades, numerous studies have made strides in identifying gene-expression based endotypes and clinico-biomarker based phenotypes among critically ill patients associated with differential outcomes and responses to treatment. In this state-of-the-art review, we summarize the biological similarities and differences across the various subclassification schemes among critically ill patients. In addition, we highlight current translational gaps, the need for advanced scientific tools, human-relevant disease models, to gain a comprehensive understanding of the molecular mechanisms underlying critical illness subclasses.

Towards personalized medicine: a scoping review of immunotherapy in sepsis

Despite significant progress in our understanding of the pathophysiology of sepsis and extensive clinical research, there are few proven therapies addressing the underlying immune dysregulation of this life-threatening condition. The aim of this scoping review is to describe the literature evaluating immunotherapy in adult patients with sepsis, emphasizing on methods providing a "personalized immunotherapy" approach, which was defined as the classification of patients into a distinct subgroup or subphenotype, in which a patient's immune profile is used to guide treatment. Subgroups are subsets of sepsis patients, based on any cut-off in a variable. Subphenotypes are subgroups that can be reliably discriminated from other subgroup based on data-driven assessments. Included studies were randomized controlled trials and cohort studies investigating immunomodulatory therapies in adults with sepsis. Studies were identified by searching PubMed, Embase, Cochrane CENTRAL and ClinicalTrials.gov, from the first paper available until January 29th, 2024. The search resulted in 15,853 studies. Title and abstract screening resulted in 1409 studies (9%), assessed for eligibility; 771 studies were included, of which 282 (37%) were observational and 489 (63%) interventional. Treatment groups included were treatments targeting the innate immune response, the complement system, coagulation and endothelial dysfunction, non-pharmalogical treatment, pleiotropic drugs, immunonutrition, concomitant treatments, Traditional Chinese Medicine, immunostimulatory cytokines and growth factors, intravenous immunoglobulins, mesenchymal stem cells and immune-checkpoint inhibitors. A personalized approach was incorporated in 70 studies (9%). Enrichment was applied using cut-offs in temperature, laboratory, biomarker or genetic variables. Trials often showed conflicting results, possibly due to the lack of patient stratification or the potential influence of severity and timing on immunomodulatory therapy results. When a personalized approach was applied, trends of clinical benefit for several interventions emerged, which hold promise for future clinical trials using personalized immunotherapy.

Immunotherapy in the context of sepsis-induced immunological dysregulation

Sepsis is a clinical syndrome caused by uncontrollable immune dysregulation triggered by pathogen infection, characterized by high incidence, mortality rates, and disease burden. Current treatments primarily focus on symptomatic relief, lacking specific therapeutic interventions. The core mechanism of sepsis is believed to be an imbalance in the host's immune response, characterized by early excessive inflammation followed by late immune suppression, triggered by pathogen invasion. This suggests that we can develop immunotherapeutic treatment strategies by targeting and modulating the components and immunological functions of the host's innate and adaptive immune systems. Therefore, this paper reviews the mechanisms of immune dysregulation in sepsis and, based on this foundation, discusses the current state of immunotherapy applications in sepsis animal models and clinical trials.

Patient stratification using plasma cytokines and their regulators in sepsis: relationship to outcomes, treatment effect and leucocyte transcriptomic subphenotypes

RATIONALE

Heterogeneity of the host response within sepsis, acute respiratory distress syndrome (ARDS) and more widely critical illness, limits discovery and targeting of immunomodulatory therapies. Clustering approaches using clinical and circulating biomarkers have defined hyper-inflammatory and hypo-inflammatory subphenotypes in ARDS associated with differential treatment response. It is unknown if similar subphenotypes exist in sepsis populations where leucocyte transcriptomic-defined subphenotypes have been reported.

OBJECTIVES

We investigated whether inflammatory clusters based on cytokine protein abundance were seen in sepsis, and the relationships with previously described transcriptomic subphenotypes.

METHODS

Hierarchical cluster and latent class analysis were applied to an observational study (UK Genomic Advances in Sepsis (GAinS)) (n=124 patients) and two clinical trial datasets (VANISH, n=155 and LeoPARDS, n=484) in which the plasma protein abundance of 65, 21, 11 circulating cytokines, cytokine receptors and regulators were quantified. Clinical features, outcomes, response to trial treatments and assignment to transcriptomic subphenotypes were compared between inflammatory clusters.

MEASUREMENTS AND MAIN RESULTS

We identified two (UK GAinS, VANISH) or three (LeoPARDS) inflammatory clusters. A group with high levels of pro-inflammatory and anti-inflammatory cytokines was seen that was associated with worse organ dysfunction and survival. No interaction between inflammatory clusters and trial treatment response was found. We found variable overlap of inflammatory clusters and leucocyte transcriptomic subphenotypes.

CONCLUSIONS

These findings demonstrate that differences in response at the level of cytokine biology show clustering related to severity, but not treatment response, and may provide complementary information to transcriptomic sepsis subphenotypes.

TRIAL REGISTRATION NUMBER

ISRCTN20769191, ISRCTN12776039.

Biomarkers in Sepsis: A Current Review of New Technologies

Sepsis syndromes have been recognized since antiquity yet still pose significant challenges to modern medicine. One of the biggest challenges lies in the heterogeneity of triggers and its protean clinical manifestations, as well as its rapidly progressive and lethal nature. Thus, there is a critical need for biomarkers that can quickly and accurately detect sepsis onset and predict treatment response. In this review, we will briefly describe the current consensus definitions of sepsis and the ideal features of a biomarker. We will then delve into currently available and in-development markers of pathogens, hosts, and their interactions that together comprise the sepsis syndrome.