Methods for Phenotyping Adult Patients in Sepsis and Septic Shock: A Scoping Review

M-ClustEHR: A multimodal clustering approach for electronic health records

Sepsis refers to a potentially life-threatening situation where the immune system of the human body has an extreme response to an infection. In the presence of underlying comorbidities, the situation can become even worse and result in death. Employing unsupervised machine learning techniques, such as clustering, can assist in providing a better understanding of patient phenotypes by unveiling subgroups characterized by distinct sepsis progression and treatment patterns. More concretely, this study introduces M-ClustEHR, a clustering approach that utilizes medical data of multiple modalities by employing a multimodal autoencoder for learning comprehensive sepsis patient representations. M-ClustEHR consistently outperforms traditional clustering approaches in terms of several internal clustering performance metrics, as well as cluster stability in identifying phenotypes in the sepsis cohort. The unveiled patterns, supported by existing medical literature and clinicians, highlight the importance of multimodal clustering for advancing personalized sepsis care.

TEMPORAL CHANGES IN INNATE AND ADAPTIVE IMMUNITY DURING SEPSIS AS DETERMINED BY ELISPOT

ABSTRACT

Background: The inability to evaluate host immunity in a rapid quantitative manner in patients with sepsis has severely hampered development of novel immune therapies. The enzyme-linked immunospot (ELISpot) assay is a functional bioassay that measures the number of cytokine-secreting cells and the relative amount of cytokine produced at the single-cell level. A key advantage of ELISpot is its excellent dynamic range enabling a more precise quantifiable assessment of host immunity. Herein, we tested the hypothesis that the ELISpot assay can detect dynamic changes in both innate and adaptive immunity as they often occur during sepsis. We also tested whether ELISpot could detect the effect of immune drug therapies to modulate innate and adaptive immunity. Methods: Mice were made septic using sublethal cecal ligation and puncture. Blood and spleens were harvested serially, and ex vivo interferon γ and TNF-α production were compared by ELISpot and enzyme-linked immunosorbent assay. The capability of ELISpot to detect changes in innate and adaptive immunity due to in vivo immune therapy with dexamethasone, IL-7, and arginine was also evaluated. Results: ELISpot confirmed a decreased innate and adaptive immunity responsiveness during sepsis progression. More importantly, ELISpot was also able to detect changes in adaptive and innate immunity in response to immune-modulatory reagents, for example, dexamethasone, arginine, and IL-7, in a readily quantifiable manner, as predicted by the reagents known mechanisms of action. ELISpot and enzyme-linked immunosorbent assay results tended to parallel one another although some differences were noted. Conclusion: ELISpot offers a unique capability to assess the functional status of both adaptive and innate immunity over time. The results presented herein demonstrate that ELISpot can also be used to detect and follow the in vivo effects of drugs to ameliorate sepsis-induced immune dysfunction. This capability would be a major advance in guiding new immune therapies in sepsis.

Resistin as a potential diagnostic biomarker for sepsis: insights from DIA and ELISA analyses

PURPOSE

The primary objective of this investigation is to systematically screen and identify differentially expressed proteins (DEPs) within the plasma of individuals afflicted with sepsis. This endeavor employs both Data-Independent Acquisition (DIA) and enzyme-linked immunosorbent assay (ELISA) methodologies. The overarching goal is to furnish accessible and precise serum biomarkers conducive to the diagnostic discernment of sepsis.

METHOD

The study encompasses 53 sepsis patients admitted to the Affiliated Hospital of Southwest Medical University between January 2019 and December 2020, alongside a control cohort consisting of 16 individuals devoid of sepsis pathology. Subsequently, a subset comprising 10 randomly selected subjects from the control group and 22 from the sepsis group undergoes quantitative proteomic analysis via DIA. The acquired data undergoes Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) analyses, facilitating the construction of a Protein-Protein Interaction (PPI) network to discern potential markers. Validation of core proteins is then accomplished through ELISA. Comparative analysis between the normal and sepsis groups ensues, characterized by Receiver Operating Characteristic (ROC) curve construction to evaluate diagnostic efficacy.

RESULT

A total of 187 DEPs were identified through bioinformatic methodologies. Examination reveals their predominant involvement in biological processes such as wound healing, coagulation, and blood coagulation. Functional pathway analysis further elucidates their engagement in the complement pathway and malaria. Resistin emerges as a candidate plasma biomarker, subsequently validated through ELISA. Notably, the protein exhibits significantly elevated levels in the serum of sepsis patients compared to the normal control group. ROC curve analysis underscores the robust diagnostic capacity of these biomarkers for sepsis.

CONCLUSION

Data-Independent Acquisition (DIA) and Enzyme-Linked Immunosorbent Assay (ELISA) show increased Resistin levels in sepsis patients, suggesting diagnostic potential, warranting further research.

Advances and Challenges in Sepsis Management: Modern Tools and Future Directions

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Exploration of Norepinephrine Dose-Capping Practices: Report From an International, Interprofessional Survey of Critical Care Clinicians

Purpose: The Joint Commission standards for titrated infusions require specification of maximum rates of infusion. This practice has led to the development of protocolized maximum doses that can be overridden by provider order ("soft maximums") and to dose caps that cannot be superseded ("hard maximums"). The purpose of this study was to determine the prevalence of and attitudes towards dose capping of norepinephrine. Methods: A 20-item cross-sectional survey assessing norepinephrine dose capping practices, perceptions of norepinephrine protocols, and respondent and practice site demographics was distributed electronically to the mailing list of an international medical podcast. Responses were stratified according to use of weight-based dosing (WBD) or non-WBD of norepinephrine. The primary objective was to characterize norepinephrine dosing practices including protocolized maximum doses and/or dose capping. Categorical and continuous variables were compared using the Chi-square test and Mann-Whitney U test, respectively, with P < .05 indicating statistical significance. Results: The survey was completed by 586 physicians, nurses, pharmacists, and advanced practice providers. WBD was used by 51% and non-WBD by 47%. A standardized titration protocol was reported by 65% and dose capping was reported by 19%. The protocolized maximum dose ranged from 20-400 mcg/min for respondents using non-WBD (median [interquartile range] 30 [30-50]) and ranged from .2-10 mcg/kg/min for respondents using WBD (1 [.5-3]). The dose cap was 50 (40-123) mcg/min with non-WBD and 2 (1-3) mcg/kg/min with WBD. Conclusions: An international, multi-professional survey of critical care and emergency medicine clinicians revealed wide variability in norepinephrine dosing practices including maximum doses allowed.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

Temporal Changes in Innate and Adaptive Immunity During Sepsis as Determined by ELISpot

Background

The inability to evaluate host immunity in a rapid quantitative manner in patients with sepsis has severely hampered development of novel immune therapies. The ELISpot assay is a functional bioassay that measures the number of cytokine-secreting cells and the relative amount of cytokine produced at the single-cell level. A key advantage of ELISpot is its excellent dynamic range enabling a more precise quantifiable assessment of host immunity. Herein, we tested the hypothesis on whether the ELISpot assay can detect dynamic changes in both innate and adaptive immunity as they often occur during sepsis. We also tested whether ELISpot could detect the effect of immune drug therapies to modulate innate and adaptive immunity.

Methods

Mice were made septic using sublethal cecal ligation and puncture (CLP). Blood and spleens were harvested serially and ex vivo IFN-γ and TNF-α production were compared by ELISpot and ELISA. The capability of ELISpot to detect changes in innate and adaptive immunity due to in vivo immune therapy with dexamethasone, IL-7, and arginine was also evaluated.

Results

ELISpot confirmed a decreased innate and adaptive immunity responsiveness during sepsis progression. More importantly, ELISpot was also able to detect changes in adaptive and innate immunity in response to immune-modulatory reagents, for example dexamethasone, arginine, and IL-7 in a readily quantifiable manner, as predicted by the reagents known mechanisms of action. ELISpot and ELISA results tended to parallel one another although some differences were noted.

Conclusion

ELISpot offers a unique capability to assess the functional status of both adaptive and innate immunity over time. The results presented herein demonstrate that ELISpot can also be used to detect and follow the in vivo effects of drugs to ameliorate sepsis-induced immune dysfunction. This capability would be a major advance in guiding new immune therapies in sepsis.

How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials

Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.

Clinical Phenotypes of Sepsis in a Cohort of Hospitalized Patients According to Infection Site

OBJECTIVES

Clinical sepsis phenotypes may be defined by a wide range of characteristics such as site of infection, organ dysfunction patterns, laboratory values, and demographics. There is a paucity of literature regarding the impact of site of infection on the timing and pattern of clinical sepsis markers. This study hypothesizes that important phenotypic variation in clinical markers and outcomes of sepsis exists when stratified by infection site.

DESIGN

Retrospective cohort study.

SETTING

Five hospitals within the Wake Forest Health System from June 2019 to December 2019.

PATIENTS

Six thousand seven hundred fifty-three hospitalized adults with a discharge International Classification of Diseases, 10th Revision code for acute infection who met systemic inflammatory response syndrome (SIRS), quick Sepsis-related Organ Failure Assessment (qSOFA), or Sequential Organ Failure Assessment (SOFA) criteria during the index hospitalization.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary outcome of interest was a composite of 30-day mortality or shock. Infection site was determined by a two-reviewer process. Significant demographic, vital sign, and laboratory result differences were seen across all infection sites. For the composite outcome of shock or 30-day mortality, unknown or unspecified infections had the highest proportion (21.34%) and CNS infections had the lowest proportion (8.11%). Respiratory, vascular, and unknown or unspecified infection sites showed a significantly increased adjusted and unadjusted odds of the composite outcome as compared with the other infection sites except CNS. Hospital time prior to SIRS positivity was shortest in unknown or unspecified infections at a median of 0.88 hours (interquartile range [IQR], 0.22-5.05 hr), and hospital time prior to qSOFA and SOFA positivity was shortest in respiratory infections at a median of 54.83 hours (IQR, 9.55-104.67 hr) and 1.88 hours (IQR, 0.47-17.40 hr), respectively.

CONCLUSIONS

Phenotypic variation in illness severity and mortality exists when stratified by infection site. There is a significantly higher adjusted and unadjusted odds of the composite outcome of 30-day mortality or shock in respiratory, vascular, and unknown or unspecified infections as compared with other sites.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups.

DESIGN

We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock.

SETTING

We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients.

PATIENTS

Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001).

CONCLUSIONS

Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

Redefying the therapeutic strategies against cardiorenal morbidity and mortality: Patient phenotypes

Chronic kidney disease (CKD) patients face an unacceptably high morbidity and mortality, mainly from cardiovascular diseases. Diabetes mellitus, arterial hypertension and dyslipidemia are highly prevalent in CKD patients. Established therapeutic protocols for the treatment of diabetes mellitus, arterial hypertension, and dyslipidemia are not as effective in CKD patients as in the general population. The role of non-traditional risk factors (RF) has gained interest in the last decades. These entail the deranged clinical spectrum of secondary hyperparathyroidism involving vascular and valvular calcification, under the term “CKD-mineral and bone disorder” (CKD-MBD), uremia per se, inflammation and oxidative stress. Each one of these non-traditional RF have been addressed in various study designs, but the results do not exhibit any applied clinical benefit for CKD-patients. The “crusade” against cardiorenal morbidity and mortality in CKD-patients is in some instances, derailed. We propose a therapeutic paradigm advancing from isolated treatment targets, as practiced today, to precision medicine involving patient phenotypes with distinct underlying pathophysiology. In this regard we propose two steps, based on current stratification management of corona virus disease-19 and sepsis. First, select patients who are expected to have a high mortality, i.e., a prognostic enrichment. Second, select patients who are likely to respond to a specific therapy, i.e., a predictive enrichment.

Identifying inflammatory phenotypes to target mechanism-specific treatments in sepsis

A clinical trial by Leventogiannis et al.¹ suggests that ferritin and HLA-DR monocyte receptor expression can identify septic patients with macrophage-activation-like syndrome (MALS), or immunoparalysis, and that targeting IL-1ra treatment with this strategy may improve outcomes.

Bulk RNA Sequencing With Integrated Single-Cell RNA Sequencing Identifies BCL2A1 as a Potential Diagnostic and Prognostic Biomarker for Sepsis

Background

Sepsis is one of the leading causes of morbidity and mortality worldwide in the intensive care unit (ICU). The prognosis of the disease strongly depends on rapid diagnosis and appropriate treatment. Thus, some new and accurate sepsis-related biomarkers are pressing needed and their efficiency should be carefully demonstrated.

Methods

Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to detect sepsis and monocyte/macrophage-related genes. Least absolute shrinkage and selection operator (LASSO) and random forest regression analyses were used in combination to screen out prognostic genes. Single-cell RNA sequence profiling was utilized to further verify the expression of these genes on a single cell level. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were also applied to verify the diagnostic value of the target biomarkers.

Results

The intersections of the genes detected by differential expression and WGCNA analyses identified 141 overlapping candidate genes that were closely related to sepsis and macrophages. The LASSO and random forest regression analyses further screened out 17 prognostic genes. Single-cell RNA sequencing analysis detected that FCGR1A and BCL2A1 might be potential biomarkers for sepsis diagnosis and the diagnostic efficacy of BCL2A1 was further validated by ROC curve and DCA.

Conclusions

It was revealed that BCL2A1 had good diagnostic and prognostic value for sepsis, and that it can be applied as a potential and novel biomarker for the management of the disease.