Sepsis biomarkers and diagnostic tools with a focus on machine learning

IDENTIFICATION OF A NOVEL SEPSIS PROGNOSIS MODEL: BASED ON TRANSCRIPTOME AND PROTEOME ANALYSIS

ABSTRACT

Sepsis is a highly prevalent and deadly disease. Currently, there is a lack of ideal biomarker prognostis models for sepsis. We attempt to construct a model capable of predicting the prognosis of sepsis patients by integrating transcriptomic and proteomic data. Through analysis of proteomic and transcriptomic data, we identified 25 differentially expressed genes (DEGs). Single-factor Cox-Lasso regression analysis identified 16 DEGs (overall survival-DEGs) associated with patient prognosis. Through multifactor Cox-Lasso regression analysis, a prognostic model based on these 16 genes was constructed. Kaplan-Meier survival analysis and receiver operating characteristic curve analysis were used to further validate the high stability and good predictive ability of this prognostic model with internal and external data. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of overall survival-DEGs and differentially expressed genes between high and low-risk groups based on the prognostic model revealed significant enrichment in immune-related pathways, particularly those associated with viral regulation.

Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs

Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51-80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions.

Machine Learning Screening and Validation of PANoptosis-Related Gene Signatures in Sepsis

Background

Sepsis is a syndrome marked by life-threatening organ dysfunction and a disrupted host immune response to infection. PANoptosis is a recent conceptual development, which emphasises the interconnectedness among multiple programmed cell deaths in various diseases. Nevertheless, the role of PANoptosis in sepsis is still unclear.

Methods

We utilized the GSE65682 dataset to identify PANoptosis-related genes (PRGs) and associated immune characteristics in sepsis, classified sepsis samples based on PRGs using the ConsensusClusterPlus method and applied the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify cluster-specific hub genes. Based on PANoptosis -specific DEGs, we compared results from machine learning models and the best-performing model was selected. Predictive efficiency was validated through external dataset, nomogram, survival analysis, quantitative real-time PCR, and western blot.

Results

The expression levels of PRGs were generally dysregulated in sepsis patients compared with normal samples, and higher PRGs expression correlated with increased immune cell infiltration. In addition, two distinct PANoptosis-related clusters were defined, and functional analysis indicated that DEGs associated with these clusters were primarily linked to immune-related pathways. The SVM model was selected as best-performing model, with lower residuals and the highest area under the curve (AUC = 0.967), which was then validated in an external dataset (AUC = 0.989) and through in vivo experiments. Additional validation through nomogram and survival analysis further confirmed its substantial predictive efficacy.

Conclusion

Our findings exposed the intricate association between PANoptosis and sepsis, offering important insights on sepsis diagnosis and potential therapeutic targets.

Navigating the Modern Landscape of Sepsis: Advances in Diagnosis and Treatment

Sepsis poses a significant threat to human health due to its high morbidity and mortality rates worldwide. Traditional diagnostic methods for identifying sepsis or its causative organisms are time-consuming and contribute to a high mortality rate. Biomarkers have been developed to overcome these limitations and are currently used for sepsis diagnosis, prognosis prediction, and treatment response assessment. Over the past few decades, more than 250 biomarkers have been identified, a few of which have been used in clinical decision-making. Consistent with the limitations of diagnosing sepsis, there is currently no specific treatment for sepsis. Currently, the general treatment for sepsis is conservative and includes timely antibiotic use and hemodynamic support. When planning sepsis-specific treatment, it is important to select the most suitable patient, considering the heterogeneous nature of sepsis. This comprehensive review summarizes current and evolving biomarkers and therapeutic approaches for sepsis.

Shenfu injection targets the PI3K-AKT pathway to regulate autophagy and apoptosis in acute respiratory distress syndrome caused by sepsis

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by an exaggerated response to infection. In the lungs, one of the most susceptible organs, this can manifest as acute respiratory distress syndrome (ARDS). Shenfu (SF) injection is a prominent traditional Chinese medicine used to treat sepsis. However, the exact mechanism of its action has rarely been reported in the literature.

PURPOSE

In the present study, we detected the protective effect of SF injection on sepsis-induced ARDS and explored its underlying mechanism.

METHODS

We investigated the potential targets and regulatory mechanisms of SF injections using a combination of network pharmacology and RNA sequencing. This study was conducted both in vivo and in vitro using a mouse model of ARDS and lipopolysaccharide (LPS)-stimulated MLE-12 cells, respectively.

RESULTS

The results showed that SF injection could effectively inhibit inflammation, oxidative stress, and apoptosis to alleviate LPS-induced ARDS. SF inhibited the PI3K-AKT pathway, which controls autophagy and apoptosis. Subsequently, MLE-12 cells were treated with 3-methyladenine to assess its effects on autophagy and apoptosis. Additional experiments were conducted by adding rapamycin, an mTOR antagonist, or SC79, an AKT agonist, to investigate the effects of SF injection on autophagy, apoptosis, and the PI3K-AKT pathway.

CONCLUSION

Overall, we found that SF administration could enhance autophagic activity, reduce apoptosis, suppress inflammatory responses and oxidative stress, and inhibit the PI3K-AKT pathway, thus ameliorating sepsis-induced ARDS.

Peripheral PD-1+NK cells could predict the 28-day mortality in sepsis patients

Background

Unbalanced inflammatory response is a critical feature of sepsis, a life-threatening condition with significant global health burdens. Immune dysfunction, particularly that involving different immune cells in peripheral blood, plays a crucial pathophysiological role and shows early warning signs in sepsis. The objective is to explore the relationship between sepsis and immune subpopulations in peripheral blood, and to identify patients with a higher risk of 28-day mortality based on immunological subtypes with machine-learning (ML) model.

Methods

Patients were enrolled according to the sepsis-3 criteria in this retrospective observational study, along with age- and sex-matched healthy controls (HCs). Data on clinical characteristics, laboratory tests, and lymphocyte immunophenotyping were collected. XGBoost and k-means clustering as ML approaches, were employed to analyze the immune profiles and stratify septic patients based on their immunological subtypes. Cox regression survival analysis was used to identify potential biomarkers and to assess their association with 28-day mortality. The accuracy of biomarkers for mortality was determined by the area under the receiver operating characteristic (ROC) curve (AUC) analysis.

Results

The study enrolled 100 septic patients and 89 HCs, revealing distinct lymphocyte profiles between the two groups. The XGBoost model discriminated sepsis from HCs with an area under the receiver operating characteristic curve of 1.0 and 0.99 in the training and testing set, respectively. Within the model, the top three highest important contributions were the percentage of CD38+CD8+T cells, PD-1+NK cells, HLA-DR+CD8+T cells. Two clusters of peripheral immunophenotyping of septic patients by k-means clustering were conducted. Cluster 1 featured higher proportions of PD1+ NK cells, while cluster 2 featured higher proportions of naïve CD4+T cells. Furthermore, the level of PD-1+NK cells was significantly higher in the non-survivors than the survivors (15.1% vs 8.6%, P<0.01). Moreover, the levels of PD1+ NK cells combined with SOFA score showed good performance in predicting the 28-day mortality in sepsis (AUC=0.91,95%CI 0.82-0.99), which is superior to PD1+ NK cells only(AUC=0.69, sensitivity 0.74, specificity 0.64, cut-off value of 11.25%). In the multivariate Cox regression, high expression of PD1+ NK cells proportion was related to 28-day mortality (aHR=1.34, 95%CI 1.19 to 1.50; P<0.001).

Conclusion

The study provides novel insights into the association between PD1+NK cell profiles and prognosis of sepsis. Peripheral immunophenotyping could potentially stratify the septic patients and identify those with a high risk of 28-day mortality.

Electroacupuncture pre-treatment exerts a protective effect on LPS-induced cardiomyopathy in mice through the delivery of miR-381 via exosomes

OBJECTIVE

This study aims to investigate the cardiac protective effects and molecular mechanisms of electroacupuncture (EA) pre-treatment in lipopolysaccharide (LPS)-Induced Cardiomyopathy.

METHODS AND RESULTS

Pre-treatment with EA was performed 30 min before intraperitoneal injection of LPS. Cardiac function changes in mice of the EA + LPS group were observed using electrocardiography, echocardiography, and enzyme linked immunosorbent assay (ELISA) and compared with the LPS group. The results demonstrated that EA pre-treatment significantly improved the survival rate of septic mice, alleviated the severity of endotoxemia, and exhibited notable cardiac protective effects. These effects were characterized by a reduction in ST-segment elevation on electrocardiography, an increase in ejection fraction (EF) and fraction shortening (FS) on echocardiography and a decrease in the expression of serum cardiac troponin I (cTn-I) levels. Serum exosomes obtained after EA pre-treatment were extracted and administered to septic mice, revealing significant cardiac protective effects of EA-derived exosomes. Furthermore, the antagonism of circulating exosomes in mice markedly suppressed the cardiac protective effects conferred by EA pre-treatment. Analysis of serum exosomes using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) revealed a significant upregulation of miR-381 expression after EA pre-treatment. Inhibition or overexpression of miR-381 through serotype 9 adeno-associated virus (AAV9)-mediated gene delivery demonstrated that overexpression of miR-381 exerted a cardiac protective effect, while inhibition of miR-381 significantly attenuated the cardiac protective effects conferred by EA pre-treatment.

CONCLUSIONS

Our research findings have revealed a novel endogenous cardiac protection mechanism, wherein circulating exosomes derived from EA pre-treatment mitigate LPS-induced cardiac dysfunction via miR-381.

Characteristics and Risk Factors for Pediatric Sepsis

OBJECTIVE

Sepsis is considered a major cause of health loss in children and had high mortality and morbidity. Currently, there is no reliable model for predicting the prognosis of pediatric patients with sepsis. This study aimed to analyze the clinical characteristics of sepsis in children and assess the risk factors associated with poor prognosis in pediatric sepsis patients to identify timely interventions and improve their outcomes.

METHODS

This study analyzed the clinical indicators and laboratory results of septic patients hospitalized in the Pediatric Intensive Care Unit of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, China, from January 1, 2019, to December 31, 2021. Risk factors for sepsis were identified by logistic regression analyses.

RESULTS

A total of 355 children with sepsis were enrolled, with 333 children (93.8%) in the good prognosis group, and 22 children (6.2%) in the poor prognosis group. Among them, there were 255 patients (71.8%) in the sepsis group, and 100 patients (28.2%) in the severe sepsis group. The length of hospital stay in the poor prognosis group was longer than that in the good prognosis group (P<0.01). The levels of interleukin 1β (IL-1β) in the poor prognosis group were higher than those in the good prognosis group (P>0.05), and the platelet (PLT), albumin (ALB), and hemoglobin (Hb) levels were lower in the poor prognosis group (P<0.01). The IL-8 levels in the severe sepsis group were higher than those in the sepsis group (P<0.05). Multiple logistic regression analysis suggested that lower Hb levels, ALB levels, peak PLT counts, and higher IL-1β levels were independent risk factors for poor prognosis in children with sepsis.

CONCLUSION

Lower Hb, ALB, and PLT counts and elevated IL-1β are independent risk factors for poor prognosis in children with sepsis.

Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterized by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility of identifying integrative patterns from clinical parameters, plasma biomarkers, and extensive phenotyping of blood immune cells. While no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90-day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90-day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T-cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical, and clinical parameters.

Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study

BACKGROUND

Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection.

METHODS

This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI.

FINDINGS

Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease class signature that achieved an AUC of 94·1% (95% CI 90·6-97·7) in distinguishing bacterial from viral infections in the RAPIDS validation cohort. A ten-gene disease severity signature achieved an AUC of 82·2% (95% CI 76·3-88·1) in predicting organ dysfunction within 24 h of sampling in the RAPIDS validation cohort. Used in tandem, the disease class and disease severity signatures predicted organ dysfunction within 24 h of sampling with an AUC of 90·5% (95% CI 83·3-97·6) for patients with predicted bacterial infection and 94·7% (87·8-100·0) for patients with predicted viral infection. In the external EUCLIDS validation dataset (n=362), the disease class and disease severity predicted organ dysfunction at time of sampling with an AUC of 70·1% (95% CI 44·1-96·2) for patients with predicted bacterial infection and 69·6% (53·1-86·0) for patients with predicted viral infection.

INTERPRETATION

In children evaluated for sepsis, novel host transcriptomic signatures specific for bacterial and viral infection can identify dysregulated host response leading to organ dysfunction.

FUNDING

Australian Government Medical Research Future Fund Genomic Health Futures Mission, Children's Hospital Foundation Queensland, Brisbane Diamantina Health Partners, Emergency Medicine Foundation, Gold Coast Hospital Foundation, Far North Queensland Foundation, Townsville Hospital and Health Services SERTA Grant, and Australian Infectious Diseases Research Centre.

Investigating the role of itaconate in macrophage activation and oxidative stress injury in sepsis-associated acute kidney injury

BACKGROUND

Sepsis may be linked to oxidative stress and can be controlled by itaconate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway. Nevertheless, the itaconate impact on sepsis-associated acute kidney injury (SA-AKI) has yet to be definitively established.

METHODS

We employed SA-AKI mouse model through a cecal ligation and puncture (CLP) procedure for the in vivo investigation of the potential nephroprotective effect of itaconate in this study. A plasmid was transfected into RAW264.7 cells to examine the Nrf2 pathway function after itaconate administration. Finally, the immune-responsive gene 1-knockout (IRG1-/-) mice were used to study the itaconate impacts on oxidative stress-induced SA-AKI.

RESULTS

We have shown that 4-octyl itaconate (OI) significantly reduced CD11b-positive macrophage aggregation and activated the Nrf2 pathway in the bone marrow-derived macrophages (BMDM). The impacts of Nrf2 inhibitor ML385 on the anti-inflammatory and antioxidant properties of itaconate were found to be partial. OI inhibited lipopolysaccharide-induced oxidative stress injury in RAW264.7 macrophages and activated Nrf2 in the nucleus to hinder the expression of nuclear factor kappa B p65, thereby suppressing oxidative stress injury in the macrophages. Additionally, the introduction of the transfected plasmid resulted in a partial inhibition of the anti-inflammatory impact of itaconate. The kidney injury caused by sepsis exhibited greater severity in the IRG1-/- mice than in the wild type mice. Exogenous OI partially attenuated the kidney injury induced by sepsis in the IRG1-/- mice and suppressed the oxidative stress injury in macrophages.

CONCLUSIONS

This investigation offers new proof to support the itaconate function in the development and progression of SA-AKI and shows a new possible therapeutic agent for the SA-AKI treatment.

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.

International Consensus Criteria for Pediatric Sepsis and Septic Shock

Importance

Sepsis is a leading cause of death among children worldwide. Current pediatric-specific criteria for sepsis were published in 2005 based on expert opinion. In 2016, the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) defined sepsis as life-threatening organ dysfunction caused by a dysregulated host response to infection, but it excluded children.

Objective

To update and evaluate criteria for sepsis and septic shock in children.

Evidence Review

The Society of Critical Care Medicine (SCCM) convened a task force of 35 pediatric experts in critical care, emergency medicine, infectious diseases, general pediatrics, nursing, public health, and neonatology from 6 continents. Using evidence from an international survey, systematic review and meta-analysis, and a new organ dysfunction score developed based on more than 3 million electronic health record encounters from 10 sites on 4 continents, a modified Delphi consensus process was employed to develop criteria.

Findings

Based on survey data, most pediatric clinicians used sepsis to refer to infection with life-threatening organ dysfunction, which differed from prior pediatric sepsis criteria that used systemic inflammatory response syndrome (SIRS) criteria, which have poor predictive properties, and included the redundant term, severe sepsis. The SCCM task force recommends that sepsis in children be identified by a Phoenix Sepsis Score of at least 2 points in children with suspected infection, which indicates potentially life-threatening dysfunction of the respiratory, cardiovascular, coagulation, and/or neurological systems. Children with a Phoenix Sepsis Score of at least 2 points had in-hospital mortality of 7.1% in higher-resource settings and 28.5% in lower-resource settings, more than 8 times that of children with suspected infection not meeting these criteria. Mortality was higher in children who had organ dysfunction in at least 1 of 4-respiratory, cardiovascular, coagulation, and/or neurological-organ systems that was not the primary site of infection. Septic shock was defined as children with sepsis who had cardiovascular dysfunction, indicated by at least 1 cardiovascular point in the Phoenix Sepsis Score, which included severe hypotension for age, blood lactate exceeding 5 mmol/L, or need for vasoactive medication. Children with septic shock had an in-hospital mortality rate of 10.8% and 33.5% in higher- and lower-resource settings, respectively.

Conclusions and Relevance

The Phoenix sepsis criteria for sepsis and septic shock in children were derived and validated by the international SCCM Pediatric Sepsis Definition Task Force using a large international database and survey, systematic review and meta-analysis, and modified Delphi consensus approach. A Phoenix Sepsis Score of at least 2 identified potentially life-threatening organ dysfunction in children younger than 18 years with infection, and its use has the potential to improve clinical care, epidemiological assessment, and research in pediatric sepsis and septic shock around the world.

Surviving Sepsis Campaign Research Priorities 2023

OBJECTIVES

To identify research priorities in the management, epidemiology, outcome, and pathophysiology of sepsis and septic shock.

DESIGN

Shortly after publication of the most recent Surviving Sepsis Campaign Guidelines, the Surviving Sepsis Research Committee, a multiprofessional group of 16 international experts representing the European Society of Intensive Care Medicine and the Society of Critical Care Medicine, convened virtually and iteratively developed the article and recommendations, which represents an update from the 2018 Surviving Sepsis Campaign Research Priorities.

METHODS

Each task force member submitted five research questions on any sepsis-related subject. Committee members then independently ranked their top three priorities from the list generated. The highest rated clinical and basic science questions were developed into the current article.

RESULTS

A total of 81 questions were submitted. After merging similar questions, there were 34 clinical and ten basic science research questions submitted for voting. The five top clinical priorities were as follows: 1) what is the best strategy for screening and identification of patients with sepsis, and can predictive modeling assist in real-time recognition of sepsis? 2) what causes organ injury and dysfunction in sepsis, how should it be defined, and how can it be detected? 3) how should fluid resuscitation be individualized initially and beyond? 4) what is the best vasopressor approach for treating the different phases of septic shock? and 5) can a personalized/precision medicine approach identify optimal therapies to improve patient outcomes? The five top basic science priorities were as follows: 1) How can we improve animal models so that they more closely resemble sepsis in humans? 2) What outcome variables maximize correlations between human sepsis and animal models and are therefore most appropriate to use in both? 3) How does sepsis affect the brain, and how do sepsis-induced brain alterations contribute to organ dysfunction? How does sepsis affect interactions between neural, endocrine, and immune systems? 4) How does the microbiome affect sepsis pathobiology? 5) How do genetics and epigenetics influence the development of sepsis, the course of sepsis and the response to treatments for sepsis?

CONCLUSIONS

Knowledge advances in multiple clinical domains have been incorporated in progressive iterations of the Surviving Sepsis Campaign guidelines, allowing for evidence-based recommendations for short- and long-term management of sepsis. However, the strength of existing evidence is modest with significant knowledge gaps and mortality from sepsis remains high. The priorities identified represent a roadmap for research in sepsis and septic shock.

Clinical value of the red blood cell distribution width to albumin ratio in the assessment of prognosis in critically ill patients with sepsis: a retrospective analysis

Background

Red blood cell (RBC) distribution width (RDW) to albumin ratio is a novel biomarker and its prognostic effect on critically ill patients with sepsis has not been extensively investigated. The objective of this study was to identify the prognostic value of the RDW to albumin ratio in these patients.

Methods

Data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. A Cox proportional hazards model and restricted cubic spline model were used to determine the association of RDW to albumin ratio with mortality. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied, and the area under the curve (AUC) was used to compare the predictive value.

Results

A total of 3,969 eligible patients were enrolled. The median RDW to albumin ratio was significantly higher in non-survivors than in survivors at 30 and 90 days. Patients were divided into groups according to the RDW to albumin ratio, and the risk of 30- and 90-day mortality markedly increased in the group with a higher ratio. The relationship between the RDW to albumin ratio as a continuous variable and 30-day mortality also showed an upward trend in the restricted cubic spline. The AUC of the RDW to albumin ratio was 0.633 in discriminating 30-day mortality which was similar to that of the lactate to albumin ratio (AUC =0.617; P=0.133) and higher than that of the neutrophil percentage to albumin ratio (AUC =0.559; P<0.001).

Conclusions

The RDW to albumin ratio is a promising biomarker for assessing the prognosis of critically ill patients with sepsis. Its predictive value in determining mortality was found to be similar to that of the lactate to albumin ratio and superior to that of the neutrophil percentage to albumin ratio.

Biomarkers for Serious Bacterial Infections in Febrile Children

Febrile infections in children are a common cause of presentation to the emergency department (ED). While viral infections are usually self-limiting, sometimes bacterial illnesses may lead to sepsis and severe complications. Inflammatory biomarkers such as C reactive protein (CRP) and procalcitonin are usually the first blood exams performed in the ED to differentiate bacterial and viral infections; nowadays, a better understanding of immunochemical pathways has led to the discovery of new and more specific biomarkers that could play a role in the emergency setting. The aim of this narrative review is to provide the most recent evidence on biomarkers and predictor models, combining them for serious bacterial infection (SBI) diagnosis in febrile children. Literature analysis shows that inflammatory response is a complex mechanism in which many biochemical and immunological factors contribute to the host response in SBI. CRP and procalcitonin still represent the most used biomarkers in the pediatric ED for the diagnosis of SBI. Their sensibility and sensitivity increase when combined, and for this reason, it is reasonable to take them both into consideration in the evaluation of febrile children. The potential of machine learning tools, which represent a real novelty in medical practice, in conjunction with routine clinical and biological information, may improve the accuracy of diagnosis and target therapeutic options in SBI. However, studies on this matter are not yet validated in younger populations, making their relevance in pediatric precision medicine still uncertain. More data from further research are needed to improve clinical practice and decision making using these new technologies.

Biomarkers and molecular endotypes of sarcoidosis: lessons from omics and non-omics studies

Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional "one-for-all" non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.

Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future

Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.

Vitexin along with verapamil downregulates efflux pump P-glycoprotein in macrophages and potentiate M1 to M2 switching via TLR4-NF-κB-TNFR2 pathway in lipopolysaccharide treated mice

The lipopolysaccharide, a microbial toxin, is one of the major causative agents of sepsis. P-gp expression and its functions are altered during inflammation. LPS has been known to impair the functions of P-gp, an efflux transporter. But the effect of LPS on P-gp expression in murine peritoneal macrophages is poorly understood. Molecular docking studies reveal that vitexin is a potent substrate and verapamil a potent inhibitor of P-gp. In the present experimental study, the curative potential of vitexin as a fruit component and verapamil treated as a control inhibitor of P-gp was examined in a murine LPS sepsis model. The effects of vitexin and verapamil on P-gp expression in macrophages correlating with changes in macrophage polarization and associated functional responses during LPS induced sepsis were studied. Peritoneal macrophages of LPS (10 mg/kg body weight) challenged mice exhibited elevated levels of H2O2, superoxide, and NO in parallel with lower antioxidant activity. LPS treatment increased P-gp expression through increased TLR4/expression. However, LPS challenged mice treated with vitexin (5 mg/kg body weight) + verapamil (5 mg/kg body weight) showed higher anti-oxidant enzyme activity (SOD, CAT and GRx) resulting in reduced oxidative stress. This combination treatment also elevated TNFR2, concomitant with down-regulation of TLR4, NF-κB and P-gp expression in murine peritoneal macrophages, resulting in a switch from M1 to M2 polarisation of macrophages and reduced inflammatory responses. In conclusion, combined vitexin and verapamil treatment could be used as a promising therapy to regulate P-gp expression and protection against LPS mediated sepsis and inflammatory damages.

(E)-2-methoxy-4-(3-(4-methoxyphenyl)prop-1-en-1-yl)phenol alleviates inflammatory responses in LPS-induced mice liver sepsis through inhibition of STAT3 phosphorylation

Sepsis is a life-threatening disease with limited treatment options, and the inflammatory process represents an important factor affecting its progression. Many studies have demonstrated the critical roles of signal transducer and activator of transcription 3 (STAT3) in sepsis pathophysiology and pro-inflammatory responses. Inhibition of STAT3 activity may therefore represent a promising treatment option for sepsis. We here used a mouse model to demonstrate that (E)-2-methoxy-4-(3-(4-methoxyphenyl)prop-1-en-1-yl)phenol (MMPP) treatment prevented the liver sepsis-related mortality induced by 30 mg/kg lipopolysaccharide (LPS) treatment and reduced LPS-induced increase in alanine transaminase, aspartate transaminase, and lactate dehydrogenase levels, all of which are markers of liver sepsis progression. These recovery effects were associated with decreased LPS-induced STAT3, p65, and JAK1 phosphorylation and proinflammatory cytokine (interleukin 1 beta, interleukin 6, and tumor necrosis factor alpha) level; expression of cyclooxygenase-2 and induced nitric oxide synthase were also reduced by MMPP. In an in vitro study using the normal liver cell line THLE-2, MMPP treatment prevented the LPS-induced increase of STAT3, p65, and JAK1 phosphorylation and inflammatory protein expression in a dose-dependent manner, and this effect was enhanced by combination treatment with MMPP and STAT3 inhibitor. The results clearly indicate that MMPP treatment prevents LPS-induced mortality by inhibiting the inflammatory response via STAT3 activity inhibition. Thus, MMPP represents a novel agent for alleviating LPS-induced liver sepsis.

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma

PURPOSE

This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD).

METHODS

Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD.

RESULTS

A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients.

CONCLUSIONS

The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.

Uncovering hub genes in sepsis through bioinformatics analysis

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.

Multilevel omics for the discovery of biomarkers in pediatric sepsis

Severe sepsis causes organ dysfunction and continues to be the leading reason for pediatric death worldwide. Early recognition of sepsis could substantially promote precision treatment and reduce the risk of pediatric death. The host cellular response to infection during sepsis between adults and pediatrics could be significantly different. A growing body of studies focused on finding markers in pediatric sepsis in recent years using multi-omics approaches. This narrative review summarized the progress in studying pediatric sepsis biomarkers from genome, transcript, protein, and metabolite levels according to the omics technique that has been applied for biomarker screening. It is most likely not a single biomarker could work for precision diagnosis of sepsis, but a panel of markers and probably a combination of markers detected at multi-levels. Importantly, we emphasize the importance of group distinction of infectious agents in sepsis patients for biomarker identification, because the host response to infection of bacteria, virus, or fungus could be substantially different and thus the results of biomarker screening. Further studies on the investigation of sepsis biomarkers that were caused by a specific group of infectious agents should be encouraged in the future, which will better improve the clinical execution of personalized medicine for pediatric sepsis.

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

Changes of Serum Pyruvate Kinase M2 Level in Patients with Sepsis and Its Clinical Value

Purpose

The glucose metabolic reprogramming is an important pathological mechanism in sepsis, which involves a series of enzymes including Pyruvate kinase M2 (PKM2). The purpose of this study is to explore the diagnostic and prognostic value of serum PKM2 in sepsis patients.

Patients and Methods

This study recruited 143 sepsis patients, 91 non-sepsis patients, and 65 physical examiners, divided into sepsis group, non-sepsis group, and control group. Measure the serum PKM2 concentration of subjects, collect and analyze clinical and laboratory indicators of all subjects. Independent risk factors were selected by Logistic regression analysis. The area under curve (AUC) was calculated by plotting the receiver operating characteristic (ROC) curve to determine the diagnostic and prognostic value of biomarkers.

Results

Compared with non-sepsis and control groups, the serum PKM2 levels in the sepsis group were significantly increased (both P<0.001). PKM2 was an independent risk factor for sepsis and had the best diagnostic efficacy when combined with procalcitonin, with the AUC value of 0.9352. Patients with high levels of PKM2 were more likely to experience organ damage and had a higher incidence of septic shock. On the 1st and 3rd days of admission, the serum PKM2 levels in the septic shock group were higher than those in the sepsis group (both P<0.05), with AUC values of 0.7296 and 0.6247, respectively. On the 3rd and 7th days of admission, the serum PKM2 levels in the non-survival group were significantly higher than those in the survival group (both P<0.001), with AUC values of 0.7033 and 0.8732, respectively.

Conclusion

The serum PKM2 levels in sepsis patients are significantly increased and correlated with disease severity and clinical outcomes. PKM2 may be a new diagnostic and prognostic biomarker for sepsis.

Decoding Sepsis-Induced Disseminated Intravascular Coagulation: A Comprehensive Review of Existing and Emerging Therapies

Disseminated intravascular coagulation (DIC) is a recurrent complication of sepsis. Since DIC not only promotes organ dysfunction but also represents a strong prognostic factor, it is important to diagnose DIC as early as possible. When coagulation is activated, fibrinolysis is inhibited, blood thinners are consumed, and a condition is created that promotes blood clotting, making it more difficult for the body to remove fibrin or prevent it from being deposited in the blood vessels. This leads to microvascular thrombosis, which plays a role in organ dysfunction. Despite efforts to understand the underlying mechanisms of sepsis-induced DIC, healthcare providers worldwide still face challenges in effectively treating this condition. In this review, we provide an in-depth analysis of the available strategies for sepsis-induced DIC, considering their effectiveness, limitations, and potential for future advances. Corticosteroids (CS), recombinant thrombomodulin (rTM), vitamin C, fibrinolytic therapy, and platelet transfusion are among the treatments discussed in the review. In addition, we are specifically addressing immunomodulatory therapy (IMT) by investigating treatments such as granulocyte colony-stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon gamma (IFN-γ), and mesenchymal stem cell therapy (MSC). Finally, we also examined how these therapies might affect COVID-19 cases, which often present with sepsis-induced DIC. The review suggests that targeted experiments with randomization are needed to verify the effectiveness of these treatments and to discover novel approaches to treat sepsis-induced DIC. By increasing our knowledge of sepsis-induced DIC, we can develop targeted treatments that have the potential to save lives and improve outcomes.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review

BACKGROUND

Sepsis, a life-threatening infection-induced inflammatory condition, has significant global health impacts. Timely detection is crucial for improving patient outcomes as sepsis can rapidly progress to severe forms. The application of machine learning (ML) and deep learning (DL) to predict sepsis using electronic health records (EHRs) has gained considerable attention for timely intervention.

METHODS

PubMed, IEEE Xplore, Google Scholar, and Scopus were searched for relevant studies. All studies that used ML/DL to detect or early-predict the onset of sepsis in the adult population using EHRs were considered. Data were extracted and analyzed from all studies that met the criteria and were also evaluated for their quality.

RESULTS

This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria. The chosen studies were predominantly retrospective (n = 38) and spanned diverse geographic settings, with a focus on the United States. Different datasets, sepsis definitions, and prevalence rates were employed, necessitating data augmentation. Heterogeneous parameter utilization, diverse model distribution, and varying quality assessments were observed. Longitudinal data enabled early sepsis prediction, and quality criteria fulfillment varied, with inconsistent funding-article quality correlation.

CONCLUSIONS

This systematic review underscores the significance of ML/DL methods for sepsis detection and early prediction through EHR data.

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.

Classification of subtypes and identification of dysregulated genes in sepsis

Background

Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis.

Methods

The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples.

Results

A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients.

Conclusion

This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.

Distinct host-response signatures in circulatory shock: a narrative review

Circulatory shock is defined syndromically as hypotension associated with tissue hypoperfusion and often subcategorized according to hemodynamic profile (e.g., distributive, cardiogenic, hypovolemic) and etiology (e.g., infection, myocardial infarction, trauma, among others). These shock subgroups are generally considered homogeneous entities in research and clinical practice. This current definition fails to consider the complex pathophysiology of shock and the influence of patient heterogeneity. Recent translational evidence highlights previously under-appreciated heterogeneity regarding the underlying pathways with distinct host-response patterns in circulatory shock syndromes. This heterogeneity may confound the interpretation of trial results as a given treatment may preferentially impact distinct subgroups. Re-analyzing results of major 'neutral' treatment trials from the perspective of biological mechanisms (i.e., host-response signatures) may reveal treatment effects in subgroups of patients that share treatable traits (i.e., specific biological signatures that portend a predictable response to a given treatment). In this review, we discuss the emerging literature suggesting the existence of distinct biomarker-based host-response patterns of circulatory shock syndrome independent of etiology or hemodynamic profile. We further review responses to newly prescribed treatments in the intensive care unit designed to personalize treatments (biomarker-driven or endotype-driven patient selection in support of future clinical trials).

Dissecting contributions of individual systemic inflammatory response syndrome criteria from a prospective algorithm to the prediction and diagnosis of sepsis in a polytrauma cohort

Background

Sepsis is the leading cause of death in intensive care units (ICUs), and its timely detection and treatment improve clinical outcome and survival. Systemic inflammatory response syndrome (SIRS) refers to the concurrent fulfillment of at least two out of the following four clinical criteria: tachycardia, tachypnea, abnormal body temperature, and abnormal leukocyte count. While SIRS was controversially abandoned from the current sepsis definition, a dynamic SIRS representation still has potential for sepsis prediction and diagnosis.

Objective

We retrospectively elucidate the individual contributions of the SIRS criteria in a polytrauma cohort from the post-surgical ICU of University Medical Center Mannheim (Germany).

Methods

We used a dynamic and prospective SIRS algorithm tailored to the ICU setting by accounting for catecholamine therapy and mechanical ventilation. Two clinically relevant tasks are considered: (i) sepsis prediction using the first 24 h after admission to our ICU, and (ii) sepsis diagnosis using the last 24 h before sepsis onset and a time point of comparable ICU treatment duration for controls, respectively. We determine the importance of individual SIRS criteria by systematically varying criteria weights when summarizing the SIRS algorithm output with SIRS descriptors and assessing the classification performance of the resulting logistic regression models using a specifically developed ranking score.

Results

Our models perform better for the diagnosis than the prediction task (maximum AUROC 0.816 vs. 0.693). Risk models containing only the SIRS level average mostly show reasonable performance across criteria weights, with prediction and diagnosis AUROCs ranging from 0.455 (weight on leukocyte criterion only) to 0.693 and 0.619 to 0.800, respectively. For sepsis prediction, temperature and tachypnea are the most important SIRS criteria, whereas the leukocytes criterion is least important and potentially even counterproductive. For sepsis diagnosis, all SIRS criteria are relevant, with the temperature criterion being most influential.

Conclusion

SIRS is relevant for sepsis prediction and diagnosis in polytrauma, and no criterion should a priori be omitted. Hence, the original expert-defined SIRS criteria are valid, capturing important sepsis risk determinants. Our prospective SIRS algorithm provides dynamic determination of SIRS criteria and descriptors, allowing their integration in sepsis risk models also in other settings.

Plasma matrix metalloproteinase-3 predicts mortality in acute respiratory distress syndrome: a biomarker analysis of a randomized controlled trial

BACKGROUND

Matrix metalloproteinase-3 (MMP-3) is a proteolytic enzyme involved in acute respiratory distress syndrome (ARDS) pathophysiology that may serve as a lung-specific biomarker in ARDS.

METHODS

This study was a secondary biomarker analysis of a subset of Albuterol for the Treatment of Acute Lung Injury (ALTA) trial patients to determine the prognostic value of MMP-3. Plasma sample MMP-3 was measured by enzyme-linked immunosorbent assay. The primary outcome was the area under the receiver operating characteristic (AUROC) of MMP-3 at day 3 for the prediction of 90-day mortality.

RESULTS

A total of 100 unique patient samples were evaluated and the AUROC analysis of day three MMP-3 showed an AUROC of 0.77 for the prediction of 90-day mortality (95% confidence interval: 0.67-0.87), corresponding to a sensitivity of 92% and specificity of 63% and an optimal cutoff value of 18.4 ng/mL. Patients in the high MMP-3 group (≥ 18.4 ng/mL) showed higher mortality compared to the non-elevated MMP-3 group (< 18.4 ng/mL) (47% vs. 4%, p < 0.001). A positive difference in day zero and day three MMP-3 concentration was predictive of mortality with an AUROC of 0.74 correlating to 73% sensitivity, 81% specificity, and an optimal cutoff value of + 9.5 ng/mL.

CONCLUSIONS

Day three MMP-3 concentration and difference in day zero and three MMP-3 concentrations demonstrated acceptable AUROCs for predicting 90-day mortality with a cut-point of 18.4 ng/mL and + 9.5 ng/mL, respectively. These results suggest a prognostic role of MMP-3 in ARDS.

Advances in Rodent Experimental Models of Sepsis

In the development of therapeutic strategies for human diseases, preclinical experimental models have a key role. However, the preclinical immunomodulatory therapies developed using rodent sepsis were not successful in human clinical trials. Sepsis is characterized by a dysregulated inflammation and redox imbalance triggered by infection. Human sepsis is simulated in experimental models using methods that trigger inflammation or infection in the host animals, most often mice or rats. It remains unknown whether the characteristics of the host species, the methods used to induce sepsis, or the molecular processes focused upon need to be revisited in the development of treatment methods that will succeed in human clinical trials. Our goal in this review is to provide a survey of existing experimental models of sepsis, including the use of humanized mice and dirty mice, and to show how these models reflect the clinical course of sepsis. We will discuss the strengths and limitations of these models and present recent advances in this subject area. We maintain that rodent models continue to have an irreplaceable role in studies toward discovering treatment methods for human sepsis.

[Relevant biomarkers in infectiology]

A biomarker in infectiology should ideally be able to identify infectious agents, monitor clinical response and determine the duration of treatment. This article answers the question to what extent C-reactive protein and procalcitonin meet these requirements and reports on the search for further biomarkers - e.g. with the help of "omics"-based technologies and the integration of artificial intelligence.

Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review

PURPOSE OF REVIEW

Sepsis is the extreme response to infection associated with high mortality, yet reliable biomarkers for its identification and stratification are lacking.

RECENT FINDINGS

Our scoping review of studies published from January 2017 to September 2022 that investigated circulating protein and lipid markers to inform non-COVID-19 sepsis diagnosis and prognosis identified interleukin (IL)-6, IL-8, heparin-binding protein (HBP), and angiopoietin-2 as having the most evidence. Biomarkers can be grouped according to sepsis pathobiology to inform biological data interpretation and four such physiologic processes include: immune regulation, endothelial injury and coagulopathy, cellular injury, and organ injury. Relative to proteins, the pleiotropic effects of lipid species' render their categorization more difficult. Circulating lipids are relatively less well studied in sepsis, however, low high-density lipoprotein (HDL) is associated with poor outcome.

SUMMARY

There is a lack of robust, large, and multicenter studies to support the routine use of circulating proteins and lipids for sepsis diagnosis or prognosis. Future studies will benefit from standardizing cohort design as well as analytical and reporting strategies. Incorporating biomarker dynamic changes and clinical data in statistical modeling may improve specificity for sepsis diagnosis and prognosis. To guide future clinical decisions at the bedside, point-of-care circulating biomarker quantification is needed.