Sepsis Research Research Articles

Targeting SphK1/S1PR3 axis ameliorates sepsis-induced multiple organ injury via orchestration of macrophage polarization and glycolysis.

Sepsis is a heterogeneous and imprecise disorder characterized by aberrant response to infection which has been accredited for detrimental impact on immune homeostasis. Recently, macrophage metabolism has been recognized as attractive targets to develop novel immunomodulatory therapy for sepsis research. However, the fine-tuning regulators dictating macrophage functions and the specific mechanisms underlying macrophage metabolic reprogramming remain largely obscure. Sphingosine-1-phosphate (S1P), a metabolic mediator of sphingolipid catabolism, predominantly formed through sphingosine kinase 1 (SphK1) catalyzing, mediates inflammation in sepsis by binding to S1P receptor 3 (S1PR3) expressed in macrophages. Here we demonstrate that SphK1/S1PR3 axis was upregulated in lipopolysaccharide (LPS)-induced macrophages and septic mice lungs, cascading the activation of proglycolytic signaling such as HIF-1α, HK2 and PFKFB3. Targeted inhibition of Sphk1 by PF-543 effectively abrogated upregulated SphK1/S1PR3 axis in vitro and in vivo. In addition, PF-543 significantly suppressed sepsis-related inflammation and multi-organ injury in vivo. Furthermore, PF-543 not only blunted key glycolytic enzymes HIF-1α, HK2, and PFKFB3 in LPS-treated macrophages but also inhibited HK2 and PFKFB3 in septic mice. Silencing or inhibiting SphK1 tempered pro-inflammatory M1 macrophages while boosted anti-inflammatory M2 macrophages. Intriguingly, S1PR3 knockdown proficiently dampened glycolysis-associated markers, retrieved LPS-modulated M1/M2 polarization and attenuated NF-κB p65 activation. In conclusion, our study provides the first evidence that PF-543 orchestrates proportional imbalance of macrophage polarization and the Warburg effect in a SphK1/S1PR3 dependent manner during sepsis, mitigating both hyperinflammation and multi-organ failure, adding a novel puzzle piece to pharmacologically exploitable therapy for sepsis.

Emerging trends and hotspots in intestinal microbiota research in sepsis: bibliometric analysis

BackgroundThe association between the gut microbiota and sepsis has garnered attention in the field of intestinal research in sepsis. This study utilizes bibliometric methods to visualize and analyze the literature on gut microbiota research in sepsis from 2011 to 2024, providing a scientific foundation for research directions and key issues in this domain.MethodsOriginal articles and reviews of gut microbiota research in sepsis, which published in English between 2011 and 2024, were obtained from the Web of Science Core Collection on June 21, 2024. Python, VOSviewer, and CiteSpace software were used for the visual analysis of the retrieved data.ResultsA total of 1,031 articles were analyzed, originating from 72 countries or regions, 1,614 research institutions, and 6,541 authors. The articles were published in 434 different journals, covering 89 different research fields. The number of publications and citations in this research area showed a significant growth trend from 2011 to 2024, with China, the United States, and the United Kingdom being the main research forces. Asada Leelahavanichkul from Thailand was identified as the most prolific author, making him the most authoritative expert in this field. “Nutrients” had the highest number of publications, while “Frontiers in Cellular and Infection Microbiology,” “Frontiers in Immunology” and “the International Journal of Molecular Sciences” have shown increasing attention to this field in the past 2 years. Author keywords appearing more than 100 times included “gut microbiota (GM),” “sepsis” and “microbiota.” Finally, this study identified “lipopolysaccharides (LPS),” “short-chain fatty acids (SCFAs),” “probiotics,” “fecal microbiota transplantation (FMT)” and “gut-liver axis” as the research hotspots and potential frontier directions in this field.ConclusionThis bibliometric study summarizes current important perspectives and offers comprehensive guidance between sepsis and intestinal microbiota, which may help researchers choose the most appropriate research directions.

Association between the serum albumin–creatinine ratio and 28-day intensive care unit mortality among patients with sepsis: a multicenter retrospective cohort study

IntroductionSepsis is a substantial global health challenge with a considerable disease burden. Despite advancements in sepsis research, the mortality rates associated with this condition remain high. The relationship between the serum albumin-to-creatinine ratio (sACR) and mortality in patients with sepsis remains unclear. Therefore, this study aimed to investigate the association between the sACR and 28-day mortality in intensive care unit (ICU) patients with sepsis.MethodsIn this retrospective cohort study, we used data sourced from the eICU Collaborative Research Database. The primary exposure variable was sACR, and the primary outcome measure was mortality within 28 days after ICU admission. Statistical analyses included univariate and multivariate logistic regression models, generalized additive models, and two-piecewise linear regression models, which were employed to explore non-linear relationships and threshold effects between sACR and mortality.ResultsThe study cohort comprised 9,690 ICU patients with sepsis, with a 28-day mortality rate of 9.99%. The results of the multivariate logistic regression model indicated that elevated sACR levels were significantly associated with a reduced risk of mortality (odds ratio = 0.78, 95% confidence interval: 0.71–0.87, p < 0.001), even after adjusting for potential confounding variables. Curve fitting revealed a non-linear relationship between sACR and 28-day mortality, with an inflection point of 4.79.DiscussionThis study demonstrated that sACR is an independent risk factor for 28-day mortality in ICU patients with sepsis, exhibiting a non-linear negative dose–response relationship and a threshold effect. These findings may serve as early warning indicators in high-risk populations.

Conceptual Model of Sepsis as a Dysregulated Host Response: Depicting Directionality of Immunologic and Metabolic Dysregulation.

Background Mortality from Sepsis continues to remain high in hospitalized patients worldwide. Previously, the conceptual model of sepsis was based on the systemic inflammatory immune response syndrome, an adaptive model. In 2016, a new definition of sepsis was proposed as a dysregulated host response to infection, however until now, no model has been proposed elucidating what immunologic mechanisms are dysregulated. Aim We sought to propose a new model of sepsis to elucidate evidence-based immunologic and metabolic mechanisms which are dysregulated in sepsis. Methods Evidence in the literature comparing and contrasting sepsis before and after the 2016 consensus definition of sepsis was reviewed for themes. Themes from research published post 2016, were described connecting immunologic and metabolic mechanisms in sepsis. This information further informed earlier research that has been published by the primary author. Results A new conceptual model of sepsis was developed to elucidate the current definition of sepsis as a dysregulated host response, incorporating themes uncovered in sepsis research post 2016. Conclusion The model proposed here supports clinicians in applying the current definition of sepsis to a physiological model. This can benefit clinicians by offering new ways of recognizing sepsis, and researchers by recommending a physiologic model to advance sepsis research. Keywords: Sepsis, sepsis model, immunologic dysregulation, metabolic dysregulation

Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis.

Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine-the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g.,trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.

Making preclinical sepsis research stronger, faster, and more responsive to patients

Making preclinical sepsis research stronger, faster, and more responsive to patients Sepsis represents a significant global burden. The National Preclinical Sepsis Platform (NPSP) is leading vital sepsis research, informing policy, driving innovation, and ultimately saving lives. Sepsis is a devastating condition triggered by the body’s extreme response to an infection. Patients with sepsis are often admitted to an intensive care unit (ICU), where they are given a barrage of life-saving treatments to combat the infection and restore basic organ functions like breathing and circulation. Despite our medical advancements, mortality rates for sepsis remain unacceptably high, with 30-50% of patients dying from this condition. In Canada, sepsis accounts for one in every 18 deaths and imposes a staggering $1.7bn burden on the healthcare system each year.

Predictive value of IL-8 for mortality risk in elderly sepsis patients of emergency department

BackgroundSepsis significantly impacts morbidity and mortality, particularly among older adults. Despite extensive research, early recognition and prognosis prediction of sepsis remain challenging. IL-8, a chemokine produced by inflammatory cells like monocytes and endothelial cells, has shown potential in predicting mortality in sepsis patients, though its role in elderly sepsis remains unexplored. ObjectivesThe present study aimed to explore the predictive ability of interleukin-8 (IL-8) for mortality risk in elderly septic patients. Methods220 elderly sepsis patients were included in the present study. Serum samples were obtained within 1 h of admission to assess serum IL-8, white blood cell (WBC), procalcitonin (PCT), C-reactive protein (CRP), and lactic acid (LAC) levels. The Sequential Organ Failure Score (SOFA) and Acute Physiological and Chronic Health Assessment II (APACHE II) were recorded. Logistic regression analysis was employed to identify independent predictors of mortality within 28 days for elderly patients diagnosed with sepsis. Further, the capacity of these factors to predict 28-day mortality within this patient cohort was evaluated. ResultsSOFA score, APACHE II score, LAC, and IL-8 were all significant independent predictors for 28-day mortality in elderly sepsis patients (P < 0.05). The AUC of the ROC curve for IL-8 was calculated to be 0.701, indicating a moderately predictive performance. In comparison, the AUC for LAC was marginally higher at 0.708. Nevertheless, the results of the statistical analysis revealed no significant difference in the predictive value between IL-8 and LAC. Moreover, the present findings indicate that the combined assessment of IL-8 and SOFA score demonstrated superior predictive value for mortality compared to using IL-8 alone. ConclusionsIL-8 LAC, APACHE II, and SOFA can be considered independent predictors factors for mortality of elderly sepsis patients. Utilizing the combination of IL-8 and SOFA demonstrates a heightened predictive capability compared to using any single index alone.

Unraveling Sepsis Epidemiology in a Low- and Middle-Income Intensive Care Setting Reveals the Alarming Burden of Tropical Infections and Antimicrobial Resistance: A Prospective Observational Study (MARS-India).

Our study addresses the sepsis research gap in lower middle-income countries, notably India. Here, we investigate community-acquired sepsis comprehensively and explore the impact of tropical microbiology on aetiology and outcomes. MARS-India was a prospective observational study from Dec-2018 to Sep-2022 in a tertiary-care hospital in South India. Adult patients within 24hrs of ICU admission meeting the Sepsis 3.0 definition were enrolled, with 6-months follow-up (http://clinicaltrials.gov number NCT03727243). Over 4000 patients were screened on ICU admission, with 1000 unique patients meeting the inclusion criteria. Median age was 55 years (IQR: 44-65) with a male preponderance (66%). Almost half the cohort resided in villages (46.5%) and 74.6% worked in the primary sector. Mortality in-hospital was 24.1%. Overall, ∼54% had confirmed microbiological diagnosis. Over 18% had a viral cause of sepsis. Surprisingly, we identified leptospirosis (10.6%), scrub typhus (4.1%), dengue (3.7%) and Kyasanur forest disease (1.6%) as notables causes of sepsis. All these infections showed seasonal variation around the monsoon. In community-acquired infections we observed substantial resistance to 3rd generation cephalosporins and carbapenems. In India, sepsis disproportionally affects a younger and lower socio-economic demographic, yielding high mortality. Tropical and viral sepsis carry a significant burden. Analyzing local data, we pinpoint priorities for public health and resources, offering valuable insights for global sepsis research.

Sepsis-Induced Immunosuppression: The Role of Co-inhibitory Molecules

Sepsis is one of the most common cause of death among hospitalized patients in the intensive care unit (ICU), with current therapeutic options falling short of a comprehensive solution. The condition&amp;apos;s pathophysiology is marked by a spectrum of immunological impairments, with a growing consensus that immunosuppression plays a decisive role in the condition&amp;apos;s rising morbidity and mortality rates. Extensive preclinical and clinical research has identified the upregulation of several co-inhibitory molecules during sepsis, including Programmed Death-1 (PD-1), Programmed Death Ligand-1 (PD-L1), Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4), B and T Lymphocyte Attenuator (BTLA), T Cell Membrane Protein-3 (TIM-3), and Lymphocyte Activation Gene-3 (LAG-3). These molecules, which exert a significant inhibitory effect on T cell function, are believed to contribute to the immunosuppressive state induced by sepsis. The elucidation of the intricate mechanisms by which these molecules induce immunosuppression is essential for devising the most efficacious treatment strategies for sepsis. The burgeoning field of immunotherapy, particularly the blockade of co-inhibitory molecules, represents a significant frontier in sepsis research. This approach holds substantial promise for the future of sepsis therapy, warranting further exploration and clinical investigation to harness its potential fully.

ADT2R: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis.

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in the clinical domain, especially from sepsis researchers. Existing sepsis DTR learning studies are mainly based on offline reinforcement learning (RL) approaches working on electronic healthcare records data. However, a trained policy may choose a treatment different from a human clinician's prescription. Furthermore, most of them do not consider: 1) heterogeneity in sepsis; 2) short-term transitions; and 3) the relationship between a patient's health state and the prescription. We propose a novel framework, an adaptive decision transformer for DTR (ADT 2 R), which recommends an optimal treatment action for each time step depending on the heterogeneity of the sepsis and a patient's evolving health states. Specifically, we devise a trajectory-optimization-based module to be trained with supervision for treatments and adaptively aggregate the multihead self-attentions by deliberating on various inherent time-varying patterns among sepsis patients. Furthermore, we estimate the patient's health state by adopting an actor-critic (AC) algorithm and inform the treatment recommendation by learning about its short-term changes. We validated the effectiveness of the proposed framework on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, an extensive intensive care database, by demonstrating performance comparable to the state-of-the-art methods.

Advances in metabolomics in critically ill patients with Sepsis and Septic Shock.

Sepsis accounts for high cases of morbidity and mortality in hospitalized patients. It has a very complex pathophysiology and swiftly progresses to a severe form of the disease, such as septic shock leading to organ dysfunction, organ failure, and death. Metabolomics has transformed sepsis's clinical and research topography with its application in prognosis, diagnosis, and risk assessment in patients with sepsis and septic shock. Metabolites in blood and urine are detected and analyzed, which helps in understanding the pathogenesis of the disease and aid in better disease management by identifying biomarkers early on. Metabolomics, sepsis and septic shock were the keywords were searched in PubMed and Scopus, from its inception to Dec 2023. This article provides information regarding metabolic profiling performed in sepsis and septic shock We demonstrated that metabolomics will change the world of sepsis by analyzing and detecting the diagnosis, prognosis, mortality, and treatment response biomarkers.

Sepsis Research Using Omics Technology in the European Union and the United Kingdom: Maps, Trends, and Future Implications.

High-throughput omics technologies have become valuable tools for systems science research and clinical management of sepsis. This article analyzes sepsis research using omics technologies in the European Union (EU) and the United Kingdom from 1990 to May 2023 using bibliometric data from the Web of Science database. Using VOSviewer for network analysis, we examined the distribution patterns, funding characteristics, and collaborations among the states, noting trends of convergence and divergence. The analysis included 2078 articles, revealing an increasing rate of publications on sepsis research using omics approaches. The United Kingdom's research output is notably high, contributing 28.3% of the total research from the EU and United Kingdom combined. Germany, France, the Netherlands, and Italy together account for 56.9% of the publications from the EU member states. The United States is the leading international collaborator, particularly with the United Kingdom, followed by Germany and France. The EU-15 countries have significantly more publication outputs in this domain with growing but limited inclusion of the newer members of the EU. We suggest that the role of EU member states and the United Kingdom in sepsis research using omics technologies can be advanced by facilitating high-value, technology-driven health research, fostering collaboration, convergence, and equity in global health and biomedical research.

Sepsis research: Heterogeneity as a foundation rather than an afterthought

Our understanding of sepsis has been hampered by the implicit assumption that sepsis is a homogeneous disease. In this issue of Cell Genomics, Burnham etal.1 have started to characterize the genetic variants and regulatory networks that underlie variations in the individual response to sepsis; this may eventually enable targeted intervention development.

Phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria.

The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of research across multiple research groups and datasets. We developed the phoenix R package and Python module to provide researchers with intuitive and simple functions to apply the Phoenix criteria to EHR data. The phoenix R package and Python module enable researchers to apply the Phoenix criteria to EHR datasets and derive the relevant indicators, total scores, and sub-scores. The transition to the Phoenix criteria marks a major change in the conceptual definition of pediatric sepsis. Applicable across differentially resourced settings, the Phoenix criteria should help improve clinical care and research. The phoenix R package and Python model are freely available on CRAN, PyPi, and GitHub. These tools enable the consistent and accurate application of the Phoenix criteria to EHR datasets.

CECAL SLURRY AS AN ALTERNATIVE MODEL TO CECAL LIGATION AND PUNCTURE FOR THE STUDY OF SEPSIS-INDUCED CARDIOVASCULAR DYSFUNCTION.

Sepsis is a life-threatening condition widely studied by animal models. Cecal ligation and puncture (CLP) is still regarded as the gold standard model for sepsis. However, CLP has limitations due to its invasiveness and variability. Cecal slurry (CS) model is a nonsurgical and thus less invasive alternative. However, the lack of standardization of the CS model in the literature limits its practical application. Additionally, it is not well studied whether CS model reproduces septic cardiovascular dysfunction in rats, which is a crucial issue in septic patients. Thus, this study aimed to standardize the CS model in Wistar rats and evaluate sepsis-induced cardiovascular dysfunction compared to CLP. Our results showed that CS model induced important features of sepsis cardiovascular dysfunction 24 h after its onset, such as hypotension, tachycardia, and decreased contractile response to vasoconstrictors both in vivo and ex vivo as well changes in renal blood flow. Increases in blood lactate, AST, ALT, creatinine, and urea indicated organ dysfunction. CS model also induced increased production of nitric oxide metabolites and bacterial spread to tissues. CS model causes less animal suffering, it is a nonsurgical model, and, more importantly, it replicates the cardiovascular dysfunction induced by sepsis with better homogeneity than CLP. Therefore, CS model serves as an alternative and possibly as a better model for sepsis research.

Basic research in sepsis: advances and challenges

Although the understanding of sepsis has evolved from "sepsis 1.0" to "sepsis 3.0", and the consensus on clinical management of sepsis has been continuously updated, the incidence rate and mortality of sepsis remain high. Therefore, in-depth investigation of the pathogenesis and related influencing factors of sepsis is of great significance for revealing the nature of sepsis and improving the clinical outcome of sepsis patients. This review will focus on the key issues in the basic research of sepsis, and summarize the recent advances and challenges in this field, mainly including genetic polymorphism, microorganisms, high-mobility group box 1 (HMGB1), endothelial dysfunction, immunotherapy, and biomarkers, aiming to provide new insights for the diagnosis and treatment of sepsis.

Advancing cell-based therapy in sepsis: An anesthesia outlook.

Sepsis poses a health challenge globally owing to markedly high rates of morbidity and mortality. Despite employing bundle therapy over two decades, approaches including transient organ supportive therapy and clinical trials focusing on signaling pathways have failed in effectively reversing multiple organ failure in patients with sepsis. Prompt and appropriate perioperative management for surgical patients with concurrent sepsis is urgent. Consequently, innovative therapies focusing on remedying organ injuries are necessitated. Cell therapy has emerged as a promising therapeutic avenue for repairing local damage to vital organs and restoring homeostasis during perioperative treatment for sepsis. Given the pivotal role of immune cell responses in the pathogenesis of sepsis, stem cell-based interventions that primarily modulate immune responses by interacting with multiple immune cells have progressed into clinical trials. The strides made in single-cell sequencing and gene-editing technologies have advanced the understanding of disease-specific immune responses in sepsis. Chimeric antigen receptor (CAR)-immune cell therapy offers an intriguing option for the treatment of sepsis. This review provides a concise overview of immune cell therapy, its current status, and the strides made in the context of sepsis research, discussing potential strategies for the management of patients with sepsis during perioperative stages.

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC–MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

Knockdown of KBTBD7 attenuates septic lung injury by inhibiting ferroptosis and improving mitochondrial dysfunction

Lung injury in sepsis is caused by an excessive inflammatory response caused by the entry of pathogenic microorganisms into the body. It is also accompanied by the production of large amounts of ROS. Ferroptosis and mitochondrial dysfunction have also been shown to be related to sepsis. Finding suitable sepsis therapeutic targets is crucial for sepsis research. BTB domain-containing protein 7 (KBTBD7) is involved in regulating inflammatory responses, but its role and mechanism in the treatment of septic lung injury are still unclear. In this study, we evaluated the role and related mechanisms of KBTBD7 in septic lung injury. In in vitro studies, we established an in vitro model by inducing human alveolar epithelial cells with lipopolysaccharide (LPS) and found that KBTBD7 was highly expressed in the in vitro model. KBTBD7 knockdown could reduce the inflammatory response by inhibiting the secretion of pro-inflammatory factors and inhibit the production of ROS, ferroptosis and mitochondrial dysfunction. Mechanistic studies show that KBTBD7 interacts with FOXA1, promotes FOXA1 expression, and indirectly inhibits SLC7A11 transcription. In vivo studies have shown that knocking down KBTBD7 improves lung tissue damage in septic lung injury mice, inhibits inflammatory factors, ROS production and ferroptosis. Taken together, knockdown of KBTBD7 shows an alleviating effect on septic lung injury in vitro and in vivo, providing a potential therapeutic target for the treatment of septic lung injury.

Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis.

Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.