Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

The combination of kidney function variables with cell cycle arrest biomarkers identifies distinct subphenotypes of sepsis-associated acute kidney injury: a post-hoc analysis (the PHENAKI study)

BACKGROUND

The severity and course of sepsis-associated acute kidney injury (SA-AKI) are correlated with the mortality rate. Early detection of SA-AKI subphenotypes might facilitate the rapid provision of individualized care.

PATIENTS AND METHODS

In this post-hoc analysis of a multicenter prospective study, we combined conventional kidney function variables with serial measurements of urine (tissue inhibitor of metalloproteinase-2 [TIMP-2])* (insulin-like growth factor-binding protein [IGFBP7]) at 0, 6, 12, and 24 h) and then using an unsupervised hierarchical clustering of principal components (HCPC) approach to identify different phenotypes of SA-AKI. We then compared the subphenotypes with regard to a composite outcome of in-hospital death or the initiation of renal replacement therapy (RRT).

RESULTS

We included 184 patients presenting SA-AKI within 6 h of the initiation of catecholamines. Three distinct subphenotypes were identified: subphenotype A (99 patients) was characterized by a normal urine output (UO), a low SCr and a low [TIMP-2]*[IGFBP7] level; subphenotype B (74 patients) was characterized by existing chronic kidney disease (CKD), a higher SCr, a low UO, and an intermediate [TIMP-2]*[IGFBP7] level; and subphenotype C was characterized by very low UO, a very high [TIMP-2]*[IGFBP7] level, and an intermediate SCr level. With subphenotype A as the reference, the adjusted hazard ratio (aHR) [95%CI] for the composite outcome was 3.77 [1.92-7.42] (p < 0.001) for subphenotype B and 4.80 [1.67-13.82] (p = 0.004) for subphenotype C.

CONCLUSIONS

Combining conventional kidney function variables with urine measurements of [TIMP-2]*[IGFBP7] might help to identify distinct SA-AKI subphenotypes with different short-term courses and survival rates.

GAB1 attenuates lipopolysaccharide‑mediated endothelial dysfunction via regulation of SOCS3

Endothelial dysfunction is a crucial pathogenetic mechanism for sepsis. GRB2-associated binder 1 (GAB1) alleviates sepsis-induced multi-organ damage; however, to the best of our knowledge, its function in endothelial dysfunction in sepsis remains unclear. HUVECs were induced by lipopolysaccharide (LPS) to simulate endothelial cell injury under sepsis. Cell transfection was conducted to achieve GAB1 overexpression or suppressor of cytokine signaling 3 (SOCS3) knockdown. The expression levels of GAB1 and SOCS3 were detected by reverse transcription-quantitative PCR and western blotting. Cell viability, apoptosis and migration were assessed using Cell Counting Kit-8, TUNEL and wound healing assays, respectively. The production of cytokines and nitric oxide (NO) was detected using commercial kits. The interaction between GAB1 and SOCS3 was confirmed using a co-immunoprecipitation assay. GAB1 was downregulated in LPS-induced HUVECs. However, GAB1 overexpression significantly mitigated LPS-induced cell viability decrease and apoptosis in HUVECs, accompanied by upregulation of Bcl2 expression, and downregulation of Bax and cleaved caspase-3 expression. GAB1 also inhibited the production of pro-inflammatory cytokines and increased NO level, increased the levels of endothelial NO synthase (eNOS) and phosphorylated (p)-eNOS, and promoted migration in LPS-induced HUVECs. However, SOCS3 knockdown partially weakened the effects of GAB1 overexpression on cell viability, apoptosis, inflammation, p-eNOS, eNOS expression and NO levels in LPS-induced HUVECs. In addition, GAB1 and SOCS3 regulated Janus kinase 2 (JAK2)/STAT3 signaling in LPS-induced HUVECs. In conclusion, GAB1 exerted a protective effect against LPS-induced endothelial cell apoptosis, inflammation and dysfunction by modulating the SOCS3/JAK2/STAT3 signaling pathway.

Understanding, assessing and treating immune, endothelial and haemostasis dysfunctions in bacterial sepsis

The interplay between the immune system, coagulation, and endothelium is critical in regulating the host response to infection. However, in sepsis and other critical illnesses, a dysregulated immune response can lead to excessive alterations in these mechanisms, resulting in coagulopathy, endothelial dysfunction, and multi-organ dysfunction. This review aims to provide a comprehensive analysis of the pathophysiological mechanisms that govern the complex interplay between immune dysfunction, endothelial dysfunction, and coagulation in sepsis. It emphasises clinical significance, evaluation methods, and potential therapeutic interventions. Understanding these mechanisms is essential for developing effective treatments that can modulate the immune response, mitigate thrombosis, restore endothelial function, and ultimately improve patient survival.

Digital solutions in paediatric sepsis: current state, challenges, and opportunities to improve care around the world

The digitisation of health care is offering the promise of transforming the management of paediatric sepsis, which is a major source of morbidity and mortality in children worldwide. Digital technology is already making an impact in paediatric sepsis, but is almost exclusively benefiting patients in high-resource health-care settings. However, digital tools can be highly scalable and cost-effective, and-with the right planning-have the potential to reduce global health disparities. Novel digital solutions, from wearable devices and mobile apps, to electronic health record-embedded decision support tools, have an unprecedented opportunity to transform paediatric sepsis research and care. In this Series paper, we describe the current state of digital solutions in paediatric sepsis around the world, the advances in digital technology that are enabling the development of novel applications, and the potential effect of advances in artificial intelligence in paediatric sepsis research and clinical care.

GENETIC ABLATION OF THE C-TYPE LECTIN RECEPTOR CLEC2D INCREASES PERITONITIS MORTALITY, INFLAMMATION, AND PHYSIOLOGY WITHOUT DIMINISHING ORGAN INJURY

ABSTRACT

Background: Sepsis accounts for substantial morbidity and mortality motivating investigators to continue the search for pathways and molecules driving the pathogenesis of the disease. The current study examined if the novel C-type lectin receptor (CLR), Clec2d, plays a significant role in the pathogenesis of sepsis. Methods: Clec2d knockout (KO) mice were fully backcrossed onto the C57/BL6 background. Acute endotoxemia was induced with an intraperitoneal injection of lipopolysaccharide (LPS). Sepsis was induced in two different models, cecal ligation and puncture (CLP) and Pseudomonas aeruginosa pneumonia. Both models were treated with antibiotics and fluid resuscitation. In the sepsis models, physiologic and hematologic measurements were measured at 24 h by collecting a small sample of peripheral blood. Mortality was followed for 14 days. Results : A total of 197 mice were studied, 58 wild type (WT) and 54 knock-out (KO) in the LPS model; 27 wild type and 21 KO mice in the CLP model; and 22 WT and 15 KO mice in the pneumonia model. Clec2d KO mice had greater mortality in the LPS and CLP studies but not the pneumonia model. There were significant differences in multiple parameters determined 24 h post sepsis between mice who subsequently died and those lived. Consistent with previous reports in the CLP model, higher concentrations of IL-6, increased numbers of peripheral blood lymphocytes and greater renal injury were found in the dying mice. In contrast, in the pneumonia model, IL-6 was higher in the surviving mice; however, the IL-6 levels in the pneumonia model (0.6 ± 0.3 ng/mL mean ± SEM) were less than 2% of the IL-6 levels of mice that died in the CLP model (41 ± 9 ng/mL, mean ± SEM). There were no differences in the lymphocyte count or renal injury between living and dying mice in the pneumonia model. In both sepsis models, dying mice had lower heart rates, respiratory rates, and body temperatures. These values were also lower in the KO mice compared to the WT in CLP, but the breath rate and body temperature were increased in the KO pneumonia mice. Conclusion: The C-type lectin receptor Clec2d plays a complicated role in the pathogenesis of sepsis, which varies with source of infection as demonstrated in the models used to study the disease. These data highlight the heterogeneity of the responses to sepsis and provide further evidence that a single common pathway driving sepsis organ injury and death likely does not exist.

Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients

PURPOSE

Disease heterogeneity in coronavirus disease 2019 (COVID-19) may render the current one-size-fits-all treatment approach suboptimal. We aimed to identify and immunologically characterize clinical phenotypes among critically ill COVID-19 patients, and to assess heterogeneity of corticosteroid treatment effect.

METHODS

We applied consensus k-means clustering on 21 clinical parameters obtained within 24 h after admission to the intensive care unit (ICU) from 13,279 COVID-19 patients admitted to 82 Dutch ICUs from February 2020 to February 2022. Derived phenotypes were reproduced in 6225 COVID-19 ICU patients from Spain (February 2020 to December 2021). Longitudinal immunological characterization was performed in three COVID-19 ICU cohorts from the Netherlands and Germany, and associations between corticosteroid treatment and survival were assessed across phenotypes.

RESULTS

We derived three phenotypes: COVIDICU1 (43% of patients) consisted of younger patients with the lowest Acute Physiology And Chronic Health Evaluation (APACHE) scores, highest body mass index (BMI), lowest PaO2/FiO2 ratio, and a 90-day in-hospital mortality rate of 18%. COVIDICU2 patients (37%) had the lowest BMI, were older and had higher APACHE scores and mortality rate (24%) than COVIDICU1. Patients with COVIDICU3 (20%) were the eldest with the most comorbidities, the highest APACHE scores, acute kidney injury and metabolic dysregulations, and the highest mortality rate (47%). These patients also displayed the most pronounced inflammatory response. Corticosteroid therapy started at day 5 [2-9] after ICU admission and administered for 5 [3-7] days was associated with an increased risk for 90-day mortality in patients with the COVIDICU1 and COVIDICU2 phenotypes (hazard ratio [HR] 1.59 [1.09-2.31], p = 0.015 and HR 1.79 [1.42-2.26], p < 0.001, respectively), but not in patients with the COVIDICU3 phenotype (HR 1.08 [0.76-1.54], p = 0.654).

CONCLUSION

Our multinational study identified three distinct clinical COVID-19 phenotypes, each exhibiting marked differences in demographic, clinical, and immunological features, and in the response to late and short-term corticosteroid treatment.

Simultaneous removal of endotoxins, inflammatory mediators and uremic toxins in ICU patients with septic shock: a retrospective cohort study

Sepsis, one of the leading causes of death, is still lacking specific treatment. OXIRIS (BAXTER, Deerfield, IL, USA) is the first device allowing combined removal of endotoxins, inflammatory mediators and uremic toxins, alongside fluid balance control. Available data is very limited. This retrospective propensity score-matched cohort study of adult patients with septic shock aimed to evaluate septic shock duration and mortality in patients treated with either standard of care renal replacement therapy (RRT) or RRT with combined hemoadsorption, who were admitted to the interdisciplinary surgical intensive care unit at Heidelberg University Hospital during the years 2018 through 2021. Main outcomes were duration of shock, thirty-day mortality and plasma interleukin-6 levels before and after initiation of hemoadsorption. Included were 117 patients (female, 33%; male 67%); median age: 67 (16) years. After matching: 42 patients (female, 33%; male, 67%); mean age: 59.1 ± 13.8 years. There was no statistically significant difference in septic shock duration (p = 0.94; hazard ratio (HR) 0.97 (95% CI, 0.48-1.97)). Thirty-day survival analysis showed a non-statistically significant survival difference. (p = 0.063; HR 0.43 (95% CI, 0.17-1.09)). A post-hoc 90-day survival analysis revealed statistically significant longer survival and lower death hazard ratio in patients treated with RRT + HA (p = 0.037; HR = 0.42 (95% CI, 0.18-0.99). In conclusion, RRT with combined hemoadsorption of endotoxins, inflammatory mediators and uremic toxins is a modality worth further investigation.

Prehospital Post-Resuscitation Vital Sign Phenotypes are Associated with Outcomes Following Out-of-Hospital Cardiac Arrest

OBJECTIVES

The use of machine learning to identify patient 'clusters' using post-return of spontaneous circulation (ROSC) vital signs may facilitate the identification of patient subgroups at high risk of rearrest and mortality. Our objective was to use k-means clustering to identify post-ROSC vital sign clusters and determine whether these clusters were associated with rearrest and mortality.

METHODS

The ESO Data Collaborative 2018-2022 datasets were used for this study. We included adult, non-traumatic OHCA patients with >2 post-ROSC vital sign sets. Patients were excluded if they had an EMS-witnessed OHCA or were encountered during an interfacility transfer. Unsupervised (k-means) clustering was performed using minimum, maximum, and delta (last minus first) systolic blood pressure (BP), heart rate, SpO2, shock index, and pulse pressure. The assessed outcomes were mortality and rearrest. To explore the association between rearrest, mortality, and cluster, multivariable logistic regression modeling was used.

RESULTS

Within our cohort of 12,320 patients, five clusters were identified. Patients in cluster 1 were hypertensive, patients in cluster 2 were normotensive, patients in cluster 3 were hypotensive and tachycardic (n = 2164; 17.6%), patients in cluster 4 were hypoxemic and exhibited increasing systolic BP, and patients in cluster 5 were severely hypoxemic and exhibited a declining systolic BP. The overall proportion of patients who experienced mortality stratified by cluster was 63.4% (c1), 68.1% (c2), 78.8% (c3), 84.8% (c4), and 86.6% (c5). In comparison to the cluster with the lowest mortality (c1), each other cluster was associated with greater odds of mortality and rearrest.

CONCLUSIONS

Unsupervised k-means clustering yielded 5 post-ROSC vital sign clusters that were associated with rearrest and mortality.

Current perspectives in the management of sepsis and septic shock

Within patients with sepsis, there exists significant heterogeneity, and while all patients should receive conventional therapy, there are subgroups of patients who may benefit from specific therapies, often referred to as rescue therapies. Therefore, the identification of these specific patient subgroups is crucial and lays the groundwork for the application of precision medicine based on the development of targeted interventions. Over the years, efforts have been made to categorize sepsis into different subtypes based on clinical characteristics, biomarkers, or underlying mechanisms. For example, sepsis can be stratified into different phenotypes based on the predominant dysregulated host response. These phenotypes can range from hyperinflammatory states to immunosuppressive states and even mixed phenotypes. Each phenotype may require different therapeutic approaches to improve patient outcomes. Rescue strategies for septic shock may encompass various interventions, such as immunomodulatory therapies, extracorporeal support (e.g., ECMO), or therapies targeted at specific molecular or cellular pathways involved in the pathophysiology of sepsis. In recent years, there has been growing interest in precision medicine approaches to sepsis and phenotype identification. Precision medicine aims to tailor treatments to each individual patient based on their unique characteristics and disease mechanisms.

Association between dietary vitamin C intake/blood level and risk of digestive system cancer: a systematic review and meta-analysis of prospective studies

Experimental studies have shown that vitamin C has anti-cancer effects, but previous meta-analyses have indicated that the role of vitamin C in digestive system cancers (DSCs) is controversial. In this study, a systematic review and meta-analysis of the relationship between dietary intake/plasma concentration of vitamin C and the risk of DSC was conducted, evaluating 32 prospective studies with 1 664 498 participants. Dose-response and subgroup analyses were also performed. Systematic literature searches were performed in PubMed, EMBASE and Web of Science databases until 9th September 2023. Vitamin C intake significantly reduced DSCs risk (RR = 0.88, 95% confidence interval (CI) 0.83 to 0.93). The subgroup analyses showed the risks of oral, pharyngeal, and esophageal (OPE) cancers (0.81, 0.72 to 0.93), gastric cancer (0.81, 0.68 to 0.95), and colorectal cancer (0.89, 0.82 to 0.98) were negatively correlated with vitamin C intake, and the effect of vitamin C was different between colon cancer (0.87, 0.77 to 0.97) and rectal cancer (1.00, 0.84 to 1.19). However, plasma vitamin C concentration was only inversely associated with gastric cancer risk (0.74, 0.59 to 0.92). Dose-response analysis revealed that 250 and 65 mg day-1 vitamin C intakes had the strongest protective effect against OPE and gastric cancers respectively. These estimates suggest that vitamin C intake could significantly reduce gastrointestinal cancer incidence, including OPE, gastric, and colon cancers. Plasma vitamin C has a significant reduction effect on the incidence of gastric cancer only, but additional large-scale clinical studies are needed to determine its impact on the incidence of DSCs.

Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan

BACKGROUND

Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes.

METHODS

This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories.

RESULTS

Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables.

CONCLUSIONS

Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

Comprehensive analysis of sialylation-related genes and construct the prognostic model in sepsis

Sepsis, a life-threatening syndrome, continues to be a significant public health issue worldwide. Sialylation is a hot potential marker that affects the surface of a variety of cells. However, the role of genes related to sialylation and sepsis has not been fully explored. Bulk RNA-seq data sets (GSE66099 and GSE65682) were obtained from the open-access databases GEO. The classification of sepsis samples into subtypes was achieved by employing the R package "ConsensusClusterPlus" on the bulk RNA-seq data. Hub genes were discerned through the application of the R package "limma" and univariate regression analysis, with the calculation of risk scores carried out using the R package "survminer". To identify the best learning method and construct a prognostic model, we used 21 different combinations of machine learning, and C-index ranking results of these combinations have been showed. ROC curves, time-dependent ROC curves, and Kaplan-Meier curves were utilized to evaluate the diagnostic accuracy of the model. The R packages "ESTIMATE" and "GSVA" were employed to quantify the fractions of immune cell infiltration in each sample. The bulk RNA-seq samples were categorized into two distinct sepsis subtypes utilizing 14 prognosis-related sialylation genes. A total of 20 differentially expressed genes (DEGs) were identified as being associated with the relationship between sepsis and sialylation. The RSF was used to identify key genes with importance scores higher than 0.01. The nine hub genes (SLA2A1, TMCC2, TFRC, RHAG, FKBP1B, KLF1, PILRA, ARL4A, and GYPA) with the importance values greater than 0.01 was selected for constructing the prognostic model. This research offers some understanding of the relationship between sepsis and sialylation. Besides, it contains one predictive model that might develop into diagnostic biomarkers for sepsis.

Development and validation of potential phenotypes of serum electrolyte disturbances in critically ill patients and a Web-based application

BACKGROUND

Electrolyte disturbances are highly heterogeneous and severely affect the prognosis of critically ill patients. Our study was to determine whether data-driven phenotypes of seven electrolytes have prognostic relevance in critically ill patients.

METHODS

We extracted patient information from three large independent public databases, and clustered the electrolyte distribution of ICU patients based on the extreme value, median value and coefficient of variation of electrolytes. Three plausible clinical phenotypes were calculated using K-means clustering algorithm as the basic clustering method. MIMIC-IV was considered a training set, and two others have been designated as verification set. The robustness of the model was then validated from different angles, providing dynamic and interactive visual charts for more detailed characterization of phenotypes.

RESULTS

15,340, 12,445 and 2147 ICU patients with electrolyte records during early ICU stay in MIMIC-IV, eICU-CRD and AmsterdamUMCdb were enrolled. After clustering, three reasonable and interpretable phenotypes are defined as α, β and γ according to the order of clusters. The α and γ phenotype, with significant differences in electrolyte distribution and clinical variables, higher 28-day mortality and longer length of ICU stay (p < 0.001), was further demonstrated by robustness analysis. The α phenotype has significant kidney injury, while the β phenotype has the best prognosis. In addition, the assignment methods of the three phenotypes were developed into a web-based tool for further verification and application.

CONCLUSIONS

Three different clinical phenotypes were identified that correlated with electrolyte distribution and clinical outcomes. Further validation and characterization of these phenotypes is warranted.

Improving Outcomes After Post-Cardiac Arrest Brain Injury: A Scientific Statement From the International Liaison Committee on Resuscitation

This scientific statement presents a conceptual framework for the pathophysiology of post-cardiac arrest brain injury, explores reasons for previous failure to translate preclinical data to clinical practice, and outlines potential paths forward. Post-cardiac arrest brain injury is characterized by 4 distinct but overlapping phases: ischemic depolarization, reperfusion repolarization, dysregulation, and recovery and repair. Previous research has been challenging because of the limitations of laboratory models; heterogeneity in the patient populations enrolled; overoptimistic estimation of treatment effects leading to suboptimal sample sizes; timing and route of intervention delivery; limited or absent evidence that the intervention has engaged the mechanistic target; and heterogeneity in postresuscitation care, prognostication, and withdrawal of life-sustaining treatments. Future trials must tailor their interventions to the subset of patients most likely to benefit and deliver this intervention at the appropriate time, through the appropriate route, and at the appropriate dose. The complexity of post-cardiac arrest brain injury suggests that monotherapies are unlikely to be as successful as multimodal neuroprotective therapies. Biomarkers should be developed to identify patients with the targeted mechanism of injury, to quantify its severity, and to measure the response to therapy. Studies need to be adequately powered to detect effect sizes that are realistic and meaningful to patients, their families, and clinicians. Study designs should be optimized to accelerate the evaluation of the most promising interventions. Multidisciplinary and international collaboration will be essential to realize the goal of developing effective therapies for post-cardiac arrest brain injury.

Sepsis in surgical patients: Personalized medicine in the future treatment of sepsis

Sepsis results when a severe infection overwhelms the normal regulatory mechanisms of the immune system, resulting in a dysregulated host response characterized by new-onset organ failure. A wide range of infectious challenges can induce sepsis, resulting in an even wider range of maladaptive immune responses. This makes sepsis a syndromic diagnosis without a unifying, underlying molecular mechanism. The next step toward personalized medicine for sepsis is to resolve the heterogeneity across the universe of septic patients in order to establish pathobiologically homogenous sepsis "endotypes" that have uniformly defined changes in physiology and immunology. Defining the mechanisms of immune dysfunction within these endotypes will provide a roadmap for the application of immunomodulatory therapies for sepsis. This approach can drive in a paradigm shift in sepsis treatment, moving beyond supportive care and toward active efforts to restore normal immune function.

Identify and mitigate bias in electronic phenotyping: A comprehensive study from computational perspective

Electronic phenotyping is a fundamental task that identifies the special group of patients, which plays an important role in precision medicine in the era of digital health. Phenotyping provides real-world evidence for other related biomedical research and clinical tasks, e.g., disease diagnosis, drug development, and clinical trials, etc. With the development of electronic health records, the performance of electronic phenotyping has been significantly boosted by advanced machine learning techniques. In the healthcare domain, precision and fairness are both essential aspects that should be taken into consideration. However, most related efforts are put into designing phenotyping models with higher accuracy. Few attention is put on the fairness perspective of phenotyping. The neglection of bias in phenotyping leads to subgroups of patients being underrepresented which will further affect the following healthcare activities such as patient recruitment in clinical trials. In this work, we are motivated to bridge this gap through a comprehensive experimental study to identify the bias existing in electronic phenotyping models and evaluate the widely-used debiasing methods' performance on these models. We choose pneumonia and sepsis as our phenotyping target diseases. We benchmark 9 kinds of electronic phenotyping methods spanning from rule-based to data-driven methods. Meanwhile, we evaluate the performance of the 5 bias mitigation strategies covering pre-processing, in-processing, and post-processing. Through the extensive experiments, we summarize several insightful findings from the bias identified in the phenotyping and key points of the bias mitigation strategies in phenotyping.

Phenotyping Refractory Cardiogenic Shock Patients Receiving Venous-Arterial Extracorporeal Membrane Oxygenation Using Machine Learning Algorithms

Background

This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous-arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition's inherent heterogeneity.

Methods

This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment.

Results

In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: "platelet preserved (I)", those with stable platelet counts, "hyperinflammatory (II)", those indicating significant inflammation, and "hepatic-renal (III)", those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (p = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management.

Conclusions

This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.

Acute Management of Sepsis beyond 24 Hours

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

HDL-based therapeutics: A promising frontier in combating viral and bacterial infections

Low levels of high-density lipoprotein (HDL) and impaired HDL functionality have been consistently associated with increased susceptibility to infection and its serious consequences. This has been attributed to the critical role of HDL in maintaining cellular lipid homeostasis, which is essential for the proper functioning of immune and structural cells. HDL, a multifunctional particle, exerts pleiotropic effects in host defense against pathogens. It functions as a natural nanoparticle, capable of sequestering and neutralizing potentially harmful substances like bacterial lipopolysaccharides. HDL possesses antiviral activity, preventing viruses from entering or fusing with host cells, thereby halting their replication cycle. Understanding the complex relationship between HDL and the immune system may reveal innovative targets for developing new treatments to combat infectious diseases and improve patient outcomes. This review aims to emphasize the role of HDL in influencing the course of bacterial and viral infections and its and its therapeutic potential.

State of the art of sepsis care for the emergency medicine clinician

Sepsis impacts 1.7 million Americans annually. It is a life-threatening disruption of organ function because of the body's host response to infection. Sepsis remains a condition frequently encountered in emergency departments (ED) with an estimated 850,000 annual visits affected by sepsis each year in the United States. The pillars of managing sepsis remain timely identification, initiation of antimicrobials while aiming for source control and resuscitation with a goal of restoring tissue perfusion. The focus herein is current evidence and best practice recommendations for state-of-the-art sepsis care that begins in the ED.

Integrated multi-omics analysis and machine learning developed diagnostic markers and prognostic model based on Efferocytosis-associated signatures for septic cardiomyopathy

Septic cardiomyopathy (SCM) is characterized by an abnormal inflammatory response and increased mortality. The role of efferocytosis in SCM is not well understood. We used integrated multi-omics analysis to explore the clinical and genetic roles of efferocytosis in SCM. We identified six module genes (ATP11C, CD36, CEBPB, MAPK3, MAPKAPK2, PECAM1) strongly associated with SCM, leading to an accurate predictive model. Subgroups defined by EFFscore exhibited distinct clinical features and immune infiltration levels. Survival analysis showed that the C1 subtype with a lower EFFscore had better survival outcomes. scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) from sepsis patients identified four genes (CEBPB, CD36, PECAM1, MAPKAPK2) associated with high EFFscores, highlighting their role in SCM. Molecular docking confirmed interactions between diagnostic genes and tamibarotene. Experimental validation supported our computational results. In conclusion, our study identifies a novel efferocytosis-related SCM subtype and diagnostic biomarkers, offering new insights for clinical diagnosis and therapy.

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

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

From Traditional Typing to Intelligent Insights: A Narrative Review of Directions Toward Targeted Therapies in Delirium

Delirium is a heterogeneous syndrome characterized by an acute change in level of consciousness that is associated with inattention and disorganized thinking. Delirium affects most critically ill patients and is associated with poor patient-oriented outcomes such as increased mortality, longer ICU and hospital length of stay, and worse long-term cognitive outcomes. The concept of delirium and its subtypes has existed since nearly the beginning of recorded medical literature, yet robust therapies have yet to be identified. Analogous to other critical illness syndromes, we suspect the lack of identified therapies stems from patient heterogeneity and prior subtyping efforts that do not capture the underlying etiology of delirium. The time has come to leverage machine learning approaches, such as supervised and unsupervised clustering, to identify clinical and pathophysiological distinct clusters of delirium that will likely respond differently to various interventions. We use sedation in the ICU as an example of how precision therapies can be applied to critically ill patients, highlighting the fact that while for some patients a sedative drug may cause delirium, in another cohort sedation is the specific treatment. Finally, we conclude with a proposition to move away from the term delirium, and rather focus on the treatable traits that may allow precision therapies to be tested.

Definition and Epidemiology of Sepsis

Here we review the epidemiology of sepsis, focusing on its definition, incidence, and mortality, as well as the demographic insights and risk factors that influence its occurrence and outcomes. We address how age, sex, and racial/ethnic disparities impact upon incidence and mortality rates. Sepsis is more frequent and severe among the elderly, males, and certain racial and ethnic groups. Poor socioeconomic status, geographic location, and pre-existing comorbidities also elevate the risk of developing and dying from sepsis. Seasonal variations, with an increased incidence during winter months, is also apparent. We delve into the predictive value of disease severity scores such as the Sequential Organ Failure Assessment score. We also highlight issues relating to coding and administrative data that can generate erroneous and misleading information, and the need for greater consistency. The Sepsis-3 definitions, offering more precise clinical criteria, are a step in the right direction. This overview will, we hope, facilitate understanding of the multi-faceted epidemiological characteristics of sepsis and current challenges.

Informative Subtyping of Patients with Sepsis

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

Clinical phenotypes of sepsis: a narrative review

Background and Objective

Sepsis, characterized by an aberrant immune response to infection leading to acute organ dysfunction, impacts millions of individuals each year and carries a substantial risk of mortality, even with prompt care. Despite notable medical advancements, managing sepsis remains a formidable challenge for clinicians and researchers, with treatment options limited to antibiotics, fluid therapy, and organ-supportive measures. Given the heterogeneous nature of sepsis, the identification of distinct clinical phenotypes holds the promise of more precise therapy and enhanced patient care. In this review, we explore various phenotyping schemes applied to sepsis.

Methods

We searched PubMed with the terms "Clinical phenotypes AND sepsis" for any type of article published in English up to September 2023. Only reports in English were included, editorials or articles lacking full text were excluded. A review of clinical phenotypes of sepsis is provided.

Key Content and Findings

While discerning clinical phenotypes may seem daunting, the application of artificial intelligence and machine learning techniques provides a viable approach to quantifying similarities among individuals within a sepsis population. These methods enable the differentiation of individuals into distinct phenotypes based on not only factors such as infectious diseases, infection sites, pathogens, body temperature changes and hemodynamics, but also conventional clinical data and molecular omics.

Conclusions

The classification of sepsis holds immense significance in improving clinical cure rates, reducing mortality, and alleviating the economic burden associated with this condition.

Role of artificial intelligence in critical care nutrition support and research

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.

Natural products act as game-changer potentially in treatment and management of sepsis-mediated inflammation: A clinical perspective

BACKGROUND

Sepsis, a life-threatening condition resulting from uncontrolled host responses to infection, poses a global health challenge with limited therapeutic options. Due to high heterogeneity, sepsis lacks specific therapeutic drugs. Additionally, there remains a significant gap in the clinical management of sepsis regarding personalized and precise medicine.

PURPOSE

This review critically examines the scientific landscape surrounding natural products in sepsis and sepsis-mediated inflammation, highlighting their clinical potential.

METHODS

Following the PRISMA guidelines, we retrieved articles from PubMed to explore potential natural products with therapeutic effects in sepsis-mediated inflammation.

RESULTS

434 relevant in vitro and in vivo studies were identified and screened. Ultimately, 55 studies were obtained as the supporting resources for the present review. We divided the 55 natural products into three categories: those influencing the synthesis of inflammatory factors, those affecting surface receptors and modulatory factors, and those influencing signaling pathways and the inflammatory cascade.

CONCLUSION

Natural products' potential as game-changers in sepsis-mediated inflammation management lies in their ability to modulate hallmarks in sepsis, including inflammation, immunity, and coagulopathy, which provides new therapeutic avenues that are readily accessible and capable of undergoing rapid clinical validation and deployment, offering a gift from nature to humanity. Innovative techniques like bioinformatics, metabolomics, and systems biology offer promising solutions to overcome these obstacles and facilitate the development of natural product-based therapeutics, holding promise for personalized and precise sepsis management and improving patient outcomes. However, standardization, bioavailability, and safety challenges arise during experimental validation and clinical trials of natural products.

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-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study

Background

The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators.

Methods

A retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients.

Results

A total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively.

Conclusion

CAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.

Temperature trajectories and mortality in hypothermic sepsis patients

OBJECTIVES

Hypothermia is associated with poor outcomes in sepsis patients, and hypothermic sepsis patients exhibit temperature alterations during initial treatment. The objective of this study was to classify hypothermic sepsis patients based on body temperature trajectories and investigate the associations of these patients with 28-day mortality.

METHODS

This was a retrospective analysis of prospectively collected data from adult sepsis or septic shock patients who visited three emergency departments between August 2014 and December 2019. Hypothermic sepsis was defined as an initial body temperature <36 °C. delta temperature was calculated by subtracting the 0 h body temperature from the 6 h body temperature. We divided the patients into three groups according to delta temperature: Group A (delta temperature ≤ 0), Group B (0 < delta temperature ≤ 1) and Group C (delta temperature > 1). The primary outcome was 28-day mortality, and a multivariable Cox proportional hazards regression model was generated.

RESULTS

Among 7344 patients with sepsis or septic shock, 325 hypothermic patients were included in the analysis, and the overall mortality rate was 36%. While initial body temperature was not different between survivors and nonsurvivors, survivors exhibited a higher body temperature at 6 h. The 28-day mortality rates for Groups A, B and C were 53.1%, 36.0%, and 30.0%, respectively, and Group A had significantly higher mortality than Group C did (p < 0.05). Group C demonstrated a 44.2% decrease in 28-day mortality compared to Group A (adjusted hazard ratio of 0.558; 95% confidence interval of 0.330-0.941).

CONCLUSIONS

In hypothermic sepsis patients, an increase of 1 °C or more in body temperature after the initial 6 h is associated with a reduced risk of 28-day mortality.

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

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

Evolving Paradigms in Sepsis Management: A Narrative Review

Sepsis, a condition characterized by life-threatening organ dysfunction due to a dysregulated host response to infection, significantly impacts global health, with mortality rates varying widely across regions. Traditional therapeutic strategies that target hyperinflammation and immunosuppression have largely failed to improve outcomes, underscoring the need for innovative approaches. This review examines the development of therapeutic agents for sepsis, with a focus on clinical trials addressing hyperinflammation and immunosuppression. It highlights the frequent failures of these trials, explores the underlying reasons, and outlines current research efforts aimed at bridging the gap between theoretical advancements and clinical applications. Although personalized medicine and phenotypic categorization present promising directions, this review emphasizes the importance of understanding the complex pathogenesis of sepsis and developing targeted, effective therapies to enhance patient outcomes. By addressing the multifaceted nature of sepsis, future research can pave the way for more precise and individualized treatment strategies, ultimately improving the management and prognosis of sepsis patients.

The potential immunological mechanisms of sepsis

Sepsis is described as a life-threatening organ dysfunction and a heterogeneous syndrome that is a leading cause of morbidity and mortality in intensive care settings. Severe sepsis could incite an uncontrollable surge of inflammatory cytokines, and the host immune system's immunosuppression could respond to counter excessive inflammatory responses, characterized by the accumulated anti-inflammatory cytokines, impaired function of immune cells, over-proliferation of myeloid-derived suppressor cells and regulatory T cells, depletion of immune effector cells by different means of death, etc. In this review, we delve into the underlying pathological mechanisms of sepsis, emphasizing both the hyperinflammatory phase and the associated immunosuppression. We offer an in-depth exploration of the critical mechanisms underlying sepsis, spanning from individual immune cells to a holistic organ perspective, and further down to the epigenetic and metabolic reprogramming. Furthermore, we outline the strengths of artificial intelligence in analyzing extensive datasets pertaining to septic patients, showcasing how classifiers trained on various clinical data sources can identify distinct sepsis phenotypes and thus to guide personalized therapy strategies for the management of sepsis. Additionally, we provide a comprehensive summary of recent, reliable biomarkers for hyperinflammatory and immunosuppressive states, facilitating more precise and expedited diagnosis of sepsis.

Subgroup analyses and heterogeneity of treatment effects in randomized trials: a primer for the clinician

PURPOSE OF REVIEW

To date, most randomized clinical trials in critical care report neutral overall results. However, research as to whether heterogenous responses underlie these results and give opportunity for personalized care is gaining momentum but has yet to inform clinical practice guidance. Thus, we aim to provide an overview of methodological approaches to estimating heterogeneity of treatment effects in randomized trials and conjecture about future paths to application in patient care.

RECENT FINDINGS

Despite their limitations, traditional subgroup analyses are still the most reported approach. More recent methods based on subphenotyping, risk modeling and effect modeling are still uncommonly embedded in primary reports of clinical trials but have provided useful insights in secondary analyses. However, further simulation studies and subsequent guidelines are needed to ascertain the most efficient and robust manner to validate these results for eventual use in practice.

SUMMARY

There is an increasing interest in approaches that can identify heterogeneity in treatment effects from randomized clinical trials, extending beyond traditional subgroup analyses. While prospective validation in further studies is still needed, these approaches are promising tools for design, interpretation, and implementation of clinical trial results.

Novel Preoperative Risk Stratification Using Digital Phenotyping Applying a Scalable Machine-Learning Approach

BACKGROUND

Classification of perioperative risk is important for patient care, resource allocation, and guiding shared decision-making. Using discriminative features from the electronic health record (EHR), machine-learning algorithms can create digital phenotypes among heterogenous populations, representing distinct patient subpopulations grouped by shared characteristics, from which we can personalize care, anticipate clinical care trajectories, and explore therapies. We hypothesized that digital phenotypes in preoperative settings are associated with postoperative adverse events including in-hospital and 30-day mortality, 30-day surgical redo, intensive care unit (ICU) admission, and hospital length of stay (LOS).

METHODS

We identified all laminectomies, colectomies, and thoracic surgeries performed over a 9-year period from a large hospital system. Seventy-seven readily extractable preoperative features were first selected from clinical consensus, including demographics, medical history, and lab results. Three surgery-specific datasets were built and split into derivation and validation cohorts using chronological occurrence. Consensus k -means clustering was performed independently on each derivation cohort, from which phenotypes' characteristics were explored. Cluster assignments were used to train a random forest model to assign patient phenotypes in validation cohorts. We reconducted descriptive analyses on validation cohorts to confirm the similarity of patient characteristics with derivation cohorts, and quantified the association of each phenotype with postoperative adverse events by using the area under receiver operating characteristic curve (AUROC). We compared our approach to American Society of Anesthesiologists (ASA) alone and investigated a combination of our phenotypes with the ASA score.

RESULTS

A total of 7251 patients met inclusion criteria, of which 2770 were held out in a validation dataset based on chronological occurrence. Using segmentation metrics and clinical consensus, 3 distinct phenotypes were created for each surgery. The main features used for segmentation included urgency of the procedure, preoperative LOS, age, and comorbidities. The most relevant characteristics varied for each of the 3 surgeries. Low-risk phenotype alpha was the most common (2039 of 2770, 74%), while high-risk phenotype gamma was the rarest (302 of 2770, 11%). Adverse outcomes progressively increased from phenotypes alpha to gamma, including 30-day mortality (0.3%, 2.1%, and 6.0%, respectively), in-hospital mortality (0.2%, 2.3%, and 7.3%), and prolonged hospital LOS (3.4%, 22.1%, and 25.8%). When combined with the ASA score, digital phenotypes achieved higher AUROC than the ASA score alone (hospital mortality: 0.91 vs 0.84; prolonged hospitalization: 0.80 vs 0.71).

CONCLUSIONS

For 3 frequently performed surgeries, we identified 3 digital phenotypes. The typical profiles of each phenotype were described and could be used to anticipate adverse postoperative events.

Early serum biomarkers to characterise different phenotypes of primary graft dysfunction after lung transplantation: a systematic scoping review

Background

Lung transplantation (LUTX) is often complicated by primary graft dysfunction (PGD). Plasma biomarkers hold potential for PGD phenotyping and targeted therapy. This scoping review aims to collect the available literature in search of serum biomarkers for PGD phenotyping.

Methods

Following JBI and PRISMA guidelines, we conducted a systematic review searching MEDLINE, Web of Science, EMBASE and The Cochrane Library for papers reporting the association between serum biomarkers measured within 72 h of reperfusion and PGD, following International Society for Heart and Lung Transplantation (ISHLT) guidelines. We extracted study details, patient demographics, PGD definition and timing, biomarker concentration, and their performance in identifying PGD cases.

Results

Among the 1050 papers screened, 25 prospective observational studies were included, with only nine conducted in the last decade. These papers included 1793 unique adult patients (1195 double LUTX, median study size 100 (IQR 44-119)). Most (n=21) compared PGD grade 3 to less severe PGD, but only four adhered to 2016 PGD definitions. Enzyme-linked immunosorbent assays and the multiplex bead array technique were utilised in 23 and two papers, respectively. In total, 26 candidate biomarkers were identified, comprising 13 inflammatory, three endothelial activation, three epithelial injury, three cellular damage and two coagulation dysregulation markers. Only five biomarkers (sRAGE, ICAM-1, PAI-1, SP-D, FSTL-1) underwent area under the receiver operating characteristic curve analysis, yielding a median value of 0.58 (0.51-0.78) in 406 patients (276 double LUTX).

Conclusions

Several biomarkers exhibit promise for future studies aimed at PGD phenotyping after LUTX. To uncover the significant existing knowledge gaps, further international prospective studies incorporating updated diagnostic criteria, modern platforms and advanced statistical approaches are essential.

Heterogeneity of treatment effect of vilobelimab in COVID-19: a secondary analysis of a randomised controlled trial

In a phase 3 trial (PANAMO, NCT04333420), vilobelimab, a complement 5a (C5a) inhibitor, reduced 28-day mortality in mechanically ventilated COVID-19 patients. This post hoc analysis of 368 patients aimed to explore treatment heterogeneity through unsupervised learning. All available clinical variables at baseline were used as input. Treatment heterogeneity was assessed using latent class analysis (LCA), Ward's hierarchical clustering (HC) and the adjudication to previously described clinical sepsis phenotypes. The primary outcome was 28-day mortality. For LCA, a 2-class latent model was deemed most suitable. In the LCA model, 82 (22%) patients were assigned to class 1 and 286 (78%) to class 2. Class 1 was defined by more severely ill patients with significantly higher mortality. In an adjusted logistic regression, no heterogeneity of treatment effect (HTE) between classes was observed (p = 0.998). For HC, no significant classes were found (p = 0.669). Using the previously described clinical sepsis subtypes, 41 patients (11%) were adjudicated subtype alpha (α), 17 (5%) beta (β), 112 (30%) delta (δ) and 198 (54%) gamma (γ). HTE was observed between clinical subtypes (p = 0.001) with improved 28-day mortality after treatment with vilobelimab for the δ subtype (OR = 0.17, 95% CI 0.07-0.40, p < 0.001). No signal for harm of treatment with vilobelimab was observed in any class or clinical subtype. Overall, treatment effect with vilobelimab was consistent across different classes and subtypes, except for the δ subtype, suggesting potential additional benefit for the most severely ill patients.

Dysregulated STAT3 signaling and T cell immunometabolic dysfunction define a targetable, high mortality subphenotype of critically ill children

Sepsis is the leading cause of death of hospitalized children worldwide. Despite the established link between immune dysregulation and mortality in pediatric sepsis, it remains unclear which host immune factors contribute causally to adverse sepsis outcomes. Identifying modifiable pathobiology is an essential first step to successful translation of biologic insights into precision therapeutics. We designed a prospective, longitudinal cohort study of 88 critically ill pediatric patients with multiple organ dysfunction syndrome (MODS), including patients with and without sepsis, to define subphenotypes associated with targetable mechanisms of immune dysregulation. We first assessed plasma proteomic profiles and identified shared features of immune dysregulation in MODS patients with and without sepsis. We then employed consensus clustering to define three subphenotypes based on protein expression at disease onset and identified a strong association between subphenotype and clinical outcome. We next identified differences in immune cell frequency and activation state by MODS subphenotype and determined the association between hyperinflammatory pathway activation and cellular immunophenotype. Using single cell transcriptomics, we demonstrated STAT3 hyperactivation in lymphocytes from the sickest MODS subgroup and then identified an association between STAT3 hyperactivation and T cell immunometabolic dysregulation. Finally, we compared proteomics findings between patients with MODS and patients with inborn errors of immunity that amplify cytokine signaling pathways to further assess the impact of STAT3 hyperactivation in the most severe patients with MODS. Overall, these results identify a potentially pathologic and targetable role for STAT3 hyperactivation in a subset of pediatric patients with MODS who have high severity of illness and poor prognosis.

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

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

Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery

Objective

To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.

Background

In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.

Methods

This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.

Results

Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K-$23.5K) vs $14.1K ($9.1K-$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care.

Conclusions

Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.

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

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

Association Between Medicare's Sepsis Reporting Policy (SEP-1) and the Documentation of a Sepsis Diagnosis in the Clinical Record

STUDY DESIGN

Interrupted time series analysis of a retrospective, electronic health record cohort.

OBJECTIVE

To determine the association between the implementation of Medicare's sepsis reporting measure (SEP-1) and sepsis diagnosis rates as assessed in clinical documentation.

BACKGROUND

The role of health policy in the effort to improve sepsis diagnosis remains unclear.

PATIENTS AND METHODS

Adult patients hospitalized with suspected infection and organ dysfunction within 6 hours of presentation to the emergency department, admitted to one of 11 hospitals in a multi-hospital health system from January 2013 to December 2017. Clinician-diagnosed sepsis, as reflected by the inclusion of the terms "sepsis" or "septic" in the text of clinical notes in the first two calendar days following presentation.

RESULTS

Among 44,074 adult patients with sepsis admitted to 11 hospitals over 5 years, the proportion with sepsis documentation was 32.2% just before the implementation of SEP-1 in the third quarter of 2015 and increased to 37.3% by the fourth quarter of 2017. Of the 9 post-SEP-1 quarters, 8 had odds ratios for a sepsis diagnosis >1 (overall range: 0.98-1.26; P value for a joint test of statistical significance = 0.005). The effects were clinically modest, with a maximum effect of an absolute increase of 4.2% (95% CI: 0.9-7.8) at the end of the study period. The effect was greater in patients who did not require vasopressors compared with patients who required vasopressors ( P value for test of interaction = 0.02).

CONCLUSIONS

SEP-1 implementation was associated with modest increases in sepsis diagnosis rates, primarily among patients who did not require vasoactive medications.

Personalized anti-reflux surgery: connecting GERD phenotypes in 690 patients to outcomes

BACKGROUND

Anti-reflux operations are effective treatments for GERD. Despite standardized surgical techniques, variability in post-operative outcomes persists. Most patients with GERD possess one or more characteristics that augment their disease and may affect post-operative outcomes-a GERD "phenotype". We sought to define these phenotypes and to compare their post-operative outcomes.

METHODS

We performed a retrospective review of a prospective gastroesophageal database at our institution, selecting all patients who underwent an anti-reflux procedure for GERD. Patients were grouped into different phenotypes based on the presence of four characteristics known to play a role in GERD: hiatal or paraesophageal hernia (PEH), hypotensive LES, esophageal dysmotility, delayed gastric emptying (DGE), and obesity. Patient-reported outcomes (GERD-HRQL, dysphagia, and reflux symptom index (RSI) scores) were compared across phenotypes using the Wilcoxon rank-sum test.

RESULTS

690 patients underwent an anti-reflux procedure between 2008 and 2022. Most patients underwent a Nissen fundoplication (302, 54%), followed by a Toupet or Dor fundoplication (205, 37%). Twelve distinct phenotypes emerged. Non-obese patients with normal esophageal motility, normotensive LES, no DGE, with a PEH represented the most common phenotype (134, 24%). The phenotype with the best post-operative GERD-HRQL scores at one year was defined by obesity, hypotensive LES, and PEH, while the phenotype with the worst scores was defined by obesity, ineffective motility, and PEH (1.5 ± 2.4 vs 9.8 ± 11.4, p = 0.010). There was no statistically significant difference in GERD-HRQL, dysphagia, or RSI scores between phenotypes after five years.

CONCLUSIONS

We have identified distinct phenotypes based on common GERD-associated patient characteristics. With further study these phenotypes may aid surgeons in prognosticating outcomes to individual patients considering an anti-reflux procedure.

Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients

OBJECTIVE

To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves.

DESIGN

Observational, retrospective, multicentre study.

SETTING

Intensive Care Unit (ICU).

PATIENTS

Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC.

RESULTS

A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08).

CONCLUSION

Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

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.

The role of thromboinflammation in acute kidney injury among patients with septic coagulopathy

Inflammation and coagulation are critical self-defense mechanisms for mitigating infection that can nonetheless induce tissue injury and organ dysfunction. In severe cases, like sepsis, a dysregulated thromboinflammatory response may result in multiorgan dysfunction. Sepsis-associated acute kidney injury (AKI) is a significant contributor to patient morbidity and mortality. The connection between AKI and thromboinflammation is largely due to unique aspects of the renal vasculature. Specifically, the interaction between blood cells with the endothelial, glomerular, and peritubular capillary systems during thromboinflammation reduces oxygen supply to tubular epithelial cells. Previous studies have focused on tubular epithelial cell damage due to hypoxia, oxidative stress, and nephrotoxins. Although these factors are pivotal in acute tubular injury or necrosis, recent studies have demonstrated that AKI in sepsis encompasses a mixture of tubular and glomerular damage subtypes. In cases of sepsis-induced coagulopathy, thromboinflammation within the glomerulus and peritubular capillaries is an important pathogenic mechanism for AKI. Unfortunately, and despite the use of renal replacement therapy, the development of AKI in sepsis continues to be associated with high morbidity, mortality, and clinical challenges requiring alternative approaches. This review introduces the important role of thromboinflammation in AKI pathogenesis and details innovative vascular-targeting therapeutic strategies.