Real-time risk monitoring is critical but challenging in intensive care units (ICUs) due to the lack of real-time updates for most clinical variables. Although real-time predictions have been integrated into various risk monitoring systems, existing systems do not address uncertainties in risk assessments. We developed a novel framework based on commonly used systems like the Sequential Organ Failure Assessment (SOFA) score by incorporating uncertainties to improve the effectiveness of real-time risk monitoring.

This study included 5351 patients admitted to the Cardiothoracic ICU in the National University Hospital in Singapore. We developed machine learning models to predict long lead-time variables and computed real-time SOFA scores using predictions. We calculated intervals to capture uncertainties in risk assessments and validated the association of the estimated real-time scores and intervals with mortality and readmission.

Our model outperforms SOFA score in predicting 24-h mortality: Nagelkerke's R-squared (0.224 vs. 0.185, p < 0.001) and the area under the receiver operating characteristic curve (AUC) (0.870 vs. 0.843, p < 0.001), and significantly outperforms quick SOFA (Nagelkerke's R-squared = 0.125, AUC = 0.778). Our model also performs better in predicting 30-day readmission. We confirmed a positive net reclassification improvement (NRI) of our model over the SOFA score (0.184, p < 0.001). Similarly, we enhanced two additional scoring systems.

Incorporating uncertainties improved existing scores in real-time monitoring, which could be used to trigger on-demand laboratory tests, potentially improving early detection, reducing unnecessary testing, and thereby lowering healthcare expenditures, mortality, and readmission rates in clinical practice.

The online version contains supplementary material available at 10.1007/s13755-024-00331-5.

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA in Figure 1) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.

BackgroundIschemic stroke is a critical neurological condition, with infection representing a significant aspect of its clinical management. Sepsis, a life-threatening organ dysfunction resulting from infection, is among the most dangerous complications in the intensive care unit (ICU). Currently, no model exists to predict the onset of sepsis in ischemic stroke patients. This study aimed to develop the first predictive model for sepsis in ischemic stroke patients using data from the MIMIC-IV database, leveraging machine learning techniques.MethodsA total of 2238 adult patients with a diagnosis of ischemic stroke, admitted to the ICU for the first time, were included from the MIMIC-IV database. The outcome of interest was the development of sepsis. Model development adhered to the TRIPOD guidelines. Feature selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, identifying 28 key variables. Multiple machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, decision trees, and XGBoost, were trained and internally validated. Performance metrics were assessed, and XGBoost was selected as the optimal model. The SHAP method was used to interpret the XGBoost model, revealing the impact of individual features on predictions. The model was also deployed on a user-friendly platform for practical use in clinical settings.ResultsThe XGBoost model demonstrated superior performance in the validation set, achieving an area under the curve (AUC) of 0.863 and offering greater net benefit compared to other models. SHAP analysis identified key factors influencing sepsis risk, including the use of invasive mechanical ventilation on the first day, excessive body weight, a Glasgow Coma Scale verbal score below 3, age, and elevated body temperature (>37.5 °C). A user interface had been developed to enable clinicians to easily access and utilize the model.ConclusionsThis study developed the first machine learning-based model to predict sepsis in ischemic stroke patients. The model exhibited high accuracy and holds potential as a clinical decision support tool, enabling earlier identification of high-risk patients and facilitating preventive measures to reduce sepsis incidence and mortality in this population.

This study investigates the impact of four laxatives-Senna, Docusate Sodium, Polyethylene Glycol, and Lactulose on 28-day mortality, ICU-free days, ventilator-free days, bowel recovery, and Clostridium difficile (C-diff) infection in critically ill sepsis patients to identify optimal bowel management strategies for improving survival and recovery.

Using the MIMIC-IV database (v3.1), we analyzed 7163 ICU sepsis patients (median age: 67.5 years; 63% male), assessing 28-day mortality, ICU-free days, vasopressor-free days, ventilator-free days, bowel sound recovery, and C-diff incidence, with propensity score matching and multivariable adjustments for confounders, alongside subgroup analyses by sex, age, Charlson Comorbidity Index, and Sequential Organ Failure Assessment score.

Docusate Sodium was associated with significantly lower 28-day mortality (adjusted HR: 0.43; 95% CI 0.36-0.52), more ICU-free days, and better bowel recovery compared to Senna, while Lactulose was linked to higher mortality (adjusted HR: 1.82; 95% CI 1.45-2.27), fewer ICU-free days, and increased C. difficile risk, with subgroup analyses confirming these trends across sex, age, and comorbidity strata.

Docusate sodium appears to be a safer and more effective bowel management option for critically ill patients with sepsis. In contrast, the association between lactulose use and adverse outcomes may primarily reflect the severity of underlying liver disease rather than a direct drug effect. These findings underscore the importance of individualized laxative selection based on patients' clinical context in critical care practice.

The Compendium of Materia Medica recorded that the traditional Chinese medicine Gastrodia elata Blume (G. elata) has applications in antibacterial and antiviral. GE05 was prepared from the rhizome of G. elata and its active ingredients have been proven to alleviate inflammatory response. However, the protective effect and the underlying mechanism of G. elata in sepsis remain unclear. Our study aims to uncover the efficacy and molecular mechanisms of GE05 in ameliorating sepsis-induced lung injury. Here, we have shown that GE05 could inhibit the expression of inflammatory cytokines in peritoneal macrophages. Bioinformatics analysis showed the activation of interleukin (IL)-17 signaling pathway in acute lung injury (ALI) mice. We established septicemia models and showed that GE05 improves survival rates and protects against sepsis-induced lung injury by downregulating the IL-23/IL-17 A axis. Quantitative real- time PCR (qPCR) analysis and immunohistochemistry have indicated that IL-17 A inhibition reduces the release of chemokine ligand (Cxcl) 1, Cxcl2, ‌granulocyte-macrophage colony stimulating factor (GM-CSF), and granulocyte colony-stimulating factor (G-CSF), mitigating neutrophil infiltration-induced lung tissue damage. Meanwhile, GE05 could inhibit the activation of the IL-17 A-related phosphatidylinositol 3-kinase/ c-Jun N-terminal kinase (PI3K/JNK) signaling pathway, suppressing the expression of tumor necrosis factor-alpha (TNF-α), IL-1β, and IL-6. These results demonstrated that GE05 is a promising agent that targets the IL-23/IL-17 A axis, providing new way for preventing and treating sepsis-induced lung injury.

Background: Adjunctive vasopressors are added to norepinephrine in one-third of adults with septic shock in the United States. However, effectiveness of this approach is unclear, and treatment recommendations are based on indirect evidence. We sought to synthesize the direct evidence for adjunctive vasopressor administration in adults with septic shock. Methods: We searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials from inception to June 7, 2023. We included randomized clinical trials of adults with septic shock comparing adjunctive treatment with a vasopressin analogue, angiotensin II, methylene blue, hydroxocobalamin, or catecholamine analog to standard care vasopressors. The primary outcome was short-term mortality (at or before 28-30 days or intensive care discharge). Secondary outcomes included kidney replacement therapy, digital/peripheral ischemia, and venous thromboembolism. Random-effects meta-analyses were conducted to derive risk ratios (RRs) and 95% CIs. The certainty of the evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation. Results: Of 6,763 records, 17 trials (3,813 participants) were included. Compared with standard care, adjunctive vasopressor administration may reduce short-term mortality risk (RR, 0.92 [95% CI, 0.85-1.00], low certainty, 17 trials [3618 participants]) and likely reduces kidney replacement therapy receipt (RR, 0.92 [95% CI, 0.84-1.01], moderate certainty, eight trials [2,408 participants]). Adjunctive vasopressor treatment may increase risk of digital/peripheral ischemia (RR, 2.44 [95% CI, 1.17-5.10], low certainty, nine trials [2,981 participants]) and venous thromboembolism (RR, 16.48 [95% CI, 0.96-283.17], low certainty, one trial [321 participants]). There was some evidence that the pooled estimate for short-term mortality was different (interaction P = 0.13) for trials adjudicated as low risk of bias (RR, 0.95 [95% CI, 0.87-1.05]) compared with trials adjudicated as some concerns or high risk of bias (RR, 0.82 [95% CI, 0.69-0.97]). The findings were robust to multiple sensitivity and subgroup analyses. Conclusions: In adults with septic shock, adjunctive vasopressors may lower short-term death risk and likely lower kidney replacement therapy risk, but may increase risk of adverse effects. In the United States, adjunctive vasopressor use prevalence in septic shock is disconnected from the low evidence certainty for a favorable mortality-to-risk profile.

Disordered coagulation is an independent risk factor for mortality in patients with sepsis and currently lacks effective therapeutic strategies. In this study, PIM1, a novel target predominantly expressed in macrophages during sepsis, is investigated by bioinformatics analysis and clinical evaluation in patients with sepsis compared with healthy individuals. The regulatory mechanism by which PIM1 promotes the release of tissue factors (TF) from macrophages by modulating the phosphorylation levels of mTOR through the AKT and MAPK signaling pathways is demonstrated both in vitro and in vivo. Based on these findings, a multifunctional co-delivery system based on mesoporous polydopamine (MPDA) nanoparticles (NPs) coated with cationic polyethyleneimine (PEI) and macrophage-targeting glucomannan (GM) (MPDA@PEI@GM NPs) is proposed for the co-delivery of the PIM1 inhibitors SMI-4a and small interfering RNA (siPIM1) to downregulate PIM1 expression and improve sepsis-induced coagulopathy. MPDA@SMI-4a@PEI/siPIM1@GM demonstrates negligible cytotoxicity, excellent macrophage-targeting efficiency, prolonged blood circulation, and significantly downregulated PIM1 expression. Notably, treatment with MPDA@SMI-4a@PEI/siPIM1@GM improves the survival rates of septic mice by ameliorating disordered coagulation and alleviating lung injury. Bioinformatic analysis and clinical research-guided MPDA@SMI-4a@PEI/siPIM1@GM co-delivery systems improve TF-mediated coagulopathy in sepsis and alleviate sepsis-induced acute lung injury, marking a significant advancement in the development of clinical antisepsis therapies.

Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock patients using data from 4872 ICU patients from February 2003 to November 2023 across three hospitals. The model integrates seven machine learning models via the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), achieving AUCs of 0.733 in internal validation, 0.808 in the pediatric ICU, 0.662 in the respiratory ICU, with external validation AUCs of 0.784 and 0.786, respectively. It demonstrated high stability and accuracy in cross-specialty and multi-center validation. This interpretable model provides clinicians with a reliable early-warning tool for septic shock mortality risk, facilitating early intervention to reduce mortality.

The difference between hematocrit and serum albumin (HCT-ALB) demonstrates diagnostic significance in infectious diseases, yet the nonlinear relationship between HCT-ALB and hospital mortality in ICU patients with sepsis remains unexplored. This retrospective multicenter cohort study analyzed 7,546 ICU sepsis patients (mean age 66 ± 16 years) to elucidate the HCT-ALB-mortality relationship. Using Cox proportional hazards models with smooth curve fitting, we identified a U-shaped association: Threshold analysis revealed an inflection point at 6.1. Below this threshold, each unit HCT-ALB increase corresponded to reduced mortality risk (adjusted HR 0.986, 95%CI 0.972-0.999; P = 0.036). Conversely, values ≥ 6.1 predicted escalating risk (adjusted HR 1.048 per unit increase, 95%CI 1.037-1.060; P < 0.0001). Significant age interaction was observed (P for interaction < 0.05), with heightened mortality risk in elderly patients (≥ 65 years: HR 1.022, 95%CI 1.014-1.031). These findings establish HCT-ALB as a non-linear predictor of sepsis outcomes, emphasizing its critical threshold dynamics and age-dependent prognostic implications.

The aim of the study was to evaluate the predictive value of cell population data (CPD) parameters in comparison with procalcitonin (PCT) and C-reactive protein (CRP) for an early diagnosis of sepsis in intensive care unit (ICU). The effect of renal function on CPD, PCT and CRP, in septic and non-septic patients was also investigated.

This is a retrospective, observational and single-center study, performed with data collected from patients consecutively admitted to the ICU of the Edoardo Bassini Hospital in Milan. Patients were divided in septic and non-septic according to Sepsis-III criteria. The control group was formed by critically ill patients without sepsis. Patients with sepsis were further divided in patients with sepsis and patients with septic shock.

A significant difference between septic and non-septic patients was found for neutrophils complexity (NE-SSC), neutrophils fluorescence intensity (NE-SFL), width of dispersion of neutrophils fluorescence (NE-WY), monocytes complexity (MO-X), monocytes fluorescence intensity (MO-Y), PCT and CRP parameters. PCT, neutrophils sixe (NE-FSC), NE-WY, width of dispersion of neutrophils size (NE-WZ) and MO-X discriminated sepsis and septic-shock patients. CPD parameters were not influenced by renal function. CPD, PCT and CRP had a heterogeneous diagnostic performance efficiency in the prediction of sepsis. Overall, NE-SSC, NE-SFL, width of dispersion of neutrophils complexity (NE-WX), MO-X, MO-Y, PCT and CRP displayed the best diagnostic performance for sepsis.

This study suggested that some CPD parameters (i.e., NE-SFL and MO-X) might provide useful information for diagnosis and management of sepsis.

Sepsis is a severe condition associated with high mortality, and hospital performance is variable. The objective of this study was to develop geospatial sepsis clusters, identify sources of variation between clusters, and test the hypothesis that redistributing sepsis patients from low-performing hospitals to higher-performing hospitals within a cluster will improve sepsis outcomes.

We conducted a cohort study of age-qualifying Medicare beneficiaries using administrative claims data from 2013 to 2015. We calculated risk-standardized mortality for hospitals then used a clustering algorithm to define geospatial cluster boundaries based on care-seeking and interhospital transfer patterns. Finally, we used simulation to model the effect of reallocating sepsis patients to higher-performing hospitals within the same cluster.

None.

We included 1,125,308 patients, and they were grouped into 222 regional clusters. High-performing clusters were located largely in the Midwest, and they tended to be in less urban regions with smaller hospitals. In our simulation, the most impactful strategy was reassigning cases from the lowest-performing hospital in a cluster to the highest-performing hospital in the cluster, which was predicted to prevent 1705 deaths per year in the United States. This aggregate benefit was lower than the 5702 deaths predicted from reducing mortality by 1% absolute in hospitals in the lower half of the performance distribution.

Geospatial clusters provide insight into regional approaches to system-based acute care. In a simulation study, targeted sepsis regionalization appears less effective than local performance improvement in reducing preventable sepsis deaths.

Sepsis is a leading cause of hospitalization and death in the United States, and rural patients are at particularly high risk. Telehealth has been proposed as one strategy to narrow rural-urban disparities. The objective of this study was to understand why rural emergency department (ED) staff use provider-to-provider telehealth (tele-ED) and how tele-ED care changes the care for rural patients with sepsis.

We conducted a qualitative interview study between February 15, 2022, and May 22, 2023, with participants from upper Midwest rural EDs and tele-ED hub physicians in a single tele-ED network that delivers provider-to-provider consultation for sepsis patients. One interviewer conducted individual telephone interviews, then we used standard qualitative methods based on modified grounded theory to identify themes and domains.

We interviewed 27 participants, and from the interviews we identified nine themes within three domains. Participants largely felt tele-ED for sepsis was valuable in their practice. We identified that telehealth was consulted to facilitate interhospital transfer, provide surge capacity for small teams, to adhere with provider scope-of-practice policies, for inexperienced providers, and for patients with increased severity of illness or complex comorbidities. Barriers to tele-ED use and impact included increased sepsis care standardization, provider reluctance, and sepsis diagnostic uncertainty. Additionally, we identified that real-time education and training were important secondary benefits identified from tele-ED use.

Tele-ED care was used by rural providers for sepsis treatment, but many barriers existed that may have limited potential benefits to its use.

Sepsis affects approximately 50 million people worldwide, resulting in 11 million deaths annually. Conflicting results and insufficient evidence comparing performance biomarkers exist. The study aimed to comprehensively compare available biomarkers and clinical scores for detecting sepsis since its redefinition in 2016 with this systematic review and Bayesian diagnostic test accuracy network meta-analysis.

We conducted searches in the PubMed, EMBASE, and Scopus databases between January 2016 and December 2023. Eligible studies assessed the diagnostic accuracies of biomarkers, the quick Sequential Organ Failure Assessment (qSOFA) score, or Systemic Inflammatory Response Syndrome (SIRS) criteria in detecting sepsis. Bivariate hierarchical random effects arm-based beta-binomial models were used for quantitative synthesis (PROSPERO Registration Number: CRD42018086545).

We included 78 studies representing 34,234 patients and compared qSOFA score, SIRS criteria alongside seven of the most studied biomarkers: procalcitonin, C-reactive protein (CRP), interleukin-6 (IL-6), presepsin (cluster of differentiation 14 subtypes), CD64, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and lipopolysaccharide-binding protein (LBP). CD64 demonstrated the highest superiority index, followed by sTREM-1 and presepsin (diagnostic odds ratio: 20.17 vs 18.73 and 10.04, 95 % credible interval [CrI]: 8.39-38.61 vs 1.31-83.98 and 6.71-14.24; quality of evidence: moderate vs low and low). Multivariable meta-regression analysis identified significant sources of heterogeneity, including study design, proportion of sepsis, sample size, and the risk of bias (patient selection).

The best diagnostic accuracy for sepsis was shown by CD64, with a moderate quality of evidence. Compared to CD64, sTREM-1 and presepsin provided suboptimal and low evidence. These biomarkers were more effective at identifying updated sepsis than clinical scores. We recommend re-considering the addition of biomarkers in screening for sepsis or sepsis-related conditions, as this could lead to more accurate and timely decisions for future clinical interventions.

Sepsis ranks among the "Big Three" conditions most prone to harmful diagnostic errors. Despite its high prevalence and severity, health systems lack effective and contextually tailored strategies to optimize diagnostic accuracy for sepsis.

The purpose of this study is to understand factors related to high sepsis diagnostic accuracy using principles and tools of safety and implementation science.

This is a multi-site study involving 20 hospitals across four states in the United States. The primary objectives are to (1) describe hospital-level variability and understand barriers and facilitators to sepsis diagnostic accuracy and (2) apply cross-case and coincidence analysis to determine minimally sufficient and necessary conditions for optimal sepsis diagnosis that minimizes under- and overtreatment. To identify barriers and facilitators of acute sepsis diagnosis, we will conduct electronic surveys and in-depth interviews with key informants from each hospital. We will use data from electronic health records (EHR) and data warehouses to operationalize sepsis diagnostic accuracy.

We have enrolled 20 hospitals and begum data collection. The findings of this study will be used to develop a context-specific toolkit that guides the selection of feasible and important strategies to promote optimal sepsis diagnosis in diverse hospitals settings.

The study uses tools and principles from safety and implementation science to generate first-of-its-kind evidence to improve diagnostic excellence in sepsis.

Sepsis is a life-threatening condition often associated with metabolic and acid-base imbalances. Alactic base excess (ABE) has emerged as a novel biomarker to assess metabolic disturbances in critically ill sepsis patients, but its prognostic value remains underexplored. We aimed to investigate the relationship between ABE and 30-day/90-day ICU all-cause mortality in a large sepsis cohort in the intensive care unit (ICU) setting.

This study utilised data from a large US ICU sepsis cohort. ABE was calculated as the sum of lactate and base excess (BE) values from the first day of ICU admission. Patients were divided into quartiles based on ABE values. Kaplan-Meier survival analysis, Cox proportional hazards models, and restricted cubic spline analyses were used to examine the associations between ABE and mortality outcomes. The predictive performance of ABE combined with the SOFA score was assessed using the area under the curve, Net Reclassification Improvement, and Integrated Discrimination Improvement.

17,099 patients with sepsis were included in this analysis, with median (IQR) age of 67.82 (56.80, 78.04) years and 59.7% males. Our analysis revealed a U-shaped association between ABE and 30-day and 90-day ICU all-cause mortality. Both the lowest (Q1) and highest (Q4) quartiles of ABE were linked to increased mortality risks, with 30-day mortality showing HRs of 1.27 (95% CI 1.13-1.44) for Q1 and 1.17 (95% CI 1.06-1.31) for Q4, while 90-day mortality showed HRs of 1.28 (95% CI 1.16-1.44) for Q1, 1.12 (95% CI 1.02-1.23) for Q2, and 1.22 (95% CI 1.11-1.34) for Q4. ABE demonstrated superior predictive performance for mortality compared to BE and lactate. Incorporating ABE into the SOFA score improved predictive performance, emphasizing ABE's value in better risk stratification. The identified thresholds (2.5 mmol/L for 30-day mortality and 2.2 mmol/L for 90-day mortality) indicate optimal ABE levels that may be associated with improved survival outcomes.

ABE demonstrated a U-shaped association with 30-day and 90-day ICU all-cause mortality in critically ill sepsis patients, suggesting its superiority over BE and lactate as a predictive biomarker. Incorporating ABE with the SOFA score may further enhance prognostic predictions. Further studies are needed to determine whether ABE should serve solely as a biomarker for monitoring the clinical course or could also be considered a potential therapeutic target.

The accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection.

To improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns. Eleven data sets were used as training data sets for the method development, and the InfectDiagno algorithm was optimized by multi-cohort training samples. Nine data sets were used as independent validation data sets for the method. We further validated the diagnostic capacity of InfectDiagno in a prospective clinical cohort.

After selecting 100 feature genes based on their gene expression ranks for infection prediction, we trained a classifier using both a noninfected-vs-infected area under the receiver-operating characteristic curve (area under the curve [AUC] 0.95 [95% CI, 0.93-0.97]) and a bacterial-vs-viral AUC 0.95 (95% CI, 0.93-0.97). We then used the noninfected/infected classifier together with the bacterial/viral classifier to build a discriminating infection diagnosis model. The sensitivity was 0.931 and 0.872, and specificity 0.963 and 0.929, for bacterial and viral infections, respectively. We then applied InfectDiagno to a prospective clinical cohort (n = 517), and found it classified 95% of the samples correctly.

Our study shows that the InfectDiagno algorithm is a powerful and robust tool to accurately identify infection in a real-world patient population, which has the potential to profoundly improve clinical care in the field of infection diagnosis.

Sepsis, characterized by immune dysregulation, inflammatory cascades, and coagulation dysfunction, remains a global health challenge with high mortality, particularly in patients with multiple organ dysfunction syndrome (MODS). Existing prognostic tools, such as SOFA and APACHE II scores, are limited by complexity and lack of real-time monitoring, necessitating simple and reliable biomarkers for risk stratification and individualized management.

This study aimed to evaluate the prognostic value of the neutrophil-to-lymphocyte-to-platelet ratio (NLPR) for mortality in sepsis patients and explore its potential utility in dynamic risk stratification and treatment optimization.

We conducted a retrospective cohort study using the MIMIC-IV database (v3.1), including adult sepsis patients meeting Sepsis-3.0 criteria. NLPR was calculated based on neutrophil, lymphocyte, and platelet counts within 24 h of admission. Patients were stratified into quartiles (Q1-Q4) based on NLPR values. Kaplan-Meier survival analysis, Cox regression models, and restricted cubic spline (RCS) analysis were performed to assess NLPR's association with 28-day, 90-day, and 365-day mortality. Subgroup analyses examined NLPR's performance in diverse clinical populations.

NLPR was a strong and independent predictor of mortality at all time points. Patients in the highest NLPR quartile (Q4) had significantly higher 28-day (28.22% vs. 12.64%), 90-day (36.82% vs. 18.06%), and 365-day (44.94% vs. 25.58%) mortality compared to the lowest quartile (Q1, all P < 0.001). Cox regression confirmed the independent association of high NLPR with mortality after adjusting for confounders such as age, gender, BMI, and SOFA scores. RCS analysis identified nonlinear relationships between NLPR and mortality, with critical thresholds (e.g.,NLPR = 6.5 for 365-day mortality) providing actionable targets for early risk identification. Subgroup analysis revealed consistent predictive performance across clinical populations, with amplified risks in younger patients, malnourished individuals, and those with acute kidney injury.

NLPR is a simple, accessible, and robust biomarker for sepsis risk stratification, integrating inflammation and coagulation data. It complements traditional scoring systems, provides actionable thresholds for early intervention, and facilitates dynamic monitoring. These findings underscore NLPR's potential to improve clinical decision-making and outcomes in sepsis management, warranting validation in prospective multicenter studies.

Sepsis is a common condition associated with significant morbidity and mortality. Emergency physicians play a key role in the diagnosis and management of this condition.

This paper evaluates key evidence-based updates concerning the management of sepsis and septic shock for the emergency clinician.

Sepsis is a life-threatening syndrome, and rapid diagnosis and management are essential. Antimicrobials should be administered as soon as possible, as delays are associated with increased mortality. Resuscitation targets include mean arterial pressure ≥ 65 mmHg, mental status, capillary refill time, lactate, and urine output. Intravenous fluid resuscitation plays an integral role in those who are fluid responsive. Balanced crystalloids and normal saline are both reasonable options for resuscitation. Early vasopressors should be initiated in those who are not fluid-responsive. Norepinephrine is the recommended first-line vasopressor, and if hypotension persists, vasopressin should be considered, followed by epinephrine. Administration of vasopressors through a peripheral 20-gauge or larger intravenous line is safe and effective. Steroids such as hydrocortisone and fludrocortisone should be considered in those with refractory septic shock.

An understanding of the recent updates in the literature concerning sepsis and septic shock can assist emergency clinicians and improve the care of these patients.

To aggregate literature on the diagnostic performance of monocyte distribution width (MDW) for sepsis detection among adults in the emergency department and inpatient settings.

We searched the MEDLINE, EMBASE, SCOPUS, and Cochrane databases for studies evaluating MDW for sepsis diagnosis in adults in the hospital setting through October 19, 2024. Two authors (G.E. and Q.H.) independently performed eligibility assessment, data extraction, and risk of bias assessment. We evaluated performance for sepsis-2 and sepsis-3 separately and applied separate diagnostic thresholds depending on the anticoagulant used in blood collection. Data were pooled using a random-effects model. We performed multiple sensitivity analyses to evaluate the stability of our findings.

Twenty-five observational studies comprising 39,041 patients were included. The area under the summary receiver operating curve (AUC) was 0.82 (95% CI, 0.78-0.85) for both sepsis-2 and sepsis-3. Sensitivity and specificity were 0.79 (95% CI, 0.74-0.83) and 0.7 (95% CI, 0.61-0.78) for sepsis-2 and 0.83 (95% CI, 0.78-0.88) and 0.64 (95% CI, 0.55-0.71) for sepsis-3. The threshold-independent weighted-average AUC was 0.76 (SD, 0.1) for sepsis-2 and 0.77 (SD, 0.07) for sepsis-3. The aggregate negative predictive value was 94% for sepsis-2 and 96% for sepsis-3. We observed similar performance across all sensitivity analyses. We assessed the overall quality of evidence to be low.

MDW performs similarly to other biomarkers such as procalcitonin for the diagnosis of sepsis, with the unique advantage of rapid availability as part of routine testing.

Acute kidney injury (AKI) is a common complication of sepsis and also a risk factor for progression of chronic kidney disease. NOP2/Sun RNA methyltransferase 3 (NSUN3) is involved in the regulation of sepsis progression. However, the mechanism by which NSUN3 regulates sepsis-associated AKI (SA-AKI) remains unclear.

SA-AKI mouse model and lipopolysaccharide (LPS)-induced injury model in HK-2 cells were constructed. Haematoxylin-eosin staining, quantitative polymerase chain reaction (qPCR), western blotting, cell counting kit 8, flow cytometry, 2',7'-dichlorofluorescein diacetate, enzyme-linked immunosorbent assay, methylation RNA immunoprecipitation-qPCR, actinomycin D and TdT-mediated dUTP Nick-End Labelling staining assays were utilised to explore the expression and related mechanism of NSUN3 in the SA-AKI models.

The expression of NSUN3 and tumour necrosis factor receptor-associated factor (TRAF)-interacting protein with forkhead-associated domain (TIFA) was upregulated in mice with SA-AKI and LPS-induced HK-2 cells. Knockdown of NSUN3 inhibited LPS-induced injury in HK-2 cells. Mechanically, NSUN3 increased TIFA mRNA stability and upregulated its expression through m5C modification. Moreover, knockdown of NSUN3 was found to alleviate LPS-induced HK-2 cell injury and SA-AKI in mice by reducing TIFA expression.

NSUN3 aggravates SA-AKI by stabilising TIFA mRNA through m5C, indicating that NSUN3 may be a biomarker for SA-AKI.

Increasingly, acute care surgeons have taken over the management of general surgery consult patients in the hospital, many of whom present with sepsis and/or in septic shock. In this article, we will discuss the intricacies of sepsis management for acute care surgery. The underlying tenants of sepsis management will be outlined with specific attention to the nuances associated with surgical patients. Ultimately, when a surgical problem is identified, this management will culminate with the need for specific source control - the unique aspect when a surgical as opposed to a medical disease process is the cause of sepsis. However, surgeons must also be competent in the other components of sepsis management including antimicrobial therapy and hemodynamic support. This article is designed for the surgeon or for any provider caring for patients with a potential acute care surgical problem, recognizing that different practice settings will vary with regard to resource availability for laboratory tests, invasive monitoring, diagnostics, and surgeon availability.

Delays in diagnosing sepsis may increase morbidity and mortality, but the frequency of delays is poorly understood.

The aim of this study was to estimate the frequency and duration of diagnostic delays for sepsis and potential risk factors for delay.

We conducted a retrospective case-crossover analysis of sepsis cases from 2016 to 2019 using claims from Merative MarketScan. We ascertained the index diagnosis of sepsis and corresponding hospitalization. We analyzed healthcare visits in the 180 days before diagnosis and then compared the observed and expected trends in signs or symptoms of infection, immune or organ dysfunction (e.g., fever, dyspnea) during the 14 days before diagnosis. A bootstrapping approach was used to estimate the frequency and duration of potential diagnostic delays along with possible risk-factors for experiencing a delay.

The number of patients who experienced a potential diagnostic delay, duration of delay, and number of potential missed opportunities.

We identified a total of 649,756 cases of sepsis from 2016 to 2019 meeting inclusion criteria. There was an increase in visits with signs or symptoms of infection, immune or organ dysfunction just before the index diagnosis of sepsis. We estimated that around 16.57% (95% CI, 16.38-16.78) of patients experienced a potential diagnostic delay, with a mean delay duration of 3.21 days (95% CI, 3.13-3.27) and a median of 2 days. Most delays occurred in outpatient settings. Potential diagnostic delays were more frequent among younger age groups and patients who received antibiotics (odds ratio [OR] 2.58 [95% CI, 2.54-2.62]), or treatments for particular symptoms, including opioids (OR 1.43 [95% CI, 1.40-1.46]) and inhalers (OR 1.37 [95% CI, 1.33-1.40]).

There may be a substantial number of potential missed opportunities to diagnose sepsis, especially in outpatient settings. Multiple factors might contribute to delays in diagnosing sepsis including commonly prescribed medications for symptoms.

Sepsis and septic shock are common conditions evaluated and managed in the emergency department (ED), and these conditions are associated with significant morbidity and mortality. There have been several recent updates in the literature, including guidelines, on the evaluation and diagnosis of sepsis and septic shock.

This is the first paper in a two-part series that provides emergency clinicians with evidence-based updates concerning sepsis and septic shock. This first paper focuses on evaluation and diagnosis of sepsis and septic shock.

The evaluation, diagnosis, and management of sepsis have evolved since the first definition in 1991. Current guidelines emphasize rapid diagnosis to improve patient outcomes. However, scoring systems have conflicting data for diagnosis, and sepsis should be considered in any patient with infection and abnormal vital signs, evidence of systemic inflammation (e.g., elevated white blood cell count or C-reactive protein), or evidence of end-organ dysfunction. The clinician should consider septic shock in any patient with infection and hypotension despite volume resuscitation or who require vasopressors to maintain a mean arterial pressure ≥ 65 mmHg. There are a variety of sources of sepsis but the most common include pulmonary, urinary tract, abdomen, and skin/soft tissue. Examples of other less common etiologies include the central nervous system (e.g., meningitis, encephalitis), spine (e.g., spinal epidural abscess, osteomyelitis), cardiac (e.g., endocarditis), and joints (e.g., septic arthritis). Evaluation may include biomarkers such as procalcitonin, C-reactive protein, and lactate, but these should not be used in isolation to exclude sepsis. Imaging is a key component of evaluation and should be based on the suspected source.

There have been several recent updates in the literature including guidelines concerning sepsis and septic shock; an understanding of these updates can assist emergency clinicians and improve the care of these patients.

Sepsis, a life-threatening condition characterized by a systemic inflammatory response, leads to organ dysfunction and high mortality rates. Honeysuckle, a traditional herbal remedy, has shown promise in attenuating organ damage and inhibiting pro-inflammatory factors in sepsis. However, the underlying molecular mechanisms remain unclear. We employed a multi-omics approach to elucidate honeysuckle's potential therapeutic effects in sepsis. RNA sequencing was performed on blood samples from 22 sepsis patients and 10 healthy controls to identify differentially expressed genes. Network pharmacology was utilized to predict effective ingredients and therapeutic targets of honeysuckle in sepsis. Meta-analysis compared gene expression between sepsis survivors and non-survivors. Single-cell RNA sequencing was employed to localize target gene expression at the cellular level. We identified 1328 differentially expressed genes in sepsis, with 221 upregulated and 1107 downregulated. Network analysis revealed 15 genes linked to 12 honeysuckle components. Four genes-DPP4, CD40LG, BCL2, and TP53-emerged as core therapeutic targets, showing decreased expression in non-survivors but upregulation in survivors. Single-cell analysis demonstrated that these genes were primarily expressed in T cells and other immune cells, suggesting their role in regulating immune response and inflammation. This study uses single-cell RNA sequencing and network analysis to identify DPP4, CD40LG, BCL2, and TP53 as key regulatory targets in sepsis, providing insights into disease mechanisms and potential therapeutic interventions. Network pharmacology analysis suggests possible interactions with honeysuckle compounds, though experimental validation is needed.

Sepsis is a life-threating cause in childhood ages. The recognition and treatment early are significant for decreasing mortality. Sepsis has many factors and various biomarkers function in the pathogenesis, the stress indicators oxidants increased and antioxidants decreased. The objective of our study was to investigate the levels of thiol disulfides with and without sepsis in a pediatric intensive care unit (PICU).

A cohort study was conducted between October 2022 and March 2023 at the PICU, comprising 64 with sepsis and 62 children without sepsis. Blood samples from sepsis and the control group were collected and centrifuged. Subsequently, the samples were stored at -80 °C until the day of the experiment. Once the requisite number of patients had been enrolled, the thiol-disulfide values in the collected samples were analysed in accordance with the ELISA kit method.

The research parameters investigated, namely total oxidant status, plasma 8-OHdG, total-native thiol and native/total thiol percent ratio, were found to be considerably elevated in the sepsis group in comparison to the control (p < 0.05). Furthermore, the oxidative stress parameters investigated (total antioxidant status, paraoxonase 1 activity, disulfide, disulfide/native thiol percent ratio, disulfide/total thiol percent ratio) were found to be significantly lower in the sepsis group than in control (p < 0.05).

In our study as well, we detected all antioxidant parameters are low and oxidant parameters are statistically significantly higher in sepsis. Our study posits that thiol-disulfide levels have the potential to serve as a diagnostic tool in conjunction with traditional established biomarkers of inflammation in critically ill children in the PICU who are being treated for sepsis.

Not applicable.

Repurposing electronic health record (EHR) or electronic medical record (EMR) data holds significant promise for evidence-based epidemic intelligence and research. Key challenges include sepsis recognition by physicians and issues with EHR and EMR data. Recent advances in data-driven techniques, alongside initiatives like the Surviving Sepsis Campaign and the Severe Sepsis and Septic Shock Management Bundle (SEP-1), have improved sepsis definition, early detection, subtype characterization, prognostication and personalized treatment. This includes identifying potential biomarkers or digital signatures to enhance diagnosis, guide therapy and optimize clinical management. Machine learning applications play a crucial role in identifying biomarkers and digital signatures associated with sepsis and its sub-phenotypes. Additionally, electronic phenotyping, leveraging EHR and EMR data, has emerged as a valuable tool for evidence-based sepsis identification and management. This review examines methods for identifying sepsis cohorts, focusing on two main approaches: utilizing health administrative data with standardized diagnostic coding via the International Classification of Diseases and integrating clinical data. This overview provides a comprehensive analysis of current cohort identification and electronic phenotyping strategies for sepsis, highlighting their potential applications and challenges. The accuracy of an electronic phenotype or signature is pivotal for precision medicine, enabling a shift from subjective clinical descriptions to data-driven insights.

Predicting and reducing the 28-day mortality in sepsis remains a challenge in this research field.

This study aimed to explore the association between hypertension and 28-day mortality in sepsis.

This study is a cross-sectional approach with Mendelian Randomization (MR). We used GWAS data for hypertension as the exposure and 28-day mortality in sepsis as the outcome and employed the main inverse variance weighted method along with other supplementary MR techniques to verify the causal association between hypertension and 28-day mortality in sepsis. We used sensitivity analyses to ensure the robustness of the research findings. Finally, we utilized clinical data from the Medical Information Mart for Intensive Care-IV database to assess the risk association between hypertension and 28-day mortality in sepsis using difference analysis and multivariate logistic regression analysis.

According to MR, hypertension increased the 28-day mortality in sepsis in both two datasets (FinnGen: odds ratio [OR] = 1.61, 95 % confidence interval [CI] = 1.15-2.26, p = 0.006; Medical Research Council-Integrative Epidemiological Unit: OR = 160, 95 % CI = 2.76-9250, p = 0.014). In our observational study, we included a total of 2012 sepsis patients, of which 60.5 % were male, and the average age was 55.4 years. By applying univariate and multivariate logistic regression models (univariate analysis p = 0.02, multivariate analysis p = 0.02), we observed a significantly increased risk of 28-day mortality due to hypertension in sepsis patients.

This study confirmed the causal relationship between hypertension and the 28-day mortality in sepsis.

Empiric antibiotics active against Pseudomonas aeruginosa are recommended by professional societies for certain infections and are commonly prescribed for hospitalized patients. The effect of this practice on mortality is uncertain.

A systematic literature search was conducted using Embase, Medline, PubMed, Web of Science, Cochrane, Scopus and Google Scholar from earliest entry through 9 October 2023. We included studies of patients hospitalized with P. aeruginosa infections that compared mortality rates depending on whether patients received active empiric antibiotics.

We found 27 studies of 12 522 patients that reported adjusted OR of active empiric antibiotics on mortality. The pooled adjusted OR was 0.40 (95% CI, 0.32-0.50), favouring active empiric antibiotics. In practice, the mortality effect of empiric antibiotics against P. aeruginosa depends on the prevalence of P. aeruginosa and baseline mortality. The estimated absolute mortality benefit was 0.02% (95% CI, 0.02-0.02) for soft tissue infections, 0.12% (95% CI, 0.10-0.13) for urinary tract infections and community-acquired pneumonia, 0.3% (0.25-0.34) for sepsis without shock, 1.1% (95% CI, 0.9-1.4) for septic shock and 2.4% (95% CI, 1.9-2.8) for nosocomial pneumonia.

The mortality effect for empiric antibiotics against P. aeruginosa depends crucially on the prevalence of P. aeruginosa and baseline mortality by type of infection. For soft tissue infections, urinary tract infections and community-acquired pneumonia, the mortality benefit is low. Meaningful benefit of empiric antibiotics against P. aeruginosa is limited to patients with approximately 30% mortality and 5% prevalence of P. aeruginosa, which is largely limited to patients in intensive care settings.

Sepsis remains a significant cause of morbidity and mortality worldwide. Although many more patients are surviving the acute event, a substantial number enters a state of persistent inflammation and immunosuppression, rendering them more vulnerable to infections. Modulating the host immune response has been a focus of sepsis research for the past 50 years, yet novel therapies have been few and far between. Although many septic patients have similar clinical phenotypes, pathways affected by the septic event differ not only between individuals but also within an individual over the course of illness. These differences ultimately impact overall immune function and response to treatment. Defining the immune state, or endotype, of an individual is critical to understanding which patients will respond to a particular therapy. In this review, we highlight current approaches to define the immune endotype and propose that these technologies may be used to "prescreen" individuals to determine which therapies are most likely to be beneficial.

Severe sepsis is cognate with life threatening multi-organ dysfunction. There is a disturbance in endocrine functions with alterations in several hormonal pathways. It has frequently been linked with dysfunction in the hypothalamic pituitary-adrenal axis (HPA). Increased cortisol or cortisolemia is evident throughout the acute phase, along with changes in the hypothalamic pituitary thyroid (HPT) axis, growth hormone-IGF-1 axis, insulin-glucose axis, leptin, catecholamines, renin angiotensin aldosterone axis, ghrelin, glucagon, hypothalamic pituitary gonadal (HGA) axis, and fibroblast growth factor-21. These changes and metabolic alterations constitute the overall response to infection in sepsis. Further research is essential to look into the hormonal changes that occur during sepsis, not only to understand their potential relevance in therapy but also because they may serve as prognostic indicators.

Background: Sepsis is a major cause of patient death in intensive care units (ICUs). Rapid diagnosis of sepsis assists in optimizing treatments and improves outcomes. Several biomarkers are employed to aid in the diagnosis, prognostication, severity grading, and sub-type discrimination of severe septic infections (SSIs), including current diagnostic parameters, hemostatic measures, and specific organ dysfunction markers. Methods: This study involved 129 critically ill adults categorized into three groups: sepsis (Se = 48), pneumonia (Pn = 48), and Se/Pn (33). Concentrations of five plasma markers (IL-6, IL-8, TREM1, uPAR, and presepsin) were compared with 13 well-established measures of SSI in critically ill patients. These measures were heart rate (HR), white blood count (WBC), C-reactive protein (CRP), procalcitonin (PCT), lactate plasma concentrations, and measures of hemostasis status (platelets count (PLT), fibrinogen, prothrombin time (PT), activated partial thromboplastin time (APTT), international normalization ratio (INR) and D-dimer). Plasma bilirubin and creatinine served as indicators of liver and kidney dysfunction, respectively. Results: Promising roles for these biomarkers were found. The best results were for presepsin, which scored 10/13, followed by IL-6 and IL-8 (each scored 7/13), and the worst were for TREM-1 and uPAR (scored 3/13). Presepsin, IL-6, and IL-8 discriminated between the SSI sub-types, whilst only presepsin correlated with bilirubin and creatinine. uPAR was positive for kidney dysfunction, and TREM-1 was the only indicator of artificial ventilation (AV). Conclusions: Presepsin is an important potential biomarker in SSIs. However, further work is needed to define this marker's diagnostic and prognostic cutoff values.

WW domain-containing oxidoreductase ( WWOX ) is a gene associated implicated in both neurologic and inflammatory diseases and is susceptible to environmental stressors. We hypothesize partial loss of Wwox function will result in increased sepsis severity and neuroinflammation.

Wwox WT/P47T mice, generated by CRISPR/Cas9, and Wwox WT/WT mice were treated with intraperitoneal PBS vs LPS (10mg/kg) and euthanized 12 hours post-injection. Open Field Testing (OFT) and Murine Sepsis Severity Scores (MSS) were utilized to measure sickness behavior and sepsis severity, respectively. Brain tissue was analyzed using immunohistochemistry and PCR to measure neuroinflammation and apoptosis.

Wwox WT/P47T LPS mice demonstrated a more significant response to sepsis with an increase in sickness behavior, sepsis severity, gliosis, and apoptosis compared to Wwow WT/WT LPS littermates.

Partial loss of Wwox function increases risk for severe sepsis and neuroinflammation. Given the susceptibility of WWOX to environmental stressors, this may be a target for future therapeutic interventions.

To investigate the potential and evolving trends in fluid management for patients with sepsis, utilizing a bibliometric approach.

Scholarly articles pertaining to fluid therapy for sepsis patients were extracted from the Web of Science (WoS) database as of June 1, 2024. The R software package, "Bibliometrix," was utilized to scrutinize the primary bibliometric attributes and to construct a three-field plot to illustrate the relationships among institutions, nations, and keywords. The VOSviewer tool was employed for author analysis, keyword co-occurrence analysis, and data visualization. Additionally, CiteSpace was used to calculate citation bursts and keywords.

A comprehensive retrieval from the Web of Science (WoS) database yielded a total of 2,569 publications. The majority of these articles were predominantly published by two countries, namely the United States (US) and China. Among the myriad of journals, Critical Care and Journal for Intensive Care Medicine emerged as the most prolific. In terms of institutional contribution, the University of California System stood out as the most productive. Recent analysis of keywords revealed a significant citation burst for terms such as "balanced crystalloids" and "critically ill children".

There is a growing focus on the connection between fluid management and the treatment of sepsis, with research in this area being at an advanced stage.

This study introduces Smart Imitator (SI), a 2-phase reinforcement learning (RL) solution enhancing personalized treatment policies in healthcare, addressing challenges from imperfect clinician data and complex environments.

Smart Imitator's first phase uses adversarial cooperative imitation learning with a novel sample selection schema to categorize clinician policies from optimal to nonoptimal. The second phase creates a parameterized reward function to guide the learning of superior treatment policies through RL. Smart Imitator's effectiveness was validated on 2 datasets: a sepsis dataset with 19 711 patient trajectories and a diabetes dataset with 7234 trajectories.

Extensive quantitative and qualitative experiments showed that SI significantly outperformed state-of-the-art baselines in both datasets. For sepsis, SI reduced estimated mortality rates by 19.6% compared to the best baseline. For diabetes, SI reduced HbA1c-High rates by 12.2%. The learned policies aligned closely with successful clinical decisions and deviated strategically when necessary. These deviations aligned with recent clinical findings, suggesting improved outcomes.

Smart Imitator advances RL applications by addressing challenges such as imperfect data and environmental complexities, demonstrating effectiveness within the tested conditions of sepsis and diabetes. Further validation across diverse conditions and exploration of additional RL algorithms are needed to enhance precision and generalizability.

This study shows potential in advancing personalized healthcare learning from clinician behaviors to improve treatment outcomes. Its methodology offers a robust approach for adaptive, personalized strategies in various complex and uncertain environments.

Septic shock is a common threat, and is the primary cause of death in almost all critical care units. Mortality of septic shock remains exceedingly high. The early use of methylene blue (MB) in different doses as adjunctive to vasopressors has promising results.

This double-blind, randomized, controlled trial comprised 90 patients divided into 3 groups: Group A received a 100 ml 0.9% NaCl placebo over 20 min; Group B received an MB bolus of 1 mg/kg in 100 ml 0.9% NaCl, and Group C received MB bolus of 4 mg/kg in 100 ml 0.9% NaCl during the same period. Groups B and C were given a 0.25 mg/kg/hour infusion of MB for 72 h after the bolus dose. All patients were started on noradrenaline at an infusion rate of 0.1-0.2 µ/kg/min and were adjusted accordingly to maintain MAP ≥ 65 mmHg. Time of vasopressor discontinuation was the primary outcome while total doses of vasopressors, ventilation days, vasopressors free days, total ICU stay, total hospital stay, and mortality rate were the secondary outcomes.

Groups B and C exhibited significantly decreased time to vasopressor termination, and vasopressor-free days at 28 days in comparison to Group A. However, there was no significant difference between Groups B and C. Groups B and C had significantly lower noradrenaline dosages compared to Group A, however, no significant difference between Group B and Group C was found. The difference between the three groups in mortality rate was near statistical significance (p = 0.083). Using the logistic regression model, the 4 mg/kg group was protective against mortality with a hazard ratio of 0.29 (95%CI: 0.09-0.90).

In cancer patients with septic shock, early adjunctive MB delivery reduces the time to a vasopressor stoppage and increases the vasopressor-free days. No significant difference between high and low MB bolus doses, and no significant adverse effects were noted. Compared to placebo, the 4 mg/kg bolus dose shows a survival advantage.

Prospectively registered at clinicaltrials.gov [NCT06005558]. (Date of registration 15/08/2023).

Sepsis is associated with significant mortality. Telehealth may improve the quality of early sepsis care, but the use and impact of telehealth applications for sepsis remain unclear. We aim to describe the telehealth interventions that have been used to facilitate sepsis care, and to summarize the reported effect of telehealth on sepsis outcomes.

We identified articles reporting telehealth use for sepsis using an English-language search of PubMed, CINAHL Plus (EBSCO), Academic Search Ultimate (EBSCO), APA PsycINFO (EBSCO), Public Health (ProQuest), and Web of Science databases with no restrictions on publication date.

Included studies described the use of telehealth as an intervention for treating sepsis. Only comparative effectiveness analyses were included.

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines, two investigators independently selected articles for inclusion and abstracted data. A random-effects subgroup analysis was conducted on patient survival treated with and without telehealth.

A total of 15 studies were included, involving 188,418 patients with sepsis. Thirteen studies used observational study designs, and the most common telehealth applications were provider-to-provider telehealth consultation and intensive care unit telehealth. Clinical and methodological heterogeneity was significantly high. Telehealth use was associated with higher survival, especially in settings with low control group survival. The effect of telehealth on other care processes and outcomes were more varied and likely dependent on hospital-level factors.

Telehealth has been used in diverse applications for sepsis care, and it may improve patient outcomes in certain contexts. Additional interventional trials and cost-based analyses would clarify the causal role of telehealth in improving sepsis outcomes.

The correlation between RAR is linked to negative outcomes in sepsis, but it remains uncertain if RAR is connected to prognosis in patients with sepsis-related NTIS. So we investigated it in this study.

Patients with sepsis-associated NTIS admitted to Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, between March 2013 and April 2017 were included in the study. Participants were divided into two groups according to the optimal threshold value for RAR determined by the receiver operating characteristic curve. Cox proportional hazards regression and graphed with Kaplan-Meier curves examined the relationship between RAR and survival in patients with sepsis-associated NTIS. To account for potential confounding variables, a propensity score matching method was conducted to verify the relationship. Subgroup analysis was performed for different sex, age, comorbidities, infection location and other scores.

A total of 328 patients with sepsis-related NTIS were analyzed in our study. The univariate and multivariate regression analysis indicated that RAR was a significant risk factor for 30-day mortality (HR 1.039(1.012, 1.067), p = 0.004). However, subgroup analysis suggested that RAR may not be an independent risk factor for 30-day mortality in sepsis patients with NTIS combined with tumor or urogenital infection. ROC analysis demonstrated that RAR had a high discriminatory ability for predicting 30-day mortality (AUC 0.751, p < 0.001). Kaplan-Meier curve analysis indicated increased 30-day mortality in the higher RAR group. Following PSM, 108 pairs of patients with matched scores were created. The multivariate regression model demonstrated that RAR was an independent factor associated with 30-day mortality risk (HR 1.049 (1.015, 1.085), p = 0.005). ROC analysis revealed that RAR was a strong discriminator for the 30d-mortality (AUC: 0.695, 95% CI: (0.598-0.792)).

A strong correlation was found between RAR and unfavorable clinical results in sepsis-related NTIS, where a greater RAR was linked to increased 30-day and in-hospital death rates.

Sepsis disproportionately affects marginalized communities. This study aims to evaluate racial and ethnic disparities in failure-to-rescue (FTR) after postoperative sepsis.

This cross-sectional study used data from the American College of Surgeons National Surgical Quality Improvement Program for patients who underwent inpatient noncardiac surgery between 2018 and 2021. Patients were categorized as non-Hispanic White (hereafter, White), non-Hispanic Black (hereafter, Black), Asian, and Hispanic individuals. The association between (1) FTR after sepsis and (2) FTR after septic shock and race and ethnicity was evaluated using multivariable logistic regression. Failure-to-rescue was defined as 30-day mortality among patients who developed postoperative sepsis or postoperative septic shock.

Among the 1388,977 patients (mean [SD] age 60.5 [16]); 783,056 (56.4%) were female, 1017,875 (73%) were White, 171,774 (12%) were Black, 138,457 (10%) were Hispanic, and 60,871 (4%) were Asian. Compared to White individuals, Black (adjusted odds ratio [aOR], 1.29; 95% CI, 1.23-1.35, P < .001) and Hispanic individuals (aOR, 1.15; 95% CI, 1.09-1.21, P < .001) were more likely to develop sepsis; Black individuals were more likely to develop septic shock (aOR, 1.28; 95% CI, 1.21-1.36; P < .001), and Asians were less likely to develop septic shock (aOR 0.84; 95% CI, 0.75-0.93, P = .002). Black individuals experienced lower rates of FTR after sepsis [Black: (aOR, 0.71; 95% CI, 0.54-0.94; P = .017), while Black (aOR, 0.93; 95% CI, 0.80-1.08; P = .35)], Hispanic (aOR, 0.87; 95% CI, 0.72-1.06; P = .16) and Asian Individuals (aOR, 1.06; 95% CI, 0.8-1.37; P = .67) experienced similar rates of FTR after septic shock compared to White individuals.

Black and Hispanic individuals experienced higher rates of postoperative sepsis but did not experience higher rates of failure-to-rescue. Reducing inequity in surgical care should focus on efforts to prevent postoperative sepsis.

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, with great clinical heterogeneity, high morbidity, and high mortality. At the same time, there are many kinds of infection sources, the pathophysiology is very complex, and the pathogenesis has not been fully elucidated. An ideal animal model of sepsis can accurately simulate clinical sepsis and promote the development of sepsis-related pathogenesis, treatment methods, and prognosis. The existing sepsis model still uses the previous Sepsis 2.0 modelling standard, which has some problems, such as many kinds of infection sources, poor repeatability, inability to take into account single-factor studies, and large differences from clinical sepsis patients. To solve these problems, this study established a new animal model of sepsis. The model uses intravenous tail injection of a single bacterial strain, simplifying the complexity of multibacterial infection, and effectively solving the above problems.

Sepsis is a leading cause of morbidity and mortality globally. The lack of specific prognostic markers necessitates tools for early risk identification in patients with suspected infections in emergency department (ED). This study evaluates the prognostic accuracy of various Early Warning Scores (EWS)-MEWS, NEWS, NEWS-2, and qSOFA-for in-hospital mortality, 30-day mortality, and ICU admission, considering the site of infection.

A retrospective analysis was conducted using data from the Acutelines cohort, which included data collected from patients admitted to the University Medical Centre Groningen ED between September 2020 and July 2023. Patients were included if they had an ICD-10 code for infection. EWS were calculated using clinical data within 8 h post-admission. Predictive performance was assessed using AUC-ROC, calibration by the Hosmer-Lemeshow test and calibration curves, and operative characteristics like sensitivity and specificity.

A total of 1661 patients were analyzed, with infections distributed as follows: lower respiratory tract (32.9%), urinary tract (30.7%), abdominal (12.5%), skin and soft tissue (9.5%), and others (8.2%). The overall in-hospital mortality was 6.7%, and ICU admission was 7.1%. The highest AUC-ROC for in-hospital mortality prediction was observed with NEWS and NEWS-2 in abdominal infections (0.86), while the lowest was for qSOFA in skin and soft tissue infections (0.57). Predictive performance varied by infection site.

The study highlights the variability in EWS performance based on infection site, emphasizing the need to consider the source of infection in EWS development for sepsis prognosis. Tailored or hybrid models may enhance predictive accuracy, balancing simplicity and specificity.