Impact of the triglyceride-glucose index on 28-day mortality in non-diabetic critically Ill patients with sepsis: a retrospective cohort analysis.

INTRODUCTION: Sepsis is a life-threatening condition that poses a globally high mortality rate. Identifying risk factors is crucial. Insulin resistance and the TYG index, associated with metabolic disorders, may play a role. This study explores their correlation with mortality in non-diabetic septic patients. METHODS: This retrospective cohort study used data from the MIMIC-IV (version 2.1) database, which includes over 50,000 ICU admissions from 2008 to 2019 at Beth Israel Deaconess Medical Center in Boston. We included adult patients with sepsis who were admitted to the intensive care unit in the study. The primary outcome was to evaluate the ability of TYG to predict death at 28-day of hospital admission in patients with sepsis. RESULTS: The study included 2213 patients with sepsis, among whom 549 (24.8%) died within 28 days of hospital admission. We observed a non-linear association between TYG and the risk of mortality. Compared to the reference group (lower TYG subgroup), the 28-day mortality increased in the higher TYG subgroup, with a fully adjusted hazard ratio of 2.68 (95% CI: 2.14 to 3.36). The area under the curve (AUC) for TYG was 67.7%, higher than for triglycerides alone (AUC = 64.1%), blood glucose (AUC = 62.4%), and GCS (AUC = 63.6%), and comparable to SOFA (AUC = 69.3%). The final subgroup analysis showed no significant interaction between TYG and each subgroup except for the COPD subgroup (interaction P-values: 0.076-0.548). CONCLUSION: In our study, TYG can be used as an independent predictor for all-cause mortality due to sepsis within 28 days of hospitalization.

Predictors of gentamicin therapy failure in neonates with sepsis.

Sepsis is a common disease with high morbidity and mortality among newborns in intensive care units world-wide. Gram-negative bacillary bacteria are the major source of infection in neonates. Gentamicin is the most widely used aminoglycoside antibiotic in empiric therapy against early-onset sepsis. However, therapy failure may result due to various factors. The purpose of this study was to identify predictors of gentamicin therapy failure in neonates with sepsis. This was a prospective cross-sectional study at the Neonatal Intensive Care Unit at Windhoek Central Hospital over a period of 5 months in 2019. Neonates received intravenous gentamicin 5 mg/kg/24 h in combination with either benzylpenicillin 100 000 IU/kg/12 h or ampicillin 50 mg/kg/8 h. Logistic regression modeling was performed to determine the predictors of treatment outcomes. 36% of the 50 neonates were classified as having gentamicin treatment failure. Increasing treatment duration by 1 day resulted in odds of treatment failure increasing from 1.0 to 2.41. Similarly, one unit increase in CRP increases odds of gentamicin treatment failure by 49%. The 1 kg increase in birthweight reduces the log odds of treatment failure by 6.848, resulting in 99.9% decrease in the odds of treatment failure. One unit increase in WBC reduces odds of gentamicin treatment failure by 27%. Estimates of significant predictors of treatment failure were precise, yielding odds ratios that were within 95% confidence interval. This study identified the following as predictors of gentamicin therapy failure in neonates: prolonged duration of treatment, elevated C-reactive protein, low birthweight, and low white blood cell count.

Impact of body mass index on mortality outcomes in intensive care patients with Staphylococcus aureus sepsis: A retrospective analysis.

BACKGROUND: Evidence associating body mass index (BMI) with the prognosis of Staphylococcus aureus sepsis remains scarce. OBJECTIVE: To explore the association between BMI and clinical outcomes in intensive care units patients with Staphylococcus aureus sepsis. METHODS: A retrospective analysis of patients with Staphylococcus aureus sepsis was conducted using the MIMIC-IV database from the Critical Care Medicine Information. Data were collected within the first 24 hours of intensive care units admission. The primary endpoint was 28-day mortality. The association between BMI and 28-day all-cause mortality was assessed using multivariable logistic regression, subgroup analyses, restricted cubic spline curves and Kaplan-Meier survival analysis. RESULTS: The study included 2,295 patients with an average age of 63.5 (16.1) years, 60.2% of whom were male. Multivariate analysis revealed that each 1 kg/m2 increase in BMI was linked to a 2.8% decrease in the risk of 28-day mortality (adjusted OR = 0.972, 95% CI: 0.955-0.990, P = 0.002). Patients in the medium and high BMI categories had significantly lower risks of 28-day mortality compared to those in the low BMI group (OR [95% CI] 0.650 [0.474-0.891]; OR [95% CI] 0.516 [0.378-0.705]; P trend < 0.0001). The RCS model showed a non-linear association between BMI and 28-day mortality (P = 0.014). Kaplan-Meier analysis showed that patients with elevated BMI had lower 28-day mortality (P < 0.0001). Notably, significant interactions between AKI and SOFA with BMI were observed (P<0.05). CONCLUSION: Increased BMI is associated with a reduced risk of 28-day all-cause mortality in patients with Staphylococcus aureus sepsis.

CTLA-4 expression on CD4+ lymphocytes in patients with sepsis-associated immunosuppression and its relationship to mTOR mediated autophagic-lysosomal disorder.

Background: The aim of this study was to clarify the relationship between expression level of CTLA-4 on CD4+ T cells and sepsis-associated immunosuppression (SAI), and to elucidate the possible mechanism of mTOR pathway mediated autophagic-lysosomal disorder in regulating CTLA-4 expression. Methods: We enrolled 63 sepsis patients admitted to our ICU between January 1 and June 30, 2023. Peripheral blood mononuclear cells were isolated from the patients within 24 hours of recruitment. Expression levels of mTOR, P62, LC3II, and CTLA-4 on circulating CD4+ T lymphocytes were quantitated using flow cytometry. The association of these markers and relationship between CTLA-4 expression and the incidence of SAI and 28-day mortality were comprehensively analyzed. Results: Compared with non-immunosuppressed patients with sepsis, patients with SAI had a higher 28-day mortality rate (37.5% vs 13.0%, P=0.039) and higher CTLA-4 mean fluorescence intensity (MFI) on CD4+ T cells (328.7 versus 78.7, P<0.0001). CTLA-4 MFI on CD4+ cells was independently associated with the occurrence of SAI (95% confidence interval: 1.00-1.14, P=0.044). In patients with sepsis and SAI, non-survivors had higher CTLA-4 expression than survivors (sepsis: 427.5 versus 130.6, P=0.002; and SAI: 506.7 versus 225.2, P<0.0001). The sensitivity and specificity of CTLA-4 MFI at predicting 28-day mortality in patients with SAI was 100% and 80% respectively with the cutoff value of 328.7 and the area under the curve of 0.949. The MFI of mTOR, P62, and LC3II on CD4+ T cells were statistically higher in patients with SAI than in non-immunosuppressed patients (267.2 versus 115.9, P<0.0001; 314.8 versus 173.7, P<0.0001; and 184.7 versus 1123.5, P=0.012, respectively); P62 and LC3II were markedly higher in non-survivors than in survivors of sepsis (302.9 versus 208.9, P=0.039; and 244.3 versus 122.8, P<0.0001 respectively). The expression of CTLA-4 statistically correlated with that of LC3II in patients with sepsis, patients with SAI, and patients with SAI who did not survive (correlation coefficient: 0.69, 0.68, and 0.73, respectively, P<0.0001). Conclusions: CTLA-4 overexpression on CD4+ T cells was markedly associated with the incidence of SAI and had great relevance to 28-day mortality. mTOR pathway mediated autophagic-lysosomal disorder showed significant association with CTLA-4 expression.

Interleukin-17 as predictor mortality of septic patients: a systematic review and meta-analysis.

OBJECTIVE: Aim: This study aimed to prove the role of IL-17 on the clinical outcomes of septic patients. PATIENTS AND METHODS: Materials and Methods: This study used a systematic review and meta-analysis design. Data were obtained by searching articles published between January 2001 and June 2022 in Pubmed, Science Direct, Scopus, and Medline databases to evaluate Interleukin-17 on clinical outcomes in septic patients. Only human studies were used in this study. Meta-analysis was undertaken using random effects models. RESULTS: Results: Fourteen published studies were eligible, and four studies were included in the meta-analysis. Meta-analysis of the ratio of means (RoM) IL-17 concentration demonstrated a 5.96-fold higher level in non-survivor septic patients compared with survivors (four studies; n = 194 patients; RoM=5.96; 95% CI, 3.51-10.31; p < 0.00001; I2 = 92%). CONCLUSION: Conclusions: IL-17 levels were significantly elevated in non-survivor and predicted mortality of septic patients.

Prediction of 30-day mortality for ICU patients with Sepsis-3.

BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation. METHODS: In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power. RESULTS: The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980-0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM's gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve. CONCLUSIONS: Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients.

Carbapenem-resistant Enterobacterales sepsis following endoscopic retrograde cholangiopancreatography: risk factors for 30-day all-cause mortality and the development of a nomogram based on a retrospective cohort.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) has become a routine endoscopic procedure that is essential for diagnosing and managing various conditions, including gallstone extraction and the treatment of bile duct and pancreatic tumors. Despite its efficacy, post-ERCP infections - particularly those caused by carbapenem-resistant Enterobacterales (CRE) - present significant risks. These risks highlight the need for accurate predictive models to enhance postprocedural care, reduce the mortality risk associated with post-ERCP CRE sepsis, and improve patient outcomes in the context of increasing antibiotic resistance. OBJECTIVE: This study aimed to examine the risk factors for 30-day mortality in patients with CRE sepsis following ERCP and to develop a nomogram for accurately predicting 30-day mortality risk. METHODS: Data from 195 patients who experienced post-ERCP CRE sepsis between January 2010 and December 2022 were analyzed. Variable selection was optimized via the least absolute shrinkage and selection operator (LASSO) regression model. Multivariate logistic regression analysis was then employed to develop a predictive model, which was evaluated in terms of discrimination, calibration, and clinical utility. Internal validation was achieved through bootstrapping. RESULTS: The nomogram included the following predictors: age > 80 years (hazard ratio [HR] 2.61), intensive care unit (ICU) admission within 90 days prior to ERCP (HR 2.64), hypoproteinemia (HR 4.55), quick Pitt bacteremia score ≥ 2 (HR 2.61), post-ERCP pancreatitis (HR 2.52), inappropriate empirical therapy (HR 3.48), delayed definitive therapy (HR 2.64), and short treatment duration (< 10 days) (HR 5.03). The model demonstrated strong discrimination and calibration. CONCLUSIONS: This study identified significant risk factors associated with 30-day mortality in patients with post-ERCP CRE sepsis and developed a nomogram to accurately predict this risk. This tool enables healthcare practitioners to provide personalized risk assessments and promptly administer appropriate therapies against CRE, thereby reducing mortality rates.

The predictive value of peripheral blood cell mitochondrial gene expression in identifying the prognosis in pediatric sepsis at preschool age.

Background: Sepsis represents a severe manifestation of infection often accompanied by metabolic disorders and mitochondrial dysfunction. Notably, mitochondrial DNA copy number (mtDNA-CN) and the expression of specific mitochondrial genes have emerged as sensitive indicators of mitochondrial function. To investigate the utility of mitochondrial gene expression in peripheral blood cells for distinguishing severe infections and predicting associated outcomes, we conducted a prospective cohort study. Methods: We established a prospective cohort comprising 74 patients with non-sepsis pneumonia and 67 cases of sepsis induced by respiratory infections, aging from 2 to 6 years old. We documented corresponding clinical data and laboratory information and collected blood samples upon initial hospital admission. Peripheral blood cells were promptly isolated, and both total DNA and RNA were extracted. We utilized absolute quantification PCR to assess mtDNA-CN, as well as the expression levels of mt-CO1, mt-ND1, and mt-ATP6. Subsequently, we extended these comparisons to include survivors and non-survivors among patients with sepsis using univariate and multivariate analyses. Receiver operating characteristic (ROC) curves were constructed to assess the diagnostic potential. Results: The mtDNA-CN in peripheral blood cells was significantly lower in the sepsis group. Univariate analysis revealed a significant reduction in the expression of mt-CO1, mt-ND1, and mt-ATP6 in patients with sepsis. However, multivariate analysis did not support the use of mitochondrial function in peripheral blood cells for sepsis diagnosis. In the comparison between pediatric sepsis survivors and non-survivors, univariate analysis indicated a substantial reduction in the expression of mt-CO1, mt-ND1, and mt-ATP6 among non-survivors. Notably, total bilirubin (TB), mt-CO1, mt-ND1, and mt-ATP6 levels were identified as independent risk factors for sepsis-induced mortality. ROC curves were then established for these independent risk factors, revealing areas under the curve (AUCs) of 0.753 for TB (95% CI 0.596-0.910), 0.870 for mt-CO1 (95% CI 0.775-0.965), 0.987 for mt-ND1 (95% CI 0.964-1.000), and 0.877 for mt-ATP6 (95% CI 0.793-0.962). Conclusion: MtDNA-CN and mitochondrial gene expression are closely linked to the severity and clinical outcomes of infectious diseases. Severe infections lead to impaired mitochondrial function in peripheral blood cells. Notably, when compared to other laboratory parameters, the expression levels of mt-CO1, mt-ND1, and mt-ATP6 demonstrate promising potential for assessing the prognosis of pediatric sepsis.

Exploring the Mediating Role of Multiple Organ Dysfunction in Sepsis-Induced Disseminated Intravascular Coagulation and Its Impact on Worsening Prognosis.

Disseminated intravascular coagulation (DIC) poses a high mortality risk, yet its exact impact remains contentious. This study investigates DIC's association with mortality in individuals with sepsis, emphasizing multiple organ function. Using data from the Peking University People's Hospital Investigation on Sepsis-Induced Coagulopathy database, we categorized patients into DIC and non-DIC groups based on DIC scores within 24 h of ICU admission (< 5 cutoff). ICU mortality was the main outcome. Initial data comparison preceded logistic regression analysis of mortality factors post-propensity score matching (PSM). Employing mediation analysis estimated direct and indirect associations. Of 549 participants, 131 were in the DIC group, with the remaining 418 in the non-DIC group. Following baseline characteristic presentation, PSM was conducted, revealing significantly higher nonplatelet sequential organ failure assessment (nonplt-SOFA) scores (6.3 ± 2.7 vs 5.0 ± 2.5, P < 0.001) and in-hospital mortality rates (47.3% vs 29.5%, P = 0.003) in the DIC group. A significant correlation between DIC and in-hospital mortality persisted (OR 2.15, 95% CI 1.29-3.59, P = 0.003), with nonplt-SOFA scores (OR 1.16, 95% CI 1.05-1.28, P = 0.004) and hemorrhage (OR 2.33, 95% CI 1.08-5.03, P = 0.032) as predictors. The overall effect size was 0.1786 (95% CI 0.0542-0.2886), comprising a direct effect size of 0.1423 (95% CI 0.0153-0.2551) and an indirect effect size of 0.0363 (95% CI 0.0034-0.0739), with approximately 20.3% of effects mediated. These findings underscore DIC's association with increased mortality risk in patients with sepsis, urging anticoagulation focus over bleeding management, with organ dysfunction assessment recommended for anticoagulant treatment efficacy.

Risk and mediation analyses of hemoglobin glycation index and survival prognosis in patients with sepsis.

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤ - 0.50%), Q2 (- 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan-Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89-3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29-1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45-2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08-1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

Predictive model of risk factors for 28-day mortality in patients with sepsis or sepsis-associated delirium based on the MIMIC-IV database.

Research on the severity and prognosis of sepsis with or without progressive delirium is relatively insufficient. We constructed a prediction model of the risk factors for 28-day mortality in patients who developed sepsis or sepsis-associated delirium. The modeling group of patients diagnosed with Sepsis-3 and patients with progressive delirium of related indicators were selected from the MIMIC-IV database. Relevant independent risk factors were determined and integrated into the prediction model. Receiver operating characteristic (ROC) curves and the Hosmer-Lemeshow (HL) test were used to evaluate the prediction accuracy and goodness-of-fit of the model. Relevant indicators of patients with sepsis or progressive delirium admitted to the intensive care unit (ICU) of a 3A hospital in Xinjiang were collected and included in the verification group for comparative analysis and clinical validation of the prediction model. The total length of stay in the ICU, hemoglobin levels, albumin levels, activated partial thrombin time, and total bilirubin level were the five independent risk factors in constructing a prediction model. The area under the ROC curve of the predictive model (0.904) and the HL test result (χ2 = 8.518) indicate a good fit. This model is valuable for clinical diagnosis and treatment and auxiliary clinical decision-making.

High expression of L-GILZ transcript variant 1 (GILZ TV 1) is associated with increased 30-day sepsis mortality, and a high expression ratio possibly contraindicates hydrocortisone administration.

BACKGROUND: Sepsis presents a challenge due to its complex immune responses, where balance between inflammation and anti-inflammation is critical for survival. Glucocorticoid-induced leucine zipper (GILZ) is key protein in achieving this balance, suppressing inflammation and mediating glucocorticoid response. This study aims to investigate GILZ transcript variants in sepsis patients and explore their potential for patient stratification and optimizing glucocorticoid therapy. METHODS: Sepsis patients meeting the criteria outlined in Sepsis-3 were enrolled, and RNA was isolated from whole blood samples. Quantitative mRNA expression of GILZ transcript variants in both sepsis patient samples (n = 121) and the monocytic U937 cell line (n = 3), treated with hydrocortisone and lipopolysaccharides, was assessed using quantitative PCR (qPCR). RESULTS: Elevated expression of GILZ transcript variant 1 (GILZ TV 1) serves as a marker for heightened 30-day mortality in septic patients. Increased levels of GILZ TV 1 within the initial day of sepsis onset are associated with a 2.2-[95% CI 1.2-4.3] fold rise in mortality, escalating to an 8.5-[95% CI 2.0-36.4] fold increase by day eight. GILZ TV1 expression is enhanced by glucocorticoids in cell culture but remains unaffected by inflammatory stimuli such as LPS. In septic patients, GILZ TV 1 expression increases over the course of sepsis and in response to hydrocortisone treatment. Furthermore, a high expression ratio of transcript variant 1 relative to all GILZ mRNA TVs correlates with a 2.3-fold higher mortality rate in patients receiving hydrocortisone treatment. CONCLUSION: High expression of GILZ TV 1 is associated with a higher 30-day sepsis mortality rate. Moreover, a high expression ratio of GILZ TV 1 relative to all GILZ transcript variants is a parameter for identifying patient subgroups in which hydrocortisone may be contraindicated.

Prediction of sepsis mortality in ICU patients using machine learning methods.

PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have utilized machine learning for prognosis, but have limitations in feature sets and model interpretability. AIM: This study aims to develop a machine learning model that enhances prediction accuracy for sepsis outcomes using a reduced set of features, thereby addressing the limitations of previous studies and enhancing model interpretability. METHODS: This study analyzes intensive care patient outcomes using the MIMIC-IV database, focusing on adult sepsis cases. Employing the latest data extraction tools, such as Google BigQuery, and following stringent selection criteria, we selected 38 features in this study. This selection is also informed by a comprehensive literature review and clinical expertise. Data preprocessing included handling missing values, regrouping categorical variables, and using the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data. We evaluated several machine learning models: Decision Trees, Gradient Boosting, XGBoost, LightGBM, Multilayer Perceptrons (MLP), Support Vector Machines (SVM), and Random Forest. The Sequential Halving and Classification (SHAC) algorithm was used for hyperparameter tuning, and both train-test split and cross-validation methodologies were employed for performance and computational efficiency. RESULTS: The Random Forest model was the most effective, achieving an area under the receiver operating characteristic curve (AUROC) of 0.94 with a confidence interval of ±0.01. This significantly outperformed other models and set a new benchmark in the literature. The model also provided detailed insights into the importance of various clinical features, with the Sequential Organ Failure Assessment (SOFA) score and average urine output being highly predictive. SHAP (Shapley Additive Explanations) analysis further enhanced the model's interpretability, offering a clearer understanding of feature impacts. CONCLUSION: This study demonstrates significant improvements in predicting sepsis outcomes using a Random Forest model, supported by advanced machine learning techniques and thorough data preprocessing. Our approach provided detailed insights into the key clinical features impacting sepsis mortality, making the model both highly accurate and interpretable. By enhancing the model's practical utility in clinical settings, we offer a valuable tool for healthcare professionals to make data-driven decisions, ultimately aiming to minimize sepsis-induced fatalities.

An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality.

Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death. Physicians face challenges in diagnosing and treating sepsis due to its complex pathogenesis. This work aims to provide an overview of the recent studies that propose explainable AI models in the prediction of sepsis onset and sepsis mortality using intensive care data. The general findings showed that explainable AI can provide the most significant features guiding the decision-making process of the model. Future research will investigate explainability through argumentation theory using intensive care data focused on sepsis patients.

Characterization of Histophilus somni sialic acid uptake mutant (ΔnanP-ΔnanU) using a mouse septicemia and mortality model.

Histophilus somni is an important pathogen of the bovine respiratory disease complex, yet the mechanisms underlying its virulence remain poorly understood. It is known that H. somni can incorporate sialic acid into lipooligosaccharide (LOS), and sialylated H. somni is more resistant to phagocytosis and complement-mediated killing by serum compared to non-sialylated bacteria in vitro. However, the virulence of non-sialylated H. somni has not been evaluated in vivo using an animal model. In this study, we investigated the contribution of sialic acid to virulence by constructing an H. somni sialic acid uptake mutant (ΔnanP-ΔnanU) and comparing the parent and mutant strains in a mouse septicemia and mortality model. Intraperitoneal challenge of mice with wildtype H. somni (1 × 108 colony forming units/mouse, CFU) was lethal to all animals. Mice challenged with three different doses (1, 2, or 5 × 108 CFU/mouse) of an H. somni ΔnanP-ΔnanU sialic acid uptake mutant exhibited survival rates of 90 %, 60 %, and 0 % respectively. High-performance anion exchange chromatography analyses revealed that LOS prepared from both parent and the ΔnanP-ΔnanU mutant strains of H. somni were sialylated. These findings suggest the presence of de novo sialic acid synthesis pathway, although the genes associated with de novo sialic acid synthesis (neuB and neuC) were not identified by genomic analysis. The lower attenuation in mice is most likely attributed to the sialylated LOS of H. somni nanPU mutant.

Comparison of Three Different Disseminated Intravascular Coagulation (DIC) Criteria and Diagnostic and Prognostic Value of Antithrombin Investigation in Patients with Confirmed Sepsis-Induced Coagulopathy (SIC).

A new scoring system termed sepsis-induced coagulopathy (SIC) has been proposed to diagnose early sepsis-induced disseminated intravascular coagulation (DIC). This study performed DIC-related analyses in patients with confirmed SIC. Data from the intensive care unit (ICU) departments of the three hospitals between 2020 and 2022 were retrospectively analyzed. Finally, 125 patients with confirmed SIC were enrolled in the study. The diagnostic value of three widely used DIC criteria was assessed in patients with newly diagnosed SIC. In addition, the diagnostic and prognostic value of antithrombin (AT) was analyzed in patients with SIC. The Japanese Association for Acute Medicine DIC criteria (JAAM) exhibited the highest DIC diagnostic rate, while the mortality risk of SIC patients demonstrated a proportional increase with higher International Society on Thrombosis and Haemostasis (ISTH) and Chinese DIC scoring system (CDSS) scores. Low AT activity (<70%) in septic patients upon SIC diagnosis predicted a very high 28-day mortality rate, almost twice as high as in the normal AT activity (≥70%) group. A decreasing tendency in AT activity after clinical interventions was correlated with increased mortality. The area under the ROC curve (AU-ROC) of AT in DIC diagnosis was statistically significant when CDSS and ISTH were used as diagnostic criteria, but not JAAM. Each of the three DIC diagnostic criteria showed diagnostic and prognostic advantages for SIC. AT could be an independent prognostic indicator for SIC but demonstrated a relatively limited DIC diagnostic value. Adding AT to the SIC scoring system may increase its prognostic power.

The role of serum albumin in critical illness, predicting poor outcomes, and exploring the therapeutic potential of albumin supplementation.

OBJECTIVE: Serum albumin (ALB) plays a vital role in maintaining oncotic pressure and contributing to hemodynamic stability, with low levels associated with adverse outcomes in critically ill patients. This study aimed to assess the association between serum ALB concentrations and poor outcomes and the possible benefits of ALB supplementation. METHODS: A retrospective study involving 300 intensive care unit (ICU) patients. Albumin levels were recorded upon admission and throughout the stay, and patients were categorized based on a cutoff of 2.49 g/dl. The associations between low ALB levels and mortality were assessed using regression analysis. Additionally, it investigated the association of albumin supplementation with patient outcomes and mortality in specific patient populations. RESULTS: The mean age was 54.9 years, with 68% having sepsis. Patients with low baseline ALB concentrations exhibited higher overall mortality (71% vs. 52%) and 28-day mortality (50% vs. 39%). Adjusted analyses confirmed associations with mortality. Albumin supplementation was administered to 53% of the patients. Its use demonstrated potential benefits, particularly in reducing mortality, when given to specific groups, such as sepsis and hypoalbuminemia patients. DISCUSSION: The study confirms that low serum albumin levels are strongly associated with higher mortality rates in ICU patients. Albumin supplementation showed potential benefits, particularly in patients with sepsis and low albumin levels. Further analyses explored the dosage-response relationship and identified potential groups that would benefit from albumin supplementation. CONCLUSION: Albumin can play a major role in predicting mortality in critically ill patients. Moreover, ALB supplementation may improve survival, especially in resource-limited settings. Future research should refine protocols through clinical trials for optimal survival in critically ill patients.

Analysis of early death in critically ill patients with acute promyelocytic leukaemia in the HICU.

This study was conducted to identify the characteristics and risk factors for early death in critically ill acute promyelocytic leukaemia (APL) patients in the Hemato-oncology ICU (HICU). A total of 44 APL patients from 2017 to 2023 were included. The mortality among APL patients in the HICU was high (27/44, 61.36%). Compared with patients who survived, nonsurvivors had a longer prothrombin time (P = 0.002), lower fibrinogen (P = 0.022), higher white blood cell count (P = 0.004) and higher creatinine (P = 0.037) on hosipital admission. Severe bleeding was the most frequent complication (34 cases, 77.27%), which occurred either preinduction or on Day 5 (IQR 3-7.5 days) of induction. Cerebral bleeding associated with consciousness disturbance was the main reason for HICU admission (18 cases, 40.9%). The leading cause of death was fatal haemorrhage (18/34, 52.94%), which occurred either preinduction or on Day 4 (IQR 3-7 days) of induction. Another common cause of death was sepsis (8/18, 44.44%), which occurred on Day 12 (IQR 9.5-24.75 days) during induction. In conclusion, the main cause of death in APL patients treated in the HICU was primary being attributed to fatal bleeding, followed by sepsis.

Triglyceride-glucose index and clinical outcomes in sepsis: A retrospective cohort study of MIMIC-IV.

Although accumulating researches were done for investigating the relationship between triglyceride-glucose index (TyG index) and different diseases, none of the researches have been made in sepsis yet. In this study, we aimed to explore the relationship between TyG index and clinical outcomes in sepsis based on a large critical care public database. Sepsis patients in Medical Information Mart for Intensive Care IV (MIMIC-IV) Database were included. The exposure was TyG index, which was calculated by the equation: ln (TG (mg/dL) × FBG (mg/dL)/2). The outcomes were in-hospital mortality and 1-year mortality. The relationship between TyG index and outcomes was performed by Cox regression analysis. Smooth fitting curves were constructed by using generalized additive model. Kaplan-Meier analyses for cumulative hazard of 1-year mortality in different groups were done. 1103 sepsis patients were included with a median TyG index of 9.78. The mortalities of in-hospital and 1-year were 37.53% (n = 414) and 42.25% (n = 466), respectively. After adjusting confounders, there was a significantly negative relationship between TyG index and mortalities of in-hospital and 1-year. With the per unit increment in TyG index, the risk of in-hospital and 1-year mortality both decreased by 21% (HR = 0.79, 95% CI: 0.66-0.94, p = 0.0086 and HR = 0.79, 95% CI: 0.66-0.94, p = 0.0080, respectively). A negative relationship between TyG index and clinical outcomes in sepsis was found.

Effects of dynamic versus static parameter-guided fluid resuscitation in patients with sepsis: A randomized controlled trial.

Background: Fluid resuscitation is an essential component for sepsis treatment. Although several studies demonstrated that dynamic variables were more accurate than static variables for prediction of fluid responsiveness, fluid resuscitation guidance by dynamic variables is not standard for treatment. The objectives were to determine the effects of dynamic inferior vena cava (IVC)-guided versus (vs.) static central venous pressure (CVP)-guided fluid resuscitation in septic patients on mortality; and others, i.e., resuscitation targets, shock duration, fluid and vasopressor amount, invasive respiratory support, length of stay and adverse events. Methods: A single-blind randomized controlled trial was conducted at Thammasat University Hospital between August 2016 and April 2020. Septic patients were stratified by acute physiologic and chronic health evaluation II (APACHE II) <25 or ≥25 and randomized by blocks of 2 and 4 to fluid resuscitation guidance by dynamic IVC or static CVP. Results: Of 124 patients enrolled, 62 were randomized to each group, and one of each was excluded from mortality analysis. Baseline characteristics were comparable. The 30-day mortality rates between dynamic IVC vs. static CVP groups were not different (34.4% vs. 45.9%, p=0.196). Relative risk for 30-day mortality of dynamic IVC group was 0.8 (95%CI=0.5-1.2, p=0.201). Different outcomes were median (interquartile range) of shock duration (0.8 (0.4-1.6) vs. 1.5 (1.1-3.1) days, p=0.001) and norepinephrine (NE) dose (6.8 (3.9-17.8) vs. 16.1 (7.6-53.6) milligrams, p=0.008 and 0.1 (0.1-0.3) vs. 0.3 (0.1-0.8) milligram⋅kilogram -1, p=0.017). Others were not different. Conclusions: Dynamic IVC-guided fluid resuscitation does not affect mortality of septic patients. However, this may reduce shock duration and NE dose, compared with static CVP guidance.