UNASSIGNED: As one of the serious complications of sepsis in children, sepsis-associated encephalopathy (SAE) is associated with significantly poor prognosis and increased mortality. However, predictors of outcomes for pediatric SAE patients have yet to be identified. The aim of this study was to develop nomograms to predict the 14-day and 90-day mortality of children with SAE, providing early warning to take effective measures to improve prognosis and reduce mortality.
UNASSIGNED: In this multicenter, retrospective study, we screened 291 patients with SAE admitted to the PICU between January 2017 and September 2022 in Shandong Province. A least absolute shrinkage and selector operation (LASSO) method was used to identify the optimal prognostic factors predicting the outcomes in pediatric patients with SAE. Then, multivariable logistic regression analysis was performed based on these variables, and two nomograms were built for visualization. We used the area under the curve (AUC), calibration curves and decision curves to test the accuracy and discrimination of the nomograms in predicting outcomes.
UNASSIGNED: There were 129 patients with SAE in the training cohort, and there were 103 and 59 patients in the two independent validation cohorts, respectively. Vasopressor use, procalcitonin (PCT), lactate and pediatric critical illness score (PCIS) were independent predictive factors for 14-day mortality, and vasopressor use, PCT, lactate, PCIS and albumin were independent predictive factors for 90-day mortality. Based on the variables, we generated two nomograms for the early identification of 14-day mortality (AUC 0.853, 95% CI 0.787-0.919, sensitivity 72.4%, specificity 84.5%) and 90-day mortality (AUC 0.857, 95% CI 0.792-0.923, sensitivity 72.3%, specificity 90.6%), respectively. The calibration plots for nomograms showed excellent agreement of mortality probabilities between the observed and predicted values in both training and validation cohorts. Decision curve analyses (DCA) indicated that nomograms conferred high clinical net benefit.
UNASSIGNED: The nomograms in this study revealed optimal prognostic factors for the mortality of pediatric patients with SAE, and individualized quantitative risk evaluation by the models would be practical for treatment management.

BACKGROUND: Sepsis-associated encephalopathy (SAE) impairs hippocampal microglial efferocytosis, causing cognitive deficits. Previous research found that milk fat globule epidermal growth factor 8 protein (MFGE8) stimulates efferocytosis, reducing hippocampal inflammation in SAE rats. In this study, we explore MFGE8\'s role in alleviating cognitive impairment and its impact on neural activity using functional magnetic resonance imaging (fMRI).
METHODS: Sixty male Sprague Dawley rats were divided into four groups: Sham, cecal ligation and puncture (CLP), CLP+MFGE8, and CLP+MFGE8+CGT (Cilengitide). After CLP, CLP+MFGE8 rats received intracerebroventricular MFGE8 (3.3 µg), while CLP+MFGE8+CGT rats received intraperitoneal Cilengitide (10 mg/kg). We assessed cognitive function with the Morris water maze and open field test over five days. Eight days post-surgery, rats underwent T2-weighted magnetic resonance imaging (MRI) and resting state (rs)-fMRI scans. Brain tissues were collected for western blot, hematoxylin-eosin (HE) staining, and immunofluorescence. Statistical analysis employed one-way analysis of variance (ANOVA) followed by Tukey\'s post-test for multiple comparisons.
RESULTS: MFGE8 improved neurobehavioral performance in open field task (OFT) and morris water maze (MWM) tests. fMRI indicated a significant reduction in abnormal neural activity in the right hippocampal CA1, CA3, and dentate gyrus of SAE rats following MFGE8 treatment. Voxel-based morphometry (VBM) analysis revealed decreased high-signal areas in the hippocampus, along with reduced hippocampal volume due to alleviated neural edema. Western blot analysis demonstrated that MFGE8 enhanced ras-related C3 botulinum toxin substrate 1 (Rac1) and microtubule-associated protein 1A/1B-light chain 3 (LC3) expression in the rat hippocampus, while CGT reduced these protein levels. Behavioral experiments and fMRI results confirmed that CGT reversed the cognitive effects of MFGE8 by inhibiting microglial αVβ3/αVβ5 integrin receptors.
CONCLUSIONS: Our findings show that MFGE8 reduced amplitude of low-frequency fluctuations (ALFF) values in the right hippocampal CA1, CA3, and the dentate gyrus, mitigating abnormal neural activity and decreasing hippocampal volume. This led to an improvement in cognitive dysfunction in SAE rats. These results suggest that MFGE8 enhances microglial efferocytosis by activating αVβ3 and αVβ5 integrin receptors on microglial surfaces, ultimately improving cognitive function in SAE rats.

Sepsis-associated encephalopathy (SAE) is defined as a dysfunction of the central nervous system experienced during sepsis with variable clinical features. The study aims to identify the prognostic role of urinary ketone bodies in relation to clinical outcomes in patients with SAE. The Medical Information Mart for Intensive Care III (MIMIC-III) database was used to conduct a retrospective cohort study. We recruited 427 patients with SAE admitted to the intensive care unit (ICU) from the MIMIC-III database. Patients with SAE were divided into a survival group (380 patients) and a non-survival group (47 patients). We used the Wilcoxon signed-rank test and the multivariate logistic regression analysis to analyze the relationship between the level of urinary ketone bodies and the clinical prognosis in patients with SAE. The primary outcome was the relationship between urinary ketone body levels and 28-day mortality of SAE. The secondary outcomes were the relationship between urinary ketone body levels and length of ICU stays, Simplified Acute Physiology Score II, Sequential Organ Failure Assessment (SOFA), Glasgow Coma Scale, mechanical ventilation, renal replacement therapy, and the use of vasopressors. The 28-day mortality of patients with SAE was 11.0%. Urinary ketone body levels were not significantly associated with the 28-day mortality of patients with SAE. Urinary ketone body levels were associated with SOFA score and the use of vasopressors in patients with SAE. The SOFA score was an independent risk factor for the 28-day mortality in patients with SAE. Urinary ketone body levels were significantly associated with SOFA score and the use of vasopressors in patients with SAE. Furthermore, the SOFA score can predict the prognosis of short-term outcomes of patients with SAE. Therefore, we should closely monitor the changes of urinary ketone bodies and SOFA score and intervene in time.

Accurate identification of delirium in sepsis patients is crucial for guiding clinical diagnosis and treatment. However, there are no accurate biomarkers and indicators at present. We aimed to identify which combinations of cognitive impairment-related biomarkers and other easily accessible assessments best predict delirium in sepsis patients.
One hundred and one sepsis patients were enrolled in a prospective study cohort. S100B, NSE, and BNIP3 L biomarkers were detected in plasma and cerebrospinal fluid and patients\' optic nerve sheath diameter (ONSD). The optimal biomarkers identified by Logistic regression are combined with other factors such as ONSD to filter out the perfect model to predict delirium in sepsis patients through Logistic regression, Naïve Bayes, decision tree, and neural network models.
Among all biomarkers, compared with BNIP3 L (AUC = .706, 95% CI = .597-.815) and NSE (AUC = .711, 95% CI = .609-.813) in cerebrospinal fluid, plasma S100B (AUC = .729, 95% CI = .626-.832) had the best discrimination performance for delirium in sepsis patients. Logistic regression analysis showed that the combination of cerebrospinal fluid BNIP3 L with plasma S100B, ONSD, neutrophils, and age provided the best discrimination to cognitive impairment in sepsis patients (accuracy = .901, specificity = .923, sensitivity = .911), which was better than Naïve Bayes, decision tree, and neural network models. Neutrophils, ONSD, and cerebrospinal fluid BNIP3 L were consistently the major contributors in a few models.
The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

BACKGROUND: Patients with sepsis-associated encephalopathy (SAE) have higher mortality rates and longer ICU stays. Predictors of SAE are yet to be identified. We aimed to establish an effective and simple-to-use nomogram for the individual prediction of SAE in patients with sepsis admitted to pediatric intensive care unit (PICU) in order to prevent early onset of SAE.
METHODS: In this retrospective multicenter study, we screened 790 patients with sepsis admitted to the PICU of three hospitals in Shandong, China. Least absolute shrinkage and selection operator regression was used for variable selection and regularization in the training cohort. The selected variables were used to construct a nomogram to predict the risk of SAE in patients with sepsis in the PICU. The nomogram performance was assessed using discrimination and calibration.
RESULTS: From January 2017 to May 2022, 613 patients with sepsis from three centers were eligible for inclusion in the final study. The training cohort consisted of 251 patients, and the two independent validation cohorts consisted of 193 and 169 patients. Overall, 237 (38.7%) patients developed SAE. The morbidity of SAE in patients with sepsis is associated with the respiratory rate, blood urea nitrogen, activated partial thromboplastin time, arterial partial pressure of carbon dioxide, and pediatric critical illness score. We generated a nomogram for the early identification of SAE in the training cohort (area under curve [AUC] 0.82, 95% confidence interval [CI] 0.76-0.88, sensitivity 65.6%, specificity 88.8%) and validation cohort (validation cohort 1: AUC 0.80, 95% CI 0.74-0.86, sensitivity 75.0%, specificity 74.3%; validation cohort 2: AUC 0.81, 95% CI 0.73-0.88, sensitivity 69.1%, specificity 83.3%). Calibration plots for the nomogram showed excellent agreement between SAE probabilities of the observed and predicted values. Decision curve analysis indicated that the nomogram conferred a high net clinical benefit.
CONCLUSIONS: The novel nomogram and online calculator showed performance in predicting the morbidity of SAE in patients with sepsis admitted to the PICU, thereby potentially assisting clinicians in the early detection and intervention of SAE.

UNASSIGNED: Sepsis-associated encephalopathy (SAE) is prevalent in intensive care unit (ICU) environments but lacks established treatment protocols, necessitating prompt diagnostic methods for early intervention. Traditional symptom-based diagnostics are non-specific and confounded by sedatives, while emerging biomarkers like neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B) have limited specificity. Transcranial Doppler (TCD) indicators, although is particularly relevant for SAE, requires high operator expertise, limiting its clinical utility.
UNASSIGNED: This pilot study aims to utilize cerebral circulation time (CCT) assessed via contrast-enhanced ultrasound (CEUS) as an innovative approach to investigate the accuracy of SAE prediction. Further, these CCT measurements are integrated into a nomogram to optimize the predictive performance.
UNASSIGNED: This study employed a prospective, observational design, enrolling 67 ICU patients diagnosed with sepsis within the initial 24 h. Receiver operating characteristic (ROC) curve analyses were conducted to assess the predictive accuracy of potential markers including NSE, S100B, TCD parameters, and CCT for SAE. A nomogram was constructed via multivariate Logistic Regression to further explore the combined predictive potential of these variables. The model\'s predictive performance was evaluated through discrimination, calibration, and decision curve analysis (DCA).
UNASSIGNED: SAE manifested at a median of 2 days post-admission in 32 of 67 patients (47.8%), with the remaining 35 sepsis patients constituting the non-SAE group. ROC curves revealed substantial predictive utility for CCT, pulsatility index (PI), and S100B, with CCT emerging as the most efficacious predictor, evidenced by an area under the curve (AUC) of 0.846. Multivariate Logistic Regression identified these markers as independent predictors for SAE, leading to the construction of a nomogram with excellent discrimination, substantiated by an AUC of 0.924 through bootstrap resampling. The model exhibited satisfactory concordance between observed and predicted probabilities, and DCA confirmed its clinical utility for the prompt identification of SAE.
UNASSIGNED: This study highlighted the enhanced predictive value of CCT in SAE detection within ICU settings. A novel nomogram incorporating CCT, PI, and S100B demonstrated robust discrimination, calibration, and clinical utility, solidifying it as a valuable tool for early SAE intervention.

Patients with sepsis-associated delirium (SAD) show severe neurological impairment, often require an intensive care unit (ICU) stay and have a high risk of mortality. Hence, useful biomarkers for early detection of SAD are urgently needed. Extracellular vesicles (EVs) and their cargo are known to maintain normal physiology but also have been linked to numerous disease states. Here, we sought to identify differentially expressed proteins in plasma EVs from SAD patients as potential biomarkers for SAD. Plasma EVs from 11 SAD patients and 11 age-matched septic patients without delirium (non-SAD) were isolated by differential centrifugation, characterized by nanoparticle tracking analysis, transmission electron microscopy and Western blot analysis. Differential EV protein expression was determined by mass spectrometry and the resulting proteomes were characterized by Gene Ontology term and between-group statistics. As preliminary results because of the small group size, five distinct proteins showed significantly different expression pattern between SAD and non-SAD patients (p ≤ 0.05). In SAD patients, upregulated proteins included paraoxonase-1 (PON1), thrombospondin 1 (THBS1), and full fibrinogen gamma chain (FGG), whereas downregulated proteins comprised immunoglobulin (IgHV3) and complement subcomponent (C1QC). Thus, plasma EVs of SAD patients show significant changes in the expression of distinct proteins involved in immune system regulation and blood coagulation as well as in lipid metabolism in this pilot study. They might be a potential indicator for to the pathogenesis of SAD and thus warrant further examination as potential biomarkers, but further research is needed to expand on these findings in longitudinal study designs with larger samples and comprehensive polymodal data collection.

OBJECTIVE: Cerebral edema is common in patients with sepsis-associated encephalopathy (SAE) and is a major cause of elevated intracranial pressure (ICP); however, the relationship between elevated ICP and SAE is unclear. The aim of this study was to investigate the association between optic nerve sheath diameter (ONSD), a surrogate of ICP, and the incidence of SAE.
METHODS: A prospective observational study was performed in a medical-surgical adult intensive care unit (ICU). All patients in the ICU who were consecutively diagnosed with sepsis during the study period were evaluated for eligibility. Ultrasound measurements of ONSD were performed within 6 h of enrollment and every two days thereafter until the patient developed SAE. Clinical and blood test data were collected throughout this period. Patients underwent a daily conscious and cognitive assessment. SAE was diagnosed as delirium or Glasgow Coma Scale (GCS) <15 points. Multivariate modified Poisson regression analysis was performed to identify risk factors for SAE.
RESULTS: A total of 123 patients with sepsis were included in the analysis. 58 patients (47.2%) developed SAE. The levels of ONSD0 (the first measured value) and ONSDmax (the maximum measured value) in the SAE group were significantly higher than those in the non-SAE group (5.23±0.52 mm vs. 5.85±0.54 mm for ONSD0 and 5.41±0.46 mm vs. 6.09±0.58 mm for ONSDmax, respectively; all p-values <0.001). The area under the curves (AUCs) for the ONSD0 and ONSDmax values in predicting SAE were 0.801 (95%CI=0.723-0.880, p<0.001) and 0.829 (95%CI=0.754-0.903, p<0.001), respectively. A higher ONSD0 level was significantly associated with an increased risk of SAE (adjusted risk ratio 3.241; 95%CI=1.686-6.230, p<0.001).
CONCLUSIONS: The levels of ONSD correlate with risk of SAE, indicating that increased ICP level is an independent risk factor for the development of SAE. Dynamic monitoring of ONSD/ICP has a high predictive value for SAE. Measures to prevent increases in ICP are helpful to reduce the incidence of SAE in sepsis patients.

The relationship between the levels of the first 24-h PaCO2 and the prognosis of sepsis-associated encephalopathy (SAE) remains unclear, and the first 24-h optimal target for PaCO2 is currently inconclusive. This study was performed to investigate the correlation between PaCO2 and all-cause mortality for SAE patients, establish a reference range of the initial 24-hour PaCO2 for clinicians in critical care, and explain the possible pathophysiological mechanisms of abnormal PaCO2 levels as a higher mortality risk factor for SAE.
The baseline information and clinical data of patients were extracted from the fourth edition Medical Information Mart for Intensive Care database (MIMIC-IV 2.0). Multivariate logistic regressions were performed to assess the relationship between PaCO2 and all-cause mortality of SAE. Additionally, restricted cubic splines, Kaplan-Meier Survival analyses, propensity score matching (PSM) analyses, and subgroup analyses were conducted.
A total of 5471 patients were included in our cohort. In the original and matched cohort, multivariate logistic regression analysis showed that normocapnia and mild hypercapnia may be associated with a more favorable prognosis of SAE patients, and survival analysis supported the findings. In addition, a U-shaped association emerged when examining the initial 24-hour PaCO2 levels in relation to 30-day, 60-day, and 90-day mortality using restricted cubic splines, with an average cut-off value of 36.3mmHg (P for nonlinearity<0.05). Below the cut-off value, higher PaCO2 was associated with lower all-cause mortality, while above the cut-off value, higher PaCO2 was associated with higher all-cause mortality. Subsequent subgroup analyses revealed similar results for the subcohort of GCS≤8 compared to the original cohort. Additionally, when examining the subcohort of GCS>8, a L-shaped relationship between PaCO2 and the three clinical endpoints emerged, in contrast to the previously observed U-shaped pattern. The findings from the subcohort of GCS>8 suggested that patients experiencing hypocapnia had a more unfavorable prognosis, which aligns with the results obtained from corresponding multivariate logistic regression analyses.
The retrospective study revealed the association between the first 24-h PaCO2 and all-cause mortality risk (30-day, 60-day, and 90-day) for patients with SAE in ICU. The range (35mmHg-50mmHg) of PaCO2 may be the optimal target for patients with SAE in clinical practice.

To develop a nomogram for predicting the occurrence of sepsis-associated delirium (SAD).
Data from a total of 642 patients were retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database to build a prediction model. Multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of SAD. The performance of the nomogram was assessed in terms of discrimination and calibration by bootstrapping with 1000 resamples.
Multivariate logistic regression identified 4 independent predictors for patients with SAD, including Sepsis-related Organ Failure Assessment(SOFA) (p = 0.004; OR: 1.131; 95% CI 1.040 to 1.231), mechanical ventilation (P < 0.001; OR: 3.710; 95% CI 2.452 to 5.676), phosphate (P = 0.047; OR: 1.165; 95% CI 1.003 to 1.358), and lactate (P = 0.023; OR: 1.135; 95% CI 1.021 to 1.270) within 24 h of intensive care unit (ICU) admission. The area under the curve (AUC) of the predictive model was 0.742 in the training set and 0.713 in the validation set. The Hosmer - Lemeshow test showed that the model was a good fit (p = 0.471). The calibration curve of the predictive model was close to the ideal curve in both the training and validation sets. The DCA curve also showed that the predictive nomogram was clinically useful.
We constructed a nomogram for the personalized prediction of delirium in sepsis patients, which had satisfactory performance and clinical utility and thus could help clinicians identify patients with SAD in a timely manner, perform early intervention, and improve their neurological outcomes.