The dynamic change of serum S100B levels from day 1 to day 3 is more associated with sepsis-associated encephalopathy

Advances in understanding the effects of cardiopulmonary bypass on gut microbiota during cardiac surgery

Cardiopulmonary bypass (CPB) is an indispensable technique in cardiac surgery; however, its impact on gut microbiota and metabolites remains insufficiently studied. CPB may disrupt the intestinal mucosal barrier, altering the composition and function of gut microbiota, thereby triggering local immune responses and systemic inflammation, which may lead to postoperative complications. This narrative review examines relevant literature from PubMed, Web of Science, Google Scholar, and CNKI databases over the past decade. Keywords such as "gut microbiota," "cardiopulmonary bypass," "cardiac surgery," and "postoperative complications" were employed, with Boolean operators used to refine the search results. The review examines changes in gut microbiota before and after CPB, their role in postoperative complications, and potential strategies for modulation to improve outcomes.

Sepsis Biomarkers: Advancements and Clinical Applications-A Narrative Review

Sepsis is now defined as a life-threatening syndrome of organ dysfunction triggered by a dysregulated host response to infection, posing significant challenges in critical care. The main objective of this review is to evaluate the potential of emerging biomarkers for early diagnosis and accurate prognosis in sepsis management, which are pivotal for enhancing patient outcomes. Despite advances in supportive care, traditional biomarkers like C-reactive protein and procalcitonin have limitations, and recent studies have identified novel biomarkers with increased sensitivity and specificity, including circular RNAs, HOXA distal transcript antisense RNA, microRNA-486-5p, protein C, triiodothyronine, and prokineticin 2. These emerging biomarkers hold promising potential for the early detection and prognostication of sepsis. They play a crucial role not only in diagnosis but also in guiding antibiotic therapy and evaluating treatment effectiveness. The introduction of point-of-care testing technologies has brought about a paradigm shift in biomarker application, enabling swift and real-time patient evaluation. Despite these advancements, challenges persist, notably concerning biomarker variability and the lack of standardized thresholds. This review summarizes the latest advancements in sepsis biomarker research, spotlighting the progress and clinical implications. It emphasizes the significance of multi-biomarker strategies and the feasibility of personalized medicine in sepsis management. Further verification of biomarkers on a large scale and their integration into clinical practice are advocated to maximize their efficacy in future sepsis treatment.

Enhancing acute inflammatory and sepsis treatment: superiority of membrane receptor blockade

Conditions such as acute pancreatitis, ulcerative colitis, delayed graft function and infections caused by a variety of microorganisms, including gram-positive and gram-negative organisms, increase the risk of sepsis and therefore mortality. Immune dysfunction is a characterization of sepsis, so timely and effective treatment strategies are needed. The conventional approaches, such as antibiotic-based treatments, face challenges such as antibiotic resistance, and cytokine-based treatments have shown limited efficacy. To address these limitations, a novel approach focusing on membrane receptors, the initiators of the inflammatory cascade, is proposed. Membrane receptors such as Toll-like receptors, interleukin-1 receptor, endothelial protein C receptor, μ-opioid receptor, triggering receptor expressed on myeloid cells 1, and G-protein coupled receptors play pivotal roles in the inflammatory response, offering opportunities for rapid regulation. Various membrane receptor blockade strategies have demonstrated efficacy in both preclinical and clinical studies. These membrane receptor blockades act as early stage inflammation modulators, providing faster responses compared to conventional therapies. Importantly, these blockers exhibit immunomodulatory capabilities without inducing complete immunosuppression. Finally, this review underscores the critical need for early intervention in acute inflammatory and infectious diseases, particularly those posing a risk of progressing to sepsis. And, exploring membrane receptor blockade as an adjunctive treatment for acute inflammatory and infectious diseases presents a promising avenue. These novel approaches, when combined with antibiotics, have the potential to enhance patient outcomes, particularly in conditions prone to sepsis, while minimizing risks associated with antibiotic resistance and immune suppression.

Meta-analysis of evaluating neuron specific enolase as a serum biomarker for sepsis-associated encephalopathy

INTRODUCTION

Brain dysfunction in sepsis is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Neuron specific enolase (NSE) may serve as an important neurocritical biomarker for detection and longitudinal monitoring in SAE patients. Our Meta-analysis aimed to explore the diagnostic and prognostic value of serum NSE in SAE patients. Currently, no systematic Review and Meta-analysis have been assessed that NSE as a biomarker of SAE.

METHODS

The study protocol was registered in the PROSPERO database (CRD42023398736) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a systematic review and Meta-analysis to evaluate the serum NSE's diagnostic accuracy for SAE and prognostic strength for probability of death of septic patients. We systematic searched electronic bibliographic databases from PubMed, MEDLINE, Web of Science, Embase, Cochrane databases, CNKI, CQVIP, and WFSD. QUADAS-2 assessment tool was used to evaluate quality and risk of bias of the selected studies. Subgroup analyses, funnel plots, sensitivity analyses were also carried out. Review Manager version 5.4 and Stata16.0. was used for statistical analysis.

RESULTS

This Meta-analysis included 22 studies with 1361 serum samples from SAE patients and 1580 serum samples from no-encephalopathy septic (NE) patients. The Meta-analysis showed that individuals with SAE had higher serum NSE level than NE controls (SMD 1.93 (95 % CI 1.51-2.35), P < 0.00001). In addition, there are 948 serum samples from survival septic patients and 446 serum samples from non-survival septic patients, septic patients with survival outcomes had lower serum NSE levels than those with death outcomes (SMD -1.87 (95 % CI -2.43 to -1.32), P < 0.00001).

CONCLUSION

Our Meta-analysis reveals a significant association between elevated NSE concentrations and the increased likelihood of concomitant SAE and mortality during septic patients. This comprehensive analysis will equip ICU physicians with up-to-date insights to accurately identify patients at risk of SAE and implement appropriate intervention strategies to mitigate morbidity and improve neurological outcomes. However, it is important to note that the presence of substantial heterogeneity among studies poses challenges in determining the most effective discrimination cutoff values and optimal sampling collection time.

Combining biomarkers of BNIP3 L, S100B, NSE, and accessible measures to predict sepsis-associated encephalopathy: a prospective observational study

BACKGROUND

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.

METHODS

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.

MAIN RESULTS

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.

CONCLUSIONS

The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

Albumin and C-reactive protein as diagnostic markers for neonatal sepsis: a retrospective study

OBJECTIVE

To assess the applicability of albumin (ALB) and C-reactive protein (CRP) concentrations in the diagnosis of sepsis in neonates on the day of admission, and to help with early identification and intervention in the development of sepsis.

METHODS

This retrospective study included all neonates who were admitted to the neonatal intensive care unit from January 2020 to June 2023. We studied 160 full-term neonates, including 80 with sepsis and 80 healthy controls. A multivariate analysis was conducted to evaluate the associations between ALB, CRP, and sepsis.

RESULTS

CRP concentrations were significantly higher in neonates with sepsis than in controls (26.5 ± 8.6  vs. 3.6 ± 1.2 ng/L). At a cut-off point of 10.8 ng/L, CRP showed a sensitivity of 74.3% and a specificity of 80%. Moreover, ALB concentrations were significantly lower in neonates with sepsis than in controls (25.4 ± 2.5 g/L vs. 29.2 ± 2.6 g/L). At a cut-off point of 26.8, ALB showed a sensitivity of 75.6% and a specificity of 84.2%.

CONCLUSIONS

Our findings suggest that ALB and CRP concentrations on the first day of admission are different between neonates who do and those who do not develop sepsis. Higher CRP concentrations and lower ALB concentrations may indicate an increased risk of sepsis.

Improved prediction of sepsis-associated encephalopathy in intensive care unit sepsis patients with an innovative nomogram tool

Background

Sepsis-associated encephalopathy (SAE) occurs as a result of systemic inflammation caused by sepsis. It has been observed that the majority of sepsis patients experience SAE while being treated in the intensive care unit (ICU), and a significant number of survivors continue suffering from cognitive impairment even after recovering from the illness. The objective of this study was to create a predictive nomogram that could be used to identify SAE risk factors in patients with ICU sepsis.

Methods

We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We defined SAE as a Glasgow Coma Scale (GCS) score of 15 or less, or delirium. The patients were randomly divided into training and validation cohorts. We used least absolute shrinkage and selection operator (LASSO) regression modeling to optimize feature selection. Independent risk factors were determined through a multivariable logistic regression analysis, and a prediction model was built. The performance of the nomogram was evaluated using various metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

Among the 4,476 sepsis patients screened, 2,781 (62.1%) developed SAE. In-hospital mortality was higher in the SAE group compared to the non-SAE group (9.5% vs. 3.7%, p < 0.001). Several variables were analyzed, including the patient's age, gender, BMI on admission, mean arterial pressure, body temperature, platelet count, sodium level, and use of midazolam. These variables were used to create and validate a nomogram. The nomogram's performance, assessed by AUC, NRI, IDI, and DCA, was found to be superior to the conventional SOFA score combined with delirium. Calibration plots and the Hosmer-Lemeshow test confirmed the accuracy of the nomogram. The enhanced NRI and IDI values demonstrated that our scoring system outperformed traditional diagnostic approaches. Additionally, the DCA curve indicated the practicality of the nomogram in clinical settings.

Conclusion

This study successfully identified autonomous risk factors associated with the emergence of SAE in sepsis patients and utilized them to formulate a predictive model. The outcomes of this investigation have the potential to serve as a valuable clinical resource for the timely detection of SAE in patients.

Optimizing the prediction of sepsis-associated encephalopathy with cerebral circulation time utilizing a nomogram: a pilot study in the intensive care unit

Background

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.

Objective

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.

Methods

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).

Results

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.

Conclusion

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.

Neurofilament light chains to assess sepsis-associated encephalopathy: Are we on the track toward clinical implementation?

Sepsis is the most common cause of admission to intensive care units worldwide. Sepsis patients frequently suffer from sepsis-associated encephalopathy (SAE) reflecting acute brain dysfunction. SAE may result in increased mortality, extended length of hospital stay, and long-term cognitive dysfunction. The diagnosis of SAE is based on clinical assessments, but a valid biomarker to identify and confirm SAE and to assess SAE severity is missing. Several blood-based biomarkers indicating neuronal injury have been evaluated in sepsis and their potential role as early diagnosis and prognostic markers has been studied. Among those, the neuroaxonal injury marker neurofilament light chain (NfL) was identified to potentially serve as a prognostic biomarker for SAE and to predict long-term cognitive impairment. In this review, we summarize the current knowledge of biomarkers, especially NfL, in SAE and discuss a possible future clinical application considering existing limitations.

The modulatory effects of gut microbes and metabolites on blood–brain barrier integrity and brain function in sepsis-associated encephalopathy

Background

Intestinal microbiota homeostasis and the gut-brain axis are key players associated with host health and alterations in metabolic, inflammatory, and neurodegenerative disorders. Sepsis-associated encephalopathy (SAE), which is closely associated with bacterial translocation, is a common secondary organ dysfunction and an urgent, unsolved problem affecting patient quality of life. Our study examined the neuroprotective effects of the gut microbiome and short-chain fatty acid (SCFA) metabolites on SAE.

Methods

Male C57BL/6 mice were administered SCFAs in drinking water, then subjected to cecal ligation and puncture (CLP) surgery to induce SAE. 16S rRNA sequencing was used to investigate gut microbiome changes. The open field test (OFT) and Y-maze were performed to evaluate brain function. The permeability of the blood–brain barrier (BBB) was assessed by Evans blue (EB) staining. Hematoxylin and eosin (HE) staining was used to examine intestinal tissue morphology. The expression levels of tight junction (TJ) proteins and inflammatory cytokines was assessed by western blots and immunohistochemistry. In vitro, bEND.3 cells were incubated with SCFAs and then with lipopolysaccharide (LPS). Immunofluorescence was used to examine the expression of TJ proteins.

Results

The composition of the gut microbiota was altered in SAE mice; this change may be related to SCFA metabolism. SCFA treatment significantly alleviated behavioral dysfunction and neuroinflammation in SAE mice. SCFAs upregulated occludin and ZO-1 expression in the intestine and brain in SAE mice and LPS-treated cerebromicrovascular cells.

Conclusions

These findings suggested that disturbances in the gut microbiota and SCFA metabolites play key roles in SAE. SCFA supplementation could exert neuroprotective effects against SAE by preserving BBB integrity.

Brain injury biomarkers do not predict delirium in acutely ill older patients: a prospective cohort study

Delirium is a common, serious, and often preventable neuropsychiatric emergency mostly characterized by a disturbance in attention and awareness. Systemic insult and inflammation causing blood-brain-barrier (BBB) damage and glial and neuronal activation leading to more inflammation and cell death is the most accepted theory behind delirium's pathophysiology. This study aims to evaluate the relationship between brain injury biomarkers on admission and delirium in acutely ill older patients. We performed a prospective cohort study which analyzed plasma S100B levels at admission in elderly patients. Our primary outcome was delirium diagnosis. Secondary outcomes were association between S100B, NSE and Tau protein and delirium diagnosis and patients' outcomes (admissions to intensive care, length of hospital stay, and in-hospital mortality). We analyzed 194 patients, and 46 (24%) developed delirium, 25 on admission and 21 during hospital stay. Median of S100B at admission in patients who developed delirium was 0.16 and median was 0.16 in patients who didn't develop delirium (p: 0.69). Levels S100B on admission did not predict delirium in acutely ill elderly patients.Trial registration: The study was approved by the local institutional review board (CAPPESq, no. 77169716.2.0000.0068, October 11, 2017) and registered in Brazilian Clinical Trials Registry (ReBEC, no. RBR-233bct).

What's new on septic encephalopathy? Ten things you need to know

Sepsis associated encephalopathy (SAE) is a frequent complication of sepsis and is associated with a higher risk of short-term mortality and long-term cognitive impairment. The EEG is a sensitive complement of the clinical examination that can also detect and quantify encephalopathy and identify features with prognostic value, such as lack of reactivity. Moreover, despite their effect on outcome is still debated, the EEG is the only tool to detect non-convulsive seizures which can occur in a septic setting. Understanding the pathophysiology of SAE is fundamental to define potential therapeutic targets. Neuroinflammation plays an important role in the development of SAE and many blood and imaging biomarkers have recently shown a promising ability to distinguish SAE form non-SAE patient. In recent years, some interesting mediators of inflammation were successfully targeted in animal models, with a significant reduction in the neuroinflammation and in sepsis-induced cognitive decline. However, the complexity of the host response to sepsis currently limits the use of immunomodulation therapies in humans. Alteration in regulatory systems of cerebral blood flow, namely cerebral autoregulation (CA) and neurovascular coupling, contribute to SAE development. Nowadays, clinicians have access to different tools to assess them at the bedside and CA-based blood pressure protocols should be implemented to optimize cerebral perfusion. Its inauspicious consequences, its complex physiopathology and the lack of efficacious treatment make of SAE a highly active research subject.

Paediatric sepsis-associated encephalopathy (SAE): a comprehensive review

Sepsis-associated encephalopathy (SAE) is one of the most common types of organ dysfunction without overt central nervous system (CNS) infection. It is associated with higher mortality, low quality of life, and long-term neurological sequelae, its mortality in patients diagnosed with sepsis, progressing to SAE, is 9% to 76%. The pathophysiology of SAE is still unknown, but its mechanisms are well elaborated, including oxidative stress, increased cytokines and proinflammatory factors levels, disturbances in the cerebral circulation, changes in blood-brain barrier permeability, injury to the brain's vascular endothelium, altered levels of neurotransmitters, changes in amino acid levels, dysfunction of cerebral microvascular cells, mitochondria dysfunction, activation of microglia and astrocytes, and neuronal death. The diagnosis of SAE involves excluding direct CNS infection or other types of encephalopathies, which might hinder its early detection and appropriate implementation of management protocols, especially in paediatric patients where only a few cases have been reported in the literature. The most commonly applied diagnostic tools include electroencephalography, neurological imaging, and biomarker detection. SAE treatment mainly focuses on managing underlying conditions and using antibiotics and supportive therapy. In contrast, sedative medication is used judiciously to treat those showing features such as agitation. The most widely used medication is dexmedetomidine which is neuroprotective by inhibiting neuronal apoptosis and reducing a sepsis-associated inflammatory response, resulting in improved short-term mortality and shorter time on a ventilator. Other agents, such as dexamethasone, melatonin, and magnesium, are also being explored in vivo and ex vivo with encouraging results. Managing modifiable factors associated with SAE is crucial in improving generalised neurological outcomes. From those mentioned above, there are still only a few experimentation models of paediatric SAE and its treatment strategies. Extrapolation of adult SAE models is challenging because of the evolving brain and technical complexity of the model being investigated. Here, we reviewed the current understanding of paediatric SAE, its pathophysiological mechanisms, diagnostic methods, therapeutic interventions, and potential emerging neuroprotective agents.

Texture Feature-Based Machine Learning Classification on MRI Image for Sepsis-Associated Encephalopathy Detection: A Pilot Study

Objective

The objective of this study was to assess the performance of combining MRI-based texture analysis with machine learning for differentiating sepsis-associated encephalopathy (SAE) from sepsis alone.

Method

Sixty-six MRI-T1WI images of an SAE patient and 125 images of patients with sepsis alone were collected. Frontal lobe, brain stem, hippocampus, and amygdala were selected as regions of interest (ROIs). 279 texture features of each ROI were obtained using MaZda software. After the dimension reduction, 30 highly discriminative features of each ROI were adopted to differentiate SAE from sepsis alone using the CatBoost model.

Results

The classification models of frontal, brain stem, hippocampus, and amygdala were constructed. The classification accuracy was above 0.83, and the area under the curve (AUC) exceeded 0.90 in the validation set.

Conclusion

The texture features differed between SAE patients and patients with sepsis alone in different anatomical locations, suggesting that MRI-based texture analysis with machine learning might be helpful in differentiating SAE from sepsis alone.

Distinct patterns of serum and urine macrophage migration inhibitory factor kinetics predict death in sepsis: a prospective, observational clinical study

Macrophage migration inhibitory factor (MIF) has been considered as a biomarker in sepsis, however the predictive value of the pattern of its kinetics in the serum and in the urine has remained unclarified. It is also unclear whether the kinetics of MIF are different between males and females. We conducted a single-center prospective, observational study with repeated measurements of MIF in serum and urine on days 0, 2, and 4 from admission to the intensive care unit (ICU) in 50 adult septic patients. We found that in patients who died within 90 days, there was an increase in serum MIF level from day 0 to 4, whereas in the survivors there was rather a decrease (p = 0.018). The kinetics were sex-dependent as the same difference in the pattern was present in males (p = 0.014), but not in females (p = 0.418). We also found that urine MIF was markedly lower in patients who died than in survivors of sepsis (p < 0.050). Urine MIF levels did not show temporal changes: there was no meaningful difference between day 0 and 4. These results suggest that kinetics of serum MIF during the initial days from ICU admission can predict death, especially in male patients. Additionally, lower urine MIF levels can also indicate death without showing meaningful temporal kinetics.

Malvidin alleviates mitochondrial dysfunction and ROS accumulation through activating AMPK-α/UCP2 axis, thereby resisting inflammation and apoptosis in SAE mice

This study aimed to explore the protective roles of malvidin in life-threatened sepsis-associated encephalopathy (SAE) and illustrate the underlying mechanism. SAE mice models were developed and treated with malvidin for subsequently protective effects evaluation. Malvidin restored neurobehavioral retardation, declined serum S100β and NSE levels, sustained cerebrum morphological structure, improved blood-brain barrier integrity with elevated tight junction proteins, and decreased evans blue leakage, and finally protect SAE mice from brain injury. Mechanistically, malvidin prevented cerebrum from mitochondrial dysfunction with enhanced JC-1 aggregates and ATP levels, and ROS accumulation with decreased lipid peroxidation and increased antioxidant enzymes. UCP2 protein levels were found to be decreased after LPS stimulation in the cerebrum and BV-2 cells, and malvidin recovered its levels in a ROS dependent manner. In vivo inhibition of UCP2 with genipin or in vitro interference with siRNA UCP2 both disrupted the mitochondrial membrane potential, decreased ATP levels and intensified DCF signals, being a key target for malvidin. Moreover, dorsomorphin block assays verified that malvidin upregulated UCP2 expression through phosphorylating AMPK in SAE models. Also, malvidin alleviated SAE progression through inhibition of ROS-dependent NLRP3 inflammasome activation mediated serum pro-inflammatory cytokines secretion and mitochondrial pathway mediated apoptosis with weakened apoptosis body formation and tunel positive signals, and decreased Bax, cytochrome C, caspase-3 and increased Bcl-2 protein levels. Overall, this study illustrated that malvidin targeted AMPK-α/UCP2 axis to restore LPS-induced mitochondrial dysfunction and alleviate ROS accumulation, which further inhibits NLRP3 inflammasome activation and mitochondrial apoptosis in a ROS dependent way, and ultimately protected SAE mice, providing a reference for the targeted development of SAE prophylactic approach.

Diagnostic and prognostic value of serum S100B in sepsis-associated encephalopathy: A systematic review and meta-analysis

Background

In sepsis, brain dysfunction is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Our systematic review and meta-analysis aimed to explore the diagnostic and prognostic value of serum S100 calcium-binding protein B (S100B) in SAE patients.

Methods

We conducted a systematic search of the databases PubMed, Web of Science, Embase, Cochrane databases, CNKI, VIP, and WFSD from their inception dates until August 20, 2022. A Meta-analysis of the included studies was also performed using Review Manager version 5.4 and Stata16.0.

Results

This meta-analysis included 28 studies with 1401 serum samples from SAE patients and 1591 serum samples from no-encephalopathy septic (NE) patients. The Meta-Analysis showed that individuals with SAE had higher serum S100B level than NE controls (MD, 0.49 [95% CI (0.37)-(0.60), Z =8.29, P < 0.00001]), and the baseline level of serum S100B in septic patients with burn was significantly higher than average (1.96 [95% CI (0.92)-(2.99), Z =3.71, P < 0.0002]) In addition, septic patients with favorable outcomes had lower serum S100B levels than those with unfavorable outcomes (MD, -0.35 [95% CI (-0.50)-(-0.20), Z =4.60, P < 0.00001]).

Conclusion

Our Meta-Analysis indicates that higher serum S100B level in septic patients are moderately associated with SAE and unfavorable outcomes (The outcomes here mainly refer to the mortality). The serum S100B level may be a useful diagnostic and prognostic biomarker of SAE.

Cerebral dysfunctions caused by sepsis during ageing

Systemic inflammation elicited by sepsis can induce an acute cerebral dysfunction known as sepsis-associated encephalopathy (SAE). Recent evidence suggests that SAE is common but shows a dynamic trajectory over time. Half of all patients with sepsis develop SAE in the intensive care unit, and some survivors present with sustained cognitive impairments for several years after initial sepsis onset. It is not clear why some, but not all, patients develop SAE and also the factors that determine the persistence of SAE. Here, we first summarize the chronic pathology and the dynamic changes in cognitive functions seen after the onset of sepsis. We then outline the cerebral effects of sepsis, such as neuroinflammation, alterations in neuronal synapses and neurovascular changes. We discuss the key factors that might contribute to the development and persistence of SAE in older patients, including premorbid neurodegenerative pathology, side effects of sedatives, renal dysfunction and latent virus reactivation. Finally, we postulate that some of the mechanisms that underpin neuropathology in SAE may also be relevant to delirium and persisting cognitive impairments that are seen in patients with severe COVID-19.

Sepsis Encephalopathy Is Partly Mediated by miR370-3p-Induced Mitochondrial Injury but Attenuated by BAM15 in Cecal Ligation and Puncture Sepsis Male Mice

BAM15 (a mitochondrial uncoupling agent) was tested on cecal ligation and puncture (CLP) sepsis mice with in vitro experiments. BAM15 attenuated sepsis as indicated by survival, organ histology (kidneys and livers), spleen apoptosis (activated caspase 3), brain injury (SHIRPA score, serum s100β, serum miR370-3p, brain miR370-3p, brain TNF-α, and apoptosis), systemic inflammation (cytokines, cell-free DNA, endotoxemia, and bacteremia), and blood-brain barrier (BBB) damage (Evan's blue dye and the presence of green fluorescent E. coli in brain after an oral administration). In parallel, brain miR arrays demonstrated miR370-3p at 24 h but not 120 h post-CLP, which was correlated with metabolic pathways. Either lipopolysaccharide (LPS) or TNF-α upregulated miR370-3p in PC12 (neuron cells). An activation by sepsis factors (LPS, TNF-α, or miR370-3p transfection) damaged mitochondria (fluorescent color staining) and reduced cell ATP, possibly through profound mitochondrial activity (extracellular flux analysis) that was attenuated by BAM15. In bone-marrow-derived macrophages, LPS caused mitochondrial injury, decreased cell ATP, enhanced glycolysis activity (extracellular flux analysis), and induced pro-inflammatory macrophages (iNOS and IL-1β) which were neutralized by BAM15. In conclusion, BAM15 attenuated sepsis through decreased mitochondrial damage, reduced neuronal miR370-3p upregulation, and induced anti-inflammatory macrophages. BAM15 is proposed to be used as an adjuvant therapy against sepsis hyperinflammation.

[Early predictive value of high density lipoprotein cholesterol for secondary acute kidney injury in sepsis patients]

Objective: To investigate the changes of high density lipoprotein cholesterol (HDL-C) in sepsis patients and its early predictive value for secondary acute kidney injury (AKI) in such patients. Methods: A retrospective case series study was conducted. From June 2019 to June 2021, 232 sepsis patients who met the inclusion criteria were admitted to the Second Hospital of Hebei Medical University, including 126 males and 106 females, aged 24 to 71 years. According to whether complicating secondary AKI, the patients were divided into non-AKI group (n=158) and AKI group (n=74). Data of patients between the two groups were compared and statistically analyzed with independent sample t test or chi-square test, including the sex, age, body mass index (BMI), body temperature, heart rate, primary infection site, combined underlying diseases, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score and sepsis-related organ failure assessment (SOFA) score at admission, and the serum levels of C-reactive protein (CRP), procalcitonin, creatinine, cystatin C, and HDL-C measured at diagnosis of sepsis. The multivariate logistic regression analysis was performed on the indicators with statistically significant differences between the two groups to screen the independent risk factors for developing secondary AKI in 232 sepsis patients, and the joint prediction model was established based on the independent risk factors. The receiver operating characteristic (ROC) curve of the independent risk factors and the joint prediction model predicting secondary AKI in 232 sepsis patients were drawn, and the area under the curve (AUC), the optimal threshold, and the sensitivity and specificity under the optimal threshold were calculated. The quality of the above-mentioned AUC was compared by Delong test, and the sensitivity and specificity under the optimal threshold were compared using chi-square test. Results: The sex, age, BMI, body temperature, heart rate, primary infection site, combined underlying diseases, and CRP level of patients between the two groups were similar (P>0.05). The procalcitonin, creatinine, cystatin C, and scores of APACHE Ⅱ and SOFA of patients in AKI group were all significantly higher than those in non-AKI group (with t values of -3.21, -16.14, -12.75, -11.13, and -12.88 respectively, P<0.01), while the HDL-C level of patients in AKI group was significantly lower than that in non-AKI group (t=6.33, P<0.01). Multivariate logistic regression analysis showed that creatinine, cystatin C, and HDL-C were the independent risk factors for secondary AKI in 232 sepsis patients (with odds ratios of 2.45, 1.68, and 2.12, respectively, 95% confidence intervals of 1.38-15.35, 1.06-3.86, and 0.86-2.56, respectively, P<0.01). The AUCs of ROC curves of creatinine, cystatin C, HDL-C, and the joint prediction model for predicting secondary AKI in 232 sepsis patients were 0.69, 0.79, 0.89, and 0.93, respectively (with 95% confidence intervals of 0.61-0.76, 0.72-0.85, 0.84-0.92, and 0.89-0.96, respectively, P values all below 0.01); the optimal threshold were 389.53 μmol/L, 1.56 mg/L, 0.63 mmol/L, and 0.48, respectively; the sensitivity under the optimal threshold were 76.6%, 81.4%, 89.7%, and 95.5%, respectively; the specificity under the optimal threshold values were 78.6%, 86.7%, 88.6%, and 96.6%, respectively. The AUC quality of cystatin C was significantly better than that of creatinine (z=2.34, P<0.05), the AUC quality and sensitivity and specificity under the optimal threshold of HDL-C were all significantly better than those of cystatin C (z=3.33, with χ2 values of 6.43 and 7.87, respectively, P<0.01) and creatinine (z=5.34, with χ2 values of 6.32 and 6.41, respectively, P<0.01); the AUC quality and sensitivity and specificity under the optimal threshold of the joint prediction model were all significantly better than those of creatinine, cystatin C, and HDL-C (with z values of 6.18, 4.50, and 2.06, respectively, χ2 values of 5.31, 7.23, 3.99, 6.56, 7.34, and 4.00, respectively, P<0.05 or P<0.01). Conclusions: HDL-C level in sepsis patients with secondary AKI is significantly lower than that in patients without secondary AKI. This is an independent risk factor for secondary AKI in sepsis patients with a diagnostic value being superior to that of creatinine and cystatin C. The combination of the aforementioned three indicators would have higher predicative valuable for secondary AKI in sepsis patients.

Biomarkers for sepsis: more than just fever and leukocytosis-a narrative review

A biomarker describes a measurable indicator of a patient's clinical condition that can be measured accurately and reproducibly. Biomarkers offer utility for diagnosis, prognosis, early disease recognition, risk stratification, appropriate treatment (theranostics), and trial enrichment for patients with sepsis or suspected sepsis. In this narrative review, we aim to answer the question, "Do biomarkers in patients with sepsis or septic shock predict mortality, multiple organ dysfunction syndrome (MODS), or organ dysfunction?" We also discuss the role of pro- and anti-inflammatory biomarkers and biomarkers associated with intestinal permeability, endothelial injury, organ dysfunction, blood-brain barrier (BBB) breakdown, brain injury, and short and long-term mortality. For sepsis, a range of biomarkers is identified, including fluid phase pattern recognition molecules (PRMs), complement system, cytokines, chemokines, damage-associated molecular patterns (DAMPs), non-coding RNAs, miRNAs, cell membrane receptors, cell proteins, metabolites, and soluble receptors. We also provide an overview of immune response biomarkers that can help identify or differentiate between systemic inflammatory response syndrome (SIRS), sepsis, septic shock, and sepsis-associated encephalopathy. However, significant work is needed to identify the optimal combinations of biomarkers that can augment diagnosis, treatment, and good patient outcomes.

Biomarker Analysis for Combination Therapy of Vitamin C and Thiamine in Septic Shock: A Post-Hoc Study of the ATESS Trial

INTRODUCTION

We evaluated the effects of vitamin C and thiamine administration on biomarkers in patients with septic shock.

METHODS

This was a post-hoc analysis of the Ascorbic Acid and Thiamine Effect in Septic Shock (ATESS) trial, a multicenter, double-blind, randomized controlled trial. Patients were randomized to either a treatment group (intravenous vitamin C and thiamine for 48 h) or a control group. Interleukin (IL)-6, IL-10, angiopoietin-II (AP2), and S100β were assessed at baseline and at 72 h. The primary outcomes were the biomarker levels at 72 h, and the secondary outcome was reduction rate.

RESULTS

Forty-five patients were assigned to the treatment group and 52 were assigned to the control group. Baseline biomarker levels and at 72 h were not significantly different between the treatment and the placebo groups. The reduction rates were not significantly different between the two groups. These outcome variables showed fair diagnostic accuracy for predicting 28-day mortality according to the area under the receiver operating characteristic curve.

CONCLUSION

Vitamin C and thiamine administration during the early phase of septic shock did not significantly change prognostic biomarker levels of IL-6, IL-10, AP2, and S100β.

TRIAL REGISTRATION

NCT, ClinicalTrials.gov NCT03756220, ATESS. Registered 28 November 2018, https://clinicaltrials.gov/ct2/show/NCT03756220.

Brain injury, endothelial injury and inflammatory markers are elevated and express sex-specific alterations after COVID-19

Objective

Although COVID-19 is a respiratory disease, all organs can be affected including the brain. To date, specific investigations of brain injury markers (BIM) and endothelial injury markers (EIM) have been limited. Additionally, a male bias in disease severity and mortality after COVID-19 is evident globally. Sex differences in the immune response to COVID-19 may mediate this disparity. We investigated BIM, EIM and inflammatory cytokine/chemokine (CC) levels after COVID-19 and in across sexes.

Methods

Plasma samples from 57 subjects at < 48 h of COVID-19 hospitalization, and 20 matched controls were interrogated for the levels of six BIMs—including GFAP, S100B, Syndecan-1, UCHLI, MAP2 and NSE, two EIMs—including sICAM1 and sVCAM1. Additionally, several cytokines/chemokines were analyzed by multiplex. Statistical and bioinformatics methods were used to measure differences in the marker profiles across (a) COVID-19 vs. controls and (b) men vs. women.

Results

Three BIMs: MAP2, NSE and S100B, two EIMs: sICAM1 and sVCAM1 and seven CCs: GRO IL10, sCD40L, IP10, IL1Ra, MCP1 and TNFα were significantly (p < 0.05) elevated in the COVID-19 cohort compared to controls. Bioinformatics analysis reveal a stronger positive association between BIM/CC/EIMs in the COVID-19 cohort. Analysis across sex revealed that several BIMs and CCs including NSE, IL10, IL15 and IL8 were significantly (p < 0.05) higher in men compared to women. Men also expressed a more robust BIM/ EIM/CC association profile compared to women.

Conclusion

The acute elevation of BIMs, CCs, and EIMs and the robust associations among them at COVID-19 hospitalization are suggestive of brain and endothelial injury. Higher BIM and inflammatory markers in men additionally suggest that men are more susceptible to the risk compared to women.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-021-02323-8.

Efficiency of Monocyte/High-Density Lipoprotein Cholesterol Ratio Combined With Neutrophil/Lymphocyte Ratio in Predicting 28-Day Mortality in Patients With Sepsis

Background: Sepsis can cause unpredictable harm, and early identification of risk for mortality may be conducive to clinical diagnosis. The present study proposes to assess the efficacy of the monocyte/high-density lipoprotein cholesterol ratio (MHR) combined with the neutrophil/lymphocyte ratio (NLR) on the day of admission in predictive efficacy in the 28-day mortality risk in critical patients with sepsis.

Material and Methods: We administered observational and retrospective cohort research from a single center. The correlation of the clinical variables, together with the system severity scores of APACHE II and SOFA, are displayed by correlation analysis, and a Cox regression model could be performed to screen the independent risk factors and estimate the capacity of multiple markers in predicting 28-day mortality. The receiver operating characteristic (ROC) curve served as an applied method to output cutoff values for the diagnosis and prognostic risk, and the area under the ROC curve and net reclassification improvement index (NRI), as well as integrated discrimination improvement index (IDI) were employed to assess the feasibility of multiple parameters for predictive value in 28-day mortality of septic patients.

Results: The study enrolled 274 eligible patients with sepsis. The correlation analysis indicated NLR and MHR were related to the sepsis severity. A multivariate Cox regression analysis indicated that NLR together with MHR displayed a close relation to death rate after adjusting for other potential confounders (NLR, HR = 1.404 [95% CI 1.170–1.684], P < 0.001; MHR, HR = 1.217 [95% CI 1.112–1.331], P < 0.001). The AUC of NLR, MHR, NLR_MHR was 0.827, 0.876, and 0.934, respectively. The addition on the biomarker NLR_MHR to the prediction model improved IDI by 18.5% and NRI by 37.8%.

Conclusions: Our findings suggest that NLR and MHR trend to an elevated level in non-surviving patients with sepsis. Evaluation of NLR_MHR, an independent risk factor for increased mortality, might improve the predictive efficacy for 28-day mortality risk in septic patients.

Gut Microbiota as Early Predictor of Infectious Complications before Cardiac Surgery: A Prospective Pilot Study

Cardiac surgery remains a field of medicine with a high percentage of postoperative complications, including infectious ones. Modern data indicate a close relationship of infectious disorders with pathological changes in the composition of the gut microbiome; however, the extent of such changes in cardiac surgery patients is not fully clarified. In this prospective, observational, single center, pilot study, 72 patients were included, 12 among them with the infectious complications. We analyzed the features of the fecal microbiota before and in the early postoperative period, as one of the markers for predicting the occurrence of bacterial infection. We also discovered the significant change in microbial composition in the group of patients with infectious complications compared to the non-infectious group before and after cardiac surgery, despite the intra-individual variation in composition of gut microbiome. Our study demonstrated that the group of patients that had a bacterial infection in the early postoperative period already had an altered microbial composition even before the surgery. Further studies will evaluate the clinical significance of the identified proportions of individual taxa of the intestinal microbiota and consider the microbiota as a novel target for reducing the risk of infectious complications.

Association Between S100b Levels and COVID-19 Pneumonia: A Case Control Study

BACKGROUND/AIM

Extracellular S100b effects are mediated by the receptor for advanced glycation end products (RAGE), which is the S100b membrane receptor. RAGE belongs to the immunoglobulin superfamily of cell surface molecules and serves as a multiligand receptor and is expressed in high abundance by alveolar type I (AT-I) cells in adult pulmonary tissue. This study aimed to provide an insight into the association between the severity of COVID-19 disease and serum S100b levels during admission to the emergency department (ED).

PATIENTS AND METHODS

A total of 64 patients (34 mild cases; 30 severe cases) were diagnosed with COVID-19 pneumonia and 30 healthy volunteers were admitted to study. Serum S100b levels were measured by using enzymle linked immunoassay method from blood serum samples.

RESULTS

Serum S100b levels showed a significantly higher mean value in mild and severe disease cohorts than in healthy controls (p=0.036 and p=0.028 respectively). Receiver operating characteristic (ROC) analysis indicated greater area under the curve (AUC) for serum S100b levels of the COVID-19 patients (AUC=0.663, 95% CI=0.541-0.785; p=0.014). In addition, serum S100b concentration was measured as 151.7 ng/ml at 79.3% sensitivity and 51.7% specificity (p=0.014). Serum S100b protein levels can serve as a valuable clinical marker in establishing diagnosis of patients. Though not useful in identifying different stages of COVID-19 infection, serum S100b concentration along with other known markers can be utilized to reliably predict clinical severity along with other clinical parameters.

Brain dysfunction in COVID-19 and CAR-T therapy: cytokine storm-associated encephalopathy

OBJECTIVE

Many neurological manifestations are associated with COVID-19, including a distinct form of encephalopathy related to cytokine storm, the acute systemic inflammatory syndrome present in a subgroup of COVID-19 patients. Cytokine storm is also associated with immune effector cell-associated neurotoxicity syndrome (ICANS), a complication of chimeric antigen receptor T-cell (CAR-T) therapy, a highly effective treatment for refractory hematological malignancies. We investigated whether COVID-19-related encephalopathy, ICANS, and other encephalopathies associated with cytokine storm, share clinical and investigative findings.

METHODS

Narrative literature review.

RESULTS

Comparisons between COVID-19-related encephalopathy and ICANS revealed several overlapping features. Clinically, these included dysexecutive syndrome, language disturbances, akinetic mutism and delirium. EEG showed a prevalence of frontal abnormalities. Brain MRI was often unrevealing. CSF elevated cytokine levels have been reported. A direct correlation between cytokine storm intensity and severity of neurological manifestations has been shown for both conditions. Clinical recovery occurred spontaneously or following immunotherapies in most of the patients. Similar clinical and investigative features were also reported in other encephalopathies associated with cytokine storm, such as hemophagocytic lymphohistiocytosis, sepsis, and febrile infection-associated encephalopathies.

INTERPRETATION

COVID-19-related encephalopathy and ICANS are characterized by a predominant electro-clinical frontal lobe dysfunction and share several features with other encephalopathies associated with cytokine storm, which may represent the common denominator of a clinical spectrum of neurological disorders. Therefore, we propose a unifying definition of cytokine storm-associated encephalopathy (CySE), and its diagnostic criteria.

Effects of Neutrophil-to-Lymphocyte Ratio Combined With Interleukin-6 in Predicting 28-Day Mortality in Patients With Sepsis

Background

The current study aimed to evaluate the relationship between the neutrophil-to-lymphocyte ratio (NLR) combined with interleukin (IL)-6 on admission day and the 28-day mortality of septic patients.

Material and Methods

We conducted an observational retrospective study. Patients with presumed sepsis were included. We observed the correlation of studied biomarkers (NLR, IL-6, PCT, and CRP) and the severity scores (APACHE II and SOFA scores) by plotting scatter plots. The relationships of the studied biomarkers and 28-day mortality were evaluated by using Cox regression model, receiver-operating characteristic (ROC) curve, and reclassification analysis.

Results

A total of 264 patients diagnosed with sepsis were enrolled. It was revealed that IL-6 had the strongest correlation with both APACHE II and SOFA scores, followed by the NLR and PCT, and there was no obvious correlation between CRP and the illness severity. NLR and IL-6 were independent predictors of the 28-day mortality in septic patients in the Cox regression model [NLR, odds ratio 1.281 (95% CI 1.159-1.414), P < 0.001; IL-6, odds ratio 1.017 (95% CI 1.005-1.028), P=0.004]. The area under the ROC curve (AUC) of NLR, IL-6 and NLR plus IL-6 (NLR_IL-6) was 0.776, 0.849, and 0.904, respectively.

Conclusion

Our study showed that the levels of NLR and IL-6 were significantly higher in the deceased patients with sepsis. NLR and IL-6 appeared to be independent predictors of 28-day mortality in septic patients. Moreover, NLR combined with IL-6 could dramatically enhance the prediction value of 28-day mortality.

Electrochemical Immunosensor for the Quantification of S100B at Clinically Relevant Levels Using a Cysteamine Modified Surface

Neuronal damage secondary to traumatic brain injury (TBI) is a rapidly evolving condition, which requires therapeutic decisions based on the timely identification of clinical deterioration. Changes in S100B biomarker levels are associated with TBI severity and patient outcome. The S100B quantification is often difficult since standard immunoassays are time-consuming, costly, and require extensive expertise. A zero-length cross-linking approach on a cysteamine self-assembled monolayer (SAM) was performed to immobilize anti-S100B monoclonal antibodies onto both planar (AuEs) and interdigitated (AuIDEs) gold electrodes via carbonyl-bond. Surface characterization was performed by atomic force microscopy (AFM) and specular-reflectance FTIR for each functionalization step. Biosensor response was studied using the change in charge-transfer resistance (Rct) from electrochemical impedance spectroscopy (EIS) in potassium ferrocyanide, with [S100B] ranging 10–1000 pg/mL. A single-frequency analysis for capacitances was also performed in AuIDEs. Full factorial designs were applied to assess biosensor sensitivity, specificity, and limit-of-detection (LOD). Higher Rct values were found with increased S100B concentration in both platforms. LODs were 18 pg/mL(AuES) and 6 pg/mL(AuIDEs). AuIDEs provide a simpler manufacturing protocol, with reduced fabrication time and possibly costs, simpler electrochemical response analysis, and could be used for single-frequency analysis for monitoring capacitance changes related to S100B levels.

The blood-brain barrier dysfunction in sepsis

Sepsis is a life-threatening organ dysfunction triggered by a dysregulated host immune response attempting to eliminate the infection. After hospital discharge, half of the sepsis survivors recover, one-third of the patients die the following year, and one-sixth have a long-term cognitive impairment, including memory dysfunction, anxiety, depression, and post-traumatic stress disorder. The infection triggers the host immune response, and both can cause vascular endothelial damage, interrupting tight junctions proteins; consequently, the blood-brain barrier (BBB) breaks down, allowing and facilitating the entry of peripheral immune cells into the brain, which triggers or exacerbates the activation of glial cells and neuroinflammation. The focus of this review is to identify biochemical abnormalities induced by sepsis, which is associated with BBB dysfunction; provide evidence of biomarkers involved in the tight junction disruption and BBB damage, and draw attention to the role of the BBB as a bridge between systemic infection and brain inflammation.

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

BACKGROUND

Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models.

METHODS

Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model.

RESULTS

A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800-0.838], 0.797 [95% CI 0.781-0.813] and 0.857 [95% CI 0.839-0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value.

CONCLUSIONS

Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

Prediction of Neurocognitive Outcome after Moderate-Severe Traumatic Brain Injury Using Serum Neuron-Specific Enolase and S100 biomarkers

Seric biomarkers have been tested in a large number of studies on traumatic brain injuries (TBI) patients in order to predict severity, especially related to the short-term outcome. However, TBI patients have a high risk of developing long-term complications such as physical disability, cognitive impairment, psychiatric pathology, epilepsy, and others. The aim of this study was to assess the correlation between protein biomarkers S100 and neuron-specific enolase (NSE) and neurocognitive status at 10- and 90-days post-injury. Both biomarkers were tested in the first 4h and after 72h post-injury in 62 patients with moderate-severe TBI. The patients were evaluated by a series of neurocognitive tests: Early Rehabilitation Barthel Index (ERBI), Glasgow Outcome Scale-Extended (GOSE), The Mini-Mental State Examination (MMSE), Processing Speed Index (PSI), and Stroop Test, at 10 and 90 days post-injury and supplementary by the Hospital Anxiety and Depression Scale at 90 days. For evaluating the whole neurocognitive status instead of every scale separately, we used Structural Equation Modeling (SEM), while for anxiety and depressive symptoms, we used multiple regression analyses. SEM showed that NSE values at 4 hours were significant predictors of the cognitive status at 10 (p=0.034) and 90 days (p= 0.023). Also, there were found significant correlations between NSE at 4h and the anxiety level. This study demonstrated a significant correlation between NSE at 4h and short and medium-term neuropsychological outcomes, which recommends using this biomarker for selecting patients with a higher risk of cognitive dysfunction.