Serum levels of S100B from jugular bulb as a biomarker of poor prognosis in patients with severe acute brain injury

A predictive model for consciousness recovery of comatose patients after acute brain injury

Background

Predicting the consciousness recovery for comatose patients with acute brain injury is an important issue. Although some efforts have been made in the study of prognostic assessment methods, it is still unclear which factors can be used to establish model to directly predict the probability of consciousness recovery.

Objectives

We aimed to establish a model using clinical and neuroelectrophysiological indicators to predict consciousness recovery of comatose patients after acute brain injury.

Methods

The clinical data of patients with acute brain injury admitted to the neurosurgical intensive care unit of Xiangya Hospital of Central South University from May 2019 to May 2022, who underwent electroencephalogram (EEG) and auditory mismatch negativity (MMN) examinations within 28 days after coma onset, were collected. The prognosis was assessed by Glasgow Outcome Scale (GOS) at 3 months after coma onset. The least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select the most relevant predictors. We combined Glasgow coma scale (GCS), EEG, and absolute amplitude of MMN at Fz to develop a predictive model using binary logistic regression and then presented by a nomogram. The predictive efficiency of the model was evaluated with AUC and verified by calibration curve. The decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model.

Results

A total of 116 patients were enrolled for analysis, of which 60 had favorable prognosis (GOS ≥ 3). Five predictors, including GCS (OR = 13.400, P < 0.001), absolute amplitude of MMN at Fz site (FzMMNA, OR = 1.855, P = 0.038), EEG background activity (OR = 4.309, P = 0.023), EEG reactivity (OR = 4.154, P = 0.030), and sleep spindles (OR = 4.316, P = 0.031), were selected in the model by LASSO and binary logistic regression analysis. This model showed favorable predictive power, with an AUC of 0.939 (95% CI: 0.899-0.979), and calibration. The threshold probability of net benefit was between 5% and 92% in the DCA.

Conclusion

This predictive model for consciousness recovery in patients with acute brain injury is based on a nomogram incorporating GCS, EEG background activity, EEG reactivity, sleep spindles, and FzMMNA, which can be conveniently obtained during hospitalization. It provides a basis for care givers to make subsequent medical decisions.

Prognostic properties of the association between the S‑100B protein levels and the mean cerebral blood flow velocity in patients diagnosed with severe traumatic brain injury

Craniocerebral injury (CBI) is tissue damage caused by a sudden mechanical force. CBI can result in neurological, neuropsychological and psychiatric dysfunctions. Currently, the severity of CBI is assessed using the Glasgow Coma Scale, brain perfusion pressure measurements, transcranial Doppler tests and biochemical markers. This study aimed to determine the applicability of the S-100B protein levels and the time-averaged mean maximum cerebral blood flow velocity (V_mean) as a means of predicting the treatment outcomes of CBI in the first 4 days of hospitalization. The results validated the standard reference ranges previously proposed for the concentration of S-100B (0.05-0.23 µg/l) and the mean of cerebral blood flow velocity (30.9 to 74.1 cm/sec). The following stratification scheme was used to predict the success of treatment: Patients with a Glasgow Outcome Scale (GOS) score ≥4 or GOS <4 were stratified into ‘favorable’ and ‘unfavorable’ groups, respectively. The favorable group showed relatively constant levels of the S-100B protein close to the normal range and exhibited an increase in V_mean, but this was still within the normal range. The unfavorable group exhibited a high level of S-100B protein and increased V_mean outside of the normal ranges. The changes in the levels of S-100B in the unfavorable and favorable groups were -0.03 and -0.006 mg/l/h, respectively. Furthermore, the rate of decrease in the V_mean value in the unfavorable and favorable groups were -0.26 and -0.18 cm/sec/h, respectively. This study showed that constant levels of S-100B protein, even slightly above the normal range, associated with an increase in V_mean was indicative of a positive therapeutic outcome. However, additional research is required to obtain the appropriate statistical strength required for clinical practice.

Blood-based traumatic brain injury biomarkers - Clinical utilities and regulatory pathways in the United States, Europe and Canada

INTRODUCTION

Traumatic brain injury (TBI) is a major global health issue, resulting in debilitating consequences to families, communities, and health-care systems. Prior research has found that biomarkers aid in the pathophysiological characterization and diagnosis of TBI. Significantly, the FDA has recently cleared both a bench-top assay and a rapid point-of-care assays of tandem biomarker (UCH-L1/GFAP)-based blood test to aid in the diagnosis mTBI patients. With the global necessity of TBI biomarkers research, several major consortium multicenter observational studies with biosample collection and biomarker analysis have been created in the USA, Europe, and Canada. As each geographical region regulates its data and findings, the International Initiative for Traumatic Brain Injury Research (InTBIR) was formed to facilitate data integration and dissemination across these consortia.

AREAS COVERED

This paper covers heavily investigated TBI biomarkers and emerging non-protein markers. Finally, we analyze the regulatory pathways for converting promising TBI biomarkers into approved in-vitro diagnostic tests in the United States, European Union, and Canada.

EXPERT OPINION

TBI biomarker research has significantly advanced in the last decade. The recent approval of an iSTAT point of care test to detect mild TBI has paved the way for future biomarker clearance and appropriate clinical use across the globe.

Predictors of poor prognosis in patients with heat stroke

Objective

The predictors of poor prognosis in heat stroke (HS) remain unknown. This study investigated the predictive factors of poor prognosis in patients with HS.

Methods

Data were obtained and analyzed from the health records of patients diagnosed with heat illness at Ajou university hospital between January 2008 and December 2017. Univariate and multivariate analyses were performed to identify the independent predictors of poor prognosis.

Results

Thirty-six patients (median age, 54.5 years; 33 men) were included in the study. Poor prognosis was identified in 27.8% of the study population (10 patients). The levels of S100B protein, troponin I, creatinine, alanine aminotransferase, and serum lactate were statistically significant in the univariate analysis. Multiple regression analysis revealed that poor prognosis was significantly associated with an increased S100B protein level (odds ratio, 177.37; 95% confidence interval, 2.59 to 12,143.80; P=0.016). The S100B protein cut-off level for predicting poor prognosis was 0.610 μg/L (area under the curve, 0.906; 95% confidence interval, 0.00 to 1.00), with 86% sensitivity and 86% specificity.

Conclusion

An increased S100B protein level on emergency department admission is an independent prognostic factor of poor prognosis in patients with HS. Elevation of the S100B protein level represents a potential target for specific and prompt therapies in these patients.

S100B Serum Level as a Mortality Predictor for Traumatic Brain Injury: A Meta-Analysis

BACKGROUND:

The pathogenesis of inflammatory neuronal cell damage will continue after traumatic brain injury in which contributed to subsequent mortality. Serum S100B levels were shown to be an early predictor of mortality due to traumatic brain injury.

AIM:

This Meta-Analysis will analyse the mean and diagnostic strength of serum S100B levels between survived and died subjects with head injuries based on the various follow-up times of nine studies.

METHODS:

We conducted a meta-anelysis in accordance with PRISMA guidelines and adhering to Cochrane Handbook for Systematic Review of Interventions. Literature search was conducted on March 16, 2018 from Medline and Scopus in the past 10 years, using various keywords related to S100, brain injury, and outcome. Duplicate journals were sorted out via EndNote. Included articles were as follows: original data from the group, clinical trials, case series, patients undergoing serum S100B levels with both short- and long-term follow-up mortality. Data were collected for mortality, serum S100B levels, and its diagnostic strength. All data were analyzed using Review Manager 5.3 (Cochrane, Denmark).

RESULTS:

The results of the meta-analysis showed a significant difference in S100B levels between survived and died subjects with head injuries on overall follow-up timeline (0.91, 95.9% CI 0.7-1.12, I2 = 98%, p < 0.001), during treatment (1.43, 95% CI 0.97 to 1.89, I2 = 98%, p < 0.001), or 6 months (0.19; 95%CI 0.1-0.29, I2 = 76%, p < 0.001) with an average threshold value that varies according to the study method used. The mean diagnostic strength was also promising to predict early mortality (sensitivity of 77.18% and 92.33%, specificity of 78.35% and 50.6%, respectively).

CONCLUSION:

S100B serum levels in the future will be potential biomarkers, and it is expected that there will be standardised guidelines for their application.