Abstract:Objective: To prospectively study S-100B and neuron specific enolase (NSE) levels in subjects treated for severe head injury (sTBI), and investigate the prognostic value of these biomarkers. Methods: Subjects included in a prospective double blind randomised study for sTBI. Inclusion criteria: Glasgow Coma Score (GCS) (8, age 15-70 years, first recorded cerebral perfusion pressure of .10 mm Hg and arrival ,24 h after trauma. Subjects were treated with an intracranial pressure (ICP) targeted therapy. Blood samp… Show more
“…When compared to NSE, S100B has a higher overall predictive power, in accordance with other groups analyzing and comparing both biomarkers [59–61], where the predictive capability of NSE was found limited in the presence of S100B in multivariate outcome models [39, 62]. An explanation is, again, the notable covariance between serum S100B and NSE, which has been shown to be 0.50–0.78 (correlation coefficient) in previous studies [39, 58, 63, 64], similar to ours of 0.67. However, in our study this correlation decreases over time, highlighting the need for more granular temporal considerations when assessing biomarkers [65].…”
BackgroundIn order to improve assessment and outcome prediction in patients suffering from traumatic brain injury (TBI), cerebral protein levels in serum have been suggested as biomarkers of injury. However, despite much investigation, biomarkers have yet to reach broad clinical utility in TBI. This study is a 9-year follow-up and clinical experience of the two most studied proteins, neuron-specific enolase (NSE) and S100B, in a neuro-intensive care TBI population. Our aims were to investigate to what extent NSE and S100B, independently and in combination, could predict outcome, assess injury severity, and to investigate if the biomarker levels were influenced by extracranial factors.MethodsAll patients treated at the neuro-intensive care unit at Karolinska University Hospital, Stockholm, Sweden between 2005 and 2013 with at least three measurements of serum S100B and NSE (sampled twice daily) were retrospectively included. In total, 417 patients fulfilled the criteria. Parameters were extracted from the computerized hospital charts. Glasgow Outcome Score (GOS) was used to assess long-term functional outcome. Univariate, and multivariate, regression models toward outcome and what explained the high levels of the biomarkers were performed. Nagelkerke’s pseudo-R2 was used to illustrate the explained variance of the different models. A sliding window assessed biomarker correlation to outcome and multitrauma over time.ResultsS100B was found a better predictor of outcome as compared to NSE (area under the curve (AUC) samples, the first 48 hours had Nagelkerke’s pseudo-R2 values of 0.132 and 0.038, respectively), where the information content of S100B peaks at approximately 1 day after trauma. In contrast, although both biomarkers were independently correlated to outcome, NSE had limited additional predictive capabilities in the presence of S100B in multivariate models, due to covariance between the two biomarkers (correlation coefficient 0.673 for AUC 48 hours). Moreover, NSE was to a greater extent correlated to multitrauma the first 48 hours following injury, whereas the effect of extracerebral trauma on S100B levels appears limited to the first 12 hours.ConclusionsWhile both biomarkers are independently correlated to long-term functional outcome, S100B is found a more accurate outcome predictor and possibly a more clinically useful biomarker than NSE for TBI patients.
“…When compared to NSE, S100B has a higher overall predictive power, in accordance with other groups analyzing and comparing both biomarkers [59–61], where the predictive capability of NSE was found limited in the presence of S100B in multivariate outcome models [39, 62]. An explanation is, again, the notable covariance between serum S100B and NSE, which has been shown to be 0.50–0.78 (correlation coefficient) in previous studies [39, 58, 63, 64], similar to ours of 0.67. However, in our study this correlation decreases over time, highlighting the need for more granular temporal considerations when assessing biomarkers [65].…”
BackgroundIn order to improve assessment and outcome prediction in patients suffering from traumatic brain injury (TBI), cerebral protein levels in serum have been suggested as biomarkers of injury. However, despite much investigation, biomarkers have yet to reach broad clinical utility in TBI. This study is a 9-year follow-up and clinical experience of the two most studied proteins, neuron-specific enolase (NSE) and S100B, in a neuro-intensive care TBI population. Our aims were to investigate to what extent NSE and S100B, independently and in combination, could predict outcome, assess injury severity, and to investigate if the biomarker levels were influenced by extracranial factors.MethodsAll patients treated at the neuro-intensive care unit at Karolinska University Hospital, Stockholm, Sweden between 2005 and 2013 with at least three measurements of serum S100B and NSE (sampled twice daily) were retrospectively included. In total, 417 patients fulfilled the criteria. Parameters were extracted from the computerized hospital charts. Glasgow Outcome Score (GOS) was used to assess long-term functional outcome. Univariate, and multivariate, regression models toward outcome and what explained the high levels of the biomarkers were performed. Nagelkerke’s pseudo-R2 was used to illustrate the explained variance of the different models. A sliding window assessed biomarker correlation to outcome and multitrauma over time.ResultsS100B was found a better predictor of outcome as compared to NSE (area under the curve (AUC) samples, the first 48 hours had Nagelkerke’s pseudo-R2 values of 0.132 and 0.038, respectively), where the information content of S100B peaks at approximately 1 day after trauma. In contrast, although both biomarkers were independently correlated to outcome, NSE had limited additional predictive capabilities in the presence of S100B in multivariate models, due to covariance between the two biomarkers (correlation coefficient 0.673 for AUC 48 hours). Moreover, NSE was to a greater extent correlated to multitrauma the first 48 hours following injury, whereas the effect of extracerebral trauma on S100B levels appears limited to the first 12 hours.ConclusionsWhile both biomarkers are independently correlated to long-term functional outcome, S100B is found a more accurate outcome predictor and possibly a more clinically useful biomarker than NSE for TBI patients.
“…There was no statistically significant difference between the control and prostacyclin groups in regard to age (un-paired Student's t test), gender (χ 2 test), GCS, GOS at 3, 6, and 12 months, Marshall, Rotterdam, and Morris-Marshall classifications (Wilcoxon rank-sum test). As previously reported, there was no difference in ISS, Sum score a +1 a Add plus 1 to make the grading numerically consistent with the grading of the motor score of the GCS and with the Marshall CT classification APACHII, S-100B, NSE, APOE, and microdialysis showed no significant differences in intracerebral lactat/pyruvate ratio or glucose between the placebo and prostacyclin group [31][32][33][34]. Thus, for further analysis of the results, the enrolled subjects are treated as one group.…”
The Rotterdam classification seems to be appropriate for describing the evolution of the injuries on the CT scans and contributes in predicting of outcome in patients treated with an ICP-targeted therapy. The Morris-Marshall classification can also be used for prognostication of outcome but it describes only the impact of traumatic subarachnoid hemorrhage (tSAH).
“…Previous studies have demonstrated that serum NSE levels often reflect the extent of brain injury, and that increased serum NSE within the first three days after cardiac arrest is associated with poor outcome [33–35]. However, serum NSE levels after brain injury displayed a gradual decline over time [36], which indicates that NSE collected from patients in the late phase of brain injury cannot be used as a prognostic factor to predict the outcome of patients with UWS.…”
IntroductionAccurate assessment of prognosis for patients with unresponsive wakefulness syndrome (UWS; formerly vegetative state) may help clinicians and families guide the type and intensity of therapy; however, there is no suitable and accurate means to predict the outcome so far. We aimed to develop a simple bedside scoring system to predict the likelihood of awareness recovery in patients with UWS.MethodsWe prospectively enrolled 56 patients (age range 10 to 73 years) with UWS 3 to 12 weeks post-onset. We collected demographic data and performed neurological, serological and neurophysiological tests at study entry. Each patient received a one year follow-up, during which awareness recovery was assessed by experienced physicians on the basis of clinical criteria. Univariate and multivariable analyses were employed to assess the relationships between predictors and awareness recovery.ResultsA total of 56 participants were included in the study; of these, 24 patients recovered awareness, 3 with moderate disabilities, 8 with severe disabilities, 12 were in a minimally conscious state, and 1 died after recovery. During the study, 23 patients remained in UWS and 9 died in UWS. Motor response, type of brain injury, electroencephalogram reactivity, sleep spindles and N20 were shown to be independent predictors for awareness recovery. Based on their coefficients in the model, we assigned these predictors with 1 point each and created a 5-point score for prediction of awareness recovery. The resulting score showed good predictive accuracy in the derivation cohort. The area under the receiver operating characteristic curve for the score was 0.918 with 87.50% sensitivity.ConclusionThis simple bedside prognostic score can be used to predict the probability of awareness recovery in UWS, thus provide families and clinicians with useful outcome information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.