Moderate Traumatic Brain Injury Patients Research Articles

The Effect of sCOX-2 inhibitor on S100B Protein Level, Cognitive Dysfunction and Glasgow Outcome Scale (GOS) in Moderate Traumatic Brain Injury Patients

The aim of this study was to explore the relationship between the protein S100B level, cognitive dysfunction, and Glasgow Outcome Scale (GOS) in moderate traumatic brain injury patients. Thirty patients suffering from head injuries undergoing neurosurgery in Dr. Hasan Sadikin General Hospital Bandung, Indonesia, and who were considered eligible based on the inclusion and exclusion criteria participated in this double-blind experimental Randomized Controlled Trial (RCT). The treatment groups (COX-2) were assigned as COX2-I, COX2II, and COX3-IV, six patients in each group. The treatment group was given another dose of sCOX-2 inhibitor 12 hours, 12 and 24 hours after the first dose, and 12, 24 and 36 hours after pre-induction sCOP-2 inhibitor was administered. The GOS score was obtained during the before the surgery and 1 month, 1.5 months, and 2 months postoperative days. The MMSE was performed preoperatively and then on the first to seventh day after the surgery. The results showed that there was a significant decrease in the protein level in all treatment groups including the control group, where the resulting p-value was less than 0.05. Moreover, the treatment group showed a faster time in reaching GOS 5 or be discharged from the hospital compared to the control. In conclusion, it has been demonstrated that the provision of 3 or 4 times of the sCox-2 inhibitor leads to the achievement of a score of 5, while the recovery of the cognitive dysfunction into the normal level is obtained when the SCox2 inhibitor is given 4 times.

Serum Glial Fibrillary Acidic Protein and S100B Profiles in Severity and Outcome Assessment of Moderate and Severe Head Injury Patients in India

Abstract Background: Traumatic brain injury (TBI) is a significant global health issue, with India witnessing approximately 150,000 deaths and 50,000 TBI-related fatalities annually. Severity is classified as mild, moderate, or severe using the Glasgow Coma Scale (GCS). Imaging and blood biomarkers such as serum glial fibrillary acidic protein (GFAP) and S100B aid in diagnosis and outcome prediction, yet imaging facilities are scarce in India. This highlights the necessity for dependable biomarkers. GFAP indicates astroglial injury, while S100B suggests neuronal injury, both in TBI patients’ blood. However, their associations and utility in the Indian population require further exploration. Our study addresses this gap by examining serum GFAP and S100B levels in moderate and severe TBI patients, correlating them with radiological findings and clinical outcomes. Methodology: This prospective observational study was conducted in a tertiary care hospital on 212 patients (106 each for moderate and severely injured groups). Initial disease severity was assessed by GCS score. Outcome assessments were required of surgical intervention and mortality within 5 days. Results: S100B superseded GFAP in assessing disease severity. Receiver operating characteristic analysis showed that S100B was successful with 66% sensitivity and specificity for a cutoff value of 78.77 pg/ml. For early mortality prediction, sensitivity (S100B = 81%–87%, GFAP = 64.1%–78.6%), specificity (S100B = 60.2%–99.2%, GFAP = 54.5%–74.6%), and area under the curve-wise (S100B = 0.721–0.909, GFAP = 0.614–0.763) S100B model performed better than the GFAP model for all corresponding cutoffs (ranged S100B = 86.53–118.56 pg/ml, GFAP = 30.87–34.5 ng/ml). Conclusion: Taken together, our study provides strong evidence that S100B is a better marker of severity and outcome assessment than GFAP.

Association Between Admission Systolic Blood Pressure and Outcomes in Patients with Isolated Traumatic Brain Injury: A Cross-National Multicenter Cohort Study.

The optimal prehospital blood pressure in patients following traumatic brain injury (TBI) remains controversial. We aimed to assess the association between the systolic blood pressure (SBP) at emergency department triage and patient outcomes following isolated moderate-to-severe TBI. We conducted a cross-national multicenter retrospective cohort study using the Pan-Asia Trauma Outcomes Study database from January 1, 2016, to November 30, 2018. The enrollees were adult patients with isolated moderate-to-severe TBI defined by the International Classification of Diseases code, a Glasgow Coma Scale (GCS) <13 at triage, and a nonhead Abbreviated Injury Scale ≤3. The studied variables were SBPs at triage categorized into different ranges. The primary outcome was 30-day mortality, and the secondary outcome was poor functional status at hospital discharge defined by the modified Rankin Scale ≥4. Multivariable logistic regression was applied to adjust for confounders including country, sex, age, mechanism of injury, prehospital vascular access, respiratory rate, GCS, oxygen saturation, intubation, Injury Severity Score, head surgery, intensive care unit admission, and length of hospital stay. Subgroup analyses were performed on different severity of TBI. A total of 785 patients (median age, 42 years; male patients 77.5%; mean SBP at triage, 136.3 ± 33.1 mmHg) were included in the primary analysis. The lowest 30-day mortality rate existed in patients with SBP of 100-119 mmHg. Taking it as baseline, the adjusted odds ratios (aORs) and 95% confidence intervals (CIs) of SBP <100 mmHg, 120-139 mmHg, 140-159 mmHg, and ≥160 mmHg were 7.05 (2.51-19.78), 3.14 (1.14-8.65), 2.91 (1.04-8.17), and 3.28 (1.14-9.42). As for the secondary outcome, the aORs and 95% CIs were 1.36 (0.68-2.68) of <100 mmHg, 0.99 (0.57-1.70) of 120-139 mmHg, 1.23 (0.67-2.25) of 140-159 mmHg, and 1.52 (0.78-2.95) of ≥160 mmHg. Subgroup analyses revealed trends of the best outcomes in both moderate and severe TBI patients with SBP 100-119 mmHg, whereas statistical significance appeared only in patients with severe TBI. SBP of 110-119 mmHg at triage is associated with the lowest 30-day mortality in patients following isolated moderate-to-severe TBI and possibly related to a better functional outcome.

A Nomogram to Predict Intracranial Hypertension in Moderate Traumatic Brain Injury Patients

ObjectivePatients with moderate traumatic brain injury (TBI) are under the threat of intracranial hypertension (IHT). However, it is unclear which moderate TBI patient will develop IHT and should receive intracranial pressure (ICP)-lowering treatment or invasive ICP monitoring after admission. The purpose of the present study was to develop and validate a prediction model that estimates the risk of IHT in moderate TBI patients. MethodsBaseline data collected on admission of 296 moderate TBI patients with Glasgow Coma Scale (GCS) score of 9-11 was collected and analyzed. Multi-variable logistic regression modeling with backward stepwise elimination was used to develop a prediction model for IHT. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. Finally, the prediction model was validated in a separate cohort of 122 patients from 3 hospitals. ResultsFour independent prognostic factors for IHT were identified: GCS score, Marshall head computed tomography score, injury severity score and location of contusion. The C-statistic of the prediction model in internal validation was 84.30% (95% confidence interval [CI]: 0.794–0.892). The area under the curve for the prediction model in external validation was 82.80% (95% CI: 0.747∼0.909). ConclusionsA prediction model based on baseline parameters was found to be highly sensitive in distinguishing moderate TBI patients with GCS score of 9-11 who would suffer IHT. The high discriminative ability of the prediction model supports its use in identifying moderate TBI patients with GCS score of 9-11 who need ICP-lowering therapy or invasive ICP monitoring.

Optimizing a deep learning model for the prediction of electric field induced by transcranial magnetic stimulation for mild to moderate traumatic brain injury patients

Transcranial magnetic stimulation (TMS) is a non-invasive method for treating neurological and psychiatric disorders. It is being tested as an experimental treatment for patients with mild to moderate traumatic brain injuries (mTBI). Due to the complex, heterogeneous composition of the brain, it is difficult to determine if targeted brain regions receive the correct amount of electric field (E-field) induced by the TMS coil. E-field distributions can be calculated by running time-consuming finite element analysis (FEA) simulations of TMS on patient head models. Using machine learning, the E-field can be predicted in real-time. Our prior work used a Deep Convolutional Neural Network (DCNN) to predict the E-field in healthy patients. This study applies the same DCNN to mTBI patients and investigates how model depth and color space of E-field images affect model performance. Nine DCNNs were created using combinations of 3, 4, or 5 encoder and decoder blocks with the color spaces RGB, LAB, and YCbCr. As depth increased, training and testing peak signal-to-noise ratios (PSNR) increased and mean squared errors (MSE) decreased. The depth 5 YCbCr model had the highest training and testing PSNRs of 34.77 and 29.08 dB and lowest training and testing MSEs of 3.335∗10−4 and 1.237∗10−3 respectively. Compared to the model in our prior work, models of depth 5 have higher testing PSNRs and lower MSEs and, except for RGB. Thus, DCNNs with depth 5 and alternative color spaces, despite losing information through color space conversions, resulted in higher PSNRs and lower MSEs.

Seizure Prophylaxis in Young Patients Following Traumatic Brain Injury.

Phenytoin is one of the commonly used anti.seizure medications in nontraumatic seizures. However, its utility and safety in young patients with traumatic brain injury (TBI) for the prevention of early-onset seizures (EOS) are debatable. We sought to explore the use of phenytoin as a seizure prophylaxis following TBI. We hypothesized that administering phenytoin is not effective in preventing EOS after TBI. This was a retrospective observational study conducted on adult TBI patients. EOS was defined as a witnessed seizure within a week postinjury. Data were compared as phenytoin versus no-phenytoin use, EOS versus no-EOS, and among TBI severity groups. During 1 year, 639 TBI patients were included with a mean age of 32 years; of them, 183 received phenytoin as seizure prophylaxis, and 453 received no prophylaxis medication. EOS was documented in 13 (2.0%) patients who received phenytoin, and none had EOS among the nonphenytoin group. The phenytoin group was more likely to have a higher Marshall Score (P = 0.001), lower Glasgow Coma Scale (GCS) (P = 0.001), EOS (P = 0.001), and higher mortality (P = 0.001). Phenytoin was administrated for 15.2%, 43.2%, and 64.5% of mild, moderate, and severe TBI patients, respectively. EOS and no-EOS groups were comparable for age, gender, mechanism of injury, GCS, Marshall Score, serum phenytoin levels, liver function levels, hospital stay, and mortality. Multivariable logistic regression analysis showed that low serum albumin (odds ratio [OR] 0.81; 95% confidence interval [CI] 0.676.0.962) and toxic phenytoin level (OR 43; 95% CI 2.420.780.7) were independent predictors of EOS. In this study, the prophylactic use of phenytoin in TBI was ineffective in preventing EOS. Large-scale matched studies and well-defined hospital protocols are needed for the proper utility of phenytoin post-TBI.

Machine learning-based models to predict the need for neurosurgical intervention after moderate traumatic brain injury.

Traumatic brain injury (TBI) is a widespread global health issue with significant economic consequences. However, no existing model exists to predict the need for neurosurgical intervention in moderate TBI patients with positive initial computed tomographyscans. This study determines the efficacy of machine learning (ML)-based models in predicting the need for neurosurgical intervention. This is a retrospective study of patients admitted to the neuro-intensive care unit of Emtiaz Hospital, Shiraz, Iran, between January 2018 and December 2020. The most clinically important variables from patients that met our inclusion and exclusion criteria were collected and used as predictors. We developed models using multilayer perceptron,random forest,support vector machines (SVM), and logistic regression. To evaluate the models, their F1-score, sensitivity, specificity, and accuracy were assessed using a fourfold cross-validation method. Based on predictive models, SVM showed the highest performance in predicting the need for neurosurgical intervention, with an F1-score of 0.83, an area under curveof 0.93, sensitivity of 0.82, specificity of 0.84, a positive predictive valueof 0.83, and a negative predictive valueof 0.83. The use of ML-based models as decision-making tools can be effective in predicting with high accuracy whether neurosurgery will be necessary after moderate TBIs. These models may ultimately be used as decision-support tools to evaluate early intervention in TBI patients.

Diagnostic accuracy test of quantitative pupillary light reflex as an indicator of increased intracranial pressure in traumatic brain injury patients: a cross-sectional study

Introduction: The gold standard of intracranial pressure (ICP) measurement is an invasive procedure of inserting ICP monitor and one of the limitations of its surrogate, ultrasonography-based (US) optic nerve sheath diameter (ONSD) measurement is operator-dependent. Quantitative pupillary light reflex (PLR) measurement provides objective data promisingly able to predict ICP elevation. The aim of this study was to evaluate diagnostic accuracy of PLR as an indicator of ICP elevation in traumatic brain injury (TBI) patients compared to US-ONSD measurement. Methods: This was a cross-sectional study involving moderate and severe TBI patients aged 18-65 years without any eye lesion, nor medication and illness that may influence PLR. Healthy volunteers were recruited as control group. ONSD and quantitative PLR measurements (baseline diameter, maximum constriction, constriction velocity, latency and amplitude) were assessed to both groups. The Student t-test or Mann-Whitney U-test was used to compare the ONSD and PLR between case and control group. Further analyses were done to determine the sensitivity, specificity and cut-off value of PLR compared to ONSD in case group. Result: ONSD measurements or parameters were significantly different between case and control group (p&lt;0.001), while all PLR parameters were significantly different (p&lt;0.005) except for right eye constriction latency. The baseline diameter of the right eye had the highest accuracy with cut-off value of &gt;3.9 mm (specificity of 100%, sensitivity of 50%). Other PLR components had variable in accuracy. Conclusion: The baseline diameter of the right eye was the most accurate PLR component as an indicator of ICP elevation, represented by ONSD.

The effect of recombinant erythropoietin on long-term outcome after moderate-to-severe traumatic brain injury

PurposeRecombinant erythropoietin (EPO) administered for traumatic brain injury (TBI) may increase short-term survival, but the long-term effect is unknown.MethodsWe conducted a pre-planned long-term follow-up of patients in the multicentre erythropoietin in TBI trial (2010–2015). We invited survivors to follow-up and evaluated survival and functional outcome with the Glasgow Outcome Scale-Extended (GOSE) (categories 5–8 = good outcome), and secondly, with good outcome determined relative to baseline function (sliding scale). We used survival analysis to assess time to death and absolute risk differences (ARD) to assess favorable outcomes. We categorized TBI severity with the International Mission for Prognosis and Analysis of Clinical Trials in TBI model. Heterogeneity of treatment effects were assessed with interaction p-values based on the following a priori defined subgroups, the severity of TBI, and the presence of an intracranial mass lesion and multi-trauma in addition to TBI.ResultsOf 603 patients in the original trial, 487 patients had survival data; 356 were included in the follow-up at a median of 6 years from injury. There was no difference between treatment groups for patient survival [EPO vs placebo hazard ratio (HR) (95% confidence interval (CI) 0.73 (0.47–1.14) p = 0.17]. Good outcome rates were 110/175 (63%) in the EPO group vs 100/181 (55%) in the placebo group (ARD 8%, 95% CI - 3 to 18%, p = 0.14). When good outcome was determined relative to baseline risk, the EPO groups had better GOSE (sliding scale ARD 12%, 95% CI 2–22%, p = 0.02). When considering long-term patient survival, there was no evidence for heterogeneity of treatment effect (HTE) according to severity of TBI (p = 0.85), presence of an intracranial mass lesion (p = 0.48), or whether the patient had multi-trauma in addition to TBI (p = 0.08). Similarly, no evidence of treatment heterogeneity was seen for the effect of EPO on functional outcome.ConclusionEPO neither decreased overall long-term mortality nor improved functional outcome in moderate or severe TBI patients treated in the intensive care unit (ICU). The limited sample size makes it difficult to make final conclusions about the use of EPO in TBI.

Optic nerve sheath diameter and pulsatility index for the diagnosis and follow-up in pediatric traumatic brain injury: a prospective observational cohort study.

Invasive neuromonitoring could be difficult in children with traumatic brain injury (TBI). This study aimed to determine whether noninvasive intracranial pressure (nICP), calculated via pulsatility index (PI) and optic nerve sheath diameter (ONSD) had correlated with each other and patient outcome. All moderate-severe TBI patients were eligible. Patients with a diagnosis of intoxication that did not affect the mental status or cardiovascular system were enrolled as controls. The PI measurements were routinely performed bilaterally on the middle cerebral artery. A software (QLAB's Q-Apps) was used to calculate PI, which further placed the ICP equation of Bellner et al. Linear probe with a 10MHz frequency transducer to measure ONSD, which further placed the ICP equation of Robba et al. All measurements were performed by a point-of-care ultrasound certified pediatric intensivist under the supervision of a neurocritical care specialist, before and 30min after a hypertonic saline (HTS) infusion for every 6h when the patient's mean arterial pressure, heart rate, body temperature, hemoglobin, and blood CO2 levels were within normal ranges. The secondary outcome was the effect of hypertonic saline (HTS) on nICP. Delta-sodium values of each HTS infusion were calculated as a difference between pre- and post-measurements. Twenty-five TBI patients (200 measurements) and 19 controls (57 measurements) were included. Median nICP-PI and nICP-ONSD on admission were significantly higher in the TBI group (11.03 (9.98-12.63), p = 0.004, and 13.14 (12.27-14.64), p < 0.001, respectively). Median nICP-ONSD of severe TBI patients were higher than moderate TBI patients (13.58 (13.14-15.71) and 12.30 (9.83-13.14), respectively, p = 0.013). The median nICP-PI was the same across the type of injury (falls and motor vehicle accidents), while the median nICP-ONSD of the motor vehicle accident group was higher than falls. The first nICP-PI and nICP-ONSD measurements in PICU and admission pGCS were negatively correlated (r = - 0.562, p = 0.003 and r = - 0.582, p = 0.002, respectively). The mean nICP-ONSD during the study period and admission pGCS and GOS-E peds score significantly correlated. However, the Bland-Altman plots showed significant bias between the two methods of ICP except after 5th dose of HTS. All nICP values significantly decreased in time, and it was most obvious after the 5th dose of HTS. No significant correlations were found between delta sodium levels and nICP. Noninvasive estimation of ICP is helpful for the management of pediatric severe TBI patients. nICP driven by ONSD is more consistent with clinical findings of increased ICP but not useful as a follow-up tool in acute management because of slow circulation of CSF around the optic sheath. The correlation between admission GCS scores and GOS-E peds score favors ONSD as a good candidate for determining disease severity and predicting long-term outcomes.

A clinical-radiomics based nomogram to predict progressive intraparenchymal hemorrhage in mild to moderate traumatic injury patients

PurposeTo develop a non-contrast computed tomography(NCCT)based radiomics model for predicting intraparenchymal hemorrhage progression in patients with mild to moderate traumatic brain injury(TBI). MethodsWe retrospectively analyzed 166 mild to moderate TBI patients with intraparenchymal hemorrhage from January 2018 to December 2021. The enrolled patients were divided into training cohort and test cohort with a ratio of 6:4. Uni- and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), the calibration curve, the decision curve analysis, sensitivity, and specificity. ResultsEleven radiomics features, presence with SDH, and D-dimer > 5 mg/l were selected to construct the combined clinical-radiomic model for the prediction of TICH in mild to moderate TBI patients. The AUC of the combined model was 0.81(95% confidence interval (CI), 0.72 to 0.90) in the training cohort and 0.88 (95% CI 0.79 to 0.96) in the test cohort, which were superior to the clinical model alone (AUCtraining = 0.72, AUCtest = 0.74). The calibration curve demonstrated that the radiomics nomogram had a good agreement between prediction and observation. Decision curve analysis confirmed clinically useful. ConclusionsThe combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors can serve as a reliable and powerful tool for Predicting intraparenchymal hemorrhage progression for patients with mild to moderate TBI.

A Long Short-Term Memory Network Using Resting-State Electroencephalogram to Predict Outcomes Following Moderate Traumatic Brain Injury

Although traumatic brain injury (TBI) is a global public health issue, not all injuries necessitate additional hospitalisation. Thinking, memory, attention, personality, and movement can all be negatively impacted by TBI. However, only a small proportion of nonsevere TBIs necessitate prolonged observation. Clinicians would benefit from an electroencephalography (EEG)-based computational intelligence model for outcome prediction by having access to an evidence-based analysis that would allow them to securely discharge patients who are at minimal risk of TBI-related mortality. Despite the increasing popularity of EEG-based deep learning research to create predictive models with breakthrough performance, particularly in epilepsy prediction, its use in clinical decision making for the diagnosis and prognosis of TBI has not been as widely exploited. Therefore, utilising 60s segments of unprocessed resting-state EEG data as input, we suggest a long short-term memory (LSTM) network that can distinguish between improved and unimproved outcomes in moderate TBI patients. Complex feature extraction and selection are avoided in this architecture. The experimental results show that, with a classification accuracy of 87.50 ± 0.05%, the proposed prognostic model outperforms three related works. The results suggest that the proposed methodology is an efficient and reliable strategy to assist clinicians in creating an automated tool for predicting treatment outcomes from EEG signals.

The relationship of neuroanatomy on resting motor threshold and induced electric field strength on treatment outcomes in mild to moderate traumatic brain injury patients during transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a non-invasive treatment protocol for treating several psychiatric conditions, including depression, migraine, smoking cessation, and obsessive-compulsive disorder. Past research suggests that TMS treatment outcomes vary based on neuroanatomy, functional connectivity, and tractography-based structural connectivity. In a previous study, 26 mild to moderate traumatic brain injury (mTBI) patients underwent repetitive transcranial magnetic stimulation (rTMS) and showed improvements in depression, post-concussive symptoms, and sleep dysfunction. The present study was a secondary analysis of that data. Anatomically accurate head models were derived from magnetic resonance imaging (MRI), and finite element analysis simulations were performed to mimic empirical data collection. This allowed for examination of the roles that age, brain scalp distance (BSD), gray matter volume (GMV), site-specific electrical field strength (EFS), and depolarized gray matter volume (DGMV) had on resting motor threshold (RMT) at the precentral gyrus (PreCG). We also investigated how EFS simulated at the dorsolateral prefrontal cortex (DLPFC) and RMT influenced rTMS treatment outcomes. Linear regression showed BSD was associated with EFS, RMT, and DGMV supporting efforts to derive accurate parameters from MRI-based modeling. Furthermore, linear mixed effects modeling showed RMT was associated with EFS and DGMV at the PreCG when age and individual neuroanatomy was accounted for suggesting MRI based anatomy and simulated EFS potentially determine TMS dosage. We did not observe any significant relationship between any of the measures from this study on empirically collected rTMS outcomes in mTBI suggesting further investigations into the mechanisms behind these outcomes are needed.

Transcranial Doppler as an early predictor of neurological outcome in mild and moderate traumatic brain injury: An observational study

Background: Traumatic brain injury (TBI) is a major cause of disability and mortality globaly. The transcranial Doppler (TCD) method may show low diastolic blood flow velocity (FVd) and high pulsatility index (PI) measurements brought on by high vascular bed resistance. This study aimed to assess the usefulness of the TCD-PI for the early detection of secondary neurological deterioration (SND) in mild and moderate TBI. Materials and Methods: This prospective study was carried out on 105 mild and moderate TBI patients, who had TCD measurements within 12 h of the initial trauma, and initial computerized tomography (CT) showed mild lesion or no detected abnormality. Results: Primary end point was assessed (SND) within 1st week post trauma. Of the 105 patients with mild and moderate TBI, 29 (27.6%) showed SND. We evaluated the value of our intervention (TCD) to predict SND after mild and moderate TBI in 1st week. PI could predict SND at cutoff 1.21 with good sensitivity of 96.5% and specificity of 94.7%, area under curve (AUC) value of 0.98, negative predictive value (NPV) of 98.6%, and positive predictive value (PPV) of 87.5%. To amplify our finding, we measured FVd, and at 25 cm/s it showed good sensitivity 86.2% and specificity 89.5% when AUC 0.93, NPV 94.4%, and PPV 75.8%. Conclusion: TCD on admission may provide a valuable tool of early prediction of neurological outcome for mild and moderate TBI patients. Closing the gap in poor prediction of commonly used evaluation by CT especially with mild lesion.

High Serum S100B Protein Levels As A Predictor Of Cognitive Function Disorders In Moderate Traumatic Brain Injury Patients

Objective: To prove high serum S100B protein levels as a predictor of impaired cognitive function in patients with moderate traumatic brain injury. Method: A prospective cohort analytic observational study. The subjects are patients with a moderate degree of TBI with inclusion criteria: onset of TBI 24 hours, age 17-40 years old, and the exclusion criteria were the presence of impaired cognitive function pre-traumatic brain injury (Short IQCODE) 3), depression and multiple trauma. Patients/families/guardians who agreed to the informed consent were checked for S100B levels at &lt;24 hours, then matched with the Montreal Cognitive Assessment version Indonesia (MoCA-INA) questionnaire on day 14 after TBI. This study’s data analysis consisted of univariate and bivariate analysis using Chi-Square. The significance level is stated with p &lt;0.05, 95% confidence interval (CI) assisted by the IBM SPSS version 23. Results: There were 43 research subjects; 23 in the high S100B group and 20 in the S100B group were not high. The mean age is 28 years, with a ratio of 4:1 for men and females. Most of the years, education ≥12 years. The incidence of cognitive dysfunction in patients with moderate traumatic brain injury in the high serum S100B group was 76.19%, while in the non-high serum S100B group was 23.80%, with RR = 6.85, with 95% CI between 1.78-26 .36, and p-value = 0.005. Conclusion: High serum S100B protein levels as a predictor of cognitive function disorders were statistically significant in moderate traumatic brain injury patients with a risk of 6.85 times.

Intracranial Pressure Monitoring for Acute Brain Injured Patients: When, How, What Should We Monitor.

While there is no level I recommendation for intracranial pressure (ICP) monitoring, it is typically indicated for patients with severe traumatic brain injury (TBI) with a Glasgow Coma Scale (GCS) score of 3-8 (class II). Even for moderate TBI patients with GCS 9-12, ICP monitoring should be considered for risk of increased ICP. The impact of ICP monitoring on patient outcomes is still not well-established, but recent studies reported a reduction of early mortality (class III) in TBI patients. There is no standard protocol for the application of ICP monitoring. In cases where cerebrospinal fluid drainage is required, an external ventricular drain is commonly used. In other cases, parenchymal ICP monitoring devices are generally employed. Subdural or non-invasive forms are not suitable for ICP monitoring. The mean value of ICP is the parameter recommended for observation in many guidelines. In TBI, values above 22 mmHg are associated with increased mortality. However, recent studies proposed various parameters including cumulative time with ICP above 20 mmHg (pressure-time dose), pressure reactivity index, ICP waveform characteristics (pulse amplitude of ICP, mean ICP wave amplitude), and the compensatory reserve of the brain (reserve-amplitude-pressure), which are useful in predicting patient outcomes and guiding treatment. Further research is required for validation of these parameters compared to simple ICP monitoring.

Intracranial-Pressure-Monitoring-Assisted Management Associated with Favorable Outcomes in Moderate Traumatic Brain Injury Patients with a GCS of 9-11.

Objective: With a mortality rate of 10−30%, a moderate traumatic brain injury (mTBI) is one of the most variable traumas. The indications for intracranial pressure (ICP) monitoring in patients with mTBI and the effects of ICP on patients’ outcomes are uncertain. The purpose of this study was to examine the indications of ICP monitoring (ICPm) and its effects on the long-term functional outcomes of mTBI patients. Methods: Patients with Glasgow Coma Scale (GCS) scores of 9−11 at Tangdu hospital, between January 2015 and December 2021, were enrolled and treated in this retrospective cohort study. We assessed practice variations in ICP interventions using the therapy intensity level (TIL). Six-month mortality and a Glasgow Outcome Scale Extended (GOS-E) score were the main outcomes. The secondary outcome was neurological deterioration (ND) events. The indication and the estimated impact of ICPm on the functional outcome were investigated by using binary regression analyses. Results: Of the 350 patients, 145 underwent ICP monitoring-assisted management, and the other 205 patients received a standard control based on imaging or clinical examinations. A GCS ≤ 10 (OR 1.751 (95% CI 1.216−3.023), p = 0.003), midline shift (mm) ≥ 2.5 (OR 3.916 (95% CI 2.076−7.386) p < 0.001), and SDH (OR 1.772 (95% CI 1.065−2.949) p = 0.028) were predictors of ICP. Patients who had ICPm (14/145 (9.7%)) had a decreased 6-month mortality rate compared to those who were not monitored (40/205 (19.5%), p = 0.011). ICPm was linked to both improved neurological outcomes at 6 months (OR 0.815 (95% CI 0.712−0.933), p = 0.003) and a lower ND rate (2 = 11.375, p = 0.010). A higher mean ICP (17.32 ± 3.52, t = −6.047, p < 0.001) and a more significant number of ICP > 15 mmHg (27 (9−45.5), Z = −5.406, p < 0.001) or ICP > 20 mmHg (5 (0−23), Z = −4.635, p < 0.001) 72 h after injury were associated with unfavorable outcomes. The best unfavorable GOS-E cutoff value of different ICP characteristics showed that the mean ICP was >15.8 mmHg (AUC 0.698; 95% CI, 0.606−0.789, p < 0.001), the number of ICP > 15 mmHg was >25.5 (AUC 0.681; 95% CI, 0.587−0.774, p < 0.001), and the number of ICP > 20 mmHg was >6 (AUC 0.660; 95% CI, 0.561−0.759, p < 0.001). The total TIL score during the first 72 h post-injury in the non-ICP group (9 (8, 11)) was lower than that of the ICP group (13 (9, 17), Z = −8.388, p < 0.001), and was associated with unfavorable outcomes. Conclusion: ICPm-assisted management was associated with better clinical outcomes six months after discharge and lower incidences of ND for seven days post-injury. A mean ICP > 15.8 mmHg, the number of ICP > 15 mmHg > 25.5, or the number of ICP > 20 mmHg > 6 implicate an unfavorable long-term prognosis after 72 h of an mTBI.

Adherence to Head-of-Bed Elevation in Traumatic Brain Injury: An Audit

Abstract Background An important factor affecting the outcome of traumatic brain injury (TBI) is the early management of raised intracranial pressure (ICP). Head-of-bed elevation (HBE) is a simple and effective method to reduce ICP and prevent aspiration in head injury. Methods This audit was carried out in a level one trauma center. All adult TBI patients were included in the study except patients who had relative contraindication to HBE, managed in prone or Trendelenburg position or who were able to be seated themselves. Patients were observed twice daily, to check adherence to HBE. Adequate HBE angle was referred as an angle of 20 to 30 degrees. A digital protractor was used to measure the head-end angle. Following the first audit cycle, after discussion with nursing staff, a bedside checklist was formulated and two postintervention audit cycles were carried out. Results The first cycle showed that 40.35% of patients had inadequate HBE. Following implementation of the checklist, this percentage dropped to 11.27 and 7.5% in the second and third cycles, respectively. Agitation (p-value = 0.038) and Glasgow coma scale at admission (p-value = 0.028) were found to be confounders for adherence to HBE. Conclusion Agitation among mild and moderate TBI patients contributed to noncompliance for HBE. There was an increasing trend in adherence to maintaining adequate HBE following the use of a bedside checklist. Sustainability of improvement was confirmed with third audit cycle.

Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury

BackgroundRapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI.MethodsRetrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction.Results2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate.ConclusionMachine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment.

Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.