Computed Tomography and Outcome in Moderate and Severe Traumatic Brain Injury: Hematoma Volume and Midline Shift Revisited

Modelling midline shift and ventricle collapse in cerebral oedema following acute ischaemic stroke

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema.

Precision and Speed at Your Fingertips: An Automated Intracranial Hematoma Volume Calculation

BACKGROUND

Intracranial hemorrhage (ICH) is a severe condition that requires rapid diagnosis and treatment. Automated methods for calculating ICH volumes can reduce human error and improve clinical decisioPlease provide professional degrees (e.g., PhD, MD) for the corresponding author.n-making. A novel automated method has been developed that is comparable to the ABC/2 method in terms of speed and accuracy while providing more accurate volumetric data.

METHODS

We developed a novel automated algorithm for calculating intracranial blood volume from computed tomography (CT) scans. The algorithm consists of a Python script that processes Digital Imaging and Communications in Medicine images and determines the blood volume and ratio. The algorithm was validated against manual calculations performed by neurosurgeons.

RESULTS

Our novel automated algorithm for calculating intracranial blood volume from CT scans demonstrated excellent agreement with the ABC/2 method, with a median overall difference of just 1.46 mL. The algorithm was also validated in patient groups with ICH, epidural hematoma (EDH), and SDH, with agreement coefficients of 0.992, 0.983, and 0.997, respectively.

CONCLUSIONS

The study introduces a novel automated algorithm for calculating the volumes of various ICHs (EDH, and SDH) within CT scans. The algorithm showed excellent agreement with manual calculations and outperformed the commonly used ABC/2 method, which tends to overestimate ICH volume. The automated algorithm offers a more accurate, efficient, and time-saving approach to quantifying ICH, EDH, and SDH volumes, making it a valuable tool for clinical evaluation and decision-making.

Correlation Between Volume and Pressure of Intracranial Space With Craniectomy Surface Area and Brain Herniation: A Phantom-Based Study

There are proponents of decompressive craniectomy (DC) and its various modifications who claim reasonable clinical outcomes for each of them. Clinical outcome in cases of traumatic brain injury, managed conservatively or aided by different surgical techniques, depends on multiple factors, which vary widely among patients and have complex interplay, making it difficult to compare one case with another in absolute terms. This forms the basis of the perceived necessity to have a standard model to study, compare, and strategize in this field. We designed a phantom-based model and present the findings of the study aimed at establishing a correlation of the volume of intracranial space and changes in intracranial pressure (ICP) with surface area of the craniectomy defect created during DC and brain herniation volume. A roughly hemispherical radio-opaque container was scanned on a 128-slice computed tomography scanner. Craniectomies of different sizes and shapes were marked on the walls of the phantom. Two spherical sacs of stretchable materials were subsequently placed inside the phantom, fixed to three-way connectors, filled with water, and connected with transducers. The terminals of the transducer cables were coupled with the display monitor through a signal amplifier and processor module. Parts of the wall of the phantom were removed to let portions of the sac herniate through the defect, simulating a DC. Volume measurements using AW volume share 7® software were done. Resection of a 12.7 × 11.5 cm part of the wall resulted in a 10-cm-diameter defect in the wall. Volume differential of 35 mL created a midline shift of 5 mm to the side with lesser volume. When measuring pressure in two stretchable sacs contained inside the phantom, there always remained a pressure differential ranging from 1 to 2 mm Hg in different recordings, even with sacs on both sides containing an equal volume of fluids. Creating a circular wall defect of 10 cm in diameter with an intracavitary pressure of 35 mm Hg on the ipsilateral sac and 33 mm on the contralateral sac recorded with intact walls, resulted in a true volume expansion of 48.411 cm3. The herniation resulted in a reduction of pressure in both sacs, with the pressure recorded as 25 mm in the ipsilateral sac and 24 mm in the contralateral sac. The findings closely matched those of the other model-based studies. Refinement of the materials used is likely to provide a valid platform to study cranial volume, ICP, craniectomy size, and brain prolapse volume in real time. The model will help in pre-operatively choosing the most appropriate technique between a classical DC, a hinge craniotomy, and an expansive cranioplasty technique in cases of refractory raised ICP.

Frailty in Traumatic Brain Injury-The Significance of Temporal Muscle Thickness

BACKGROUND

Temporal muscle thickness (TMT) on cranial CT scans has recently been identified as a prognostic imaging parameter for assessing a patient's baseline frailty. Here, we analyzed whether TMT correlates with Traumatic brain injury (TBI) severity and whether it can be used to predict outcome(s) after TBI.

METHODS

We analyzed the radiological and clinical data sets of 193 patients with TBI who were admitted to our institution and correlated the radiological data with clinical outcomes after stratification for TMT.

RESULTS

Our analyses showed a significant association between high TMT and increased risk for intracranial hemorrhage (p = 0.0135) but improved mRS at 6 months (p = 0.001) as compared to patients with low TMT. Congruent with such findings, a lower TMT was associated with falls and reduced outcomes at 6 months (p < 0.0001 and p < 0.0001).

CONCLUSION

High TMT was robustly associated with head trauma sequelae but was also associated with good clinical outcomes in TBI patients. These findings consolidate the significance of TMT as an objective marker of frailty in TBI patients; such measurements may ultimately be leveraged as prognostic indicators.

Relationship between the shape of intracranial pressure pulse waveform and computed tomography characteristics in patients after traumatic brain injury

BACKGROUND

Midline shift and mass lesions may occur with traumatic brain injury (TBI) and are associated with higher mortality and morbidity. The shape of intracranial pressure (ICP) pulse waveform reflects the state of cerebrospinal pressure-volume compensation which may be disturbed by brain injury. We aimed to investigate the link between ICP pulse shape and pathological computed tomography (CT) features.

METHODS

ICP recordings and CT scans from 130 TBI patients from the CENTER-TBI high-resolution sub-study were analyzed retrospectively. Midline shift, lesion volume, Marshall and Rotterdam scores were assessed in the first CT scan after admission and compared with indices derived from the first 24 h of ICP recording: mean ICP, pulse amplitude of ICP (AmpICP) and pulse shape index (PSI). A neural network model was applied to automatically group ICP pulses into four classes ranging from 1 (normal) to 4 (pathological), with PSI calculated as the weighted sum of class numbers. The relationship between each metric and CT measures was assessed using Mann-Whitney U test (groups with midline shift > 5 mm or lesions > 25 cm3 present/absent) and the Spearman correlation coefficient. Performance of ICP-derived metrics in identifying patients with pathological CT findings was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

PSI was significantly higher in patients with mass lesions (with lesions: 2.4 [1.9-3.1] vs. 1.8 [1.1-2.3] in those without; p << 0.001) and those with midline shift (2.5 [1.9-3.4] vs. 1.8 [1.2-2.4]; p < 0.001), whereas mean ICP and AmpICP were comparable. PSI was significantly correlated with the extent of midline shift, total lesion volume and the Marshall and Rotterdam scores. PSI showed AUCs > 0.7 in classification of patients as presenting pathological CT features compared to AUCs ≤ 0.6 for mean ICP and AmpICP.

CONCLUSIONS

ICP pulse shape reflects the reduction in cerebrospinal compensatory reserve related to space-occupying lesions despite comparable mean ICP and AmpICP levels. Future validation of PSI is necessary to explore its association with volume imbalance in the intracranial space and a potential complementary role to the existing monitoring strategies.

Racial Disparities in Employment Status After Moderate/Severe Traumatic Brain Injuries in Southeast Michigan

OBJECTIVE

To examine the progress made in recent decades by assessing the employment rates of Black and non-Hispanic White (NHW) patients after traumatic brain injury (TBI), controlling for pre-TBI employment status and education status.

DESIGN

Retrospective analysis in a cohort of patients treated in Southeast Michigan at major trauma centers in more recent years (February 2010 to December 2019).

SETTING

Southeastern Michigan Traumatic Brain Injury Model System (TBIMS): 1 of 16 TBIMSs across the United States.

PARTICIPANTS

NHW (n=81) and Black (n=188) patients with moderate/severe TBI (N=269).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Employment status, which is separated into 2 categories: student plus competitive employment and noncompetitive employment.

RESULTS

In 269 patients, NHW patients had more severe initial TBI, measured by percentage brain computed tomography with compression causing >5-mm midline shift (P<.001). Controlling for pre-TBI employment status, we found NHW participants who were students or had competitive employment prior to TBI had higher rates of competitive employment at 2-year (P=.03) follow-up. Controlling for pre-TBI education status, we found no difference in competitive and noncompetitive employment rates between NHW and Black participants at all follow-up years.

CONCLUSIONS

Black patients who were students or had competitive employment before TBI experience worse employment outcomes than their NHW counterparts after TBI at 2 years post TBI. Further research is needed to understand better the factors driving these disparities and how social determinants of health affect these racial differences after TBI.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Is there a weekend effect on mortality rate and outcome for moderate and severe traumatic brain injury? A population-based, observational cohort study

Purpose

The aim of the study was to analyse patient and injury characteristics and the effects of weekend admissions on mortality rate and outcome after moderate and severe traumatic brain injuries.

Methods

This is an observational cohort study based on data from a prospectively maintained regional trauma registry in South Western Norway. Patients with moderate and severe traumatic brain injury admitted between January 1st, 2004 and December 31st, 2019 were included in this study.

Results

During the study period 688 patients were included in the study with similar distribution between moderate (n ​= ​318) and severe (n ​= ​370) traumatic brain injury. Mortality rate was 46% in severe and 13% in moderate traumatic brain injury. Two hundred and thirty-one (34%) patients were admitted during weekends. Patients admitted during weekends were significantly younger (median age (IQR) 32.0 (25.5-67.0) vs 47.0 (20.0-55.0), p ​< ​0.001). Pre-injury ASA 1 was significantly more common in patients admitted during weekends (n ​= ​146, 64%, p ​= ​0.001) while ASA 3 showed significance during weekdays compared to weekends (n ​= ​101, 22%, p ​= ​0.013). On binominal logistic regression analysis mortality rate was significantly higher with older age (OR 1.03, 95% CI for OR 1.02-1.04, p ​< ​0.001) and increasing TBI severity (OR 7.08, 95% CI for OR 4.67-10.73, p ​< ​0.001).

Conclusions

Mortality rate and poor clinical outcome remain high in severe traumatic brain injury. While a higher number of patients are admitted during the weekend, mortality rate does not differ from weekday admissions.

Automated identification and quantification of traumatic brain injury from CT scans: Are we there yet?

BACKGROUND

The purpose of this study was to conduct a systematic review for understanding the availability and limitations of artificial intelligence (AI) approaches that could automatically identify and quantify computed tomography (CT) findings in traumatic brain injury (TBI).

METHODS

Systematic review, in accordance with PRISMA 2020 and SPIRIT-AI extension guidelines, with a search of 4 databases (Medline, Embase, IEEE Xplore, and Web of Science) was performed to find AI studies that automated the clinical tasks for identifying and quantifying CT findings of TBI-related abnormalities.

RESULTS

A total of 531 unique publications were reviewed, which resulted in 66 articles that met our inclusion criteria. The following components for identification and quantification regarding TBI were covered and automated by existing AI studies: identification of TBI-related abnormalities; classification of intracranial hemorrhage types; slice-, pixel-, and voxel-level localization of hemorrhage; measurement of midline shift; and measurement of hematoma volume. Automated identification of obliterated basal cisterns was not investigated in the existing AI studies. Most of the AI algorithms were based on deep neural networks that were trained on 2- or 3-dimensional CT imaging datasets.

CONCLUSION

We identified several important TBI-related CT findings that can be automatically identified and quantified with AI. A combination of these techniques may provide useful tools to enhance reproducibility of TBI identification and quantification by supporting radiologists and clinicians in their TBI assessments and reducing subjective human factors.

Cognitive efficacy and neural mechanisms of music-based neurological rehabilitation for traumatic brain injury

Traumatic brain injury (TBI) causes lifelong cognitive deficits, most often in executive function (EF). Both musical training and music-based rehabilitation have been shown to enhance EF and neuroplasticity. Thus far, however, there is little evidence for the potential rehabilitative effects of music for TBI. Here, we review the core findings from our recent cross-over randomized controlled trial in which a 10-week music-based neurological rehabilitation (MBNR) protocol was administered to 40 patients with moderate-to-severe TBI. Neuropsychological testing and structural/functional magnetic resonance imaging were collected at three time points (baseline, 3 months, and 6 months); one group received the MBNR between time points 1 and 2, while a second group received it between time points 2 and 3. We found that both general EF and set shifting improved after the intervention, and this effect was maintained long term. Morphometric analyses revealed therapy-induced gray matter volume changes most consistently in the right inferior frontal gyrus, changes that correlated with better outcomes in set shifting. Finally, we found changes in the between- and within-network functional connectivity of large-scale resting-state networks after MBNR, which also correlated with measures of EF. Taken together, the data provide evidence for concluding that MBNR improves EF in TBI; also, the data show that morphometric and resting-state functional connectivity are sensitive markers with which to monitor the neuroplasticity induced by the MBNR intervention.

Benefits of Hypothermia for Young Patients with Acute Subdural Hematoma: A Computed Tomography Analysis of the Brain Hypothermia Study

Therapeutic hypothermia for severe traumatic brain injury (TBI) has been repeatedly studied, but no past studies have assessed the detailed head computed tomography (CT) findings. We sought to investigate individual CT findings of severe TBI patients treated with targeted temperature management utilizing the head CT database obtained from the Brain Hypothermia study. Enrolled patients underwent either mild therapeutic hypothermia (32.0°C−34.0°C) or fever control (35.5°C−37.0°C). We assessed individual head CT images on arrival and after rewarming and investigated the correlations with outcomes. The initial CT data were available for 125 patients (hypothermia group = 80, fever control group = 45). Baseline characteristics and CT findings, such as hematoma thickness and midline shift, were similar in all aspects between the two groups. The favorable outcomes in the hypothermia and fever control groups were 38 (47.5%) and 24 (53.3%; p = 0.53) for all 125 patients, respectively; 21 (46.7%) vs. 10 (38.5%; p = 0.50) for 71 patients with acute subdural hematoma (SDH), respectively; and 12 (75.0%) vs. 4 (36.4%; p = 0.045) in 27 young adults (≤50 years) with acute SDH, respectively. There was a trend toward favorable outcomes for earlier time to reach 35.5°C (190 vs. 377 min, p = 0.052) and surgery (155 vs. 180 min, p = 0.096) in young patients with acute SDH. The second CT image revealed progression of the brain injury. This study demonstrated the potential benefits of early hypothermia in young patients with acute SDH, despite no difference in CT findings between the two groups. However, the small number of cases involved hindered the drawing of definitive conclusions. Future studies are warranted to validate the results.

A Robust, Fully Automatic Detection Method and Calculation Technique of Midline Shift in Intracranial Hemorrhage and Its Clinical Application

A midline shift (MLS) is an important clinical indicator for intracranial hemorrhage. In this study, we proposed a robust, fully automatic neural network-based model for the detection of MLS and compared it with MLSs drawn by clinicians; we also evaluated the clinical applications of the fully automatic model. We recruited 300 consecutive non-contrast CT scans consisting of 7269 slices in this study. Six different types of hemorrhage were included. The automatic detection of MLS was based on modified Keypoint R-CNN with keypoint detection followed by training on the ResNet-FPN-50 backbone. The results were further compared with manually drawn outcomes and manually defined keypoint calculations. Clinical parameters, including Glasgow coma scale (GCS), Glasgow outcome scale (GOS), and 30-day mortality, were also analyzed. The mean absolute error for the automatic detection of an MLS was 0.936 mm compared with the ground truth. The interclass correlation was 0.9899 between the automatic method and MLS drawn by different clinicians. There was high sensitivity and specificity in the detection of MLS at 2 mm (91.7%, 80%) and 5 mm (87.5%, 96.7%) and MLSs greater than 10 mm (85.7%, 97.7%). MLS showed a significant association with initial poor GCS and GCS on day 7 and was inversely correlated with poor 30-day GOS (p < 0.001). In conclusion, automatic detection and calculation of MLS can provide an accurate, robust method for MLS measurement that is clinically comparable to the manually drawn method.

Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery

Objective

This study aimed to create a prediction model of postoperative pulmonary complications for the patients with emergency cerebral hemorrhage surgery.

Methods

Patients with hemorrhage surgery who underwent cerebral hemorrhage surgery were included and divided into two groups: patients with or without pulmonary complications. Patient characteristics, previous history, laboratory tests, and interventions were collected. Univariate and multivariate logistic regressions were used to predict postoperative pulmonary infection. Multiple machine learning approaches have been used to compare their importance in predicting factors, namely K-nearest neighbor (KNN), stochastic gradient descent (SGD), support vector classification (SVC), random forest (RF), and logistics regression (LR), as they are the most successful and widely used models for clinical data.

Results

Three hundred and fifty four patients with emergency cerebral hemorrhage surgery between January 1, 2017 and December 31, 2020 were included in the study. 53.7% (190/354) of the patients developed postoperative pulmonary complications (PPC). Stepwise logistic regression analysis revealed four independent predictive factors associated with pulmonary complications, including current smoker, lymphocyte count, clotting time, and ASA score. In addition, the RF model had an ideal predictive performance.

Conclusions

According to our result, current smoker, lymphocyte count, clotting time, and ASA score were independent risks of pulmonary complications. Machine learning approaches can also provide more evidence in the prediction of pulmonary complications.

Bedside detection of intracranial midline shift using portable magnetic resonance imaging

Neuroimaging is crucial for assessing mass effect in brain-injured patients. Transport to an imaging suite, however, is challenging for critically ill patients. We evaluated the use of a low magnetic field, portable MRI (pMRI) for assessing midline shift (MLS). In this observational study, 0.064 T pMRI exams were performed on stroke patients admitted to the neuroscience intensive care unit at Yale New Haven Hospital. Dichotomous (present or absent) and continuous MLS measurements were obtained on pMRI exams and locally available and accessible standard-of-care imaging exams (CT or MRI). We evaluated the agreement between pMRI and standard-of-care measurements. Additionally, we assessed the relationship between pMRI-based MLS and functional outcome (modified Rankin Scale). A total of 102 patients were included in the final study (48 ischemic stroke; 54 intracranial hemorrhage). There was significant concordance between pMRI and standard-of-care measurements (dichotomous, κ = 0.87; continuous, ICC = 0.94). Low-field pMRI identified MLS with a sensitivity of 0.93 and specificity of 0.96. Moreover, pMRI MLS assessments predicted poor clinical outcome at discharge (dichotomous: adjusted OR 7.98, 95% CI 2.07–40.04, p = 0.005; continuous: adjusted OR 1.59, 95% CI 1.11–2.49, p = 0.021). Low-field pMRI may serve as a valuable bedside tool for detecting mass effect.

Outcome Prediction in Patients of Traumatic Brain Injury Based on Midline Shift on CT Scan of Brain

Background Clinicians treating patients with head injury often take decisions based on their assessment of prognosis. Assessment of prognosis could help communication with a patient and the family. One of the most widely used clinical tools for such prediction is the Glasgow coma scale (GCS); however, the tool has a limitation with regard to its use in patients who are under sedation, are intubated, or under the influence of alcohol or psychoactive drugs. CT scan findings such as status of basal cistern, midline shift, associated traumatic subarachnoid hemorrhage (SAH), and intraventricular hemorrhage are useful indicators in predicting outcome and also considered as valid options for prognostication of the patients with traumatic brain injury (TBI), especially in emergency setting.

Materials and Methods 108 patients of head injury were assessed at admission with clinical examination, history, and CT scan of brain. CT findings were classified according to type of lesion and midline shift correlated to GCS score at admission. All the subjects in this study were managed with an identical treatment protocol. Outcome of these patients were assessed on GCS score at discharge.

Results Among patients with severe GCS, 51% had midline shift. The degree of midline shift in CT head was a statistically significant determinant of outcome (p = 0.023). Seventeen out of 48 patients (35.4%) with midline shift had poor outcome as compared with 8 out of 60 patients (13.3%) with no midline shift.

Conclusion In patients with TBI, the degree of midline shift on CT scan was significantly related to the severity of head injury and resulted in poor clinical outcome.

Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology

Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer's disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

Random Forest-Based Prediction of Outcome and Mortality in Patients with Traumatic Brain Injury Undergoing Primary Decompressive Craniectomy

BACKGROUND

Various prognostic models are used to predict mortality and functional outcome in patients after traumatic brain injury with a trend to incorporate machine learning protocols. None of these models is focused exactly on the subgroup of patients indicated for decompressive craniectomy. Evidence regarding efficiency of this surgery is still incomplete, especially in patients undergoing primary decompressive craniectomy with evacuation of traumatic mass lesions.

METHODS

In a prospective study with a 6-month follow-up period, we assessed postoperative outcome and mortality of 40 patients who underwent primary decompressive craniectomy for traumatic brain injuries during 2018-2019. The results were analyzed in relation to a wide spectrum of preoperatively available demographic, clinical, radiographic, and laboratory data. Random forest algorithms were trained for prediction of both mortality and unfavorable outcome, with their accuracy quantified by area under the receiver operating curves (AUCs) for out-of-bag samples.

RESULTS

At the end of the follow-up period, we observed mortality of 57.5%. Favorable outcome (Glasgow Outcome Scale [GOS] score 4-5) was achieved by 30% of our patients. Random forest-based prediction models constructed for 6-month mortality and outcome reached a moderate predictive ability, with AUC = 0.811 and AUC = 0.873, respectively. Random forest models trained on handpicked variables showed slightly decreased AUC = 0.787 for 6-month mortality and AUC = 0.846 for 6-month outcome and increased out-of-bag error rates.

CONCLUSIONS

Random forest algorithms show promising results in prediction of postoperative outcome and mortality in patients undergoing primary decompressive craniectomy. The best performance was achieved by Classification Random forest for 6-month outcome.

Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model

Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee (AUP: A2015 11-001) on December 22, 2015.

Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach

OBJECTIVES

We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Traumatic Brain Injury (TBI).

METHODS

This study included TBI patients who were hospitalized in a level 1 trauma center between January 2014 and February 2019. Data were analyzed for all adult patients who received mechanical ventilation following TBI with abbreviated injury severity (AIS) score for the head region of ≥ 3. This study designed three sets of machine learning models: set A defined PMV to be greater than 7 days, set B (PMV > 10 days) and set C (PMV >14 days) to determine the optimal model for deployment. Patients' demographics, injury characteristics and CT findings were used as predictors. Logistic regression (LR), Artificial neural networks (ANN) Support vector machines (SVM), Random Forest (RF) and C.5 Decision Tree (C.5 DT) were used to predict the PMV.

RESULTS

The number of eligible patients that were included in the study were 674, 643 and 622 patients in sets A, B and C respectively. In set A, LR achieved the optimal performance with accuracy 0.75 and Area under the curve (AUC) 0.83. SVM achieved the optimal performance among other models in sets B with accuracy/AUC of 0.79/0.84 respectively. ANNs achieved the optimal performance in set C with accuracy/AUC of 0.76/0.72 respectively. Machine learning models in set B demonstrated more stable performance with higher prediction success and discrimination power.

CONCLUSION

This study not only provides evidence that machine learning methods outperform the traditional multivariate analytical methods, but also provides a perspective to reach a consensual definition of PMV.

Association of the bleeding time test with aspects of traumatic brain injury in patients with alcohol use disorder

BACKGROUND-AIM

Traumatic brain injury (TBI) and alcohol use disorder (AUD) can occur concomitantly and be associated with coagulopathy that influences TBI outcome. The use of bleeding time tests in TBI management is controversial. We hypothesized that in TBI patients with AUD, a prolonged bleeding time is associated with more severe injury and poor outcome.

MATERIAL AND METHODS

Moderate and severe TBI patients with evidence of AUD were examined with bleeding time according to IVY bleeding time on admission during neurointensive care. Baseline clinical and radiological characteristics were recorded. A standardized IVY bleeding time test was determined by staff trained in the procedure. Bleeding time test results were divided into normal (≤ 600 s), prolonged (> 600 s), and markedly prolonged (≥ 900 s). Normal platelet count (PLT) was defined as > 150,000/μL. This cohort was compared with another group of TBI patients without evidence of AUD.

RESULTS

Fifty-two patients with TBI and AUD were identified, and 121 TBI patients without any history of AUD were used as controls. PLT was low in 44.2% and bleeding time was prolonged in 69.2% of patients. Bleeding time values negatively correlated with PLT (p < 0.05). TBI patients with markedly prolonged values (≥ 900 s) had significantly increased hematoma size, and more frequently required intracranial pressure measurement and mechanical ventilation compared with those with bleeding times < 900 s (p < 0.05). Most patients (88%) with low platelet count had prolonged bleeding time. No difference in 6-month outcome between the bleeding time groups was observed (p > 0.05). Subjects with TBI and no evidence for AUD had lower bleeding time values and higher platelet count compared with those with TBI and history of AUD (p < 0.05).

CONCLUSIONS

Although differences in the bleeding time values between TBI cohorts exist and prolonged values may be seen even in patients with normal platelet count, the bleeding test is a marker of primary hemostasis and platelet function with low specificity. However, it may provide an additional assessment in the interpretation of the overall status of TBI patients with AUD. Therefore, the bleeding time test should only be used in combination with the patient's bleeding history and careful assessment of other hematologic parameters.

Factors Associated with Poor Outcomes in Patients with Mild or Moderate Acute Subdural Hematomas

The factors influencing the outcomes of mild/moderate acute subdural hematoma (ASDH) are still unclear. Retrospective analyses were performed to identify such factors. The medical records of all patients who were admitted to Saiseikai Shiga Hospital with mild (Glasgow Coma Scale [GCS] score of 14–15) or moderate (GCS score of 9–13) ASDH between April 2008 and March 2017 were reviewed. Comparisons between the patients who exhibited favorable and poor outcomes were performed. Then, independent factors that contributed to poor outcomes were identified via logistic regression analyses. A total of 266 patients with a mean age of 70.2 were included in this study. The most common concomitant injuries were subarachnoid hemorrhages (SAHs; 56.8%). The patients’ Injury Severity Scores (ISS) ranged from 16 to 75 (median: 21). The 66 moderate ASDH patients exhibited significantly higher frequencies of surgery and mortality (24.2% and 13.6%, respectively) than the 200 mild ASDH patients (8.0% and 4.5%, respectively). The factors associated with poor outcomes were age (odds ratio [OR]: 1.06) and the ISS (OR: 1.24) in the mild ASDH patients, and older age (OR: 1.09) and the higher ISS (OR: 1.15) in the moderate group, too.

Automated hematoma segmentation and outcome prediction for patients with traumatic brain injury

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. Automated brain hematoma segmentation and outcome prediction for patients with TBI can effectively facilitate patient management. In this study, we propose a novel Multi-view convolutional neural network with a mixed loss to segment total acute hematoma on head CT scans collected within 24 h after the injury. Based on the automated segmentation, the volumetric distribution and shape characteristics of the hematoma were extracted and combined with other clinical observations to predict 6-month mortality. The proposed hematoma segmentation network achieved an average Dice coefficient of 0.697 and an intraclass correlation coefficient of 0.966 between the volumes estimated from the predicted hematoma segmentation and volumes of the annotated hematoma segmentation on the test set. Compared with other published methods, the proposed method has the most accurate segmentation performance and volume estimation. For 6-month mortality prediction, the model achieved an average area under the precision-recall curve (AUCPR) of 0.559 and area under the receiver operating characteristic curve (AUC) of 0.853 using 10-fold cross-validation on a dataset consisting of 828 patients. The average AUCPR and AUC of the proposed model are respectively more than 10% and 5% higher than those of the widely used IMPACT model.

Quantification of Iodine Leakage on Dual-Energy CT as a Marker of Blood-Brain Barrier Permeability in Traumatic Hemorrhagic Contusions: Prediction of Surgical Intervention for Intracranial Pressure Management

BACKGROUND AND PURPOSE

Hemorrhagic contusions are associated with iodine leakage. We aimed to identify quantitative iodine-based dual-energy CT variables that correlate with the type of intracranial pressure management.

MATERIALS AND METHODS

Consecutive patients with contusions from May 2016 through January 2017 were retrospectively analyzed. Radiologists, blinded to the outcomes, evaluated CT variables from unenhanced admission and short-term follow-up head dual-energy CT scans obtained after contrast-enhanced whole-body CT. Treatment intensity of intracranial pressure was broadly divided into 2 groups: those managed medically and those managed surgically. Univariable analysis followed by logistic regression was used to develop a prediction model.

RESULTS

The study included 65 patients (50 men; median age, 48 years; Q1 to Q3, 25-65.5 years). Twenty-one patients were managed surgically (14 by CSF drainage, 7 by craniectomy). Iodine-based variables that correlated with surgical management were higher iodine concentration, pseudohematoma volume, iodine quantity in pseudohematoma, and iodine quantity in contusions. The regression model developed after inclusion of clinical variables identified 3 predictor variables: postresuscitation Glasgow Coma Scale (adjusted OR = 0.55; 95% CI, 0.38-0.79; P = .001), age (adjusted OR = 0.9; 95% CI, 0.85-0.97; P = .003), and pseudohematoma volume (adjusted OR = 2.05; 95% CI, 1.1-3.77; P = .02), which yielded an area under the curve of 0.96 in predicting surgical intracranial pressure management. The 2 predictors for craniectomy were age (adjusted OR = 0.89; 95% CI, 0.81-0.99; P = .03) and pseudohematoma volume (adjusted OR = 1.23; 95% CI, 1.03-1.45; P = .02), which yielded an area under the curve of 0.89.

CONCLUSIONS

Quantitative iodine-based parameters derived from follow-up dual-energy CT may predict the intensity of intracranial pressure management in patients with hemorrhagic contusions.

Long-term outcome in traumatic brain injury patients with midline shift: a secondary analysis of the Phase 3 COBRIT clinical trial

OBJECTIVE

Following traumatic brain injury (TBI), midline shift of the brain at the level of the septum pellucidum is often caused by unilateral space-occupying lesions and is associated with increased intracranial pressure and worsened morbidity and mortality. While outcome has been studied in this population, the recovery trajectory has not been reported in a large cohort of patients with TBI. The authors sought to utilize the Citicoline Brain Injury Treatment (COBRIT) trial to analyze patient recovery over time depending on degree of midline shift at presentation.

METHODS

Patient data from the COBRIT trial were stratified into 4 groups of midline shift, and outcome measures were analyzed at 30, 90, and 180 days postinjury. A recovery trajectory analysis was performed identifying patients with outcome measures at all 3 time points to analyze the degree of recovery based on midline shift at presentation.

RESULTS

There were 892, 1169, and 895 patients with adequate outcome data at 30, 90, and 180 days, respectively. Rates of favorable outcome (Glasgow Outcome Scale-Extended [GOS-E] scores 4-8) at 6 months postinjury were 87% for patients with no midline shift, 79% for patients with 1-5 mm of shift, 64% for patients with 6-10 mm of shift, and 47% for patients with > 10 mm of shift. The mean improvement from unfavorable outcome (GOS-E scores 2 and 3) to favorable outcome (GOS-E scores 4-8) from 1 month to 6 months in all groups was 20% (range 4%-29%). The mean GOS-E score for patients in the 6- to 10-mm group crossed from unfavorable outcome (GOS-E scores 2 and 3) into favorable outcome (GOS-E scores 4-8) at 90 days, and the mean GOS-E of patients in the > 10-mm group nearly reached the threshold of favorable outcome by 180 days postinjury.

CONCLUSIONS

In this secondary analysis of the Phase 3 COBRIT trial, TBI patients with less than 10 mm of midline shift on admission head CT had significantly improved functional outcomes through 180 days after injury compared with those with greater than 10 mm of midline shift. Of note, nearly 50% of patients with > 10 mm of midline shift achieved a favorable outcome (GOS-E score 4-8) by 6 months postinjury.

Midline Shift vs. Mid-Surface Shift: Correlation with Outcome of Traumatic Brain Injuries

Traumatic brain injury (TBI) is a major health and socioeconomic problem globally that is associated with a high level of mortality. Early and accurate diagnosis and prognosis of TBI is important in patient management and preventing any secondary injuries. Computer tomography (CT) imaging assists physicians in diagnosing injury and guiding treatment. One of the clinical parameters extracted from CT images is midline shift, a measure of linear displacement in brain structure, which is correlated with TBI patient outcomes. However, only a tiny fraction of the overall tissue displacement is quantified through this parameter. In this paper, a novel measurement of overall mid-surface shift is proposed that quantifies the total volume of brain tissue shifted across the midline. When compared to traditional midline shift, mid-surface shift has a stronger correlation with TBI patient outcomes.

Hematoma Segmentation Using Dilated Convolutional Neural Network

Traumatic brain injury (TBI) is a global health challenge. Accurate and fast automatic detection of hematoma in the brain is essential for TBI diagnosis and treatment. In this study, we developed a fully automated system to detect and segment hematoma regions in head Computed Tomography (CT) images of patients with acute TBI. We adapted the structure of a fully convolutional network by introducing dilated convolution and removing down-sampling and up-sampling layers. Skip layers are also used to combine low-level features and high-level features. By integrating the information from different scales without losing spatial resolution, the network can perform more accurate segmentation. Our final hematoma segmentations achieved the Dice, sensitivity, and specificity of 0.62, 0.81, and 0.96, respectively, which outperformed the results from previous methods.

Chitinase-3-Like Protein 1, Serum Amyloid A1, C-Reactive Protein, and Procalcitonin Are Promising Biomarkers for Intracranial Severity Assessment of Traumatic Brain Injury: Relationship with Glasgow Coma Scale and Computed Tomography Volumetry

OBJECTIVE

The volume and location of intracranial hematomas are well-known prognostic factors for traumatic brain injury. The aim of this study was to determine the relationship of serum biomarkers S100β, glial fibrillary acidic protein, neuron-specific enolase, total tau, phosphorylated neurofilament heavy chain, serum amyloid A1 (SAA1), C-reactive protein, procalcitonin (PCT), and chitinase-3-like protein 1 (YKL-40) with traumatic brain injury severity and the amount and location of hemorrhagic traumatic lesions.

METHODS

A prospective observational cohort of 115 patients with a Glasgow Coma Scale (GCS) score of 3-15 were evaluated. Intracranial lesion volume was measured from the semiautomatic segmentation of hematoma on computed tomography using Analyze software. The establishment of possible biomarker cutoff points for intracranial lesion detection was estimated using the Youden Index (J) obtained from the area under the receiver operating characteristic curve.

RESULTS

SAA1, YKL-40, PCT, and S100β showed the most robust association with level of consciousness, both with total GCS and motor score. Biomarkers significantly correlated with volumetric measurements of subdural hematoma, traumatic subarachnoid hemorrhage, intraparenchymal hemorrhage, intraventricular hemorrhage, and total amount of bleeding. The type of intracranial hemorrhage was associated with various release patterns of neurobiochemical markers.

CONCLUSIONS

YKL-40, SAA1, C-reactive protein, and PCT combined with S100β were the most promising biomarkers to determine the presence, location, and extent of traumatic intracranial lesions. Combination of biomarkers further increased the discriminatory capacity for the detection of intracranial bleeding.

Traumatic Brain Injury Results in Dynamic Brain Structure Changes Leading to Acute and Chronic Motor Function Deficits in a Pediatric Piglet Model

Traumatic brain injury (TBI) is a leading cause of death and disability in children. Pediatric TBI patients often suffer from crippling cognitive, emotional, and motor function deficits that have negative lifelong effects. The objective of this study was to longitudinally assess TBI pathophysiology using multi-parametric magnetic resonance imaging (MRI), gait analysis, and histological approaches in a pediatric piglet model. TBI was produced by controlled cortical impact in Landrace piglets. MRI data, including from proton magnetic resonance spectroscopy (MRS), were collected 24 hours and 12 weeks post-TBI, and gait analysis was performed at multiple time-points over 12 weeks post-TBI. A subset of animals was sacrificed 24 hours, 1 week, 4 weeks, and 12 weeks post-TBI for histological analysis. MRI results demonstrated that TBI led to a significant brain lesion and midline shift as well as microscopic tissue damage with altered brain diffusivity, decreased white matter integrity, and reduced cerebral blood flow. MRS showed a range of neurochemical changes after TBI. Histological analysis revealed neuronal loss, astrogliosis/astrocytosis, and microglia activation. Further, gait analysis showed transient impairments in cadence, cycle time, % stance, step length, and stride length, as well as long-term impairments in weight distribution after TBI. Taken together, this study illustrates the distinct time course of TBI pathoanatomic and functional responses up to 12 weeks post-TBI in a piglet TBI model. The study of TBI injury and recovery mechanisms, as well as the testing of therapeutics in this translational model, are likely to be more predictive of human responses and clinical outcomes compared to traditional small animal models.

Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury

Traumatic brain injury is a complex and diverse medical condition with a high frequency of intracranial abnormalities. These can typically be visualized on a computed tomography (CT) scan, which provides important information for further patient management, such as the need for operative intervention. In order to quantify the extent of acute intracranial lesions and associated secondary injuries, such as midline shift and cisternal compression, visual assessment of CT images has limitations, including observer variability and lack of quantitative interpretation. Automated image analysis can quantify the extent of intracranial abnormalities and provide added value in routine clinical practice. In this article, we present icobrain, a fully automated method that reliably computes acute intracranial lesions volume based on deep learning, cistern volume, and midline shift on the noncontrast CT image of a patient. The accuracy of our method is evaluated on a subset of the multi-center data set from the CENTER-TBI (Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury) study for which expert annotations were used as a reference. Median volume differences between expert assessments and icobrain are 0.07 mL for acute intracranial lesions and -0.01 mL for cistern segmentation. Correlation between expert assessments and icobrain is 0.91 for volume of acute intracranial lesions and 0.94 for volume of the cisterns. For midline shift computations, median error is -0.22 mm, with a correlation of 0.93 with expert assessments.

Final outcome trends in severe traumatic brain injury: a 25-year analysis of single center data

BACKGROUND

Evidence from the last 25 years indicates a modest reduction of mortality after severe traumatic head injury (sTBI). This study evaluates the variation over time of the whole Glasgow Outcome Scale (GOS) throughout those years.

METHODS

The study is an observational cohort study of adults (≥ 15 years old) with closed sTBI (GCS ≤ 8) who were admitted within 48 h after injury. The final outcome was the 1-year GOS, which was divided as follows: (1) dead/vegetative, (2) severely disabled (dependent patients), and (3) good/moderate recovery (independent patients). Patients were treated uniformly according to international protocols in a dedicated ICU. We considered patient characteristics that were previously identified as important predictors and could be determined easily and reliably. The admission years were divided into three intervals (1987-1995, 1996-2004, and 2005-2012), and the following individual CT characteristics were noted: the presence of traumatic subarachnoid or intraventricular hemorrhage (tSAH, IVH), midline shift, cisternal status, and the volume of mass lesions (A × B × C/2). Ordinal logistic regression was performed to estimate associations between predictors and outcomes. The patients' estimated propensity scores were included as an independent variable in the ordinal logistic regression model (TWANG R package).

FINDINGS

The variables associated with the outcome were age, pupils, motor score, deterioration, shock, hypoxia, cistern status, IVH, tSAH, and epidural volume. When adjusting for those variables and the propensity score, we found a reduction in mortality from 55% (1987-1995) to 38% (2005-2012), but we discovered an increase in dependent patients from 10 to 21% and just a modest increase in independent patients of 6%.

CONCLUSIONS

This study covers 25 years of management of sTBI in a single neurosurgical center. The prognostic factors are similar to those in the literature. The improvement in mortality does not translate to better quality of life.

The clinical relevance of ABO blood type in 100 patients with acute subdural hematoma

OBJECTIVE

The correlation of depleted blood through midline shift in acute subdural hematoma remains the most reliable clinical predictor to date. On the other hand, patient's ABO blood type has a profound impact on coagulation and hemostasis. We conducted this study to evaluate the role of patient's blood type in terms of incidence, clinical course and outcome after acute subdural hematoma bleeding.

METHODS

100 patients with acute subdural hematoma treated between 2010 and 2015 at the author's institution were included. Baseline characteristics and clinical findings including Glasgow coma scale, Glasgow outcome scale, hematoma volume, rebleeding, midline shift, postoperative seizures and the presence of anticoagulation were analyzed for their association with ABO blood type.

RESULTS

Patient's with blood type O were found to have a lower midline shift (p<0.01) and significantly less seizures (OR: 0.43; p<0.05) compared to non-O patients. Furthermore, patients with blood type A had the a significantly higher midline shift (p<0.05) and a significantly increased risk for postoperative seizures (OR: 4.01; p<0.001). There was no difference in ABO blood type distribution between acute subdural hematoma patients and the average population.

CONCLUSION

The ABO blood type has significant influence on acute subdural hematoma sequelae. Patient's with blood type O benefit in their clinical course after acute subdural hematoma whereas blood type A patients are at highest risk for increased midline shift and postoperative seizures. Further studies elucidating the biological mechanisms of blood type depended hemostaseology and its role in acute subdural hematoma are required for the development of an appropriate intervention.

Sulfonylurea Receptor 1, Transient Receptor Potential Cation Channel Subfamily M Member 4, and KIR6.2:Role in Hemorrhagic Progression of Contusion

In severe traumatic brain injury (TBI), contusions often are worsened by contusion expansion or hemorrhagic progression of contusion (HPC), which may double the original contusion volume and worsen outcome. In humans and rodents with contusion-TBI, sulfonylurea receptor 1 (SUR1) is upregulated in microvessels and astrocytes, and in rodent models, blockade of SUR1 with glibenclamide reduces HPC. SUR1 does not function by itself, but must co-assemble with either KIR6.2 or transient receptor potential cation channel subfamily M member 4 (TRPM4) to form K_ATP (SUR1-KIR6.2) or SUR1-TRPM4 channels, with the two having opposite effects on membrane potential. Both KIR6.2 and TRPM4 are reportedly upregulated in TBI, especially in astrocytes, but the identity and function of SUR1-regulated channels post-TBI is unknown. Here, we analyzed human and rat brain tissues after contusion-TBI to characterize SUR1, TRPM4, and KIR6.2 expression, and in the rat model, to examine the effects on HPC of inhibiting expression of the three subunits using intravenous antisense oligodeoxynucleotides (AS-ODN). Glial fibrillary acidic protein (GFAP) immunoreactivity was used to operationally define core versus penumbral tissues. In humans and rats, GFAP-negative core tissues contained microvessels that expressed SUR1 and TRPM4, whereas GFAP-positive penumbral tissues contained astrocytes that expressed all three subunits. Förster resonance energy transfer imaging demonstrated SUR1-TRPM4 heteromers in endothelium, and SUR1-TRPM4 and SUR1-KIR6.2 heteromers in astrocytes. In rats, glibenclamide as well as AS-ODN targeting SUR1 and TRPM4, but not KIR6.2, reduced HPC at 24 h post-TBI. Our findings demonstrate upregulation of SUR1-TRPM4 and K_ATP after contusion-TBI, identify SUR1-TRPM4 as the primary molecular mechanism that accounts for HPC, and indicate that SUR1-TRPM4 is a crucial target of glibenclamide.

Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms

Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.

Efficiency and Limitations of Decompressive Craniectomy in Patients after Traumatic Brain Injury – Preliminary Results

Introduction: Decompressive craniectomy (DC) has been recently proven effective tier II therapeutic procedure in the treatment of refractory posttraumatic intracranial hypertension. However, its full potential and effectivity is yet to be described and this surgery remains controversial. The goals of our study include analysis of efficiency of DC and description of risk factors associated with unfavourable outcome.

Methods: 24 patients who underwent DC at the Clinic of Neurosurgery, JFM CU in Martin, during years 2015–2016 were prospectively observed. Selected demographic, clinical, and radiographic factors were analysed and compared with patient’s GOS (Glasgow Outcome Scale) at the time of their first ambulatory control (after 3.5 months in average).

Results: We observed mortality of 29.17 %. Good outcome (GOS 4–5) was achieved by 29.17 % of the patients as well. Preoperative GCS ≤ 5 (p = 0.049), intraventricular bleeding (p = 0.0268), midline shift ≥ 15 mm (p = 0.0067), and the volume of intracranial lesion (R = −0.41, p = 0.046), especially its extracerebral component (R = −0.46, p = 0.02), were identified as statistically significant negative prognostic factors.

Conclusion: DC is effective in the management of patients with traumatic brain injury. Good outcome is achieved by 29.17 % of the patients. Described negative prognostic factors (preoperative GCS ≤ 5, intraventricular bleeding, midline shift ≥ 15 mm, and increasing the volume of traumatic mass lesion) could help in targeting this surgery only to patients who are expected to benefit from it.

Erythropoietin Attenuates the Brain Edema Response after Experimental Traumatic Brain Injury

Erythropoietin (EPO) has neuroprotective effects in multiple central nervous system (CNS) injury models; however EPO's effects on traumatic brain edema are elusive. To explore EPO as an intervention in traumatic brain edema, male Sprague–Dawley (SD) rats were subjected to blunt, controlled traumatic brain injury (TBI). Animals were randomized to EPO 5000 IU/kg or saline (control group) intraperitoneally within 30 min after trauma and once daily for 4 consecutive days. Brain MRI, immunohistofluorescence, immunohistochemistry, and quantitative protein analysis were performed at days 1 and 4 post- trauma. EPO significantly prevented the loss of the tight junction protein zona occludens 1 (ZO-1) observed in control animals after trauma. The decrease of ZO-1 in the control group was associated with an immunoglobulin (Ig)G increase in the perilesional parenchyma, indicating blood–brain barrier (BBB) dysfunction and increased permeability. EPO treatment attenuated decrease in apparent diffusion coefficient (ADC) after trauma, suggesting a reduction of cytotoxic edema, and reduced the IgG leakage, indicating that EPO contributed to preserve BBB integrity and attenuated vasogenic edema. Animals treated with EPO demonstrated conserved levels of aquaporin 4 (AQP4) protein expression in the perilesional area, whereas control animals showed a reduction of AQP4. We show that post TBI administration of EPO decreases early cytotoxic brain edema and preserves structural and functional properties of the BBB, leading to attenuation of the vasogenic edema response. The data support that the mechanisms involve preservation of the tight junction protein ZO-1 and the water channel AQP4, and indicate that treatment with EPO may have beneficial effects on the brain edema response following TBI.

Radiologic Factors Predicting Deterioration of Mental Status in Patients with Acute Traumatic Subdural Hematoma

OBJECTIVE

To evaluate whether subdural hematoma (SDH) volume and other radiologic factors predict deterioration of mental status in patients with acute traumatic SDH.

METHODS

SDH volumes were measured with a semiautomated tool. The area under the receiver operating characteristic curve was used to determine optimal cutoff values for mental deterioration, including the variables midline shift, SDH volume, hematoma thickness, and Sylvian fissure ratio. Multivariate logistic regression was used to calculate the odds ratio for mental deterioration based on several predictive factors.

RESULTS

We enrolled 103 consecutive patients admitted to our hospital with acute traumatic SDH over an 8-year period. We observed an increase in SDH volume of approximately 7.2 mL as SDH thickness increased by 1 mm. A steeper slope for midline shift was observed in patients with SDH volumes of approximately 75 mL in the younger age group compared with patients in the older age group. When comparing cutoff values used to predict poor mental status at time of admission between the 2 age groups, we observed smaller midline shifts in the older patients.

CONCLUSIONS

Among younger patients, an overall tendency for more rapid midline shift progression was observed in patients with relatively low SDH volumes compared with older patients. Older patients seem to tolerate larger hematoma volumes owing to brain atrophy compared with younger patients. When there is a midline shift, older patients seem to be more vulnerable to mental deterioration than younger patients.

Acute cerebral MCA ischemia with secondary severe head injury and acute intracerebral and subdural haematoma. Case report

Generally, according to international literature, cerebral ischemia is a secondary posttraumatic lesion produced by direct compression in the context of a cerebral herniation syndrome or indirect by vasospasm produced by posttraumatic subarachnoid, subdural or intraventricular hemorrhages. We present the case of a patient with an acute MCA ischemia with severe head injury due to a fall with subsequent intracranial acute intracerebral and subdural hematoma which evolved with acute left uncal, parahipocampal and subfalcinecerebral herniation (coma, GCS 6, left mydriasis, right severe hemiparesis). Surgical emergency aspiration of the hematomas was performed. Postoperative treatment of cerebral ischemia and residual hematomas was properly done. We consider important and underdiagnosed the association of cerebral ischemia and secondary posttraumatic brain injuries.

Abbreviations: MCA-middle cerebral artery, GCS-Glasgow Coma Scale, ICA-internal carotid artery, PCA-posterior cerebral artery, ACA-anterior cerebral artery.

Conclusion: We present a case of a patient with an acute MCA ischemia with secondary head injury due to a fall with subsequent intracranial acute intracerebral and subdural hematomas. Surgical emergency aspiration of the hematomas was performed. The treatment was performed for both lesions (cerebral ischemia and posttraumatic hematomas) with vitamins B, neurotrophycs, pain killers, antibiotics. Unfortunately, due to aggravation of the Mendelson syndrome, the patient died 7 days later.

Assessment of the accuracy of ABC/2 variations in traumatic epidural hematoma volume estimation: a retrospective study

Background. The traumatic epidural hematoma (tEDH) volume is often used to assist in tEDH treatment planning and outcome prediction. ABC/2 is a well-accepted volume estimation method that can be used for tEDH volume estimation. Previous studies have proposed different variations of ABC/2; however, it is unclear which variation will provide a higher accuracy. Given the promising clinical contribution of accurate tEDH volume estimations, we sought to assess the accuracy of several ABC/2 variations in tEDH volume estimation.

Methods. The study group comprised 53 patients with tEDH who had undergone non-contrast head computed tomography scans. For each patient, the tEDH volume was automatically estimated by eight ABC/2 variations (four traditional and four newly derived) with an in-house program, and results were compared to those from manual planimetry. Linear regression, the closest value, percentage deviation, and Bland-Altman plot were adopted to comprehensively assess accuracy.

Results. Among all ABC/2 variations assessed, the traditional variations y = 0.5 × A₁B₁C₁ (or A₂B₂C₁) and the newly derived variations y = 0.65 × A₁B₁C₁ (or A₂B₂C₁) achieved higher accuracy than the other variations. No significant differences were observed between the estimated volume values generated by these variations and those of planimetry (p > 0.05). Comparatively, the former performed better than the latter in general, with smaller mean percentage deviations (7.28 ± 5.90% and 6.42 ± 5.74% versus 19.12 ± 6.33% and 21.28 ± 6.80%, respectively) and more values closest to planimetry (18/53 and 18/53 versus 2/53 and 0/53, respectively). Besides, deviations of most cases in the former fell within the range of <10% (71.70% and 84.91%, respectively), whereas deviations of most cases in the latter were in the range of 10–20% and >20% (90.57% and 96.23, respectively).

Discussion. In the current study, we adopted an automatic approach to assess the accuracy of several ABC/2 variations for tEDH volume estimation. Our initial results showed that the variations y = 0.5 × A₁B₁C₁ (or A₂B₂C₁) performed better than the other traditional variations, suggesting that the adjusted depth is favorable. In addition, linear regression has been shown to be useful for improving the estimation accuracy of the ABC/2 method, and future studies are warranted to investigate the applicability of such linear regression-derived formulas for clinical application.

A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury

The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimaging experts identified the most relevant peer-reviewed publications and assessed the quality of the literature for each of these imaging technique in the clinical and research settings. Although CT, MRI, and TCD were determined to be the most useful modalities in the clinical setting, no single imaging modality proved sufficient for all patients due to the heterogeneity of TBI. All imaging modalities reviewed demonstrated the potential to emerge as part of future clinical care. This paper describes and updates the results of the DoD report and also expands on the use of angiography in patients with TBI.

Comparison of the Outcomes and Recurrence with Three Surgical Techniques for Chronic Subdural Hematoma: Single, Double Burr Hole, and Double Burr Hole Drainage with Irrigation

Objective

Chronic subdural hematoma (CSDH), a disease commonly encountered by neurosurgeons, is treated by burr hole drainage (BHD). However, the optimal surgical technique among the three types of BHD has not been determined.

Methods

We conducted a retrospective study on BHD performed on 93 patients who were diagnosed with CSDH. The subjects were divided into three groups based on the surgical technique performed: single BHD without irrigation (Group A, n=31), double BHD without irrigation (Group B, n=32), and double BHD with irrigation (Group C, n=30). The clinical factors, radiological factors and recurrences were compared between the three groups. Moreover, independent factors affecting the recurrence were analyzed.

Results

The change in hematoma thickness was 29.77±7.94%, 49.73±12.87%, and 75.29±4.32% for Group A, B, and C, respectively, while the change in midline shift was 40.81±15.47%, 51.78±10.94%, and 56.16±16.16%, respectively. Thus, Group C showed the most effective for resolution of hematoma and midline shift (p<0.05). Group A, B, and C had 12 cases (38.7%), 8 cases (25.0%), and 3 cases (10.0%) of recurrences, respectively. Group C had a statistically significantly fewer recurrence rate than Group A (p<0.05). Double burr hole, irrigation, and coagulopathy were each identified as independent factors that reduce recurrence (p<0.05).

Conclusion

Among the three techniques, the double BHD with saline irrigation resulted in the fewest recurrences. It is probably the most effective technique for preventing the recurrence of CSDH.

Midline shift in relation to thickness of traumatic acute subdural hematoma predicts mortality

BACKGROUND

Traumatic acute subdural hematoma has a high mortality despite intensive treatment. Despite the existence of several prediction models, it is very hard to predict an outcome. We investigated whether a specific combination of initial head CT-scan findings is a factor in predicting outcome, especially non-survival.

METHODS

We retrospectively studied admission head CT scans of all adult patients referred for a traumatic acute subdural hematoma between April 2009 and April 2013. Chart review was performed for every included patient. Midline shift and thickness of the hematoma were measured by two independent observers. The difference between midline shift and thickness of the hematoma was calculated. These differences were correlated with outcome. IRB has approved the study.

RESULTS

A total of 59 patients were included, of whom 29 died. We found a strong correlation between a midline shift exceeding the thickness of the hematoma by 3 mm or more, and subsequent mortality. For each evaluation, specificity was 1.0 (95 % CI: 0.85-1 for all evaluations), positive predictive value 1.0 (95 % CI between 0.31-1 and 0.56-1), while sensitivity ranged from 0.1 to 0.23 (95 % CI between 0.08-0.39 and 0.17-0.43), and negative predictive value varied from 0.52 to 0.56 (95 % CI between 0.38-0.65 and 0.41-0.69).

CONCLUSIONS

In case of a traumatic acute subdural hematoma, a difference between the midline shift and the thickness of the hematoma ≥ 3 mm at the initial CT predicted mortality in all cases. This is the first time that such a strong correlation was reported. Especially for the future development of prediction models, the relation between midline shift and thickness of the hematoma could be included as a separate factor.

Identification of hematomas in mild traumatic brain injury using an index of quantitative brain electrical activity

Rapid identification of traumatic intracranial hematomas following closed head injury represents a significant health care need because of the potentially life-threatening risk they present. This study demonstrates the clinical utility of an index of brain electrical activity used to identify intracranial hematomas in traumatic brain injury (TBI) presenting to the emergency department (ED). Brain electrical activity was recorded from a limited montage located on the forehead of 394 closed head injured patients who were referred for CT scans as part of their standard ED assessment. A total of 116 of these patients were found to be CT positive (CT+), of which 46 patients with traumatic intracranial hematomas (CT+) were identified for study. A total of 278 patients were found to be CT negative (CT-) and were used as controls. CT scans were subjected to quantitative measurements of volume of blood and distance of bleed from recording electrodes by blinded independent experts, implementing a validated method for hematoma measurement. Using an algorithm based on brain electrical activity developed on a large independent cohort of TBI patients and controls (TBI-Index), patients were classified as either positive or negative for structural brain injury. Sensitivity to hematomas was found to be 95.7% (95% CI = 85.2, 99.5), specificity was 43.9% (95% CI = 38.0, 49.9). There was no significant relationship between the TBI-Index and distance of the bleed from recording sites (F = 0.044, p = 0.833), or volume of blood measured F = 0.179, p = 0.674). Results of this study are a validation and extension of previously published retrospective findings in an independent population, and provide evidence that a TBI-Index for structural brain injury is a highly sensitive measure for the detection of potentially life-threatening traumatic intracranial hematomas, and could contribute to the rapid, quantitative evaluation and treatment of such patients.

Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score)

BACKGROUND

Early computerized tomography (CT) abnormalities are important predictors of outcome after traumatic brain injury (TBI).

OBJECTIVE

To develop a novel CT scoring system (Helsinki CT score) and to compare it with the Marshall CT classification and the Rotterdam CT score in predicting long-term outcome of patients with TBI.

METHODS

Eight hundred sixty-nine consecutive TBI patients were included in this open-cohort, retrospective, single-center study. Logistic regression was used to develop the Helsinki CT score. The scores from the Marshall, Rotterdam, and Helsinki CT scoring methods were added to a clinical model based on age, motor score, and pupils to evaluate their value in predicting outcome. Internal validity was assessed by a bootstrap technique and expressed as area under the curve (AUC). Outcome was 6-month unfavorable neurological outcome and mortality.

RESULTS

Variables included in the Helsinki CT score were bleeding type and size, intraventricular hemorrhage, and suprasellar cisterns. In the present data set, the performance of the Helsinki CT score was superior to that of the Marshall CT and Rotterdam CT scores (AUC, 0.74-0.75 vs 0.63-0.70; P < .001). Addition of the Helsinki CT score modestly increased prognostic performance of the clinical model (AUC neurological outcome +0.02 [P = .002]; AUC mortality, +0.01 [P = .21]). In contrast, the Marshall and Rotterdam CT scores were of no additional predictive value to the clinical model (P > .05).

CONCLUSION

Use of the novel Helsinki CT score improved outcome prediction accuracy, and the Helsinki CT score is a feasible alternative to the Rotterdam and Marshall CT systems. External validation of the Helsinki CT score is advocated to show generalizability.

Blood pressure regulation to prevent progression of blunt traumatic intracranial hemorrhage in stable patients

BACKGROUND

Target blood pressure (BP) in stable (non-hypotensive) patients with acute isolated blunt traumatic intracranial hemorrhage (TICH) is unknown. To address this issue, our study correlated BP with radiological volumetric progression (RP) and neurological deterioration (ND) in these patients.

METHODS

A retrospective review of hemodynamically stable adults (n = 184) with isolated TICH not requiring emergent surgery consecutively admitted to a Level I trauma center. BPs before admission computed tomography (CT) scan (CT1) and between CT1 and a follow-up CT (CT2) were correlated with TICH volume and Glasgow Coma Scale (GCS) during these time periods. Predictors for deterioration were studied. Primary outcomes were increased measured TICH and decreased GCS at the CT1-CT2 interval.

RESULTS

Age (57 years), % male (73), ISS (17), % falls (77), comorbidities (1.2/pt), and % anticoagulation (20) were similar in patients with or without RP (n = 107, 58%) or ND (n = 34, 18%). By univariate analysis, RP patients had an average systolic (SBP), diastolic (DBP), and mean BP (MAP) similar to non-RP patients; whereas ND patients compared to non-ND patients had a higher mean admission DBP (p < 0.02) and MAP (p < 0.04), a higher mean admission peak MAP (p < 0.01) and DBP (p < 0.01), a higher CT1-CT2 interval peak DBP (p < 0.01) and peak MAP (p < 0.01), and a lower CT1-CT2 nadir SBP (p < 0.04). Spearman rank correlation test did not show association among average SBP, MAP, DBP, absolute or % change in BPs, and absolute or % change in TICH volumes in any phase. Multivariate analysis identified higher nadir admission SBP [adjusted odds ratio (AOR) 1.29 per 10 mmHg increase] and lower peak MAP during the CT1-CT2 period (AOR 0.71 per 10 mmHg decrease) as independent predictors of RP, and a peak DBP in the CT1-CT2 interval (AOR 1.48) as an independent predictor of ND. Other predictors of ND included bilateral admission TICH (AOR 3.31) and increased injury volume (AOR 1.36), while the number of comorbidities/patient (AOR 4.34), bilateral injury (AOR 3.12), and midline shift (AOR 4.34) predicted RD.

CONCLUSIONS

A comprehensive dynamic analysis correlating repeated BP determinations with quantifiable repeated parameters of TICH deterioration (injury volume and GCS) did not demonstrate a clinically relevant protective target BP value. Current practices of BP control in this specific group of patients should be further investigated.

LEVEL OF EVIDENCE III

Prognostic, Level II study.