Evaluation of novel computerized tomography scoring systems in human traumatic brain injury: An observational, multicenter study

Assessing outcomes in traumatic brain injury: Helsinki score versus Glasgow coma scale

BACKGROUND

The precision of assessment and prognosis in traumatic brain injury (TBI) is paramount for effective triage and informed therapeutic strategies. While the Glasgow Coma Scale (GCS) remains the cornerstone for TBI evaluation, it overlooks critical primary imaging findings. The Helsinki Score (HS), a novel tool designed to incorporate radiological data, offers a promising approach to predicting TBI outcomes. This study aims to evaluate the prognostic efficacy of HS in comparison to GCS across a substantial TBI patient cohort.

METHODS

This retrospective study encompassed TBI patients treated at our institution between 2008 and 2019, specifically those with an admission GCS of 14 or lower. We assessed both the initial GCS and the HS derived from primary CT scans. Key outcome metrics included the Glasgow Outcome Scale (GOS) and mortality rates at hospital discharge and at 6 and 12-month intervals post-discharge. Predictive performances of GCS and HS were analyzed through Receiver Operating Characteristic (ROC) curves and Kendall tau-b correlation coefficients against each outcome.

RESULTS

The study included 544 patients, with an average age of 62.2 ± 21.5 years, median initial GCS of 14, and a median HS of 3. The mortality rate at discharge stood at 8.6%, with a median GOS of 4. Both GCS and HS demonstrated significant correlations with mortality and GOS outcomes (p < 0.05). Notably, HS showed a markedly superior correlation with mortality (τb = 0.36) compared to GCS (τb = -0.11) and with GOS outcomes (τb = -0.40 for HS vs. τb = 0.33 for GCS). ROC analyses affirmed HS's enhanced predictive accuracy over GCS for both mortality (AUC of 0.79 for HS vs. 0.62 for GCS) and overall outcomes (AUC of 0.77 for HS vs. 0.71 for GCS).

CONCLUSION

The findings validate the HS in a large German cohort and suggest that radiological assessments alone, as exemplified by HS, can surpass the traditional GCS in predicting TBI outcomes. However, the HS, despite its efficacy, lacks the integration of clinical evaluation, a vital component in TBI management. This underscores the necessity for a holistic approach that amalgamates both radiological and clinical insights for a more comprehensive and accurate prognostication in TBI care.

Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study

In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0.

Guidelines for Neuroprognostication in Critically Ill Adults with Moderate-Severe Traumatic Brain Injury

BACKGROUND

Moderate-severe traumatic brain injury (msTBI) carries high morbidity and mortality worldwide. Accurate neuroprognostication is essential in guiding clinical decisions, including patient triage and transition to comfort measures. Here we provide recommendations regarding the reliability of major clinical predictors and prediction models commonly used in msTBI neuroprognostication, guiding clinicians in counseling surrogate decision-makers.

METHODS

Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, we conducted a systematic narrative review of the most clinically relevant predictors and prediction models cited in the literature. The review involved framing specific population/intervention/comparator/outcome/timing/setting (PICOTS) questions and employing stringent full-text screening criteria to examine the literature, focusing on four GRADE criteria: quality of evidence, desirability of outcomes, values and preferences, and resource use. Moreover, good practice recommendations addressing the key principles of neuroprognostication were drafted.

RESULTS

After screening 8125 articles, 41 met our eligibility criteria. Ten clinical variables and nine grading scales were selected. Many articles varied in defining "poor" functional outcomes. For consistency, we treated "poor" as "unfavorable". Although many clinical variables are associated with poor outcome in msTBI, only the presence of bilateral pupillary nonreactivity on admission, conditional on accurate assessment without confounding from medications or injuries, was deemed moderately reliable for counseling surrogates regarding 6-month functional outcomes or in-hospital mortality. In terms of prediction models, the Corticosteroid Randomization After Significant Head Injury (CRASH)-basic, CRASH-CT (CRASH-basic extended by computed tomography features), International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT)-core, IMPACT-extended, and IMPACT-lab models were recommended as moderately reliable in predicting 14-day to 6-month mortality and functional outcomes at 6 months and beyond. When using "moderately reliable" predictors or prediction models, the clinician must acknowledge "substantial" uncertainty in the prognosis.

CONCLUSIONS

These guidelines provide recommendations to clinicians on the formal reliability of individual predictors and prediction models of poor outcome when counseling surrogates of patients with msTBI and suggest broad principles of neuroprognostication.

Predictors of outcomes 3 to 12 months after traumatic brain injury: a systematic review and meta-analysis

The exact factors predicting outcomes following traumatic brain injury (TBI) remain elusive. In this systematic review and meta-analysis, we examined factors influencing outcomes in adult patients with TBI, from 3 months to 1 year after injury. A search of four electronic databases-PubMed, Scopus, Web of Science, and ScienceDirect-yielded 29 studies for review and 16 for meta-analysis, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. In patients with TBI of any severity, mean differences were observed in age (8.72 years; 95% confidence interval [CI], 4.77-12.66 years), lymphocyte count (-0.15 109/L; 95% CI, -0.18 to -0.11), glucose levels (1.20 mmol/L; 95% CI, 0.73-1.68), and haemoglobin levels (-0.91 g/dL; 95% CI, -1.49 to -0.33) between those with favourable and unfavourable outcomes. The prevalence rates of unfavourable outcomes were as follows: abnormal cisterns, 65.7%; intracranial pressure above 20 mmHg, 52.9%; midline shift of 5 mm or more, 63%; hypotension, 71%; hypoxia, 86.8%; blood transfusion, 70.3%; and mechanical ventilation, 90%. Several predictors were strongly associated with outcome. Specifically, age, lymphocyte count, glucose level, haemoglobin level, severity of TBI, pupillary reaction, and type of injury were identified as potential predictors of long-term outcomes.

Effect of antiplatelet and anticoagulant medication use on injury severity and mortality in patients with traumatic brain injury treated in the intensive care unit

BACKGROUND

Antiplatelet and anticoagulant medication are increasingly common and can increase the risks of morbidity and mortality in traumatic brain injury (TBI) patients. Our study aimed to quantify the association of antiplatelet or anticoagulant use in intensive care unit (ICU)-treated TBI patients with 1-year mortality and head CT findings.

METHOD

We conducted a retrospective, multicenter observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted to four university hospital ICUs during 2003-2013. The patients were followed up until the end of 2016. The national drug reimbursement database provided information on prescribed medication for our study. We used multivariable logistic regression models to assess the association between TBI severity, prescribed antiplatelet and anticoagulant medication, and their association with 1-year mortality.

RESULTS

Of 3031 patients, 128 (4%) had antiplatelet and 342 (11%) anticoagulant medication before their TBI. Clopidogrel (2%) and warfarin (9%) were the most common antiplatelets and anticoagulants. Three patients had direct oral anticoagulant (DOAC) medication. The median age was higher among antiplatelet/anticoagulant users than in non-users (70 years vs. 52 years, p < 0.001), and their head CT findings were more severe (median Helsinki CT score 3 vs. 2, p < 0.05). In multivariable analysis, antiplatelets (OR 1.62, 95% CI 1.02-2.58) and anticoagulants (OR 1.43, 95% CI 1.06-1.94) were independently associated with higher odds of 1-year mortality. In a sensitivity analysis including only patients over 70, antiplatelets (OR 2.28, 95% CI 1.16-4.22) and anticoagulants (1.50, 95% CI 0.97-2.32) were associated with an increased risk of 1-year mortality.

CONCLUSIONS

Both antiplatelet and anticoagulant use before TBI were risk factors in our study for 1-year mortality. Antiplatelet and anticoagulation medication users had a higher radiological intracranial injury burden than non-users defined by the Helsinki CT score. Further investigation on the effect of DOACs on mortality should be done in ICU-treated TBI patients.

Correction: Evaluation of novel computerized tomography scoring systems in human traumatic brain injury: An observational, multicenter study

[This corrects the article DOI: 10.1371/journal.pmed.1002368.].

Computed tomography findings as early predictors of long-term language impairment in patients with traumatic brain injury

This study aims to assess the relationship between computed tomography (CT) findings, during the acute phase of hospitalization, and long-term language impairment in people with traumatic brain injury (TBI). Another aim was to assess the receptive and expressive abilities of subjects with TBI based on the location of the injury. This is a retrospective observational study including 49 participants with TBI due to war injuries. The Arabic Diagnostic Aphasia Battery (A-DAB-1) was administered to the participants and the Helsinki CT score was computed to quantify brain damage. The results showed that the Helsinki CT score was negatively correlated with the total score of the A-DAB-1 (r = -0.544, p-value < 0.0001). Simple linear regression supported such findings and reflected an inversely proportional relationship between both variables (p-value < 0.0001). When compared with subjects having right hemisphere damage, subjects with left hemisphere and bilateral brain damage performed more poorly on language tasks respectively as follows: A-DAB-1 overall score (92.08-66.08-70.28, p-value = 0.021), Content of descriptive speech (9.57-6.69-7.22, p-value = 0.034), Verbal fluency (6.57-3.54-3.89, p-value = 0.002), Auditory comprehension (9.71-7.54-7.78, p-value = 0.039), Complex auditory commands (9.71-7.65-7.56, p-value = 0.043), Repetition (9.75-7.08-7.61, p-value = 0.036), Naming (9.93-7.15-8.11, p-value = 0.046). Following TBI, CT findings on admission can significantly predict long-term language abilities, with left side lesions inducing poorer outcomes.

Comparison of the predictive value of the Helsinki, Rotterdam, and Stockholm CT scores in predicting 6-month outcomes in patients with blunt traumatic brain injuries

PURPOSE

Despite advances in modern medicine, traumatic brain injuries (TBIs) are still a major medical problem. Early diagnosis of TBI is crucial for clinical decision-making and prognosis. This study aims to compare the predictive value of Helsinki, Rotterdam, and Stockholm CT scores in predicting the 6-month outcomes in blunt TBI patients.

METHODS

This cohort study was conducted on blunt TBI patients of 15 years or older. All of them were admitted to the surgical emergency department of Shahid Beheshti Hospital in Kashan, Iran from 2020 to 2021 and had abnormal trauma-related findings on brain CT images. The patients' demographic data such as age, gender, history of comorbid conditions, mechanism of trauma, Glasgow coma scale, CT images, length of hospital stay, and surgical procedures were recorded. The Helsinki, Rotterdam, and Stockholm CT scores were simultaneously determined according to the existing guidelines. The included patients' 6-month outcome was determined using the Glasgow outcome scale extended. M Data were analyzed by SPSS software version 16.0. Sensitivity, specificity, negative/positive predictive value and the area under the receiver operating characteristic curve were calculated for each test. The Kappa agreement coefficient and Kuder Richardson-20 were used to compare the scoring systems.

RESULTS

Altogether 171 TBI patients met the inclusion and exclusion criteria, with the mean age of (44.9 ± 20.2) years. Most patients were male (80.7%), had traffic related injuries (83.1%) and mild TBIs (64.3%). Patients with lower Glasgow coma scale had higher Helsinki, Rotterdam, and Stockholm CT scores and lower Glasgow outcome scale extended scores. Among all the scoring systems, the Helsinki and Stockholm scores showed the highest agreement in predicting patients' outcomes (kappa = 0.657, p < 0.001). The Rotterdam scoring system had the highest sensitivity (90.1%) in predicting death of TBI patients, whereas the Helsinki scoring system had the highest sensitivity (89.8%) in predicting the 6-month outcome in TBI patients.

CONCLUSION

The Rotterdam scoring system was superior in predicting death in TBI patients, whereas the Helsinki scoring system was more sensitive in predicting the 6-month outcome.

Diagnosis and management of elevated intracranial pressure in the emergency department

BACKGROUND

Elevated intracranial pressure is a devastating complication of catastrophic brain injury. Intracranial hypertension is commonly seen in neurologic injury secondary to traumatic brain injuries. Uncontrolled pressures can lead to permanent neurologic damage, but acute medical management is often overlooked when pursuing surgical management options that may not always be indicated.

DISCUSSION

Traumatic brain injury is the leading cause of death in patients with severe neurologic injury. Diagnosing elevated intracranial pressures is imperative in initiating prompt treatment to reduce secondary central nervous system injury, morbidity, and mortality. Although the initial injury to the brain is typically irreversible, intracranial pressure control can assist in salvaging the remaining brain tissue from additional damage. We will discuss the initial medical and surgical management of traumatic brain injury to prevent further neurologic deterioration and reduce mortality.

CONCLUSION

Recent literature has reported several methods to detect elevated intracranial pressure easily and studies describing multiple treatment modalities. These investigations suggest that early detection and timely treatment of intracranial hypertension are beneficial in reducing mortality.

Prediction of Mortality and Morbidity After Severe Traumatic Brain Injury: A Comparison Between Rotterdam and Richmond Computed Tomography Scan Scoring System

OBJECTIVE

Accurate prediction of the morbidity and mortality outcomes of traumatic brain injury patients is still challenging. In the present study, we aimed to compare the predictive value of the Richmond and Rotterdam scoring systems as two novel computed tomography-based predictive models.

METHODS

We retrospectively analyzed 1400 subjects who suffered from severe traumatic brain injury and were admitted to Emtiaz Hospital, a tertiary referral trauma center in Shiraz, south of Iran, from January 2018 to December 2019. We evaluated the 1-month results; considering two primary factors: mortality and morbidity. The patients' condition was the basis for this assessment. We conducted a logistic regression analysis to determine the association between scoring systems and outcomes. To determine the optimal threshold value, we utilized the receiver operating characteristic curve model.

RESULTS

The mean age of participants was 36.61 ± 17.58 years, respectively. Concerning predicting the mortality rate, the area under the curve (AUC) for the Rotterdam score was relatively low 0.64 (95% confidence interval: 0.60, 0.67), while the Richmond score had a higher AUC 0.74 (0.71-0.77), which demonstrated the superiority of this scoring system. Moreover, the Richmond score was more accurate for predicting 1-month morbidity with AUC: 0.71 (0.69, 0.74) versus 0.62 (0.59, 0.65).

CONCLUSIONS

The Richmond scoring system demonstrated more accurate predictions for the present outcomes. The simplicity and predictive value of the Richmond score make this system an ideal option for use in emergency settings and centers with high patient loads.

Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury

Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.

Intracranial lesion features in moderate-to-severe traumatic brain injury: relation to neurointensive care variables and clinical outcome

BACKGROUND

The primary aim was to determine the association of intracranial hemorrhage lesion type, size, mass effect, and evolution with the clinical course during neurointensive care and long-term outcome after traumatic brain injury (TBI).

METHODS

In this observational, retrospective study, 385 TBI patients treated at the neurointensive care unit at Uppsala University Hospital, Sweden, were included. The lesion type, size, mass effect, and evolution (progression on the follow-up CT) were assessed and analyzed in relation to the percentage of secondary insults with intracranial pressure > 20 mmHg, cerebral perfusion pressure < 60 mmHg, and cerebral pressure autoregulatory status (PRx) and in relation to Glasgow Outcome Scale-Extended.

RESULTS

A larger epidural hematoma (p < 0.05) and acute subdural hematoma (p < 0.001) volume, greater midline shift (p < 0.001), and compressed basal cisterns (p < 0.001) correlated with craniotomy surgery. In multiple regressions, presence of traumatic subarachnoid hemorrhage (p < 0.001) and intracranial hemorrhage progression on the follow-up CT (p < 0.01) were associated with more intracranial pressure-insults above 20 mmHg. In similar regressions, obliterated basal cisterns (p < 0.001) were independently associated with higher PRx. In a multiple regression, greater acute subdural hematoma (p < 0.05) and contusion (p < 0.05) volume, presence of traumatic subarachnoid hemorrhage (p < 0.01), and obliterated basal cisterns (p < 0.01) were independently associated with a lower rate of favorable outcome.

CONCLUSIONS

The intracranial lesion type, size, mass effect, and evolution were associated with the clinical course, cerebral pathophysiology, and outcome following TBI. Future efforts should integrate such granular data into more sophisticated machine learning models to aid the clinician to better anticipate emerging secondary insults and to predict clinical outcome.

Right-to-Left Displacement of an Airgun Lead Bullet after Transorbital Entry into the Skull Complicated by Posttraumatic Epilepsy : A Case Report

Penetrating head injury is a serious open cranial injury. In civilians, it is often caused by non-missile, low velocity flying objects that penetrate the skull through a weak cranial structure, forming intracranial foreign bodies. The intracranial foreign body can be displaced due to its special quality, shape, and location. In this paper, we report a rare case of right-to-left displacement of an airgun lead bullet after transorbital entry into the skull complicated by posttraumatic epilepsy, as a reminder to colleagues that intracranial metal foreign bodies maybe displaced intraoperatively. In addition, we have found that the presence of intracranial metallic foreign bodies may be a factor for the posttraumatic epilepsy, and their timely removal appears to be beneficial for epilepsy control.

Comparing Predictive Utility of Head Computed Tomography Scan-Based Scoring Systems for Traumatic Brain Injury: A Retrospective Study

This study compared the predictive utility of Marshall, Rotterdam, Stockholm, Helsinki, and NeuroImaging Radiological Interpretation System (NIRIS) scorings based on early non-contrast brain computed tomography (CT) scans in patients with traumatic brain injury (TBI). The area under a receiver operating characteristic curve (AUROC) was used to determine the predictive utility of scoring systems. Subgroup analyses were performed among patients with head AIS scores > 1. A total of 996 patients were included, of whom 786 (78.9%) were males. In-hospital mortality, ICU admission, neurosurgical intervention, and prolonged total hospital length of stay (THLOS) were recorded for 27 (2.7%), 207 (20.8%), 82 (8.2%), and 205 (20.6%) patients, respectively. For predicting in-hospital mortality, all scoring systems had AUROC point estimates above 0.9 and 0.75 among all included patients and patients with head AIS > 1, respectively, without any significant differences. The Marshall and NIRIS scoring systems had higher AUROCs for predicting ICU admission and neurosurgery than the other scoring systems. For predicting THLOS ≥ seven days, although the NIRIS and Marshall scoring systems seemed to have higher AUROC point estimates when all patients were analyzed, five scoring systems performed roughly the same in the head AIS > 1 subgroup.

Prediction of In-Hospital Outcomes in Patients with Traumatic Brain Injury Using Computed Tomographic Scoring Systems: A Comparison Between Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems

OBJECTIVE

This study aimed to compare the prognostic value of Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems (NIRIS) in predicting the in-hospital outcomes of patients with traumatic brain injury.

METHODS

We identified 250 patients with traumatic brain injury in a retrospective single-center cohort from 2019 to 2020. Computed tomography (CT) scans were reviewed by two radiologists and scored according to three CT scoring systems. One-month outcomes were evaluated, including hospitalization, intensive care unit admission, neurosurgical procedure, and mortality. Logistic regression analysis was performed to identify scoring systems and outcome relationships. The best cutoff value was calculated using the receiver operating characteristic curve model.

RESULTS

Eighteen patients (7.2%) died in the 1-month follow-up. The mean age and Glasgow Coma Scale of survivors differed significantly from nonsurvivors. Subarachnoid hemorrhage and compressed/absent cisterns were dead patients' most frequent CT findings. All three scoring systems had good discrimination power in mortality prediction (area under the receiver operating characteristic curve of the Marshall, Rotterdam, and NIRIS was 0.78, 0.86, and 0.84, respectively). Regarding outcome, three systems directly correlated with unfavorable outcome prediction.

CONCLUSIONS

The Marshall, Rotterdam, and NIRIS are good predictive models for mortality and outcome prediction, with slight superiority of the Rotterdam in mortality prediction and the Marshall in intensive care unit admission and neurosurgical procedures.

Automatic localisation and per-region quantification of traumatic brain injury on head CT using atlas mapping

Rationale and objectives

To develop a method for automatic localisation of brain lesions on head CT, suitable for both population-level analysis and lesion management in a clinical setting.

Materials and methods

Lesions were located by mapping a bespoke CT brain atlas to the patient's head CT in which lesions had been previously segmented. The atlas mapping was achieved through robust intensity-based registration enabling the calculation of per-region lesion volumes. Quality control (QC) metrics were derived for automatic detection of failure cases. The CT brain template was built using 182 non-lesioned CT scans and an iterative template construction strategy. Individual brain regions in the CT template were defined via non-linear registration of an existing MRI-based brain atlas.Evaluation was performed on a multi-centre traumatic brain injury dataset (TBI) (n = 839 scans), including visual inspection by a trained expert. Two population-level analyses are presented as proof-of-concept: a spatial assessment of lesion prevalence, and an exploration of the distribution of lesion volume per brain region, stratified by clinical outcome.

Results

95.7% of the lesion localisation results were rated by a trained expert as suitable for approximate anatomical correspondence between lesions and brain regions, and 72.5% for more quantitatively accurate estimates of regional lesion load. The classification performance of the automatic QC showed an AUC of 0.84 when compared to binarised visual inspection scores. The localisation method has been integrated into the publicly available Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT).

Conclusion

Automatic lesion localisation with reliable QC metrics is feasible and can be used for patient-level quantitative analysis of TBI, as well as for large-scale population analysis due to its computational efficiency (<2 min/scan on GPU).

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.

Accuracy of head computed tomography scoring systems in predicting outcomes for patients with moderate to severe traumatic brain injury: A ProTECT III ancillary study

BACKGROUND

Several types of head CT classification systems have been developed to prognosticate and stratify TBI patients.

OBJECTIVE

The purpose of our study was to compare the predictive value and accuracy of the different CT scoring systems, including the Marshall, Rotterdam, Stockholm, Helsinki, and NIRIS systems, to inform specific patient management actions, using the ProTECT III population of patients with moderate to severe acute traumatic brain injury (TBI).

METHODS

We used the data collected in the patients with moderate to severe (GCS score of 4-12) TBI enrolled in the ProTECT III clinical trial. ProTECT III was a NIH-funded, prospective, multicenter, randomized, double-blind, placebo-controlled clinical trial designed to determine the efficacy of early administration of IV progesterone. The CT scoring systems listed above were applied to the baseline CT scans obtained in the trial. We assessed the predictive accuracy of these scoring systems with respect to Glasgow Outcome Scale-Extended at 6 months, disability rating scale score, and mortality.

RESULTS

A total of 882 subjects were enrolled in ProTECT III. Worse scores for each head CT scoring systems were highly correlated with unfavorable outcome, disability outcome, and mortality. The NIRIS classification was more strongly correlated than the Stockholm and Rotterdam CT scores, followed by the Helsinki and Marshall CT classification. The highest correlation was observed between NIRIS and mortality (estimated odds ratios of 4.83).

CONCLUSION

All scores were highly associated with 6-month unfavorable, disability and mortality outcomes. NIRIS was also accurate in predicting TBI patients' management and disposition.

Effect of Red Cell Distribution Width on the Prognosis of Patients with Traumatic Brain Injury: A Retrospective Cohort Study

BACKGROUND

The link between red cell distribution width (RDW) and prognosis of traumatic brain injury (TBI) is controversial. Whether RDW can increase the prognostic value of established predictors remains unknown. This study aimed to provide supportive evidence for the prognostic value of RDW.

METHODS

Clinical data of 1488 patients with TBI were extracted from the Multiparameter Intelligent Monitoring in Intensive Care III database and classified into 2 groups: 1) one with RDW <14.5% (n = 1061) and 2) the other with RDW ≥14.5% (n = 427). Multivariable logistic regression models were used to estimate the relationship between RDW and outcomes. Stratified analyses and interactions were also performed. We compared the area under the receiver operating characteristic curve of the International Mission for Prognoses and Clinical Trial Design in TBI (IMPACT) core and extended models with and without RDW.

RESULTS

After adjusting for confounding factors, RDW was an independent risk consideration for TBI prognoses; the odds ratios were 1.62 (95% confidence interval (CI): 1.05, 2.50) and 1.89 (95% CI: 1.35, 2.64) for hospital mortality and 6-month mortality, respectively. This association was crucial for patients with a Glasgow Coma Score of 3-12 (odds ratio, 2.79; 95% CI: 1.33, 5.87). For 6-month mortality, when RDW was added to the core and extended IMPACT models, the area under the receiver operating characteristic curve increased from 0.833 to 0.851 (P = 0.001) and from 0.842 to 0.855 (P = 0.002), respectively.

CONCLUSIONS

Elevated RDW is an independent risk consideration for hospital and 6-month mortality rates. When RDW was added to the IMPACT core and extended models, it improved its predictive ability for 6-month mortality in patients with TBI.

Traumatic brain injury: progress and challenges in prevention, clinical care, and research

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research.

In LMICs, the occurrence of TBI is driven by road traffic incidents, often involving vulnerable road users such as motorcyclists and pedestrians. In HICs, most TBI is caused by falls, particularly in older people (aged ≥65 years), who often have comorbidities. Risk factors such as frailty and alcohol misuse provide opportunities for targeted prevention actions. Little evidence exists to inform treatment of older patients, who have been commonly excluded from past clinical trials—consequently, appropriate evidence is urgently required. Although increasing age is associated with worse outcomes from TBI, age should not dictate limitations in therapy. However, patients injured by low-energy falls (who are mostly older people) are about 50% less likely to receive critical care or emergency interventions, compared with those injured by high-energy mechanisms, such as road traffic incidents.

Mild TBI, defined as a Glasgow Coma sum score of 13–15, comprises most of the TBI cases (over 90%) presenting to hospital. Around 50% of adult patients with mild TBI presenting to hospital do not recover to pre-TBI levels of health by 6 months after their injury. Fewer than 10% of patients discharged after presenting to an emergency department for TBI in Europe currently receive follow-up. Structured follow-up after mild TBI should be considered good practice, and urgent research is needed to identify which patients with mild TBI are at risk for incomplete recovery.

The selection of patients for CT is an important triage decision in mild TBI since it allows early identification of lesions that can trigger hospital admission or life-saving surgery. Current decision making for deciding on CT is inefficient, with 90–95% of scanned patients showing no intracranial injury but being subjected to radiation risks. InTBIR studies have shown that measurement of blood-based biomarkers adds value to previously proposed clinical decision rules, holding the potential to improve efficiency while reducing radiation exposure. Increased concentrations of biomarkers in the blood of patients with a normal presentation CT scan suggest structural brain damage, which is seen on MR scanning in up to 30% of patients with mild TBI. Advanced MRI, including diffusion tensor imaging and volumetric analyses, can identify additional injuries not detectable by visual inspection of standard clinical MR images. Thus, the absence of CT abnormalities does not exclude structural damage—an observation relevant to litigation procedures, to management of mild TBI, and when CT scans are insufficient to explain the severity of the clinical condition.

Although blood-based protein biomarkers have been shown to have important roles in the evaluation of TBI, most available assays are for research use only. To date, there is only one vendor of such assays with regulatory clearance in Europe and the USA with an indication to rule out the need for CT imaging for patients with suspected TBI. Regulatory clearance is provided for a combination of biomarkers, although evidence is accumulating that a single biomarker can perform as well as a combination. Additional biomarkers and more clinical-use platforms are on the horizon, but cross-platform harmonisation of results is needed. Health-care efficiency would benefit from diversity in providers.

In the intensive care setting, automated analysis of blood pressure and intracranial pressure with calculation of derived parameters can help individualise management of TBI. Interest in the identification of subgroups of patients who might benefit more from some specific therapeutic approaches than others represents a welcome shift towards precision medicine. Comparative-effectiveness research to identify best practice has delivered on expectations for providing evidence in support of best practices, both in adult and paediatric patients with TBI.

Progress has also been made in improving outcome assessment after TBI. Key instruments have been translated into up to 20 languages and linguistically validated, and are now internationally available for clinical and research use. TBI affects multiple domains of functioning, and outcomes are affected by personal characteristics and life-course events, consistent with a multifactorial bio-psycho-socio-ecological model of TBI, as presented in the US National Academies of Sciences, Engineering, and Medicine (NASEM) 2022 report. Multidimensional assessment is desirable and might be best based on measurement of global functional impairment. More work is required to develop and implement recommendations for multidimensional assessment. Prediction of outcome is relevant to patients and their families, and can facilitate the benchmarking of quality of care. InTBIR studies have identified new building blocks (eg, blood biomarkers and quantitative CT analysis) to refine existing prognostic models. Further improvement in prognostication could come from MRI, genetics, and the integration of dynamic changes in patient status after presentation.

Neurotrauma researchers traditionally seek translation of their research findings through publications, clinical guidelines, and industry collaborations. However, to effectively impact clinical care and outcome, interactions are also needed with research funders, regulators, and policy makers, and partnership with patient organisations. Such interactions are increasingly taking place, with exemplars including interactions with the All Party Parliamentary Group on Acquired Brain Injury in the UK, the production of the NASEM report in the USA, and interactions with the US Food and Drug Administration. More interactions should be encouraged, and future discussions with regulators should include debates around consent from patients with acute mental incapacity and data sharing. Data sharing is strongly advocated by funding agencies. From January 2023, the US National Institutes of Health will require upload of research data into public repositories, but the EU requires data controllers to safeguard data security and privacy regulation. The tension between open data-sharing and adherence to privacy regulation could be resolved by cross-dataset analyses on federated platforms, with the data remaining at their original safe location. Tools already exist for conventional statistical analyses on federated platforms, however federated machine learning requires further development. Support for further development of federated platforms, and neuroinformatics more generally, should be a priority.

This update to the 2017 Commission presents new insights and challenges across a range of topics around TBI: epidemiology and prevention (section 1 ); system of care (section 2 ); clinical management (section 3 ); characterisation of TBI (section 4 ); outcome assessment (section 5 ); prognosis (Section 6 ); and new directions for acquiring and implementing evidence (section 7 ). Table 1 summarises key messages from this Commission and proposes recommendations for the way forward to advance research and clinical management of TBI.

Impact of Intracranial Hypertension on Outcome of Severe Traumatic Brain Injury Pediatric Patients: A 15-Year Single Center Experience

Background: Intracranial hypertension (IC-HTN) is significantly associated with higher risk for an unfavorable outcome in pediatric trauma. Intracranial pressure (ICP) monitoring is widely becoming a standard of neurocritical care for children. Methods: The present study was designed to evaluate influences of IC-HTN on clinical outcomes of pediatric TBI patients. Demographic, injury severity, radiologic characteristics were used as possible predictors of IC-HTN or of functional outcome. Results: A total of 118 pediatric intensive care unit (PICU) patients with severe TBI (sTBI) were included. Among sTBI cases, patients with GCS < 5 had significantly higher risk for IC-HTN and for mortality. Moreover, there was a statistically significant positive correlation between IC-HTN and severity scoring systems. Kaplan−Meier analysis determined a significant difference for good recovery among patients who had no ICP elevations, compared to those who had at least one episode of IC-HTN (log-rank chi-square = 11.16, p = 0.001). A multivariable predictive logistic regression analysis distinguished the ICP-monitored patients at risk for developing IC-HTN. The model finally revealed that higher ISS and Helsinki CT score increased the odds for developing IC-HTN (p < 0.05). Conclusion: The present study highlights the importance of ICP-guided clinical practices, which may lead to increasing percentages of good recovery for children.

Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

BACKGROUND

While the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as 'mild', 'moderate' or 'severe' based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights.

METHODS

We developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation.

RESULTS

Six stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with 'moderate' TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with 'severe' GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001).

CONCLUSIONS

Six stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care. Trial registration The core study was registered with ClinicalTrials.gov, number NCT02210221 , registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).

Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm

Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.

AUS-TBI: The Australian Health Informatics Approach to Predict Outcomes and Monitor Intervention Efficacy after Moderate-to-Severe Traumatic Brain Injury

Predicting and optimizing outcomes after traumatic brain injury (TBI) remains a major challenge because of the breadth of injury characteristics and complexity of brain responses. AUS-TBI is a new Australian Government–funded initiative that aims to improve personalized care and treatment for children and adults who have sustained a TBI. The AUS-TBI team aims to address a number of key knowledge gaps, by designing an approach to bring together data describing psychosocial modulators, social determinants, clinical parameters, imaging data, biomarker profiles, and rehabilitation outcomes in order to assess the influence that they have on long-term outcome. Data management systems will be designed to track a broad range of suitable potential indicators and outcomes, which will be organized to facilitate secure data collection, linkage, storage, curation, management, and analysis. It is believed that these objectives are achievable because of our consortium of highly committed national and international leaders, expert committees, and partner organizations in TBI and health informatics. It is anticipated that the resulting large-scale data resource will facilitate personalization, prediction, and improvement of outcomes post-TBI.

Prognostic Value of Different Computed Tomography Scoring Systems in Patients With Severe Traumatic Brain Injury Undergoing Decompressive Craniectomy

OBJECTIVE

In this study, we investigate the preoperative and postoperative computed tomography (CT) scores in severe traumatic brain injury (TBI) patients undergoing decompressive craniectomy (DC) and compare their predictive accuracy.

METHODS

Univariate and multivariate logistic regression analyses were used to determine the relationship between CT score (preoperative and postoperative) and mortality at 30 days after injury. The discriminatory power of preoperative and postoperative CT score was assessed by the area under the receiver operating characteristic curve (AUC).

RESULTS

Multivariate logistic regression analysis adjusted for the established predictors of TBI outcomes showed that preoperative Rotterdam CT score (odds ratio [OR], 3.60; 95% confidence interval [CI], 1.13-11.50; P = 0.030), postoperative Rotterdam CT score (OR, 4.17; 95% CI, 1.63-10.66; P = 0.003), preoperative Stockholm CT score (OR, 3.41; 95% CI, 1.42-8.18; P = 0.006), postoperative Stockholm CT score (OR, 4.50; 95% CI, 1.60-12.64; P = 0.004), preoperative Helsinki CT score (OR, 1.44; 95% CI, 1.03-2.02; P = 0.031), and postoperative Helsinki CT score (OR, 2.55; 95% CI, 1.32-4.95; P = 0.005) were significantly associated with mortality. The performance of the postoperative Rotterdam CT score was superior to the preoperative Rotterdam CT score (AUC, 0.82-0.97 vs 0.71-0.91). The postoperative Stockholm CT score was superior to the preoperative Stockholm CT score (AUC, 0.76-0.94 vs 0.72-0.92). The postoperative Helsinki CT score was superior to the preoperative Helsinki CT score (AUC, 0.88-0.99 vs 0.65-0.87).

CONCLUSIONS

In conclusion, assessing the CT score before and after DC may be more precise and efficient for predicting early mortality in severe TBI patients who undergo DC.

Clinical-Pathological Study on Expressions β-APP, GFAP, NFL, Spectrin II, CD68 to Verify Diffuse Axonal Injury Diagnosis, Grade and Survival Interval

Traumatic brain injury (TBI) is one of the leading causes of death worldwide, particularly in young people. Diffuse axonal injuries (DAI) are the result of strong rotational and translational forces on the brain parenchyma, leading to cerebral oedema and neuronal death. DAI is typically characterized by coma without focal lesions at presentation and is defined by localized axonal damage in multiple regions of the brain parenchyma, often causing impairment of cognitive and neuro-vegetative function. Following TBI, axonal degeneration has been identified as a progressive process that begins with the disruption of axonal transport, leading subsequently to axonal swelling, axonal ballooning, axonal retraction bulges, secondary disconnection and Wallerian degeneration. The objective of this paper is to report on a series of patients who have suffered fatal traumatic brain injury, in order to verify neurological outcomes in dynamics, relative to the time of injury, using antibodies for neurofilament (NFL), spectrin II, beta-amyloid (β-APP), glial fibrillary acidic protein (GFAP) and cluster of differentiation 68 (CD68). From the studied cases, a total of 50 cases were chosen, which formed two study groups. The first study group comprises 30 cases divided according to survival interval. The control group comprises 20 cases with no history of traumatic brain injury. Cardiovascular disease and history of stroke, cases suffering from loss of vital functions, a post-traumatic survival time of less than 15 min, autolysis and putrefaction were established as criteria for exclusion. Based on their expression, we tested for diagnosis and degree of DAI as a strong predictor of mortality. Immunoreactivity was significantly increased in the DAI group compared to the control group. The earliest changes were recorded for GFAP and CD68 immunolabeling, followed by β-APP, spectrin II and NFM. The most intense changes in immunostaining were recorded for spectrin II. Comparative analysis of brain apoptosis, reactive astrocytosis and inflammatory reaction using specific immunohistochemical markers can provide important information on diagnosis of DAI and prognosis, and may elucidate the timing of the traumatic event in traumatic brain injury.

Rubiano A, G. Kolias A, Silva Paiva W. Evaluation of Computed Tomography Scoring Systems in the Prediction of Short-Term Mortality in Traumatic Brain Injury Patients from a Low- to Middle-Income Country

The present study aims to evaluate the accuracy of the prognostic discrimination and prediction of the short-term mortality of the Marshall computed tomography (CT) classification and Rotterdam and Helsinki CT scores in a cohort of TBI patients from a low- to middle-income country. This is a post hoc analysis of a previously conducted prospective cohort study conducted in a university-associated, tertiary-level hospital that serves a population of >12 million in Brazil. Marshall CT class, Rotterdam and Helsinki scores, and their components were evaluated in the prediction of 14-day and in-hospital mortality using Nagelkerk's pseudo-R² and area under the receiver operating characteristic curve. Multi-variate regression was performed using known outcome predictors (age, Glasgow Coma Scale, pupil response, hypoxia, hypotension, and hemoglobin values) to evaluate the increase in variance explained when adding each of the CT classification systems. Four hundred forty-seven patients were included. Mean age of the patient cohort was 40 (standard deviation, 17.83) years, and 85.5% were male. Marshall CT class was the least accurate model, showing pseudo-R² values equal to 0.122 for 14-day mortality and 0.057 for in-hospital mortality, whereas Rotterdam CT scores were 0.245 and 0.194 and Helsinki CT scores were 0.264 and 0.229. The AUC confirms the best prediction of the Rotterdam and Helsinki CT scores regarding the Marshall CT class, which presented greater discriminative ability. When associated with known outcome predictors, Marshall CT class and Rotterdam and Helsinki CT scores showed an increase in the explained variance of 2%, 13.4%, and 21.6%, respectively. In this study, Rotterdam and Helsinki scores were more accurate models in predicting short-term mortality. The study denotes a contribution to the process of external validation of the scores and may collaborate with the best risk stratification for patients with this important pathology.

Chinese Admission Warning Strategy for Predicting the Hospital Discharge Outcome in Patients with Traumatic Brain Injury

Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.

External validation of the NeuroImaging Radiological Interpretation System and Helsinki computed tomography score for mortality prediction in patients with traumatic brain injury treated in the intensive care unit: a Finnish intensive care consortium study

BACKGROUND

Admission computed tomography (CT) scoring systems can be used to objectively quantify the severity of traumatic brain injury (TBI) and aid in outcome prediction. We aimed to externally validate the NeuroImaging Radiological Interpretation System (NIRIS) and the Helsinki CT score. In addition, we compared the prognostic performance of the NIRIS and the Helsinki CT score to the Marshall CT classification and to a clinical model.

METHODS

We conducted a retrospective multicenter observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted in four university hospital ICUs during 2003-2013. We analyzed the CT scans using the NIRIS and the Helsinki CT score and compared the results to 6-month mortality as the primary outcome. In addition, we created a clinical model (age, Glasgow Coma Scale score, Simplified Acute Physiology Score II, presence of severe comorbidity) and combined clinical and CT models to see the added predictive impact of radiological data to conventional clinical information. We measured model performance using area under curve (AUC), Nagelkerke's R² statistics, and the integrated discrimination improvement (IDI).

RESULTS

A total of 3031 patients were included in the analysis. The 6-month mortality was 710 patients (23.4%). Of the CT models, the Helsinki CT displayed best discrimination (AUC 0.73 vs. 0.70 for NIRIS) and explanatory variation (Nagelkerke's R² 0.20 vs. 0.15). The clinical model displayed an AUC of 0.86 (95% CI 0.84-0.87). All CT models increased the AUC of the clinical model by + 0.01 to 0.87 (95% CI 0.85-0.88) and the IDI by 0.01-0.03.

CONCLUSION

In patients with TBI treated in the ICU, the Helsinki CT score outperformed the NIRIS for 6-month mortality prediction. In isolation, CT models offered only moderate accuracy for outcome prediction and clinical variables outweighing the CT-based predictors in terms of predictive performance.

Traumatic Subarachnoid Hemorrhage: A Scoping Review

Sixty-nine million people have a traumatic brain injury (TBI) each year, and TBI is the most common cause of subarachnoid hemorrhage (SAH). Traumatic SAH (TSAH) has been described as an adverse prognostic factor leading to progressive neurological deterioration and increased morbidity and mortality. A limited number of studies, however, evaluate recent trends in the diagnosis and management of SAH in the context of trauma. The objective of this scoping review was to understand the extent and type of evidence concerning the diagnostic criteria and management of TSAH. This scoping review was conducted following the Joanna Briggs Institute methodology for scoping reviews. The review included adults with SAH secondary to trauma, where isolated TSAH (iTSAH) refers to the presence of SAH in the absence of any other traumatic radiographic intracranial pathology, and TSAH refers to the presence of SAH with the possibility or presence of additional traumatic radiographic intracranial pathology. Data extracted from each study included study aim, country, methodology, population characteristics, outcome measures, a summary of findings, and future directives. Thirty studies met inclusion criteria. Studies were grouped into five categories by topic: TSAH associated with mild TBI (mTBI), n = 13), and severe TBI (n = 3); clinical management and diagnosis (n = 9); imaging (n = 3); and aneurysmal TSAH (n = 1). Of the 30 studies, two came from a low- and middle-income country (LMIC), excluding China, nearly a high-income country. Patients with TSAH associated with mTBI have a very low risk of clinical deterioration and surgical intervention and should be treated conservatively when considering intensive care unit admission. The Helsinki and Stockholm computed tomography scoring systems, in addition to the American Injury Scale, creatinine level, age decision tree, may be valuable tools to use when predicting outcome and death.

Trends in mortality after intensive care of patients with traumatic brain injury in Finland from 2003 to 2019: a Finnish Intensive Care Consortium study

Background

Several studies have suggested no change in the outcome of patients with traumatic brain injury (TBI) treated in intensive care units (ICUs). This is mainly due to the shift in TBI epidemiology toward older and sicker patients. In Finland, the share of the population aged 65 years and over has increased the most in Europe during the last decade. We aimed to assess changes in 12-month and hospital mortality of patients with TBI treated in the ICU in Finland.

Methods

We used a national benchmarking ICU database (Finnish Intensive Care Consortium) to study adult patients who had been treated for TBI in four tertiary ICUs in Finland during 2003–2019. We divided admission years into quartiles and used multivariable logistic regression analysis, adjusted for case-mix, to assess the association between admission year and mortality.

Results

A total of 4535 patients were included. Between 2003–2007 and 2016–2019, the patient median age increased from 54 to 62 years, the share of patients having significant comorbidity increased from 8 to 11%, and patients being dependent on help in activities of daily living increased from 7 to 15%. Unadjusted hospital and 12-month mortality decreased from 18 and 31% to 10% and 23%, respectively. After adjusting for case-mix, a reduction in odds of 12-month and hospital mortality was seen in patients with severe TBI, intracranial pressure monitored patients, and mechanically ventilated patients. Despite a reduction in hospital mortality, 12-month mortality remained unchanged in patients aged ≥ 70 years.

Conclusion

A change in the demographics of ICU-treated patients with TBI care is evident. The outcome of younger patients with severe TBI appears to improve, whereas long-term mortality of elderly patients with less severe TBI has not improved. This has ramifications for further efforts to improve TBI care, especially among the elderly.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00701-021-05034-4.

Behavioral and Cognitive Aspects of Concussion

PURPOSE OF REVIEW

This review provides the reader with an overview of concussion and mild traumatic brain injury (TBI). Key aspects of the pathophysiology, signs, and symptoms, treatment and rehabilitation, and recovery from concussion/mild TBI are reviewed with an emphasis on the variety of factors that may contribute to cognitive concerns following injury.

RECENT FINDINGS

Concussion remains a clinical diagnosis based on symptoms that occur in the immediate aftermath of an applied force and in the hours, days, and weeks thereafter. Although advances have been made in advanced diagnostics, including neuroimaging and fluid biomarkers in hopes of developing objective indicators of injury, such markers currently lack sufficient specificity to be used in clinical diagnostics. The symptoms of concussion are heterogeneous and may be seen to form subtypes, each of which suggests a targeted rehabilitation by the interdisciplinary team. Although the majority of patients with concussion recover within the first 30 to 90 days after injury, some have persistent disabling symptoms. The concept of postconcussion syndrome, implying a chronic syndrome of injury-specific symptoms, is replaced by a broader concept of persistent symptoms after concussion. This concept emphasizes the fact that most persistent symptoms have their basis in complex somatic, cognitive, psychiatric, and psychosocial factors related to risk and resilience. This framework leads to the important conclusion that concussion is a treatable injury from which nearly all patients can be expected to recover.

SUMMARY

Concussion/mild TBI is a significant public health problem in civilian, military, and organized athletic settings. Recent advances have led to a better understanding of underlying pathophysiology and symptom presentation and efficacious treatment and rehabilitation of the resulting symptoms. An interdisciplinary team is well-positioned to provide problem-oriented, integrated care to facilitate recovery and to advance the evidence base supporting effective practice in diagnosis, treatment, and prevention.

Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms

COVID-19 (Coronavirus) went through a rapid escalation until it became a pandemic disease. The normal and manual medical infection discovery may take few days and therefore computer science engineers can share in the development of the automatic diagnosis for fast detection of that disease. The study suggests a hybrid COVID-19 framework (named HMB-HCF) based on deep learning (DL), genetic algorithm (GA), weighted sum (WS), and majority voting principles in nine phases. Its segmentation phase suggests a lung segmentation algorithm using X-Ray images (named HMB-LSAXI) for extracting lungs. Its classification phase is built from a hybrid convolutional neural network (CNN) architecture using an abstractly-designed CNN (named HMB1-COVID19) and transfer learning (TL) pre-trained models (VGG16, VGG19, ResNet50, ResNet101, Xception, DenseNet121, DenseNet169, MobileNet, and MobileNetV2). The hybrid CNN architecture is used for learning, classification, and parameters optimization while GA is used to optimize the hyperparameters. This hybrid working mechanism is combined in an overall algorithm named HMB-DLGA. The study experiments implemented the WS approach to evaluate the models' performance using the loss, accuracy, F1-score, precision, recall, and area under curve (AUC) metrics with different pre-defined ratios. A collected, combined, and unified X-Ray dataset from 8 different public datasets was used alongside the regularization, dropout, and data augmentation techniques to limit the overall overfitting. The applied experiments reported state-of-the-art metrics. VGG16 reported 100% WS metric (i.e., 0.0097, 99.78%, 0.9984, 99.89%, 99.78%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the highest WS. It also reported a 99.92% WS metric (i.e., 0.0099, 99.84%, 0.9984, 99.84%, 99.84%, and 0.9996 for the loss, accuracy, F1, precision, recall, and AUC respectively) concerning the last reported WS result. HMB-HCF was validated on 13 different public datasets to verify its generalization. The best-achieved metrics were compared with 13 related studies. These extensive experiments' target was the applicability verification and generalization.

Prognostic Value of Hemorrhagic Brainstem Injury on Early Computed Tomography: A TRACK-TBI Study

BACKGROUND

Traumatic brainstem injury has yet to be incorporated into widely used imaging classification systems for traumatic brain injury (TBI), and questions remain regarding prognostic implications for this TBI subgroup. To address this, retrospective data on patients from the multicenter prospective Transforming Research and Clinical Knowledge in TBI study were studied.

METHODS

Patients with brainstem and cerebrum injury (BSI+) were matched by age, sex, and admission Glasgow Coma Scale (GCS) score to patients with cerebrum injuries only. All patients had an interpretable head computed tomography (CT) scan from the first 48 hours after injury and a 6-month Glasgow Outcome Scale Extended (GOSE) score. CT scans were reviewed for brainstem lesions and, when present, characterized by location, size, and type (traumatic axonal injury, contusion, or Duret hemorrhage). Clinical, demographic, and outcome data were then compared between the two groups.

RESULTS

Mann-Whitney U-tests showed no significant difference in 6-month GOSE scores in patients with BSI+ (mean 2.7) compared with patients with similar but only cerebrum injuries (mean 3.9), although there is a trend (p = 0.10). However, subclassification by brainstem lesion type, traumatic axonal injury (mean 4.0) versus Duret hemorrhage or contusion (mean 1.4), did identify a proportion of BSI+ with significantly less favorable outcome (p = 0.002). The incorporation of brainstem lesion type (traumatic axonal injury vs. contusion/Duret), along with GCS into a multivariate logistic regression model of favorable outcome (GOSE score 4-8) did show a significant contribution to the prognostication of this brainstem injury subgroup (odds ratio 0.08, 95% confidence interval 0.00-0.67, p = 0.01).

CONCLUSIONS

These findings suggest two groups of patients with brainstem injuries may exist with divergent recovery potential after TBI. These data support the notion that newer CT imaging classification systems may augment traditional clinical measures, such as GCS in identifying those patients with TBI and brainstem injuries that stand a higher chance of favorable outcome.

Selection of CT variables and prognostic models for outcome prediction in patients with traumatic brain injury

Background

Traumatic brain injuries (TBI) are associated with high risk of morbidity and mortality. Early outcome prediction in patients with TBI require reliable data input and stable prognostic models. The aim of this investigation was to analyze different CT classification systems and prognostic calculators in a representative population of TBI-patients, with known outcomes, in a neurointensive care unit (NICU), to identify the most suitable CT scoring system for continued research.

Materials and methods

We retrospectively included 158 consecutive patients with TBI admitted to the NICU at a level 1 trauma center in Sweden from 2012 to 2016. Baseline data on admission was recorded, CT scans were reviewed, and patient outcome one year after trauma was assessed according to Glasgow Outcome Scale (GOS). The Marshall classification, Rotterdam scoring system, Helsinki CT score and Stockholm CT score were tested, in addition to the IMPACT and CRASH prognostic calculators. The results were then compared with the actual outcomes.

Results

Glasgow Coma Scale score on admission was 3–8 in 38%, 9–13 in 27.2%, and 14–15 in 34.8% of the patients. GOS after one year showed good recovery in 15.8%, moderate disability in 27.2%, severe disability in 24.7%, vegetative state in 1.3% and death in 29.7%. When adding the variables from the IMPACT base model to the CT scoring systems, the Stockholm CT score yielded the strongest relationship to actual outcome. The results from the prognostic calculators IMPACT and CRASH were divided into two subgroups of mortality (percentages); ≤50% (favorable outcome) and > 50% (unfavorable outcome). This yielded favorable IMPACT and CRASH scores in 54.4 and 38.0% respectively.

Conclusion

The Stockholm CT score and the Helsinki score yielded the closest relationship between the models and the actual outcomes in this consecutive patient series, representative of a NICU TBI-population. Furthermore, the Stockholm CT score yielded the strongest overall relationship when adding variables from the IMPACT base model and would be our method of choice for continued research when using any of the current available CT score models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13049-021-00901-6.

Novel severe traumatic brain injury blood outcome biomarkers identified with proximity extension assay

OBJECTIVES

Severe traumatic brain injury (sTBI) patients suffer high mortality. Accurate prognostic biomarkers have not been identified. In this exploratory study, we performed targeted proteomics on plasma obtained from sTBI patients to identify potential outcome biomarkers.

METHODS

Blood sample was collected from patients admitted to the ICU suffering a sTBI, using standardized clinical and computerized tomography (CT) imaging criteria. Age- and sex-matched healthy control subjects and sTBI patients were enrolled. Targeted proteomics was performed on plasma with proximity extension assays (1,161 proteins).

RESULTS

Cohorts were well-balanced for age and sex. The majority of sTBI patients were injured in motor vehicle collisions and the most frequent head CT finding was subarachnoid hemorrhage. Mortality rate for sTBI patients was 40%. Feature selection identified the top performing 15 proteins for identifying sTBI patients from healthy control subjects with a classification accuracy of 100%. The sTBI proteome was dominated by markers of vascular pathology, immunity/inflammation, cell survival and macrophage/microglia activation. Receiver operating characteristic (ROC) curve analyses demonstrated areas-under-the-curves (AUC) for identifying sTBI that ranged from 0.870-1.000 (p≤0.005). When mortality was used as outcome, ROC curve analyses identified the top 3 proteins as Willebrand factor (vWF), Wnt inhibitory factor-1 (WIF-1), and colony stimulating factor-1 (CSF-1). Combining vWF with either WIF-1 or CSF-1 resulted in excellent mortality prediction with AUC of 1.000 for both combinations (p=0.011).

CONCLUSIONS

Targeted proteomics with feature classification and selection distinguished sTBI patients from matched healthy control subjects. Two protein combinations were identified that accurately predicted sTBI patient mortality. Our exploratory findings require confirmation in larger sTBI patient populations.

Contribution of CT-Scan Analysis by Artificial Intelligence to the Clinical Care of TBI Patients

The gold standard to diagnose intracerebral lesions after traumatic brain injury (TBI) is computed tomography (CT) scan, and due to its accessibility and improved quality of images, the global burden of CT scan for TBI patients is increasing. The recent developments of automated determination of traumatic brain lesions and medical-decision process using artificial intelligence (AI) represent opportunities to help clinicians in screening more patients, identifying the nature and volume of lesions and estimating the patient outcome. This short review will summarize what is ongoing with the use of AI and CT scan for patients with TBI.

Mortality After Trauma Craniotomy Is Decreasing in Older Adults-A Nationwide Population-Based Study

OBJECTIVE

No evidence-based guidelines are available for operative neurosurgical treatment of older patients with traumatic brain injuries (TBIs), and no population-based results of current practice have been reported. The objective of the present study was to investigate the rates of trauma craniotomy operations and later mortality in older adults with TBI in Finland.

METHODS

Nationwide databases were searched for all admissions with a TBI diagnosis and after trauma craniotomy, and later deaths for persons aged ≥60 years from 2004 to 2018.

RESULTS

The study period included 2166 patients (64% men; mean age, 70.3 years) who had undergone TBI-related craniotomy. The incidence rate of operations decreased with a concomitant decrease in adjusted mortality (30-day mortality, P < 0.001; 1-year mortality, P < 0.001) and increase in mean patient age (R² = 0.005; P < 0.001) during the study period. The cumulative mortality was 25% at 30 days and 38% at 1 year. The comorbidities increasing the hazard for 30-day mortality were diabetes, a history of malignancy, peripheral vascular disease, and a history of myocardial infarction. For 1-year mortality, the comorbidities were heart failure and a history of myocardial infarction. Evacuation of an epidural hematoma decreased the hazard for mortality. In contrast, evacuation of an intracerebral hematoma and decompressive craniectomy increased the risk at both 30 days and 1 year.

CONCLUSIONS

Among older adults in Finland, the rate of trauma craniotomy and later mortality has been decreasing although the mean age of operated patients has been increasing. This can be expected to be related to an improved understanding of geriatric TBIs and, consequently, improved selection of patients for targeted therapy.

Densitometric analysis of brain computed tomography as a new prognostic factor in patients with acute subdural hematoma

OBJECTIVE

Acute subdural hematoma (ASDH) is a major cause of mortality and morbidity after traumatic brain injury (TBI). Surgical evacuation is the mainstay of treatment in patients with altered neurological status or significant mass effect. Nevertheless, concerns regarding surgical indication still persist. Given that clinicians often make therapeutic decisions on the basis of their prognosis assessment, to accurately evaluate the prognosis is of great significance. Unfortunately, there is a lack of specific and reliable prognostic models. In addition, the interdependence of certain well-known predictive variables usually employed to guide surgical decision-making in ASDH has been proven. Because gray matter and white matter are highly susceptible to secondary insults during the early phase after TBI, the authors aimed to assess the extent of these secondary insults with a brain parenchyma densitometric quantitative CT analysis and to evaluate its prognostic capacity.

METHODS

The authors performed a retrospective analysis among their prospectively collected cohort of patients with moderate to severe TBI. Patients with surgically evacuated, isolated, unilateral ASDH admitted between 2010 and 2017 were selected. Thirty-nine patients were included. For each patient, brain parenchyma density in Hounsfield units (HUs) was measured in 10 selected slices from the supratentorial region. In each slice, different regions of interest (ROIs), including and excluding the cortical parenchyma, were defined. The injured hemisphere, the contralateral hemisphere, and the absolute differences between them were analyzed. The outcome was evaluated using the Glasgow Outcome Scale–Extended at 1 year after TBI.

RESULTS

Fifteen patients (38.5%) had a favorable outcome. Collected demographic, clinical, and radiographic data did not show significant differences between favorable and unfavorable outcomes. In contrast, the densitometric analysis demonstrated that greater absolute differences between both hemispheres were associated with poor outcome. These differences were detected along the supratentorial region, but were greater at the high convexity level. Moreover, these HU differences were far more marked at the cortical parenchyma. It was also detected that these differences were more prone to ischemic and/or edematous insults than to hyperemic changes. Age was significantly correlated with the side-to-side HU differences in patients with unfavorable outcome.

CONCLUSIONS

The densitometric analysis is a promising prognostic tool in patients diagnosed with ASDH. The supplementary prognostic information provided by the densitometric analysis should be evaluated in future studies.

Mismatch between midline shift and hematoma thickness as a prognostic factor of mortality in patients sustaining acute subdural hematoma

Background

Acute subdural hematoma (ASDH) is a traumatic lesion commonly found secondary to traumatic brain injury. Radiological findings on CT, such as hematoma thickness (HT) and structures midline shift (MLS), have an important prognostic role in this disease. The relationship between HT and MLS has been rarely studied in the literature. Thus, this study aimed to assess the prognostic accuracy of the difference between MLS and HT for acute outcomes in patients with ASDH in a low-income to middle-income country.

Methods

This was a post-hoc analysis of a prospective cohort study conducted in a university-associated tertiary-level hospital in Brazil. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) statement guidelines were followed. The difference values between MLS and HT (Zumkeller index, ZI) were divided into three categories (<0.00, 0.01–3, and >3). Logistic regression analyses were performed to reveal the OR of categorized ZI in predicting primary outcome measures. A Cox regression was also performed and the results were presented through HR. The discriminative ability of three multivariate models including clinical and radiological variables (ZI, Rotterdam score, and Helsinki score) was demonstrated.

Results

A total of 114 patients were included. Logistic regression demonstrated an OR value equal to 8.12 for the ZI >3 category (OR 8.12, 95% CI 1.16 to 40.01; p=0.01), which proved to be an independent predictor of mortality in the adjusted model for surgical intervention, age, and Glasgow Coma Scale (GCS) score. Cox regression analysis demonstrated that this category was associated with 14-day survival (HR 2.92, 95% CI 1.38 to 6.16; p=0.005). A multivariate analysis performed for three models including age and GCS with categorized ZI or Helsinki or Rotterdam score demonstrated area under the receiver operating characteristic curve values of 0.745, 0.767, and 0.808, respectively.

Conclusions

The present study highlights the potential usefulness of the difference between MLS and HT as a prognostic variable in patients with ASDH.

Level of evidence

Level III, epidemiological study.

Predictive Value of Rotterdam Score and Marshall Score in Traumatic Brain Injury: A Contemporary Review

This article conducts a contemporary comparative review of the medical literature to update and establish evidence as to which framework among Rotterdam and Marshall computed tomography (CT)-based scoring systems predicts traumatic brain injury (TBI) outcomes better. The scheme followed was following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines for literature search. The search started on August 15, 2020 and ended on December 31, 2020. The combination terms used were Medical Subject Headings terms, combination keywords, and specific words used for describing various pathologies of TBI to identify the most relevant article in each database. PICO question to guide the search strategy was: “what is the use of Marshall (I) versus Rotterdam score (C) in TBI patients (P) for mortality risk stratification (O).” The review is based on 46 references which included a full review of 14 articles for adult TBI patients and 6 articles for pediatric TBI articles comparing Rotterdam and Marshall CT scores. The review includes 8,243 patients, of which 2,365 were pediatric and 5,878 were adult TBI patients. Marshall CT classification is not ordinal, is more descriptive, has better inter-rater reliability, and poor performance in a specific group of TBI patients requiring decompressive craniectomy. Rotterdam CT classification is ordinal, has better discriminatory power, and a better description of the dynamics of intracranial changes. The two scoring systems are complimentary. A combination of clinical parameters, severity, ischemic and hemodynamic parameters, and CT scoring system could predict the prognosis of TBI patients with significant accuracy. None of the classifications has good evidence for use in pediatric patients.

Utility of the Marshall & Rotterdam Classification Scores in Predicting Outcomes in Trauma Patients

BACKGROUND

Traumatic Brain Injury (TBI) is a leading cause of mortality in the trauma population. Accurate prognosis remains a challenge. Two common Computed Tomography (CT)-based prognostic models include the Marshall Classification and the Rotterdam CT Score. This study aims to determine the utility of the Marshall and Rotterdam scores in predicting mortality for adult patients in coma with severe TBI.

METHOD

Retrospective review of our Level 1 Trauma Center's registry for patients ≥ 18 years of age with blunt TBI and a Glasgow Coma Scale (GCS) of 3-5, with no other significant injuries. Admission Head CT was evaluated for the presence of extra-axial blood (SDH, EDH, SAH, IVH), intra-axial blood (contusions, diffuse axonal injury), midline shift and mass effect on basilar cisterns. Rotterdam and Marshall scores were calculated for all patients; subsequently patients were divided into two groups according to their score (< 4, ≥ 4).

RESULTS

106 patients met inclusion criteria; 75.5% were males (n = 80) and 24.5% females (n = 26). The mean age was 52. The odds ratio (OR) of dying from severe TBI for patients in coma with a Rotterdam score of ≥ 4 compared to < 4 was OR = 17 (P < 0.05). The odds of dying from severe TBI for patients in coma with a Marshall score of ≥ 4 versus < 4 was OR = 11 (P < 0.05).

CONCLUSION

Higher scores in the Marshall classification and the Rotterdam system are associated with increased odds of mortality in adult patients in come from severe TBI after blunt injury. The results of our study support these scoring systems and revealed that a cutoff score of < 4 was associated with improved survival.

Fluid proteomics of CSF and serum reveal important neuroinflammatory proteins in blood-brain barrier disruption and outcome prediction following severe traumatic brain injury: a prospective, observational study

BACKGROUND

Severe traumatic brain injury (TBI) is associated with blood-brain barrier (BBB) disruption and a subsequent neuroinflammatory process. We aimed to perform a multiplex screening of brain enriched and inflammatory proteins in blood and cerebrospinal fluid (CSF) in order to study their role in BBB disruption, neuroinflammation and long-term functional outcome in TBI patients and healthy controls.

METHODS

We conducted a prospective, observational study on 90 severe TBI patients and 15 control subjects. Clinical outcome data, Glasgow Outcome Score, was collected after 6-12 months. We utilized a suspension bead antibody array analyzed on a FlexMap 3D Luminex platform to characterize 177 unique proteins in matched CSF and serum samples. In addition, we assessed BBB disruption using the CSF-serum albumin quotient (QA), and performed Apolipoprotein E-genotyping as the latter has been linked to BBB function in the absence of trauma. We employed pathway-, cluster-, and proportional odds regression analyses. Key findings were validated in blood samples from an independent TBI cohort.

RESULTS

TBI patients had an upregulation of structural CNS and neuroinflammatory pathways in both CSF and serum. In total, 114 proteins correlated with QA, among which the top-correlated proteins were complement proteins. A cluster analysis revealed protein levels to be strongly associated with BBB integrity, but not carriage of the Apolipoprotein E4-variant. Among cluster-derived proteins, innate immune pathways were upregulated. Forty unique proteins emanated as novel independent predictors of clinical outcome, that individually explained ~ 10% additional model variance. Among proteins significantly different between TBI patients with intact or disrupted BBB, complement C9 in CSF (p = 0.014, ΔR2 = 7.4%) and complement factor B in serum (p = 0.003, ΔR2 = 9.2%) were independent outcome predictors also following step-down modelling.

CONCLUSIONS

This represents the largest concomitant CSF and serum proteomic profiling study so far reported in TBI, providing substantial support to the notion that neuroinflammatory markers, including complement activation, predicts BBB disruption and long-term outcome. Individual proteins identified here could potentially serve to refine current biomarker modelling or represent novel treatment targets in severe TBI.

Early CT in dogs following traumatic brain injury has limited value in predicting short-term prognosis

Traumatic brain injury is associated with a high risk of mortality in veterinary patients, however publications describing valid prognostic indicators are currently lacking. The objective of this retrospective observational study was to determine whether early CT findings are associated with short-term prognosis following traumatic brain injury (TBI) in dogs. An electronic database was searched for dogs with TBI that underwent CT within 72 h of injury; 40 dogs met the inclusion criteria. CT findings were graded based on a Modified Advanced Imaging System (MAIS) from grade I (normal brain parenchyma) to VI (bilateral lesions affecting the brainstem with or without any foregoing lesions of lesser grades). Other imaging features recorded included presence of midline shift, intracranial hemorrhage, brain herniation, skull fractures, and percentage of total brain parenchyma affected. Outcome measures included survival to discharge and occurrence of immediate onset posttraumatic seizures. Thirty dogs (75%) survived to discharge. Seven dogs (17.5%) suffered posttraumatic seizures. There was no association between survival to discharge and posttraumatic seizures. No imaging features evaluated were associated with the study outcome measures. Therefore, the current study failed to identify any early CT imaging features with prognostic significance in canine TBI patients. Limitations associated with CT may preclude its use for prognostication; however, modifications to the current MAIS and evaluation in a larger study population may yield more useful results. Despite this, CT is a valuable tool in the detection of structural abnormalities following TBI in dogs that warrants further investigation.

Functional recovery after brain injury: Independent predictors of psychosocial outcome one year after TBI

INTRODUCTION

Traumatic Brain Injury (TBI) is the leading cause of death and disability in people aged under 40 in the UK. Many patients suffer residual deficits, which limits their functional recovery. The aim of this study was to determine independent predictors of functional outcome at 1-year post-TBI.

METHODS

Utilising a prospective observational cohort design, 1131 consecutive adult admissions with non-recurrent TBI were recruited from the ED (Emergency Department). Using routine consultant-led follow up clinics, data was collected between August 2011 and July 2015. The Rivermead Head Injury Follow Up Questionnaire (RHFUQ) was used to measure psychosocial function at 1 year.

RESULTS

A multiple linear regression model showed that previous psychiatric history (p < 0.001), lower Glasgow Coma Scale (p < 0.001), a severe CT scan (p = 0.002), aetiology of assault compared to sport (p = 0.011) and falls (p = 0.005), initial unemployment (p < 0.001) and no job at 8-10 weeks (p < 0.001) after TBI had a significant association with a worse RHFUQ score at 1 year. Follow up rate was >90 %.

CONCLUSIONS

This study adds valuable information on the prognostic indicators of TBI recovery and possible targets for intervention. Future development of a validated prognostic model to predict long term functional outcomes after TBI will help improve long-term treatment of the condition.

Delayed Neurosurgical Intervention in Traumatic Brain Injury Patients Referred From Primary Hospitals Is Not Associated With an Unfavorable Outcome

Background: Secondary transports of patients suffering from traumatic brain injury (TBI) may result in a delayed management and neurosurgical intervention, which is potentially detrimental. The aim of this study was to study the effect of triaging and delayed transfers on outcome, specifically studying time to diagnostics and neurosurgical management.

Methods: This was a retrospective observational cohort study of TBI patients in need of neurosurgical care, 15 years and older, in the Stockholm Region, Sweden, from 2008 throughout 2014. Data were collected from pre-hospital and in-hospital charts. Known TBI outcome predictors, including the protein biomarker of brain injury S100B, were used to assess injury severity. Characteristics and outcomes of direct trauma center (TC) and those of secondary transfers were evaluated and compared. Functional outcome, using the Glasgow Outcome Scale, was assessed in survivors at 6–12 months after trauma. Regression models, including propensity score balanced models, were used for endpoint assessment.

Results: A total of n = 457 TBI patients were included; n = 320 (70%) patients were direct TC transfers, whereas n = 137 (30%) were secondary referrals. In all, n = 295 required neurosurgery for the first 24 h after trauma (about 75% of each subgroup). Direct TC transfers were more severely injured (median Glasgow Coma Scale 8 vs. 13) and more often suffered a high energy trauma (31 vs. 2.9%) than secondary referrals. Admission S100B was higher in the TC transfer group, though S100B levels 12–36 h after trauma were similar between cohorts. Direct or indirect TC transfer could be predicted using propensity scoring. The secondary referrals had a shorter distance to the primary hospital, but had later radiology and surgery than the TC group (all p < 0.001). In adjusted multivariable analyses with and without propensity matching, direct or secondary transfers were not found to be significantly related to outcome. Time from trauma to surgery did not affect outcome.

Conclusions: TBI patients secondary transported to a TC had surgical intervention performed hours later, though this did not affect outcome, presumably demonstrating that accurate pre-hospital triaging was performed. This indicates that for selected patients, a wait-and-see approach with delayed neurosurgical intervention is not necessarily detrimental, but warrants further research.

Outcome Prediction after Moderate and Severe Traumatic Brain Injury: External Validation of Two Established Prognostic Models in 1742 European Patients

The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict functional outcome after moderate and severe traumatic brain injury (TBI). We aimed to assess their performance in a contemporary cohort of patients across Europe. The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) core study is a prospective, observational cohort study in patients presenting with TBI and an indication for brain computed tomography. The CENTER-TBI core cohort consists of 4509 TBI patients available for analyses from 59 centers in 18 countries across Europe and Israel. The IMPACT validation cohort included 1173 patients with GCS ≤12, age ≥14, and 6-month Glasgow Outcome Scale-Extended (GOSE) available. The CRASH validation cohort contained 1742 patients with GCS ≤14, age ≥16, and 14-day mortality or 6-month GOSE available. Performance of the three IMPACT and two CRASH model variants was assessed with discrimination (area under the receiver operating characteristic curve; AUC) and calibration (comparison of observed vs. predicted outcome rates). For IMPACT, model discrimination was good, with AUCs ranging between 0.77 and 0.85 in 1173 patients and between 0.80 and 0.88 in the broader CRASH selection (n = 1742). For CRASH, AUCs ranged between 0.82 and 0.88 in 1742 patients and between 0.66 and 0.80 in the stricter IMPACT selection (n = 1173). Calibration of the IMPACT and CRASH models was generally moderate, with calibration-in-the-large and calibration slopes ranging between -2.02 and 0.61 and between 0.48 and 1.39, respectively. The IMPACT and CRASH models adequately identify patients at high risk for mortality or unfavorable outcome, which supports their use in research settings and for benchmarking in the context of quality-of-care assessment.

Recovery of Theta Frequency Oscillations in Rats Following Lateral Fluid Percussion Corresponds With a Mild Cognitive Phenotype

Whether from a fall, sports concussion, or even combat injury, there is a critical need to identify when an individual is able to return to play or work following traumatic brain injury (TBI). Electroencephalogram (EEG) and local field potentials (LFP) represent potential tools to monitor circuit-level abnormalities related to learning and memory: specifically, theta oscillations can be readily observed and play a critical role in cognition. Following moderate traumatic brain injury in the rat, lasting changes in theta oscillations coincide with deficits in spatial learning. We hypothesized, therefore, that theta oscillations can be used as an objective biomarker of recovery, with a return of oscillatory activity corresponding with improved spatial learning. In the current study, LFP were recorded from dorsal hippocampus and anterior cingulate in awake, behaving adult Sprague Dawley rats in both a novel environment on post-injury days 3 and 7, and Barnes maze spatial navigation on post-injury days 8–11. Theta oscillations, as measured by power, theta-delta ratio, peak theta frequency, and phase coherence, were significantly altered on day 3, but had largely recovered by day 7 post-injury. Injured rats had a mild behavioral phenotype and were not different from shams on the Barnes maze, as measured by escape latency. Injured rats did use suboptimal search strategies. Combined with our previous findings that demonstrated a correlation between persistent alterations in theta oscillations and spatial learning deficits, these new data suggest that neural oscillations, and particularly theta oscillations, have potential as a biomarker to monitor recovery of brain function following TBI. Specifically, we now demonstrate that oscillations are depressed following injury, but as oscillations recover, so does behavior.

Admission Levels of Interleukin 10 and Amyloid β 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury

Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI).

Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI.

Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of β-amyloid isoforms 1–40 (Aβ40) and 1–42 (Aβ42), glial fibrillary acidic protein, heart fatty acid-binding protein, interleukin 10 (IL-10), neurofilament light, S100 calcium-binding protein B, and total tau were measured within 24 h from admission. The patients were divided into favorable (Glasgow Outcome Scale—Extended 5–8, n = 49) and unfavorable (Glasgow Outcome Scale—Extended 1–4, n = 33) groups. The outcome was assessed 6–12 months after injury. An optimal predictive panel was investigated with the sensitivity set at 90–100%.

Results: The HCTS alone yielded a sensitivity of 97.0% (95% CI: 90.9–100) and specificity of 22.4% (95% CI: 10.2–32.7) and partial area under the curve of the receiver operating characteristic of 2.5% (95% CI: 1.1–4.7), in discriminating patients with favorable and unfavorable outcomes. The threshold to detect a patient with unfavorable outcome was an HCTS > 1. The three best individually performing biomarkers in outcome prediction were Aβ40, Aβ42, and neurofilament light. The optimal panel included IL-10, Aβ40, and the HCTS reaching a partial area under the curve of the receiver operating characteristic of 3.4% (95% CI: 1.7–6.2) with a sensitivity of 90.9% (95% CI: 81.8–100) and specificity of 59.2% (95% CI: 40.8–69.4).

Conclusion: Admission plasma levels of IL-10 and Aβ40 significantly improve the prognostication ability of the HCTS after TBI.