Neuroimaging Radiological Interpretation System for Acute Traumatic Brain Injury

The Florida Geriatric Head Trauma CT Clinical Decision Rule

BACKGROUND

Several clinical decision rules have been devised to guide head computed tomography (CT) use in patients with minor head injuries, but none have been validated in patients 65 years or older. We aimed to derive and validate a head injury clinical decision rule for older adults.

METHODS

We conducted a secondary analysis of an existing dataset of consecutive emergency department (ED) patients >65 years old with blunt head trauma. The main predictive outcomes were significant intracranial injury and Need for Neurosurgical Intervention on CT. The secondary outcomes also considered in the model development and validation were All Injuries and All Intracranial Injuries. Predictor variables were identified using multiple variable logistic regression, and clinical decision rule models were developed in a split-sample derivation cohort and then tested in an independent validation cohort.

RESULTS

Of 5776 patients, 233 (4.0%) had significant intracranial injury and an additional 104 (1.8%) met CT criteria for Need for Neurosurgical Intervention. The best performing model, the Florida Geriatric Head Trauma CT Clinical Decision Rule, assigns points based on several clinical variables. If the points totaled 25 or more, a CT scan is indicated. The included predictors were arrival via Emergency Medical Services (+30 points), Glasgow Coma Scale (GCS) <15 (+20 points), GCS <14 (+50 points), antiplatelet medications (+17 points), loss of consciousness (+16 points), signs of basilar skull fracture (+50 points), and headache (+20 points). Utilizing this clinical decision rule in the validation cohort, a point total ≥25 had a sensitivity and specificity of 100.0% (95% CI: 96.0-100) and 12.3% (95% CI: 10.9-13.8), respectively, for significant intracranial injury and Need for Neurosurgical Intervention.

CONCLUSIONS

The Florida Geriatric Head Trauma CT Clinical Decision Rule has the potential to reduce unnecessary CT scans in older adults, without compromising safe emergency medicine practice.

Traumatic Brain Injury and Traumatic Spinal Cord Injury

OBJECTIVE

This article reviews the mechanisms of primary traumatic injury to the brain and spinal cord, with an emphasis on grading severity, identifying surgical indications, anticipating complications, and managing secondary injury.

LATEST DEVELOPMENTS

Serum biomarkers have emerged for clinical decision making and prognosis after traumatic injury. Cortical spreading depolarization has been identified as a potentially modifiable mechanism of secondary injury after traumatic brain injury. Innovative methods to detect covert consciousness may inform prognosis and enrich future studies of coma recovery. The time-sensitive nature of spinal decompression is being elucidated.

ESSENTIAL POINTS

Proven management strategies for patients with severe neurotrauma in the intensive care unit include surgical decompression when appropriate, the optimization of perfusion, and the anticipation and treatment of complications. Despite validated models, predicting outcomes after traumatic brain injury remains challenging, requiring prognostic humility and a model of shared decision making with surrogate decision makers to establish care goals. Penetrating injuries, especially gunshot wounds, are often devastating and require public health and policy approaches that target prevention.

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.

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

PURPOSE

We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients.

METHODS

In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables.

RESULTS

Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis.

CONCLUSION

Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

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.

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement

PURPOSE

This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI).

METHODS

This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements.

RESULTS

One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2.

CONCLUSION

While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.

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.

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.

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.

Mild-to-Moderate Traumatic Brain Injury: A Review with Focus on the Visual System

Traumatic Brain Injury (TBI) is a major global public health problem. Neurological damage from TBI may be mild, moderate, or severe and occurs both immediately at the time of impact (primary injury) and continues to evolve afterwards (secondary injury). In mild (m)TBI, common symptoms are headaches, dizziness and fatigue. Visual impairment is especially prevalent. Insomnia, attentional deficits and memory problems often occur. Neuroimaging methods for the management of TBI include computed tomography and magnetic resonance imaging. The location and the extent of injuries determine the motor and/or sensory deficits that result. Parietal lobe damage can lead to deficits in sensorimotor function, memory, and attention span. The processing of visual information may be disrupted, with consequences such as poor hand-eye coordination and balance. TBI may cause lesions in the occipital or parietal lobe that leave the TBI patient with incomplete homonymous hemianopia. Overall, TBI can interfere with everyday life by compromising the ability to work, sleep, drive, read, communicate and perform numerous activities previously taken for granted. Treatment and rehabilitation options available to TBI sufferers are inadequate and there is a pressing need for new ways to help these patients to optimize their functioning and maintain productivity and participation in life activities, family and community.

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.

CT findings and the prognostic value of the Koret CT score in cats with traumatic brain injury

OBJECTIVES

The aims of this study were to evaluate associations between abnormal head CT findings and outcome, and to examine the prognostic value of the Koret CT score (KCTS) in cats sustaining acute traumatic brain injury (TBI).

METHODS

The medical records of cats hospitalised with TBI that underwent head CT scans within 72 h of admission were retrospectively reviewed. CT scans were evaluated independently by a radiologist and a neurologist who were blinded to the outcome. A KCTS and modified Glasgow Coma Scale (MGCS) were assigned to each cat and the association between abnormal CT findings, KCTS, MGCS and outcome were analysed.

RESULTS

Fourteen cats were included in the study: nine (64.2%) survivors and five (35.7%) non-survivors. Of the nine cats that were discharged, one was a short-term survivor (10 days) and eight (57.1%) were long-term survivors (⩾6 months). Abnormal CT findings included lateral ventricle asymmetry/midline shift (42.8%), intracranial haemorrhage (35.7%), caudotentorial lesions (14.2%) and cranial vault fractures (14.2%), all of which were depressed. Intracranial haemorrhage was found to be significantly and negatively associated with short-term (P = 0.005) and long-term (P = 0.023) survival. KCTS was significantly associated with short-term survival (P = 0.002) and long-term survival (P = 0.004). A KCTS cut-off value of 2 yielded a 100% sensitivity and 100% specificity for short-term survival and 100% sensitivity and 80% specificity for long-term survival. A MGCS cut-off value of ⩾13 was associated with a 100% sensitivity and 100% specificity for short-term survival, and with a 100% sensitivity and 80% specificity for long-term survival.

CONCLUSIONS AND RELEVANCE

KCTS, performed up to 72 h from injury, can be used as an additional diagnostic tool for the prediction of survival in cats with TBI.

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.

Validation of the Revised Neuroimaging Radiological Interpretation System For Acute Traumatic Brain Injury in Adult and Pediatric Population

Aim Our study aimed to validate the revised neuroimaging radiological interpretation system (NIRIS), which would standardize the interpretation of noncontrast head CT of acute traumatic brain injury (TBI) patient and consolidate imaging finding into ordinal severity categories that would not only inform specific patient management actions but could also be used as a clinical decision support tool.

Methods We retrospectively studied dispositions and their outcomes of consecutive patients brought to the Sawai Man Singh Hospital Trauma Centre, Jaipur, India, by any means of transport and who underwent a noncontrast CT scan for suspected TBI between April and December 2018.

Results The revised NIRIS correctly predicted disposition and outcome in 62.9% (750/1192) of patients. After excluding patients with OMEI (other major extracranial injuries) and OMII (other major intracranial injuries), a correct prediction was observed in 88.3% (670/758) of patients. After excluding OMEI and OMII, the predictability of revised NIRIS in the adult population is 87.6% (446/509), while predictability in the pediatric population is 92.1% (224/249).

Conclusion Revised NIRIS is a good tool for predicting patient dispositions, to specific management categories, and outcomes in TBI patients after noncontrast CT head.

Blood Biomarkers for Detection of Brain Injury in COVID-19 Patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus attacks multiple organs of coronavirus disease 2019 (COVID-19) patients, including the brain. There are worldwide descriptions of neurological deficits in COVID-19 patients. Central nervous system (CNS) symptoms can be present early in the course of the disease. As many as 55% of hospitalized COVID-19 patients have been reported to have neurological disturbances three months after infection by SARS-CoV-2. The mutability of the SARS-COV-2 virus and its potential to directly affect the CNS highlight the urgency of developing technology to diagnose, manage, and treat brain injury in COVID-19 patients. The pathobiology of CNS infection by SARS-CoV-2 and the associated neurological sequelae of this infection remain poorly understood. In this review, we outline the rationale for the use of blood biomarkers (BBs) for diagnosis of brain injury in COVID-19 patients, the research needed to incorporate their use into clinical practice, and the improvements in patient management and outcomes that can result. BBs of brain injury could potentially provide tools for detection of brain injury in COVID-19 patients. Elevations of BBs have been reported in cerebrospinal fluid (CSF) and blood of COVID-19 patients. BB proteins have been analyzed in CSF to detect CNS involvement in patients with infectious diseases, including human immunodeficiency virus and tuberculous meningitis. BBs are approved by the U.S. Food and Drug Administration for diagnosis of mild versus moderate traumatic brain injury and have identified brain injury after stroke, cardiac arrest, hypoxia, and epilepsy. BBs, integrated with other diagnostic tools, could enhance understanding of viral mechanisms of brain injury, predict severity of neurological deficits, guide triage of patients and assignment to appropriate medical pathways, and assess efficacy of therapeutic interventions in COVID-19 patients.

The prognostic value of the Koret CT score in dogs following traumatic brain injury

Traumatic brain injury (TBI) is a common condition in veterinary medicine with relatively high mortality rate. Recently, a study that correlated abnormal computed tomography (CT) findings with outcome in dogs with head trauma established a prognostic scoring system termed Koret CT score (KCTS). The purpose of this study was to evaluate the accuracy of the KCTS in making short- and long-term prognosis in dogs presented within 72 h of TBI. Thirty-five dogs that were admitted to a hospital during 2010-2019 with TBI and were CT-scanned within 72 h of injury were included in the study. Retrospectively collected data included signalment, modified Glasgow Coma Scale score (MGCS), CT findings, and outcome, i.e. short-term (defined as 10 days) and long-term (6 months) survival. CT images were reviewed and the KCTS was calculated for all dogs. Association between KCTS and outcome was examined. A significant negative association was found between KCTS and both short- and long-term survival. The area under receiver operating characteristic curve for KCTS for short- and long-term survival was 0.9 and 0.87, respectively. Furthermore, the probability of survival in the short term was predicated by the KCTS in an almost linear fashion and a score of 3 points or less on the KCTS was associated with survival with 85% sensitivity and 100% specificity. These results validate the prognostic value of the KCTS in dogs with TBI and provide a complementary tool for serial clinical and neurological evaluation.

Traumatic Brain Injury: Imaging Patterns and Complications

While the diagnosis of traumatic brain injury (TBI) is a clinical decision, neuroimaging remains vital for guiding management on the basis of identification of intracranial pathologic conditions. CT is the mainstay of imaging of acute TBI for both initial triage and follow-up, as it is fast and accurate in detecting both primary and secondary injuries that require neurosurgical intervention. MRI is more sensitive for the detection of certain intracranial injuries (eg, axonal injuries) and blood products 24-48 hours after injury, but it has limitations (eg, speed, accessibility, sensitivity to motion, and cost). The evidence primarily supports the use of MRI when CT findings are normal and there are persistent unexplained neurologic findings or at subacute and chronic periods. Radiologists should understand the role and optimal imaging modality to use, in addition to patterns of primary brain injury and their influence on the risk of developing secondary brain injuries related to herniation. ^©RSNA, 2019 See discussion on this article by Mathur and Nicolaou.

Interobserver Agreement for the Computed Tomography Severity Grading Scales for Acute Traumatic Brain Injury

The purpose of this study was to determine the interobserver variability among providers of different specialties and levels of experience across five established computed tomography (CT) scoring systems for acute traumatic brain injury (TBI). One hundred cases were selected at random from a retrospective population of adult patients transported to our emergency department and subjected to a non-contrast head CT due to suspicion of TBI. Eight neuroradiologists and neurosurgeons in trainee (residents and fellows) and attending roles independently scored each non-contrast head CT scan on the Marshall, Rotterdam, Helsinki, Stockholm, and NeuroImaging Radiological Interpretation System (NIRIS) head CT scales. Interobserver variability of scale scores-overall and by specialty and level of training-was quantified using the intraclass correlation coefficient (ICC), and agreement with respect to National Institutes of Health Common Data Elements (NIH CDEs) was assessed using Cohen's kappa. All CT severity scoring systems showed high interobserver agreement as evidenced by high ICCs, ranging from 0.75-0.89. For all scoring systems, neuroradiologists (ICC range from 0.81-0.94) tended to have higher interobserver agreement than neurosurgeons (ICC range from 0.63-0.76). For all scoring systems, attendings (ICC range from 0.76-0.89) had similar interobserver agreement to trainees (ICC range from 0.73-0.89). Agreement with respect to NIH CDEs was high for ascertaining presence/absence of hemorrhage, skull fracture, and mass effect, with estimated kappa statistics of least 0.89. Acute TBI CT scoring systems demonstrate high interobserver agreement. These results provide scientific rigor for future use of these systems for the classification of acute TBI.

Prognostic Validation of the NINDS Common Data Elements for the Radiologic Reporting of Acute Traumatic Brain Injuries: A CENTER-TBI Study

The aim of this study is to investigate the prognostic value of using the National Institute of Neurological Disorders and Stroke (NINDS) standardized imaging-based pathoanatomic descriptors for the evaluation and reporting of acute traumatic brain injury (TBI) lesions. For a total of 3392 patients (2244 males and 1148 females, median age = 51 years) enrolled in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study, we extracted 96 Common Data Elements (CDEs) from the structured reports, spanning all three levels of pathoanatomic information (i.e., 20 "basic," 60 "descriptive," and 16 "advanced" CDE variables per patient). Six-month clinical outcome scores were dichotomized into favorable (Glasgow Outcome Scale Extended [GOS-E] = 5-8) versus unfavorable (GOS-E = 1-4). Regularized logistic regression models were constructed and compared using the optimism-corrected area under the curve (AUC). An abnormality was reported for the majority of patients (64.51%). In 79.11% of those patients, there was at least one coexisting pathoanatomic lesion or associated finding. An increase in lesion severity, laterality, and volume was associated with more unfavorable outcomes. Compared with the full set of pathoanatomic descriptors (i.e., all three categories of information), reporting "basic" CDE information provides at least equal discrimination between patients with favorable versus unfavorable outcome (AUC = 0.8121 vs. 0.8155, respectively). Addition of a selected subset of "descriptive" detail to the basic CDEs could improve outcome prediction (AUC = 0.8248). Addition of "advanced" or "emerging/exploratory" information had minimal prognostic value. Our results show that the NINDS standardized-imaging based pathoanatomic descriptors can be used in large-scale studies and provide important insights into acute TBI lesion patterns. When used in clinical predictive models, they can provide excellent discrimination between patients with favorable and unfavorable 6-month outcomes. If further validated, our findings could support the development of structured and itemized templates in routine clinical radiology.

Admission Perfusion CT for Classifying Early In-Hospital Mortality of Patients With Severe Traumatic Brain Injury: A Pilot Study

OBJECTIVE. The purposes of this study were to assess the feasibility and safety of perfusion CT of patients with severe traumatic brain injury (TBI) at hospital admission and to examine whether early in-hospital mortality could be characterized with perfusion CT (PCT). The hypothesis was that PCT can be used to characterize brain death, when present, in patients with severe TBI at hospital admission. SUBJECTS AND METHODS. In this prospective cohort pilot study, PCT was performed on patients with severe TBI at first imaging workup at hospital admission. PCT images were processed at the end of the study and assessed for features of brain death. The PCT features were then compared with the clinical outcome of in-hospital mortality. RESULTS. A total of 19 patients (13 men [68.4%]; six women [31.6%]; mean age, 36.4 years; median, 27.5 years) had a mean hospital stay longer than 1 month. No complications of PCT were found. In the first 48 hours after admission, four patients (21%) died. Admission PCT changes suggesting brainstem death were sensitive (75%) and specific (100%) and had high positive (100%) and negative (93.75%) predictive value for correct classification early in-hospital mortality. CONCLUSION. Admission PCT of patients with severe TBI was feasible and safe. Admission PCT findings helped in correctly classifying early in-hospital mortality in the first 48 hours of hospital admission.

Development of a Clinical Decision Rule for the Early Safe Discharge of Patients with Mild Traumatic Brain Injury and Findings on Computed Tomography Brain Scan: A Retrospective Cohort Study

International guidelines recommend routine hospital admission for all patients with mild traumatic brain injury (TBI) who have injuries on computed tomography (CT) brain scan. Only a small proportion of these patients require neurosurgical or critical care intervention. We aimed to develop an accurate clinical decision rule to identify low-risk patients safe for discharge from the emergency department (ED) and facilitate earlier referral of those requiring intervention. A retrospective cohort study of case notes of patients admitted with initial Glasgow Coma Scale 13-15 and injuries identified by CT was completed. Data on a primary outcome measure of clinically important deterioration (indicating need for hospital admission) and secondary outcome of neurosurgery, intensive care unit admission, or intubation (indicating need for neurosurgical admission) were collected. Multi-variable logistic regression was used to derive models and a risk score predicting deterioration using routinely reported clinical and radiological candidate variables identified in a systematic review. We compared the performance of this new risk score with the Brain Injury Guideline (BIG) criteria, derived in the United States. A total of 1699 patients were included from three English major trauma centers. A total of 27.7% (95% confidence interval [CI], 25.5-29.9) met the primary and 13.1% (95% CI, 11.6-14.8) met the secondary outcomes of deterioration. The derived clinical decision rule suggests that patients with simple skull fractures or intracranial bleeding <5 mm in diameter who are fully conscious could be safely discharged from the ED. The decision rule achieved a sensitivity of 99.5% (95% CI, 98.1-99.9) and specificity of 7.4% (95% CI, 6.0-9.1) to the primary outcome. The BIG criteria achieved the same sensitivity, but lower specificity (5%). Our empirical models showed good predictive performance and outperformed the BIG criteria. This would potentially allow ED discharge of 1 in 20 patients currently admitted for observation. However, prospective external validation and economic evaluation are required.

Demographics and clinical characteristics of acute traumatic brain injury patients in the different Neuroimaging Radiological Interpretation System (NIRIS) categories

PURPOSE

To characterize the demographics, clinical and imaging findings, and outcomes of traumatic brain injury (TBI) patients in each of NeuroImaging Radiological Interpretation System (NIRIS) categories.

MATERIAL AND METHODS

We considered all consecutive patients transported to Stanford Hospital's emergency department by ambulance or helicopter between November 2015 and April 2017. We retained adult patients (> 18 years old) for whom a trauma alert was triggered and who underwent a non-contrast head computer tomography (CT) because of suspected TBI. We reviewed the non-contrast CT scans in these patients for the NIH TBI common data elements (CDEs). We recorded, then assessed differences in terms of demographics, clinical characteristics, imaging CDEs, and outcomes in patients from the different NIRIS categories.

RESULTS

In all, 1152 patients were included in this study. Patients with NIRIS 0 imaging findings were significantly younger than patients in other NIRIS categories (P<0.001). Motor vehicle accidents and falls from height were the most common mechanisms of injury across NIRIS categories. GCS scores decreased with increasing NIRIS category imaging findings and were significantly lower in patients with NIRIS 4 imaging findings (P<0.001). Significant differences in NIRIS categories were observed for all imaging CDEs (P<0.001), in agreement with the definition of the different NIRIS categories. Mortality increased progressively with increasing NIRIS severity.

CONCLUSIONS

TBI patients in different NIRIS categories have different clinical characteristics, hospital courses and outcomes. This natural history assessment of patients from different NIRIS categories could thus serve as a reference standard for future TBI clinical trials.