Prehospital clinical signs are a poor predictor of raised intracranial pressure following traumatic brain injury

The Australian Traumatic Brain Injury Initiative: Systematic Review of Clinical Factors Associated with Outcomes in People with Moderate-Severe Traumatic Brain Injury

The aim of the Australian Traumatic Brain Injury Initiative (AUS-TBI) is to design a data dictionary to inform data collection and facilitate prediction of outcomes for moderate-severe traumatic brain injury (TBI) across Australia. The process has engaged diverse stakeholders across six areas: social, health, clinical, biological, acute interventions, and long-term outcomes. Here, we report the results of the clinical review. Standardized searches were implemented across databases to April 2022. English-language reports of studies evaluating an association between a clinical factor and any clinical outcome in at least 100 patients with moderate-severe TBI were included. Abstracts, and full-text records, were independently screened by at least two reviewers in Covidence. The findings were assessed through a consensus process to determine inclusion in the AUS-TBI data resource. The searches retrieved 22,441 records, of which 1137 were screened at full text and 313 papers were included. The clinical outcomes identified were predominantly measures of survival and disability. The clinical predictors most frequently associated with these outcomes were the Glasgow Coma Scale, pupil reactivity, and blood pressure measures. Following discussion with an expert consensus group, 15 were recommended for inclusion in the data dictionary. This review identified numerous studies evaluating associations between clinical factors and outcomes in patients with moderate-severe TBI. A small number of factors were reported consistently, however, how and when these factors were assessed varied. The findings of this review and the subsequent consensus process have informed the development of an evidence-informed data dictionary for moderate-severe TBI in Australia.

Brain Injury Associated Shock: An Under-Recognized and Challenging Prehospital Phenomenon

OBJECTIVE

Hemodynamic collapse in multi-trauma patients with severe traumatic brain injury (TBI) poses both a diagnostic and therapeutic challenge for prehospital clinicians. Brain injury associated shock (BIAS), likely resulting from catecholamine storm, can cause both ventricular dysfunction and vasoplegia but may present clinically in a manner similar to hemorrhagic shock. Despite different treatment strategies, few studies exist describing this phenomenon in the early post-injury phase. This retrospective observational study aimed to describe the frequency of shock in isolated TBI in prehospital trauma patients and to compare their clinical characteristics to those patients with hemorrhagic shock and TBI without shock.

METHODS

All prehospital trauma patients intubated by prehospital medical teams from New South Wales Ambulance Aeromedical Operations (NSWA-AO) with an initial Glasgow Coma Scale (GCS) of 12 or less were investigated. Shock was defined as a pre-intubation systolic blood pressure under 90mmHg and the administration of blood products or vasopressors. Injuries were classified from in-hospital computed tomography (CT) reports. From this, three study groups were derived: BIAS, hemorrhagic shock, and isolated TBI without shock. Descriptive statistics were then produced for clinical and treatment variables.

RESULTS

Of 1,292 intubated patients, 423 had an initial GCS of 12 or less, 24 patients (5.7% of the original cohort) had shock with an isolated TBI, and 39 patients had hemorrhagic shock. The hemodynamic parameters were similar amongst these groups, including values of tachycardia, hypotension, and elevated shock index. Prehospital clinical interventions including blood transfusion and total fluids administered were also similar, suggesting they were indistinguishable to prehospital clinicians.

CONCLUSIONS

Hemodynamic compromise in the setting of isolated severe TBI is a rare clinical entity. Current prehospital physiological data available to clinicians do not allow for easy delineation between these patients from those with hemorrhagic shock.

Anaesthetic management in a cat undergoing emergency craniotomy for meningioma excision

Case summary

A 15-year-old female spayed domestic shorthair cat underwent an emergency craniotomy to remove an intracranial meningioma causing marked midline shift, caudal transtentorial and foramen magnum herniation. Because intracranial structures are enclosed in the cranium, any volume-occupying lesions might raise intracranial pressure (ICP), compromising cerebral perfusion.

Relevance and novel information

This case report discusses the anaesthetic management of a cat that presented with marked bradycardia and concomitant hypotension. Cushing's reflex (CR) is a well-recognised cardiovascular reflex following sudden ICP increase, and it features an irregular breathing pattern and increased arterial blood pressure with reflex bradycardia. However, CR is reported to have a low sensitivity for the detection of raised ICP in humans with traumatic brain injury. In a previous study reporting seven cats undergoing surgical removal of intracranial meningioma, ICP was measured in four cases and, in these patients, CR was not observed during surgery. Because bradycardia was not secondary to hypertension, in this case, it might have been the result of direct compression of the nucleus of the vagus nerve. Based on the literature search, there is paucity of reports of cardiovascular changes in cats with increased ICP and their perianaesthetic management.

Initial Diagnosis and Management of Acutely Elevated Intracranial Pressure

Acutely elevated intracranial pressure (ICP) may have devastating effects on patient mortality and neurologic outcomes, yet its initial detection remains difficult because of the variety of manifestations that it can cause disease states it is associated with. Several treatment guidelines exist for specific disease processes such as trauma or ischemic stroke, but their recommendations may not apply to other causes. In the acute setting, management decisions must often be made before the underlying cause is known. In this review, we present an organized, evidence-based approach to the recognition and management of patients with suspected or confirmed elevated ICP in the first minutes to hours of resuscitation. We explore the utility of invasive and noninvasive methods of diagnosis, including history, physical examination, imaging, and ICP monitors. We synthesize various guidelines and expert recommendations and identify core management principles including noninvasive maneuvers, neuroprotective intubation and ventilation strategies, and pharmacologic therapies such as ketamine, lidocaine, corticosteroids, and the hyperosmolar agents mannitol and hypertonic saline. Although an in-depth discussion of the definitive management of each etiology is beyond the scope of this review, our goal is to provide an empirical approach to these time-sensitive, critical presentations in their initial stages.

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

Background

Rapid referral of traumatic brain injury (TBI) patients requiring emergency neurosurgery to a specialized trauma center can significantly reduce morbidity and mortality. Currently, no model has been reported to predict the need for acute neurosurgery in severe to moderate TBI patients. This study aims to evaluate the performance of Machine Learning-based models to establish to predict the need for neurosurgery procedure within 24 h after moderate to severe TBI.

Methods

Retrospective multicenter cohort study using data from a national trauma registry (Traumabase®) from November 2011 to December 2020. Inclusion criteria correspond to patients over 18 years old with moderate or severe TBI (Glasgow coma score ≤ 12) during prehospital assessment. Patients who died within the first 24 h after hospital admission and secondary transfers were excluded. The population was divided into a train set (80% of patients) and a test set (20% of patients). Several approaches were used to define the best prognostic model (linear nearest neighbor or ensemble model). The Shapley Value was used to identify the most relevant pre-hospital variables for prediction.

Results

2159 patients were included in the study. 914 patients (42%) required neurosurgical intervention within 24 h. The population was predominantly male (77%), young (median age 35 years [IQR 24–52]) with severe head injury (median GCS 6 [3–9]). Based on the evaluation of the predictive model on the test set, the logistic regression model had an AUC of 0.76. The best predictive model was obtained with the CatBoost technique (AUC 0.81). According to the Shapley values method, the most predictive variables in the CatBoost were a low initial Glasgow coma score, the regression of pupillary abnormality after osmotherapy, a high blood pressure and a low heart rate.

Conclusion

Machine learning-based models could predict the need for emergency neurosurgery within 24 h after moderate and severe head injury. Potential clinical benefits of such models as a decision-making tool deserve further assessment. The performance in real-life setting and the impact on clinical decision-making of the model requires workflow integration and prospective assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13017-022-00449-5.

Relationship between admission vitals and brain herniation in 32 cats: a retrospective study

OBJECTIVES

The aim of the study was to evaluate whether any admission vitals correlated with the presence of brain herniation diagnosed via MRI in cats presenting with neurologic signs.

METHODS

Medical records at two veterinary university referral centers were reviewed to identify cats that underwent brain MRI between 2010 and 2019. A control group of cats with intracranial lesions without concurrent brain herniation was analyzed for comparison. Data relating to signalment, vitals on admission, abnormalities observed on initial neurologic examination, underlying etiology, advanced imaging findings and outcome were reviewed. A Modified Glasgow Coma Scale (MGCS) score was determined retrospectively based on initial neurologic examination. Logistic regressions were performed to investigate the relationship between each risk factor and the odds of brain herniation as diagnosed on MRI.

RESULTS

Thirty-two cats with brain herniation and 44 cats with abnormal brain MRI without evidence of herniation (as a control group) based on MRI findings were included. Cats with intracranial neoplasia vs other diagnoses were found to be at increased risk of herniation (odds ratio [OR] 4.8, 95% confidence interval [CI] 1.8-13.8; P = 0.001). The odds of herniation increased with age (OR 1.1, 95% CI 1.01-1.2; P = 0.031). Cats with herniation had a significantly lower level of consciousness in their MGCS score (P <0.0001) than cats without herniation. There was no significant difference in either motor activity or brainstem reflexes between the groups (P >0.05).

CONCLUSIONS AND RELEVANCE

Admission heart rate and blood pressure were not associated with brain herniation. Cats with herniation were presented with a significantly lower level of consciousness in their MGCS score; however, this clinical feature cannot be directly attributable to and predictive of herniation. Older cats with intracranial neoplasia are more likely to have brain herniation.

A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

IMPORTANCE

An adequate system for triaging patients with head trauma in prehospital settings and choosing optimal medical institutions is essential for improving the prognosis of these patients. To our knowledge, there has been no established way to stratify these patients based on their head trauma severity that can be used by ambulance crews at an injury site.

OBJECTIVES

To develop a prehospital triage system to stratify patients with head trauma according to trauma severity by using several machine learning techniques and to evaluate the predictive accuracy of these techniques.

DESIGN, SETTING, AND PARTICIPANTS

This single-center retrospective cohort study was conducted by reviewing the electronic medical records of consecutive patients who were transported to Tokyo Medical and Dental University Hospital in Japan from April 1, 2018, to March 31, 2021. Patients younger than 16 years with cardiopulmonary arrest on arrival or with a significant amount of missing data were excluded.

MAIN OUTCOMES AND MEASURES

Machine learning-based prediction models to detect the presence of traumatic intracranial hemorrhage were constructed. The predictive accuracy of the models was evaluated with the area under the receiver operating curve (ROC-AUC), area under the precision recall curve (PR-AUC), sensitivity, specificity, and other representative statistics.

RESULTS

A total of 2123 patients (1527 male patients [71.9%]; mean [SD] age, 57.6 [19.8] years) with head trauma were enrolled in this study. Traumatic intracranial hemorrhage was detected in 258 patients (12.2%). Among several machine learning algorithms, extreme gradient boosting (XGBoost) achieved the mean (SD) highest ROC-AUC (0.78 [0.02]) and PR-AUC (0.46 [0.01]) in cross-validation studies. In the testing set, the ROC-AUC was 0.80, the sensitivity was 74.0% (95% CI, 59.7%-85.4%), and the specificity was 74.9% (95% CI, 70.2%-79.3%). The prediction model using the National Institute for Health and Care Excellence (NICE) guidelines, which was calculated after consultation with physicians, had a sensitivity of 72.0% (95% CI, 57.5%-83.8%) and a specificity of 73.3% (95% CI, 68.7%-77.7%). The McNemar test revealed no statistically significant differences between the XGBoost algorithm and the NICE guidelines for sensitivity or specificity (P = .80 and P = .55, respectively).

CONCLUSIONS AND RELEVANCE

In this cohort study, the prediction model achieved a comparatively accurate performance in detecting traumatic intracranial hemorrhage using only the simple pretransportation information from the patient. Further validation with a prospective multicenter data set is needed.

Standardised data collection in prehospital critical care: a comparison of medical problem categories and discharge diagnoses

Background

Prehospital medical problem reporting is essential in the management of helicopter emergency medical services (HEMS) operations. The consensus-based template for reporting and documenting in physician-staffed prehospital services exists and the classification of medical problems presented in the template is widely used in research and quality improvement. However, validation of the reported prehospital medical problem is lacking. This study aimed to describe the in-hospital diagnoses, patient characteristics and medical interventions in different categories of medical problems.

Methods

This retrospective, observational registry study examined the 10 most common in-hospital International Statistical Classification of Disease (ICD-10) diagnoseswithin different prehospital medical problem categories, defined by the HEMS physician/paramedic immediately after the mission was completed. Data were gathered from a national HEMS quality registry and a national hospital discharge registry. Patient characteristics and medical interventions related to different medical problem categories are also described.

Results

A total of 33,844 patients were included in the analyses. All the medical problem categories included a broad spectrum of ICD-10 diagnoses (the number of diagnosis classes per medical problem category ranged from 73 to 403). The most frequent diagnoses were mainly consistent with the reported medical problems. Overlapping of ICD-10 diagnoses was mostly seen in two medical problem categories: stroke and acute neurology excluding stroke. Additionally, typical patient characteristics and disturbances in vital signs were related to adequate medical problem categories.

Conclusions

Medical problems reported by HEMS personnel have adequate correspondence to hospital discharge diagnoses. However, the classification of cerebrovascular accidents remains challenging.

Accuracy of Optic Nerve Sheath Diameter Measurements in Pocket-Sized Ultrasound Devices in a Simulation Model

Introduction

Transorbital sonographic measurement of optic nerve sheath diameter (ONSD) is an emerging non-invasive technique for the identification and monitoring of intracranial hypertension. In recent years, new pocket ultrasound devices have become available, and it is uncertain if they have the resolution to measure such small structures appropriately as compared to their predecessors. In this study, we measure the performance of three ultrasound units on a simulation model to establish their precision and accuracy.

Methods

ONSD was measured by three expert point-of-care sonographers using ultrasound machines three times on each of seven discrete ONS model sizes ranging from 3.5 to 7.9 mm. Two pocket ultrasounds (IVIZ, Sonosite, and Lumify, Philips) and one standard-sized portable ultrasound (M-Turbo, Sonosite) were used. Measurements were analyzed for mean error and variance and tested for significance using blocked covariance matrix regression analyses.

Results

The devices differed in their variances (Lumify: 0.19 mm², M-Turbo: 0.26 mm², IVIZ: 0.34 mm²) and their mean error (Lumify: -0.05 mm, M-Turbo: 0.10 mm, IVIZ: -0.10 mm). The difference in mean error between users is not significant (p = 0.45), but there is a significant difference in mean error between devices (p = 0.02).

Conclusions

Accurate ONSD measurement is possible utilizing pocket-sized ultrasound, and in some cases, may be more accurate than larger portable ultrasound units. While the differences in these devices were statistically significant, all three were highly accurate, with one pocket device (Lumify) outperforming the rest. Further study in human subjects should be conducted prior to using pocket ultrasound devices for in vivo diagnosis of intracranial hypertension.

Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea

OBJECTIVES

Predicting diagnosis and prognosis of traumatic brain injury (TBI) at the prehospital stage is challenging; however, using comprehensive prehospital information and machine learning may improve the performance of the predictive model. We developed and tested predictive models for TBI that use machine learning algorithms using information that can be obtained in the prehospital stage.

DESIGN

This was a multicentre retrospective study.

SETTING AND PARTICIPANTS

This study was conducted at three tertiary academic emergency departments (EDs) located in an urban area of South Korea. The data from adult patients with severe trauma who were assessed by emergency medical service providers and transported to three participating hospitals between 2014 to 2018 were analysed.

RESULTS

We developed and tested five machine learning algorithms-logistic regression analyses, extreme gradient boosting, support vector machine, random forest and elastic net (EN)-to predict TBI, TBI with intracranial haemorrhage or injury (TBI-I), TBI with ED or admission result of admission or transferred (TBI with non-discharge (TBI-ND)) and TBI with ED or admission result of death (TBI-D). A total of 1169 patients were included in the final analysis, and the proportions of TBI, TBI-I, TBI-ND and TBI-D were 24.0%, 21.5%, 21.3% and 3.7%, respectively. The EN model yielded an area under receiver-operator curve of 0.799 for TBI, 0.844 for TBI-I, 0.811 for TBI-ND and 0.871 for TBI-D. The EN model also yielded the highest specificity and significant reclassification improvement. Variables related to loss of consciousness, Glasgow Coma Scale and light reflex were the three most important variables to predict all outcomes.

CONCLUSION

Our results inform the diagnosis and prognosis of TBI. Machine learning models resulted in significant performance improvement over that with logistic regression analyses, and the best performing model was EN.