Cushing's sign and severe traumatic brain injury in children after blunt trauma: a nationwide retrospective cohort study in Japan

PREHOSPITAL SHOCK INDEX MULTIPLIED BY AVPU SCALE AS A PREDICTOR OF CLINICAL OUTCOMES IN TRAUMATIC INJURY

ABSTRACT

Objectives: Many prehospital trauma triage scores have been proposed, but none has emerged as a criterion standard. Therefore, a rapid and accurate tool is necessary for field triage. The shock index (SI) multiplied by the AVPU (Alert, responds to Voice, responds to Pain, Unresponsive) score (SIAVPU) reflected the hemodynamic and neurological conditions through a combination of the SI and AVPU. This study aimed to investigate the prediction performance of SI multiplied by the AVPU and to compare the prediction performance of other prehospital trauma triage scores in a population with traumatic injury. Patients and Methods: This study included 6,156 patients with trauma injury from the Taipei Tzu Chi trauma database. We investigated the accuracy of four scoring systems in predicting mortality, intensive care unit (ICU) admission, and prolonged hospital stay (defined as a duration of hospitalization >14 days). In the subgroup analysis, we also analyzed the effects of age, injury mechanism and severity, underlying diseases, and traumatic brain injury. Results: The predictive accuracy of SIAVPU for mortality, ICU admission, and prolonged hospital stay was significantly higher than that of SI, modified SI, and SI multiplied by age in the traumatic injury population, with an area under the receiver operating characteristic curve of 0.738 for mortality, 0.641 for ICU admission, and 0.606 for prolonged hospital stay. In the subgroup analysis, the prediction accuracy of mortality, ICU admission, and prolonged hospital stay of SIAVPU was also better in patients with younger age, older age, major trauma (Injury Severity Score ≥16), motor vehicle collisions, fall injury, healthy, cardiovascular disease, mixed traumatic brain injury, and isolated traumatic brain injury. The best cutoff levels of SIAVPU score to predict mortality, ICU admission, and total length of stay ≥14 days in trauma injury patients were 0.90, 0.82, and 0.80, with accuracies of 88.56%, 79.84%, and 78.62%, respectively. Conclusions: In conclusion, SIAVPU is a rapid and accurate field triage score with better prediction accuracy for mortality, ICU admission, and prolonged hospital stay than SI, modified SI, and SI multiplied by age in patients with trauma. Patients with SIAVPU ≥0.9 should be considered for the highest-level trauma center available within the geographic constraints of regional trauma systems.

Shock index, modified shock index, age shock index score, and reverse shock index multiplied by Glasgow Coma Scale predicting clinical outcomes in traumatic brain injury: Evidence from a 10-year analysis in a single center

OBJECTIVES

Early identification of traumatic brain injury (TBI) patients at a high risk of mortality is very important. This study aimed to compare the predictive accuracy of four scoring systems in TBI, including shock index (SI), modified shock index (MSI), age-adjusted shock index (ASI), and reverse shock index multiplied by the Glasgow Coma Scale (rSIG).

PATIENTS AND METHODS

This is a retrospective analysis of a registry from the Taipei Tzu Chi trauma database. Totally, 1,791 patients with TBI were included. We investigated the accuracy of four major shock indices for TBI mortality. In the subgroup analysis, we also analyzed the effects of age, injury mechanism, underlying diseases, TBI severity, and injury severity.

RESULTS

The predictive accuracy of rSIG was significantly higher than those of SI, MSI, and ASI in all the patients [area under the receiver operating characteristic curve (AUROC), 0.710 vs. 0.495 vs. 0.527 vs. 0.598], especially in the moderate/severe TBI (AUROC, 0.625 vs. 0.450 vs. 0.476 vs. 0.529) and isolated head injury populations (AUROC 0.689 vs. 0.472 vs. 0.504 vs. 0.587). In the subgroup analysis, the prediction accuracy of mortality of rSIG was better in TBI with major trauma [Injury Severity Score (ISS) ≥ 16], motor vehicle collisions, fall injury, and healthy and cardiovascular disease population. rSIG also had a better prediction effect, as compared to SI, MSI, and ASI, both in the non-geriatric (age < 65 years) and geriatric (age ≥ 65 years).

CONCLUSION

rSIG had a better prediction accuracy for mortality in the overall TBI population than SI, MSI, and ASI. Although rSIG have better accuracy than other indices (ROC values indicate poor to moderate accuracy), the further clinical studies are necessary to validate our results.

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.

Retrospective evaluation of the relationship between admission variables and brain herniation in dogs (2010-2019): 54 cases

OBJECTIVE

To document the admission systolic blood pressure (SBP), heart rate (HR), and modified Glasgow coma scale (MGCS) score in dogs with and without brain herniation and to determine their relationship with brain herniation.

DESIGN

Retrospective study between 2010 and 2019.

SETTING

University veterinary teaching hospital.

ANIMALS

Fifty-four client-owned dogs with brain herniation and 40 client-owned dogs as a control group, as determined on magnetic resonance imaging.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

SBP, HR, MGCS score, and outcome were extracted from medical records. MGCS score was retrospectively calculated based on initial neurological examination in dogs with adequate available information. Dogs with brain herniation had a significantly higher SBP (P = 0.0078), greater SBP-HR difference (P = 0.0006), and lower MGCS score (P < 0.0001) compared to control dogs. A cutoff value of an SBP ≥ 178 mm Hg, SBP-HR ≥ 60, and MGCS score ≤ 14 each provides a specificity of 90%-98%. A combination of an SBP > 140 mm Hg and HR < 80/min provided 24% sensitivity and 100% specificity to diagnose dogs with brain herniation (P < 0.0001).

CONCLUSIONS

A high SBP, a greater difference between SBP and HR, a combination of higher SBP and lower HR, and a low MGCS score were associated with brain herniation in dogs presenting with neurological signs upon admission. Early recognition of these abnormalities may help veterinarians to suspect brain herniation and determine timely treatment.

Isolated Bradycardia During Aneurysmal Clipping: Rebleed or Trigeminocardiac Reflex?

The most common cause of nontraumatic subarachnoid hemorrhage is the rupture of intracranial aneurysm. After initial bleed, the risk of rebleeding is highest in the early postictal period and this rebleed is strongly associated with poor neurological outcome. The major goal of anesthesia in these surgeries is to prevent the rebleed. If rebleeding occurs prior to the craniotomy, it results in the acute rise of intracranial pressure and usually presents as bradycardia and hypertension (Cushing’s reflex). Here we reported a case where rebleeding presented unusually as isolated bradycardia without associated hypertension and was mistaken as trigeminocardiac reflex. The surgeon was informed about the event and they planned to proceed. After craniotomy, despite all the efforts the brain was persistently tight and surgery could not be completed. Postoperative scan showed rebleeding and the patient died after a few days in ICU.

We highlighted in this case report the fact that isolated transient bradycardia may also be the presentation of rebleed with closed cranial vault. It is not always necessary to see all the features of Cushing’s traid in every patient. If bradycardia occurs before the craniotomy, the surgeon should be notified, the severity of bleed should be assessed, and further management should be planned according to the severity of bleed.

[Causes and clinical features of children with traumatic brain injury: a retrospective analysis of 126 cases]

OBJECTIVE

To investigate the causes and clinical features of children with traumatic brain injury (TBI) who need hospitalization or emergency observation.

METHODS

A retrospective analysis was performed for the clinical data of 126 children with TBI who were admitted to the emergency department from January 1, 2014 to August 31, 2016, including causes of injury and clinical features.

RESULTS

Of the 126 children, there were 95 boys and 31 girls, with a mean age of 2.8 years (range 0.8-5.5 years). The children aged <1 year accounted for 38.1% (48/126), and 26 children died. The two most common types of TBI were epidural hematoma (54.0%) and subarachnoid hemorrhage (50.8%). Of the 126 children, 83 (65.9%) had a Glasgow Coma Scale score of ≤8 within 24 hours after admission. There were different causes of TBI and places where TBI occurred in different age groups. The two leading causes of TBI were falls (51.6%) and road traffic injuries (42.9%). Compared with those in the other age groups, the children in the age <1 year group were most likely to experience injury due to falls (46%; P=0.023). Thirty-five percent of all TBI due to road traffic injuries occurred in the children aged 3-6 years (P<0.001). Most TBI cases occurred at home (47.6%) or on roads/streets (45.2%). Among those who experienced TBI at home, the children aged <1 year accounted for the highest proportion of 48% (P=0.002), and 53% of the patients aged 3-6 years experienced TBI on roads/streets. The most common cause of death in children with TBI was road traffic injury, which accounted for 69%. Among those who died, the children aged <1 year accounted for the highest proportion (62%).

CONCLUSIONS

There are different causes of TBI and places where TBI occurs in different age groups. Among children with TBI, the children aged <1 year account for the highest proportion and have the highest number of deaths, with falls at home as the most common cause of TBI. Children aged 3-6 years tend to suffer TBI due to road traffic injury. Road traffic injury is the leading cause of death.