Facial Fracture Repair in the Traumatic Brain Injury Patient

Traumatic Brain Injury and Its Association With Orbital Fracture Characteristics and Repair

Traumatic brain injury (TBI) is common in up to 50% of patients with facial fractures. Orbital fractures account for 25% of all facial fractures. The authors sought to determine the prevalence and risk factors for TBI in patients undergoing orbital fracture repair (OFR) and assess the impact of TBI on surgical timing. A retrospective review of trauma patients who underwent OFR at a single trauma center from 2015 to 2020 was conducted. Excluded were patients <18 years old and those with unreported GCS on presentation. TBI was defined as GCS <15 or any neurological symptom on presentation. TBI was categorized into mild (GCS=14-15), moderate (GCS=9-13), and severe TBI (GCS=3-8). Our primary and secondary outcomes were the prevalence of TBI on presentation and duration from injury to surgery, respectively. Of the 200 patients analyzed, 99 (49.5%) had concomitant TBI on presentation. The most common neurological symptom on presentation was loss of consciousness [n=80 (40%)]. Patients with TBI were significantly more likely to have an orbital roof [n=11 (11.1%), n=4 (4.0%), P=0.048] and lateral wall fractures [n=25 (25.3%), n=14 (13.9%), P=0.031] compared with patients without TBI. Patients with severe TBI were more likely to have delayed OFR-a significantly greater proportion of patients who had severe TBI had OFR after 60 days of injury compared with those without TBI or with mild TBI [5 (39%), 12 (12%), 4 (5%), P=0.032]. Craniofacial surgeons must suspect and screen for TBI in patients presenting with facial trauma, especially those with orbital roof and lateral wall fractures.

Facial Fractures: Independent Prediction of Neurosurgical Intervention

Context  Over half of patients with facial fractures have associated traumatic brain injury (TBI). Based on force dynamic cadaveric studies, Le Fort type 2 and 3 fractures are associated with severe injury. Correlation to neurosurgical intervention is not well characterized.

Aims  This study characterizes fracture pattern types in patients requiring neurosurgical intervention and assesses whether this is different from those not requiring intervention.

Settings and Design  Retrospective data was collected from the trauma registry from 2010 to 2019.

Methods and Materials  Patients over 18 years, with confirmed facial fracture, reported TBI, available neuroimaging, and hospital admission were included.

Statistical Analysis  Retrospective contingency analysis with fraction of total comparison was used with chi-square analysis for demographic and injury characteristic data.

Results  Note that 1,001 patients required no neurosurgical intervention and 171 required intervention. The intervention group had a significantly greater number of patients with Glasgow Coma Scale (GCS) < 8 compared with the nonintervention group. Subset analysis revealed a twofold increase in Le Fort type 2 fractures and notable increase in Le Fort type 3 and panfacial fractures in the intervention group. Patients requiring craniectomy, craniotomy, or burr holes were much more likely to have Le Fort type 2 or 3 fractures compared with those only requiring external ventricular drains or intracranial pressure monitoring. Subset analysis accounting for GCS supported these results.

Conclusion  Le Fort type 2 and type 3 fractures are significantly associated with requiring neurosurgical intervention. An improved algorithm for managing these patients has been proposed in the discussion. Ongoing work will focus on validating and refining the algorithm to improve patient care.

Facial Fractures: Independent Prediction of Neurosurgical Intervention

CONTEXT

Over half of patients with facial fractures have associated traumatic brain injury (TBI). Based on force dynamic cadaveric studies, Lefort type 2 and 3 fractures are associated with severe injury. Correlation to neurosurgical intervention is not well characterized.

AIMS

This study characterizes fracture pattern types in patients requiring neurosurgical intervention and assesses whether this is different from those not requiring intervention.

SETTINGS AND DESIGN

Retrospective data were collected from the trauma registry from 2010 to 2019.

SUBJECTS AND METHODS

Patients over 18, with confirmed facial fracture, reported TBI, available neuroimaging, and hospital admission were included.

STATISTICAL ANALYSIS USED

Retrospective Contingency Analysis with Fraction of Total Comparison was used with Chi-square analysis for demographic and injury characteristic data.

RESULTS

One thousand and one patients required no neurosurgical intervention and 171 required intervention. The intervention group had a significantly greater number of patients with Glasgow Coma Scale (GCS) <8 compared to the nonintervention group. Subset analysis revealed a twofold increase in Lefort type 2 fractures and notable increase in Lefort type 3 and panfacial fractures in the intervention group. Patients requiring craniectomy, craniotomy, or burr holes were much more likely to have Lefort type 2 or 3 fractures compared to those only requiring external ventricular drains or intracranial pressure monitoring. Subset analysis accounting for GCS supported these results.

CONCLUSIONS

Lefort type 2 and type 3 fractures are significantly associated with requiring neurosurgical intervention. An improved algorithm for managing these patients has been proposed in the discussion. Ongoing work will focus on validating and refining the algorithm to improve patient care.

Traumatic maxillofacial and brain injuries: a systematic review

Concomitant traumatic brain injury (TBI) and maxillofacial fractures carry the risk of significant morbidity and mortality. The aim of this review was to explore the demographics, types of injury, and complications of traumatic maxillofacial and brain injuries, in order to contribute to comprehensive health strategies. The PubMed and Scopus databases were systematically searched. Inclusion criteria were clinical studies investigating combined traumatic maxillofacial and brain injuries. Exclusion criteria were duplicates, non-English publications, non-full-text publications, publication date before 1990, and studies with insufficient data. Of the 754 articles identified, 15 eligible articles representing 1421 cases were included. The mean age was 38.3 years. Most cases were male (79%). The most common mechanism of injury was traffic accidents (53.4%). The most common fracture pattern was middle third fractures (52.4%). Seven studies had an explicit definition for TBI, using the Glasgow Coma Score (GCS), radiological evidence, and/or specific symptoms. There were 147 complications reported in 62 of 253 cases (24.5%), with the most common being infection (n=54, 36.7%). Significant risk factors for complications included delayed surgical repair, low GCS, and upper third fractures. Robust longitudinal evaluations with clear definitions of TBI are required. Gaps in knowledge include risk factors for complications and fracture pattern-GCS correlations.

Comparison of four variable selection methods to determine the important variables in predicting the prognosis of traumatic brain injury patients by support vector machine

Background:

Large amounts of information have called for increased computational complexity. Data dimension reduction is therefore critical to preliminary analysis. In this research, four variable selection (VS) methods are compared to obtain the important variables in predicting the prognosis of traumatic brain injury (TBI) patients.

Materials and Methods:

In a retrospective follow-up study, 741 TBI patients who were hospitalized for at least 2 days and had a Glasgow Coma Scale score of at least one were followed. Their clinical data recorded during intensive care unit (ICU) admission and eight-category extended GOS conditions 6 months after discharge were utilized here. Two filter- and two wrapper-based VS methods were applied for comparison. A support vector machine (SVM) classifier was then used, and the sensitivity, specificity, accuracy, and the area under the receiver characteristic curve (AUC) values were calculated.

Results:

Theoretically, the variables selected by sequential forward selection (SFS) method would better predict the prognosis (AUC = 0.737, 95% confidence interval [0.701, 0.772], specificity = 89.2%, sensitivity = 58.9% and accuracy = 79.1%) than the others. Genetic algorithm (GA), minimum redundancy maximum relevance (MRMR), and mutual information method were in the next orders, respectively.

Conclusion:

The use of an SVM classifier on optimal subsets given by GA and SFS reveals that wrapper-based methods perform better than filter-based methods in our data set, although all selected subsets, except for the MRMR, were clinically accepted. In addition, for prognosis prediction of TBI patients, a small subset of clinical records during ICU admission is enough to achieve an accepted accuracy.

Pattern of maxillofacial injuries in patients with craniocerebral injuries: a prospective study

Introduction: This prospective study highlights the pattern of oral and maxillofacial injuries in patients with associated craniocerebral injuries. Material and Methods: This was a prospective descriptive study conducted over a 22-month period. Information was collected using a structured questionnaire and analyzed using Statistical Package for Social Sciences (SPSS) Version 13 (SPSS Inc., Chicago, IL, USA) and Microsoft Office Excel 2007 (Microsoft, Redmond, WA, USA). Test of statistical significance was set at 0.05. Results: Three hundred and three consecutive patients were studied and this consisted of 254 males and 49 females. The difference in the gender distribution was statistically significant (p = 0.008). Road traffic crashes (n = 262; 86.5%) was the most common cause of injury and soft tissues orofacial injuries accounted for 61.7% of injuries. Le Fort II fractures were the major skeletal injuries. Glasgow Coma Score (GCS) of 13–15 had the highest frequency (n = 157; 53.4%). Intracerebral haemorrhage was the most common cerebral injury recorded and the commonest complication noted was dysocclusion. Discussion: Although middle third facial fractures were the most common skeletal injury, fractures of the upper third facial skeleton appear to be associated with lower GCS. Conclusion: Fractures of the facial skeleton are fairly common in craniocerebral injuries.

Identifying Important Attributes for Prognostic Prediction in Traumatic Brain Injury Patients

Background: Generally, traumatic brain injury (TBI) patients do not have a stable condition, particularly after the first week of TBI. Hence, indicating the attributes in prognosis through a prediction model is of utmost importance since it helps caregivers with treatment-decision options, or prepares the relatives for the most-likely outcome. Objectives: This study attempted to determine and order the attributes in prognostic prediction in TBI patients, based on early clinical findings. A hybrid method was employed, which combines a decision tree (DT) and an artificial neural network (ANN) in order to improve the modeling process. Methods: The DT approach was applied as the initial analysis of the network architecture to increase accuracy in prediction. Afterwards, the ANN structure was mapped from the initial DT based on a part of the data. Subsequently, the designed network was trained and validated by the remaining data. 5-fold cross-validation method was applied to train the network. The area under the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy rate were utilized as performance measures. The important attributes were then determined from the trained network using two methods: change of mean squared error (MSE), and sensitivity analysis (SA). Results: The hybrid method offered better results compared to the DT method. The accuracy rate of 86.3 % vs. 82.2 %, sensitivity value of 55.1 % vs. 47.6 %, specificity value of 93.6 % vs. 91.1 %, and the area under the ROC curve of 0.705 vs. 0.695 were achieved for the hybrid method and DT, respectively. However, the attributes’ order by DT method was more consistent with the clinical literature. Conclusions: The combination of different modeling methods can enhance their performance. However, it may create some complexities in computations and interpretations. The outcome of the present study could deliver some useful hints in prognostic prediction on the basis of early clinical findings for TBI patients.