Impact of Road Traffic Injury to Pediatric Traumatic Brain Injury in Southern Thailand

Transition of EMS workflow from radio to bell signals to shorten activation time in multiple casualty incident

Multiple casualty incident (MCI) are critical situations mandating an immediate response. Traditionally, members of emergency medical services (EMS) are notified about MCI through radio signals. However, communication failures can lead to delays in activation time of EMS operations. The use of bell signals is proposed as a solution to address these issues. This study uses a retrospective pre-post design evaluating the impact of the bell and radio signal on activation times for EMS operation in MCI. Data were collected from January 2020 to December 2023 and divided into two phases: radio signal use during 2020-2021 (pre-design), and bell signal use during 2022-2023 (post-design). In the event of MCI, the bell or radio is used primarily to alert medical personnel. After the MCI was recognized during the pre-design phase, the dispatcher utilized the radio signal, calling out all EMS personnel twice via radio at 171.425 MHz, with a one-minute interval between communication to notify them of the incident. The ED staff would be informed of these incidents through radio or telephone communication by EMS personnel. In the post-design phase, the dispatcher utilized the bell signal, ringing it three times to alert all staff. Activation time and equipment used by EMS during MCI operations was recorded for both phases. A total of 105 MCI with EMS operations were recorded. In the bell signal group, 52.1% (n = 199) of the participants were male. Mass transportation incidents accounted for the most of the MCI, comprising 73.6% in the bell signal group and 73.1% in the radio signal group. The average activation time was significantly shorter for the bell signal (1.54 min) compared to the radio signal (3.60 min) (P < 0.001). The average response time for the bell signal was 13.20 min, while the radio signal response time averaged 16.10 min (P = 0.042). Early activation time (less than 2 min after EMS dispatch) was significantly more likely in the Bell signal group (adjusted odds ratio, 1.25; 95% confidence interval, 1.10-2.45) than in the Radio signal group. The activation and response times for EMS operations during MCI were significantly reduced by using bell signals to alert EMS staff.

Prevention of "bygone futures" due to road traffic injuries in children

Road traffic injuries remain one of the common and leading causes of death among children and adolescents till the age of 19 years. Road safety is important for children since their physical activity, active travel, independence and development are largely affected by it. Solutions for road safety with benefits for people as well as an economy exist which should be implemented effectively and efficiently. These solutions which combine engineering, legislation and behavioural interventions should be implemented in an integrated Safe Systems Approach. The future of the children must be safeguarded from these injuries and every effort towards it being converted into "bygone figures" must be done diligently and honestly. The various risk factors and interventions possibly explained in this review article shall help in better understanding of the causes and possible guidelines at a policy level to prevent road traffic injuries in children.

Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury

BACKGROUND

Traumatic brain injury (TBI) is a major cause of death and functional disability in the general population. The nomogram is a clinical prediction tool that has been researched for a wide range of medical conditions. The purpose of this study was to identify prognostic factors associated with in-hospital mortality. The secondary objective was to develop a clinical nomogram for TBI patients' in-hospital mortality based on prognostic factors.

METHODS

A retrospective cohort study was conducted to analyze 14,075 TBI patients who were admitted to a tertiary hospital in southern Thailand. The total dataset was divided into the training and validation datasets. Several clinical characteristics and imaging findings were analyzed for in-hospital mortality in both univariate and multivariable analyses using the training dataset. Based on binary logistic regression, the nomogram was developed and internally validated using the final predictive model. Therefore, the predictive performances of the nomogram were estimated by the validation dataset.

RESULTS

Prognostic factors associated with in-hospital mortality comprised age, hypotension, antiplatelet, Glasgow coma scale score, pupillary light reflex, basilar skull fracture, acute subdural hematoma, subarachnoid hemorrhage, midline shift, and basal cistern obliteration that were used for building nomogram. The predictive performance of the nomogram was estimated by the training dataset; the area under the receiver operating characteristic curve (AUC) was 0.981. In addition, the AUCs of bootstrapping and cross-validation methods were 0.980 and 0.981, respectively. For the temporal validation with an unseen dataset, the sensitivity, specificity, accuracy, and AUC of the nomogram were 0.90, 0.88, 0.88, and 0.89, respectively.

CONCLUSION

A nomogram developed from prognostic factors had excellent performance; thus, the tool had the potential to serve as a screening tool for prognostication in TBI patients. Furthermore, future research should involve geographic validation to examine the predictive performances of the clinical prediction tool.

Development and internal validation of a nomogram for predicting outcomes in children with traumatic subdural hematoma

Background

A subdural hematoma (SDH) following a traumatic brain injury (TBI) in children can lead to unexpected death or disability. The nomogram is a clinical prediction tool used by physicians to provide prognosis advice to parents for making decisions regarding treatment. In the present study, a nomogram for predicting outcomes was developed and validated. In addition, the predictors associated with outcomes in children with traumatic SDH were determined.

Methods

In this retrospective study, 103 children with SDH after TBI were evaluated. According to the King’s Outcome Scale for Childhood Head Injury classification, the functional outcomes were assessed at hospital discharge and categorized into favorable and unfavorable. The predictors associated with the unfavorable outcomes were analyzed using binary logistic regression. Subsequently, a two-dimensional nomogram was developed for presentation of the predictive model.

Results

The predictive model with the lowest level of Akaike information criterion consisted of hypotension (odds ratio [OR], 9.4; 95% confidence interval [CI], 2.0–42.9), Glasgow coma scale scores of 3–8 (OR, 8.2; 95% CI, 1.7–38.9), fixed pupil in one eye (OR, 4.8; 95% CI, 2.6–8.8), and fixed pupils in both eyes (OR, 3.5; 95% CI, 1.6–7.1). A midline shift ≥5 mm (OR, 1.1; 95% CI, 0.62–10.73) and co-existing intraventricular hemorrhage (OR, 6.5; 95% CI, 0.003–26.1) were also included.

Conclusions

SDH in pediatric TBI can lead to mortality and disability. The predictability level of the nomogram in the present study was excellent, and external validation should be conducted to confirm the performance of the clinical prediction tool.

Epidemiological Trends and Rehabilitation Utilization of Traumatic Brain Injury in Korea (2008-2018)

This study aimed to estimate the trend of traumatic brain injuries (TBIs) and TBI-related medical usage in Korea. Patients first diagnosed with disease codes of TBIs were included. We calculated the crude incidence and age-adjusted incidence, as well as medical cost, length of stay (LOS), clinic visits, and the number of specialized rehabilitation therapy for 1 year. Patients first diagnosed as TBI was higher in national health insurance (NH-I) than in automobile insurance (AUTO-I). In contrast with the gradual decrease of the crude incidence, total medical costs both in NH-I and AUTO-I were generally and steadily increased. For oriental medicine, total medical costs dramatically increased in both inpatient and outpatient. LOS, clinic visits, and the number of specialized rehabilitation therapy were higher in AUTO-I than in NH-I. The most frequent age groups in NH-I were the young (0-9) and old (70 or over), whereas in AUTO-I, the working age group was prominent. Our results show differences in the incidence of TBI and medical usage between NH-I and AUTO-I, which could be associated with the policy for strengthening health insurance coverage, automobile-related regulations to prevent accidents and injuries, as well as rapid changes in the structure of the population in Korea.

Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury

OBJECTIVE

The overuse of head CT examinations has been much discussed, especially those for minor traumatic brain injury (TBI). In the disruptive era, machine learning (ML) is one of the prediction tools that has been used and applied in various fields of neurosurgery. The objective of this study was to compare the predictive performance between ML and a nomogram, which is the other prediction tool for intracranial injury following cranial CT in children with TBI.

METHODS

Data from 964 pediatric patients with TBI were randomly divided into a training data set (75%) for hyperparameter tuning and supervised learning from 14 clinical parameters, while the remaining data (25%) were used for validation purposes. Moreover, a nomogram was developed from the training data set with similar parameters. Therefore, models from various ML algorithms and the nomogram were built and deployed via web-based application.

RESULTS

A random forest classifier (RFC) algorithm established the best performance for predicting intracranial injury following cranial CT of the brain. The area under the receiver operating characteristic curve for the performance of RFC algorithms was 0.80, with 0.34 sensitivity, 0.95 specificity, 0.73 positive predictive value, 0.80 negative predictive value, and 0.79 accuracy.

CONCLUSIONS

The ML algorithms, particularly the RFC, indicated relatively excellent predictive performance that would have the ability to support physicians in balancing the overuse of head CT scans and reducing the treatment costs of pediatric TBI in general practice.

Application of machine learning to predict the outcome of pediatric traumatic brain injury

PURPOSE

Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.

METHODS

A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.

RESULTS

There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.

CONCLUSION

The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

Traumatic Brain Injury-Related Pediatric Mortality and Morbidity in Low- and Middle-Income Countries: A Systematic Review

BACKGROUND

The burden of pediatric traumatic brain injury (pTBI) in low- and middle-income countries (LMICs) is unknown. To fill this gap, we conducted a review that aimed to characterize the causes of pTBI in LMICs, and their reported associated mortality and morbidity.

METHODS

A systematic review was conducted. MEDLINE, Embase, Global Health, and Global Index Medicus were searched from January 2000 to May 2020. Observational or experimental studies on pTBI of individuals aged between 0 and 16 years in LMICs were included. The causes of pTBI and morbidity data were descriptively analyzed, and case fatality rates were calculated.

PROSPERO ID

CRD42020171276.

RESULTS

A total of 136 studies were included. Fifty-seven studies were at high risk of bias. Of the remaining studies, 170,224 cases of pTBI were reported in 32 LMICs. The odds of having a pTBI were 1.8 times higher (95% confidence interval, 1.6-2.0) in males. The odds of a pTBI being mild were 4.4 times higher (95% confidence interval, 1.9-6.8) than a pTBI being moderate or severe. Road traffic accidents were the most common cause (n = 16,275/41,979; 39%) of pTBIs. On discharge, 24% of patients (n = 4385/17,930) had a reduction in their normal mental or physical function. The median case fatality rate was 7.3 (interquartile range, 2.1-7.7).

CONCLUSIONS

Less than a quarter (n = 32) of all LMICs have published high-quality data on the volume and burden of pTBI. From the limited data available, young male children are at a high risk of pTBIs in LMICs, particularly after road traffic accidents.

Predicting the Health-related Quality of Life in Patients Following Traumatic Brain Injury

Background  Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method  A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R ² ) and the root mean square error (RMSE). Results  A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R ² of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R ² of 0.6843 and the lowest RMSE of 0.06748. Conclusions  A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

Clinical Nomogram Predicting Intracranial Injury in Pediatric Traumatic Brain Injury

Background:

There are differences in injured mechanisms among pediatric traumatic brain injury (TBI) in developing countries. This study aimed to develop and validate clinical nomogram for predicting intracranial injury in pediatric TBI that will be implicated in balancing the unnecessary investigation in the general practice.

Materials and Methods:

The retrospective study was conducted in all patients who were younger than 15 years old and underwent computed tomography (CT) of the brain after TBI in southern Thailand. Injured mechanisms and clinical characteristics were identified and analyzed with binary logistic regression for predicting intracranial injury. Using random sampling without replacement, the total data was split into nomogram developing dataset (80%) and testing dataset (20%). Therefore, a nomogram was constructed and applied via the web-based application from the developing dataset. Using testing dataset, validation as binary classifiers was performed by various probabilities levels.

Results:

A total of 900 victims were enrolled. The mean age was 87.2 (standard deviation [SD] 57.4) months, and 65.3% of all patients injured were from road traffic accidents. The rate of positive findings in CT of the brain was 32.8%. A nomogram was developed from the significant variables, including age groups, road traffic accidents, loss of consciousness, scalp hematoma/laceration, motor weakness, signs of basilar skull fraction, low Glasgow Coma Scale score, and pupillary light reflex.

Therefore, a nomogram was developed from 80% of data and was validated from 20% of data. The accuracy, sensitivity, specificity, positive, and negative predictive values of the nomogram were 0.83, 0.42, 1.00, 1.00, and 0.81 at a cutoff value of 0.5 probability.

Conclusion:

This study provides a clinical nomogram that will be applied to making decisions in general practice as a diagnostic tool from high specificity.

Development and Validation of a Nomogram for Predicting the Mortality after Penetrating Traumatic Brain Injury

Objective:

To determine the factors associated with mortality in penetrating brain injury (PTBI) and proposed the nomogram predicting the risk of death.

Methods:

A retrospective cohort study was conducted on all patients who had sustained PTBI between 2009 and 2018. Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Prognostic factors analysis was conducted using a forest plot. Therefore, the nomogram was developed and validated. For the propose of evaluation, the nomogram’s sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Receiver Operating Characteristic (ROC) curve and the area under the receiver operating characteristic (AUC) were determined for validating the optimal cut-off point of the total scores.

Results:

During the study period, 62 individuals enrolled. In the univariate analysis, factors associated with the morality were normal pupils’ reactivity to light (OR 0.04, p < 0.001), hypotension (OR 9.91, p<0.001), hypoxia (OR 10.2, p=0.04), bihemispheric injuries (OR 19.0, p=0.001), multilobar injuries (OR 21.5, p< 0.001), subarachnoid hemorrhage (OR 6.9, p= 0.02), intraventricular hemorrhage (OR 26.6, p= 0.006), basal cistern effacement (OR 28.8, , p<0.001), midline shift >5 mm (OR 0.19, p<0.001) were significantly associated with death. In multivariable analysis, hypotension (OR 8.82, p=0.03), normal pupils’ reactivity to light (OR 0.07, p =0.01), midline shift >5 mm (OR 18.23, p<0.007) were significantly associated with death. The nomogram’s sensitivity, specificity, PPV, NPV, and AUC for predicting mortality (total score ≥ 100) were 80%, 92.6%, 72.7%, 95.0%, and, 0.86 respectively.

Conclusions:

PTBI is the fatal injury depend on both clinical and neuroimaging parameters. The nomogram is the alternative method providing prognostic parameters toward implication for clinical decision making.

Predictive Risk Factors for Early Seizures in Traumatic Brain Injury

Background Early posttraumatic seizure (PTS) is a significant cause of unfavorable outcomes in traumatic brain injury (TBI). This study was aimed to investigate the incidence and determine a predictive model for early PTS.

Materials and Methods A prospective cohort study of 484 TBI patients was conducted. All patients were evaluated for seizure activities within 7 days after the injury. Risk factors for early PTS were identified using univariate analysis. The candidate risk factors with p < 0.1 were selected into multivariable logistic regression analysis to identify predictors of early PTS. The fitting model and the power of discrimination with the area under the receiver operating characteristic (AUROC) curve were demonstrated. The nomogram for prediction of early PTS was developed for individuals.

Results There were 27 patients (5.6%) with early PTS in this study. The final model illustrated chronic alcohol use (odds ratio [OR]: 4.06, 95% confidence interval [CI]: 1.64–10.07), epidural hematoma (OR: 3.98, 95% CI: 1.70–9.33), and Glasgow Coma Scale score 3–8 (OR: 3.78, 95% CI: 1.53–9.35) as predictors of early PTS. The AUROC curve was 0.77 (95% CI: 0.66–0.87).

Conclusions The significant predictors for early PTS were chronic alcohol use, epidural hematoma, and severe TBI. Our nomogram was considered as a reliable source for prediction.

Traumatic cerebrovascular injury: Prevalence and risk factors

BACKGROUND

Traumatic cerebrovascular injury (TCVI) is uncommon in traumatic brain injury (TBI). Although TCVI is a rare condition, this complication is serious. A missed or delayed diagnosis may lead to an unexpected life-threatening hemorrhagic event or persistent neurological deficit. The object of this study was to determine the prevalence and risk factors associated with TCVI.

METHODS

The authors retrospectively reviewed medical records and neuroimaging studies of 5178 patients with TBI. The association of various factors was investigated using time-to-event statistical analysis. A TCVI which resulted in an occlusion, arteriovenous fistula, pseudoaneurysm or cerebral artery transection was defined as an event.

RESULTS

Forty-two patients developed a TCVI after injuries with an overall prevalence of 0.8%. The risk factors for an intracranial arterial injury based on univariate analysis using the Cox proportional hazard regression were penetrating injury, severe head injury, orbitofacial injury, basilar skull fracture, subdural hematoma, and cerebral contusion. In multivariable analysis, the two variables that were independently associated with TCVI were basilar skull fracture (odds ratio [OR] 22.1, 95% confidence interval [CI] 11.5-42.2) followed by orbitofacial fracture (OR 13.6, 95% CI 6.8-27.3).

CONCLUSIONS

Although TCVI is a rare complication of TBI, early investigation in high-risk patients may be necessary for early treatment before an unexpected fatal event occurs.

Blast-induced traumatic brain injury: the experience from a level I trauma center in southern Thailand

OBJECTIVE

In the ongoing conflict in southern Thailand, the improvised explosive device (IED) has been a common cause of blast-induced traumatic brain injury (bTBI). The authors investigated the particular characteristics of bTBI and the factors associated with its clinical outcome.

METHODS

A retrospective cohort study was conducted on all patients who had sustained bTBI between 2009 and 2017. Collected data included clinical characteristics, intracranial injuries, and outcomes. Factors analysis was conducted using a forest plot.

RESULTS

During the study period, 70 patients met the inclusion criteria. Fifty individuals (71.4%) were military personnel. One-third of the patients (32.9%) suffered moderate to severe bTBI, and the rate of intracerebral injuries on brain CT was 65.7%. Coup contusion was the most common finding, and primary blast injury was the most common mechanism of blast injury. Seventeen individuals had an unfavorable outcome (Glasgow Outcome Scale score 1–3), and the overall mortality rate for bTBI was 11.4%. In the univariate analysis, factors associated with an unfavorable outcome were preoperative coagulopathy, midline shift of the brain ≥ 5 mm, basal cistern effacement, moderate to severe TBI, hypotension, fixed and dilated pupils, surgical site infection, hematocrit < 30% on admission, coup contusion, and subdural hematoma. In the multivariable analysis, midline shift ≥ 5 mm (OR 29.1, 95% CI 2.5–328.1) and coagulopathy (OR 28.7, 95% CI 4.5–180.3) were the only factors predicting a poor outcome of bTBI.

CONCLUSIONS

bTBIs range from mild to severe. Midline shift and coagulopathy are treatable factors associated with an unfavorable outcome. Hence, in cases of bTBI, reversing an abnormal coagulogram is required as soon as possible to improve clinical outcomes. The management of brain shift needs further study.

The Optimal Operative Timing of Traumatic Intracranial Acute Subdural Hematoma Correlated with Outcome

Objective

Acute subdural hematoma (ASDH) has been associated with mortality in traumatic brain injury. The timing of surgical evacuation for ASDH has still been controversial. The object of this study was to determine the temporal and clinical factors associated with outcome following surgery for ASDH.

Materials and Methods

The study retrospectively viewed medical records and neuroimaging studies of ASDH patients who underwent surgical evacuation. Surgical outcomes were dichotomized into favorable and unfavorable outcomes, and operative times compared between the groups.

Results

The records of 145 ASDH patients who underwent surgery were reviewed. Almost two-thirds of the patients were admitted for surgical evacuation, of whom 71% underwent a decompressive operation. The temporal variables were as follows: mean time from scene of accident to emergency department (ED) was 70 (Standard deviation [SD] 256.0) min, mean time from ED to obtaining CT of the brain was 45.6 (SD 38.9) min, mean time from brain computed tomographic to operating room arrival was 68.6 (SD 50.0) min, and mean time from ED arrival to skin incision was 160.1 (SD 88.1) min. The mean time from ED arrival to skin incision was significantly shorter in the unfavorable outcome group. Because of this reverse association between time from ED to surgery, multivariate analysis was applied to adjust the timing factors with other clinical factors, and the results indicated that temporal factors were not associated with functional outcome, as features such as increased intracranial pressure due to obliterated basal cistern and brain herniation were significantly associated with functional outcome.

Conclusions

The optimal times for surgical evacuation of ASDH are challenging to estimate because compressed brainstem signs are more important than time factors. ASDH patients with compressed brainstem should have surgery as soon as possible.