A Population-based Outcomes Study of Persons Hospitalized With Traumatic Brain Injury

Prediction of motor function in patients with traumatic brain injury using genetic algorithms modified back propagation neural network: A data-based study

Objective

Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. In this study, the characteristics of the patients, who were admitted to the China Rehabilitation Research Center, were elucidated in the TBI database, and a prediction model based on the Fugl-Meyer assessment scale (FMA) was established using this database.

Methods

A retrospective analysis of 463 TBI patients, who were hospitalized from June 2016 to June 2020, was performed. The data of the patients used for this study included the age and gender of the patients, course of TBI, complications, and concurrent dysfunctions, which were assessed using FMA and other measures. The information was collected at the time of admission to the hospital and 1 month after hospitalization. After 1 month, a prediction model, based on the correlation analyses and a 1-layer genetic algorithms modified back propagation (GA-BP) neural network with 175 patients, was established to predict the FMA. The correlations between the predicted and actual values of 58 patients (prediction set) were described.

Results

Most of the TBI patients, included in this study, had severe conditions (70%). The main causes of the TBI were car accidents (56.59%), while the most common complication and dysfunctions were hydrocephalus (46.44%) and cognitive and motor dysfunction (65.23 and 63.50%), respectively. A total of 233 patients were used in the prediction model, studying the 11 prognostic factors, such as gender, course of the disease, epilepsy, and hydrocephalus. The correlation between the predicted and the actual value of 58 patients was R ² = 0.95.

Conclusion

The genetic algorithms modified back propagation neural network can predict motor function in patients with traumatic brain injury, which can be used as a reference for risk and prognosis assessment and guide clinical decision-making.

Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study

Objective

To investigate the mean impact value (MIV) method for discerning the most efficacious input variables for the machine learning (ML) model. Subsequently, various ML algorithms are harnessed to formulate a more accurate predictive model that can forecast both the prognosis and the length of hospital stay for patients suffering from traumatic brain injury (TBI).

Design

Retrospective cohort study.

Participants

The study retrospectively accrued data from 1128 cases of patients who sought medical intervention at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University, within the timeframe spanning from May 2017 to May 2022.

Methods

We performed a retrospective analysis of patient data obtained from the Neurosurgery Center of the Second Hospital of Anhui Medical University, covering the period from May 2017 to May 2022. Following meticulous data filtration and partitioning, 70% of the data were allocated for model training, while the remaining 30% served for model evaluation. During the construction phase of the ML models, a gamut of 11 independent variables-including, but not limited to, in-hospital complications and patient age-were utilized as input variables. Conversely, the length of stay (LOS) and the Glasgow Outcome Scale (GOS) scores were designated as output variables. The model architecture was initially refined through the MIV methodology to identify optimal input variables, whereupon five distinct predictive models were constructed, encompassing support vector regression (SVR), convolutional neural networks (CNN), backpropagation (BP) neural networks, artificial neural networks (ANN) and logistic regression (LR). Ultimately, SVR emerged as the most proficient predictive model and was further authenticated through an external dataset obtained from the First Hospital of Anhui Medical University.

Results

Upon incorporating the optimal input variables as ascertained through MIV, it was observed that the SVR model exhibited remarkable predictive prowess. Specifically, the Mean Absolute Percentage Error (MAPE) of the SVR model in predicting the GOS score in the test dataset is only 6.30%, and the MAPE in the external validation set is only 7.61%. In terms of predicting hospitalization time, the accuracy of the test and external validation sets were 9.28% and 7.91%, respectively. This error indicator is significantly lower than the error of other prediction models, thus proving the excellent efficacy and clinical reliability of the MIV-optimized SVR model.

Conclusion

This study unequivocally substantiates that the incorporation of MIV for selecting optimal input variables can substantially augment the predictive accuracy of machine learning models. Among the models examined, the MIV-SVR model emerged as the most accurate and clinically applicable, thereby rendering it highly conducive for future clinical decision-making processes.

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study

BACKGROUND

The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and significantly reduce societal health care burden. Applying novel machine learning methods to the field of TBI may be valuable for determining the prognosis and cost-effectiveness of clinical treatment.

OBJECTIVE

We aimed to combine multiple machine learning approaches to build hybrid models for predicting the prognosis and length of hospital stay for adults and children with TBI.

METHODS

We collected relevant clinical information from patients treated at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University between May 2017 and May 2022, of which 80% was used for training the model and 20% for testing via screening and data splitting. We trained and tested the machine learning models using 5 cross-validations to avoid overfitting. In the machine learning models, 11 types of independent variables were used as input variables and Glasgow Outcome Scale score, used to evaluate patients' prognosis, and patient length of stay were used as output variables. Once the models were trained, we obtained and compared the errors of each machine learning model from 5 rounds of cross-validation to select the best predictive model. The model was then externally tested using clinical data of patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022.

RESULTS

The final convolutional neural network-support vector machine (CNN-SVM) model predicted Glasgow Outcome Scale score with an accuracy of 93% and 93.69% in the test and external validation sets, respectively, and an area under the curve of 94.68% and 94.32% in the test and external validation sets, respectively. The mean absolute percentage error of the final built convolutional neural network-support vector regression (CNN-SVR) model predicting inpatient time in the test set and external validation set was 10.72% and 10.44%, respectively. The coefficient of determination (R²) was 0.93 and 0.92 in the test set and external validation set, respectively. Compared with back-propagation neural network, CNN, and SVM models built separately, our hybrid model was identified to be optimal and had high confidence.

CONCLUSIONS

This study demonstrates the clinical utility of 2 hybrid models built by combining multiple machine learning approaches to accurately predict the prognosis and length of stay in hospital for adults and children with TBI. Application of these models may reduce the burden on physicians when assessing TBI and assist clinicians in the medical decision-making process.

Vagus nerve stimulation for refractory posttraumatic epilepsy: Efficacy and predictors of seizure outcome

Background

Traumatic brain injury (TBI) has been recognized as an important and common cause of epilepsy since antiquity. Posttraumatic epilepsy (PTE) is usually associated with drug resistance and poor surgical outcomes, thereby increasing the burden of the illness on patients and their families. Vagus nerve stimulation (VNS) is an adjunctive treatment for medically refractory epilepsy. This study aimed to determine the efficacy of VNS for refractory PTE and to initially evaluate the potential predictors of efficacy.

Methods

We retrospectively collected the outcomes of VNS with at least a 1-year follow-up in all patients with refractory PTE. Subgroups were classified as responders and non-responders according to the efficacy of VNS (≥50% or <50% reduction in seizure frequency). Preoperative data were analyzed to screen for potential predictors of VNS efficacy.

Results

In total, forty-five patients with refractory PTE who underwent VNS therapy were enrolled. Responders were found in 64.4% of patients, and 15.6% of patients achieved seizure freedom at the last follow-up. In addition, the responder rate increased over time, with 37.8, 44.4, 60, and 67.6% at the 3-, 6-, 12-, and 24-month follow-ups, respectively. After multivariate analysis, generalized interictal epileptic discharges (IEDs) were found to be a negative predictor (OR: 4.861, 95% CI: 1.145–20.632) of VNS efficacy.

Conclusion

The results indicated that VNS therapy was effective in refractory PTE patients and was well tolerated over a 1-year follow-up period. Patients with focal or multifocal IEDs were recognized to have better efficacy after VNS therapy.

Dysphagia among geriatric trauma patients: A population-based study

Objective

To determine the significance of dysphagia on clinical outcomes of geriatric trauma patients.

Methods

This is a retrospective population-based study of geriatric trauma patients 65 years and older utilizing the Florida Agency for Health Care Administration dataset from 2010 to 2019. Patients with pre-admission dysphagia were excluded. Multivariable regression was used to create statistical adjustments. Primary outcomes included mortality and the development of dysphagia. Secondary outcomes included length of stay and complications. Subgroup analyses included patients with dementia, patients who received transgastric feeding tubes (GFTs) or tracheostomies, and speech language therapy consultation.

Results

A total of 52,946 geriatric patients developed dysphagia after admission during a 9-year period out of 1,150,438 geriatric trauma admissions. In general, patients who developed dysphagia had increased mortality, length of stay, and complications. When adjusted for traumatic brain and cervical spine injuries, the addition of mechanical ventilation decreased the mortality odds. This was also observed in the subset of patients with dysphagia who had GFTs placed. Of the three primary risk factors for dysphagia investigated, mechanical ventilation was the most strongly associated with later development of dysphagia and mortality.

Conclusion

The geriatric trauma population is vulnerable to dysphagia with a large number associated with traumatic brain injury, cervical spine injury, and polytraumatic injuries that lead to mechanical ventilation. Earlier intubation/mechanical ventilation in association with GFTs was found to be associated with decreased inpatient hospital mortality. Tracheostomy placement was shown to be an independent risk factor for the development of dysphagia. The utilization of speech language therapy was found to be inconsistently utilized.

An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study

Background

Posttraumatic epilepsy (PTE) is a common sequela after traumatic brain injury (TBI), and identifying high-risk patients with PTE is necessary for their better treatment. Although artificial neural network (ANN) prediction models have been reported and are superior to traditional models, the ANN prediction model for PTE is lacking.

Objective

We aim to train and validate an ANN model to anticipate the risks of PTE.

Methods

The training cohort was TBI patients registered at West China Hospital. We used a 5-fold cross-validation approach to train and test the ANN model to avoid overfitting; 21 independent variables were used as input neurons in the ANN models, using a back-propagation algorithm to minimize the loss function. Finally, we obtained sensitivity, specificity, and accuracy of each ANN model from the 5 rounds of cross-validation and compared the accuracy with a nomogram prediction model built in our previous work based on the same population. In addition, we evaluated the performance of the model using patients registered at Chengdu Shang Jin Nan Fu Hospital (testing cohort 1) and Sichuan Provincial People’s Hospital (testing cohort 2) between January 1, 2013, and March 1, 2015.

Results

For the training cohort, we enrolled 1301 TBI patients from January 1, 2011, to December 31, 2017. The prevalence of PTE was 12.8% (166/1301, 95% CI 10.9%-14.6%). Of the TBI patients registered in testing cohort 1, PTE prevalence was 10.5% (44/421, 95% CI 7.5%-13.4%). Of the TBI patients registered in testing cohort 2, PTE prevalence was 6.1% (25/413, 95% CI 3.7%-8.4%). The results of the ANN model show that, the area under the receiver operating characteristic curve in the training cohort was 0.907 (95% CI 0.889-0.924), testing cohort 1 was 0.867 (95% CI 0.842-0.893), and testing cohort 2 was 0.859 (95% CI 0.826-0.890). Second, the average accuracy of the training cohort was 0.557 (95% CI 0.510-0.620), with 0.470 (95% CI 0.414-0.526) in testing cohort 1 and 0.344 (95% CI 0.287-0.401) in testing cohort 2. In addition, sensitivity, specificity, positive predictive values and negative predictors in the training cohort (testing cohort 1 and testing cohort 2) were 0.80 (0.83 and 0.80), 0.86 (0.80 and 0.84), 91% (85% and 78%), and 86% (80% and 83%), respectively. When calibrating this ANN model, Brier scored 0.121 in testing cohort 1 and 0.127 in testing cohort 2. Compared with the nomogram model, the ANN prediction model had a higher accuracy (P=.01).

Conclusions

This study shows that the ANN model can predict the risk of PTE and is superior to the risk estimated based on traditional statistical methods. However, the calibration of the model is a bit poor, and we need to calibrate it on a large sample size set and further improve the model.

The protective effects of prolactin on brain injury

AIMS

Brain injuries based on their causes are divided into two categories, TBI and NTBI. TBI is caused by damages such as head injury, but non-physical injury causes NTBI. Prolactin is one of the blood factors that increase during brain injury. It has been assumed to play a regenerative role in post-injury recovery.

MATERIALS AND METHODS

In this review, various valid papers from electronic sources (including Web of Science, Scopus, PubMed, SID, Google Scholar, and ISI databases) used, which in them the protective effect of prolactin on brain injury investigated.

KEY FINDINGS

Inflammation following brain injury with the production of pro-inflammatory cytokines in the affected area can even lead to excitotoxicity and cell death in the damaged area. Medical brain damage treatments are long-term, and can have several side effects. Therefore, it is better to consider medication treatments that have fewer side effects and greater efficacy. Research suggests that prolactin has numerous regenerative effects on brain injury, and prevents cell death. Prolactin is one of the hormones produced in the body; therefore it has fewer side effects and may be more effective because it increases during brain injury.

SIGNIFICANCE

Prolactin can be used peripherally and centrally, and exerts its neuro regenerative effects against further damage post-TBI and NTBI.

Epidemiology of traumatic brain injury-associated epilepsy in western China: An analysis of multicenter data

OBJECTIVES

This study aims to explore the probability of developing posttraumatic epilepsy (PTE) in the following 8 years after traumatic brain injury (TBI), the risk factors associated with PTE and its cumulative prevalence.

METHODS

This is a retrospective follow-up study of patients with traumatic brain injury (TBI) discharged from the West China Hospital between January 1, 2011 and December 31, 2017, Chengdu Shang Jin Nan Fu Hospital and Sichuan Provincial People's Hospital from January 1, 2013 to March 1, 2015. We used forward stepwise method to build the final multivariate cox proportional hazard regression model to obtain estimates of hazard ratio (HR) of PTE and 95% confidence intervals (CI). We also conducted Kaplan-Meier survival analysis to investigate the cumulative prevalence of PTE.

RESULTS

The cumulative incidence of PTE rose from 6.2% in one year to 10.6% in eight years. There were more male patients in PTE group and generally older. Besides, patients with PTE tended to have abnormal CT scan results. The risk factors of PTE were male (HR = 1.6, 95% CI: 1.1-2.2, P = 0.009), early posttraumatic seizures (HR = 2.9, 95% CI: 2.2-4.1, P < 0.001), TBI severity (moderate TBI: HR = 3.0, 95% CI: 1.8-5.0, P = 0.001; severe TBI: HR = 4.3, 95% CI: 2.3-7.6, P < 0.029), loss of consciousness (LOC) more than 30 min (30 min-24 h: HR = 1.8, 95% CI: 1.02-3.1, P = 0.041; >24 h: HR = 2.4, 95% CI: 1.4-2.4, P = 0.001), subdural hematoma (SDH) (HR = 1.9, 95% CI: 1.4-2.5, P < 0.001), brain contusion sites (frontal-temporal lobe: HR = 2.7, 95% CI: 1.9-3.9, P < 0.001; other sites: HR = 1.5, 95% CI: 1.01-2.3, P = 0.042) and cranial surgery (HR = 1.7, 95% CI: 1.3-2.3, P < 0.001).

SIGNIFICANCE

The probability of developing PTE increased during the study period. In addition, the risk of developing PTE was significantly associated with gender, EPTS, LOC time, SDH, brain contusion sites, surgery and TBI severity. However, further researches may be needed to predict the risk of PTE in combination with quantitative factors.

Quality of life measures in older adults after traumatic brain injury: a systematic review

BACKGROUND

On average older adults experiencing TBI are hospitalized four times as often, have longer hospital stays, and experience slower recovery trajectories and worse functional outcomes compared to younger populations with the same injury severity. A standard measure of Qol for older adults with TBI would facilitate accurate and reliable data across the individual patient care continuum and across clinical care settings, as well as support more rigorous research studies of metadata.

PURPOSE

The aim of this systematic review was to investigate patient reported Qol measures in studies with older adults post TBI.

METHOD

A systematic review was carried out focusing on the various tools to measure Qol in older adults, ≥ 65 years of age with a diagnosis of TBI. Data bases searched included Medline, Embase, PubMed, CINAHL, and PsychInfo from date of inception to September 25, 2017.

RESULTS

A total of 20 articles met the inclusion criteria. Nine different tools were identified.

CONCLUSIONS

Findings based on the comparison of reliability and construct validity of the Qol measures reported in this review suggest that no single instrument is superior to all others for our study population. Future research in this field should include the enrollment of larger study samples of older adults. Without these future efforts, the ability to detect an optimal Qol measure will be hindered.

A Clinical Framework for Functional Recovery in a Person With Chronic Traumatic Brain Injury: A Case Study

BACKGROUND AND PURPOSE

This case study describes a task-specific training program for gait walking and functional recovery in a young man with severe chronic traumatic brain injury.

CASE DESCRIPTION

The individual was a 26-year-old man 4 years post-traumatic brain injury with severe motor impairments who had not walked outside of therapy since his injury. He had received extensive gait training prior to initiation of services. His goal was to recover the ability to walk.

INTERVENTION

The primary focus of the interventions was the restoration of walking. A variety of interventions were used, including locomotor treadmill training, electrical stimulation, orthoses, and specialized assistive devices. A total of 79 treatments were delivered over a period of 62 weeks.

OUTCOMES

At the conclusion of therapy, the client was able to walk independently with a gait trainer for approximately 1km (over 3000 ft) and walked in the community with the assistance of his mother using a rocker bottom crutch for distances of 100m (330 ft).

DISCUSSION

Specific interventions were intentionally selected in the development of the treatment plan. The program emphasized structured practice of the salient task, that is, walking, with adequate intensity and frequency. Given the chronicity of this individual's injury, the magnitude of his functional improvements was unexpected.Video Abstract available for additional insights from the Authors (see Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A175).

Long-term outcomes after moderate-to-severe traumatic brain injury among military veterans: Successes and challenges

OBJECTIVE

To assess long-term outcomes after traumatic brain injury (TBI) among veterans and service members.

SETTING

Regional Veterans Affairs medical centre.

PARTICIPANTS

One hundred and eighteen veterans and military personnel, aged 23-70 years (median = 35 years), 90% male, had moderate-to-severe TBI (82% in coma > 1 day, 85% amnesic > 7 days), followed by acute interdisciplinary rehabilitation 5-16 years ago (median = 8 years).

DESIGN

Cross-sectional analysis of live interviews conducted via telephone.

MAIN MEASURES

TBI follow-up interview (occupational, social, cognitive, neurologic and psychiatric ratings), Community Integration Questionnaire, Disability Rating Scale (four indices of independent function) and Satisfaction with Life Scale.

RESULTS

At follow-up, 52% of participants were working or attending school; 34% ended or began marriages after TBI, but the overall proportion married changed little. Finally, 22% were still moderately-to-severely disabled. However, 62% of participants judged themselves to be as satisfied or more satisfied with life than before injury. Injury severity, especially post-traumatic amnesia, was correlated with poorer outcomes in all functional domains.

CONCLUSIONS

After moderate-severe TBI, most veterans assume productive roles and are satisfied with life. However, widespread difficulties and functional limitations persist. These findings suggest that veteran and military healthcare systems should continue periodic, comprehensive follow-up evaluations long after moderate-to-severe TBI.

Traumatic brain injury and cognition

Traumatic brain injury (TBI) is a major cause of death and disability, and therefore an important health and socioeconomic problem for our society. Individuals surviving from a moderate to severe TBI frequently suffer from long-lasting cognitive deficits. Such deficits include different aspects of cognition such as memory, attention, executive functions, and awareness of their deficits. This chapter presents a review of the main neuropsychological and neuroimaging studies of patients with TBI. These studies found that patients evolve differently according to the severity of the injury, the mechanism causing the injury, and the lesion location. Further research is necessary to develop rehabilitation methods that enhance brain plasticity and recovery after TBI. In this chapter, we summarize current knowledge and controversies, focusing on cognitive sequelae after TBI. Recommendations from the Common Data Elements are provided, with an emphasis on diagnosis, outcome measures, and studies organization to make data more comparable across studies. Final considerations on neuroimaging advances, rehabilitation approaches, and genetics are described in the final section of the chapter.

Trends in the epidemiology of disability related to traumatic brain injury in the US Army and Marine Corps: 2005 to 2010

BACKGROUND

Traumatic brain injury (TBI) has been recognized as a major public health issue for several decades. Despite technological advancements in protective equipment and medical care available during recent military conflicts, TBI is the most common neurological condition among Soldiers and Marines evaluated for discharge from service. This study describes the demographic, service-related, and disability characteristics of Soldiers and Marines referred for combat-related TBI disability evaluation.

METHODS

Cross-sectional analysis of Soldiers and Marines evaluated for combat-related disability between October 1, 2004 and September 30, 2010 was performed. Traumatic brain injury cases were identified using the Veterans Affairs Schedule for Rating Disabilities code for TBI and compared with other combat-related disabilities.

RESULTS

Combat-related TBI disability rates have significantly increased in both the Army and the Marine Corps since 2005. Significantly more unfitting conditions are present on average in combat-related TBI cases than in other combat-related disability cases. Combat-related TBI disability cases are more likely to be medically retired than other types of combat-related disability.

CONCLUSIONS

Because veterans with combat-related TBI disabilities are likely to require chronic care for TBI-associated medical conditions, disability evaluation policy and programs must ensure that combat-related TBI disabilities are accurately identified and compensated, and the potential long-term care needs are addressed.

Risk of severe and repetitive traumatic brain injury in persons with epilepsy: a population-based case-control study

BACKGROUND

While traumatic brain injury (TBI) can lead to epilepsy, individuals with preexisting epilepsy or seizure disorder (ESD), depending on the type of epilepsy and the degree of seizure control, may have a greater risk of TBI from seizure activity or medication side effects. The joint occurrence of ESD and TBI can complicate recovery as signs and symptoms of TBI may be mistaken for postictal effects. Those with ESD are predicted to experience more deleterious outcomes either because of having a more severe TBI or because of the cumulative effects of repetitive TBI.

METHODS

We conducted a case-control study of all emergency department visits and hospital discharges for TBI from 1998 through 2011 in a statewide population. The severity of TBI, repetitive TBI, and other demographic and clinical characteristics were compared between persons with TBI with preexisting ESD (cases) and those without (controls). Significant differences in proportions were evaluated with confidence intervals. Logistic regression was used to examine the association of the independent variables with ESD.

RESULTS

During the study period, 236,164 individuals sustained TBI, 5646 (2.4%) of which had preexisting ESD. After adjustment for demographic and clinical characteristics, cases were more likely to have sustained a severe TBI (OR=1.49; 95% CI=1.38-1.60) and have had repetitive TBI (OR=1.54; 95% CI=1.41-1.69).

CONCLUSION

The consequences of TBI may be greater in individuals with ESD owing to the potential for a more severe or repetitive TBI. Seizure control is paramount, and aggressive management of comorbid conditions among persons with ESD and increased awareness of the hazard of repetitive TBI is warranted. Furthermore, future studies are needed to examine the long-term outcomes of cases in comparison with controls to determine if the higher risk of severe or repetitive TBI translates into permanent deficits.

Prior history of traumatic brain injury among persons in the Traumatic Brain Injury Model Systems National Database

OBJECTIVE

To determine the association between demographic, psychosocial, and injury-related characteristics and traumatic brain injury (TBI) occurring prior to a moderate or severe TBI requiring rehabilitation.

DESIGN

Secondary data analysis.

SETTING

TBI Model System inpatient rehabilitation facilities.

PARTICIPANTS

Persons (N=4464) 1, 2, 5, 10, 15, or 20 years after TBI resulting in participation in the TBI Model System National Database.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

History of TBI prior to the TBI Model System Index injury, pre-Index injury demographic and behavioral characteristics, Index injury characteristics, post-Index injury behavioral health and global outcome.

RESULTS

Twenty percent of the cohort experienced TBIs preceding the TBI Model System Index injury-80% of these were mild and 40% occurred before age 16. Pre- and post-Index injury behavioral issues, especially substance abuse, were highly associated with having had a prior TBI. Greater severity of the pre-Index injury as well as occurrence before age 6 often showed stronger associations. Unexpectedly, pre-Index TBI was associated with less severe Index injuries and better functioning on admission and discharge from rehabilitation.

CONCLUSIONS

Findings suggest that earlier life TBI may have important implications for rehabilitation after subsequent TBI, especially for anticipating behavioral health issues in the chronic stage of recovery. Results provide additional evidence for the potential consequences of early life TBI, even if mild.

Abusive head trauma in young children: a population-based study

OBJECTIVE

The objectives of this study were to provide population-based incidence estimate of abusive head trauma (AHT) in children aged 0 to 5 years from inpatient and emergency department (ED) and identify risk characteristics for recognizing high-risk children to improve public health surveillance.

METHODS

This was a retrospective cohort study based on children's first encounter in ED or hospital admission with a diagnosis of head trauma (HT), 2000-2010. The relationship between clinical markers and AHT was examined controlling for covariables in the model using Cox hazards regression. Kaplan-Meier incidence probability was plotted, and the number of weeks elapsing from date of birth to the first encounter with HT established the survival time (T).

RESULTS

Twenty-six thousand six hundred eighty-one children had HT, 502 (1.8%) resulted from abuse; 42.4% was captured from ED. Incidence varied from 28.9 (95% confidence interval [CI], 27.9-37.4) in infants to 4.1 (95% CI, 2.4-5.7) in 5-year-olds per 100,000 per year. Adjusted hazard ratio was 20.3 (95% CI, 10.9-38.0) for intracranial bleeding and 11.4 (95% CI, 8.57-15.21) for retinal hemorrhage.

CONCLUSIONS

Incidence estimates of AHT are incomplete without including ED. Intracranial bleeding is a cardinal feature of AHT to be considered in case ascertainment to improve public health surveillance.

Representativeness of the Traumatic Brain Injury Model Systems National Database

OBJECTIVE

To determine whether the Traumatic Brain Injury Model Systems National Database (TBIMS-NDB) is representative of individuals aged 16 years and older admitted for acute, inpatient rehabilitation in the United States with a primary diagnosis of traumatic brain injury (TBI).

DESIGN

Secondary analysis of existing data sets.

SETTING

Acute inpatient rehabilitation facilities.

PARTICIPANTS

Patients aged 16 years and older with a primary rehabilitation diagnosis of TBI.

MAIN OUTCOME MEASURES

Demographic characteristics, functional status, and hospital length of stay.

RESULTS

Patients included in the TBIMS-NDB from October 2001 through December 2007 were largely representative of all individuals 16 years and older admitted for rehabilitation in the United States with a primary diagnosis of TBI. The major difference in distribution was age-the TBIMS-NDB cohort did not include as large a proportion of patients older than 65 years as were admitted for rehabilitation with a primary diagnosis of TBI in the United States. Distributional differences for age-related characteristics were observed; however, groups of patients partitioned at aged 65 years differed minimally, especially within the younger than 65 years subset. Regardless of age, the proportion of patients with a rehabilitation stay of 1 to 9 days was larger nationwide. Nationwide admissions showed an age distribution similar to patients discharged alive from acute care with moderate, severe or penetrating TBI. The proportion of patients aged 70 years and older admitted for TBI rehabilitation in the United States increased every year, a trend that was not evident in the general population, TBIMS-NDB or among TBI patients in acute care.

CONCLUSIONS

These results provide substantial empirical evidence that the TBIMS-NDB is representative of patients receiving inpatient rehabilitation for TBI in the United States. Researchers utilizing the TBIMS-NDB may want to adjust statistically for the lower percentage of patients older than 65 years or those with stays less than 10 days.

Trends in Traumatic Brain Injury in the U.S. and the public health response: 1995-2009

PROBLEM

Traumatic Brain Injury (TBI) is a public health problem in the United States. In 2009, approximately 2.4 million [corrected] patients with a TBI listed as primary or secondary diagnosis were hospitalized and discharged alive (N=300,667) or were treated and released from emergency departments (EDs; N=2,077,350), outpatient departments (ODs; N=83,857), and office-based physicians (OB-P; N=1,079,338). In addition, 52,695 died with one or more TBI-related diagnoses.

METHODS

Federal TBI-related laws that have guided CDC since 1996 were reviewed. Trends in TBI were obtained by analyzing data from nationally representative surveys conducted by the National Center for Health Statistics (NCHS).

FINDINGS

CDC has developed and is implementing a strategy to reduce the burden of TBI in the United States. Currently, 20 states have TBI surveillance and prevention systems. From 1995-2009, the TBI rates per 100,000 population increased in EDs (434.1 vs. 686.0) and OB-Ps (234.6 vs. 352.3); and decreased in ODs (42.6 vs. 28.1) and in TBI-related deaths (19.9 vs. 16.6). TBI Hospitalizations decreased from 95.5 in 1995 to 77.9 in 2000 and increased to 95.7 in 2009.

CONCLUSIONS

The rates of TBI have increased since 1995 for ED and PO visits. To reduce of the burden and mitigate the impact of TBI in the United States, an improved state- and territory-specific TBI surveillance system that accurately measures burden and includes information on the acute and long-term outcomes of TBI is needed.

Association of depressive symptoms with functional outcome after traumatic brain injury

OBJECTIVE

To test whether improved functional status correlates with more depressive symptoms after traumatic brain injury (TBI). This is based on the concept that increasing awareness of deficits may exacerbate depression, even while survivors are making functional improvements.

PARTICIPANTS

A total of 471 individuals with TBI (72% white; 71% men; median Glasgow Coma Scale (GCS) score = 11) enrolled during acute care or inpatient rehabilitation and followed up at a median of 6 months.

MAIN MEASURE

Beck Depression Inventory-II (BDI-II), Glasgow Outcome Scale-Extended, and Functional Status Examination (FSE).

RESULTS

We found significant Spearman rank order correlations between BDI-II scores and the total FSE as well as all domains of the FSE. Lower functional levels correlated with more depressive symptoms. Modeling of predictive factors, including subject characteristics, injury-related characteristics, and outcome measures, resulted in 2 models, both containing age and GCS along with other factors.

CONCLUSION

The relation between depressive symptoms and functional outcomes is complex and a fertile area for further research. The authors would encourage clinicians to monitor patients for depressive symptoms to help to prevent the detrimental impact on recovery.

Risk factors for reduced survival after traumatic brain injury: a 30-year follow-up study

PRIMARY OBJECTIVE

To evaluate risk factors for reduced survival in subjects with traumatic brain injury (TBI).

PARTICIPANTS AND METHODS

A retrospective follow-up of three decades included 192 subjects with TBI. Cognitive testing was carried out on average 2 years after the injury (at mean age of 39.0 years), during the years 1966-1972. Cox's regression and logistic regression analyses were used and the survival of the subjects was compared with the general population using the standardized mortality ratio (SMR).

RESULTS

Reduced survival was significantly associated with age at injury (p < 0.001) and vocational outcome (p = 0.003). Vocational outcome in turn was associated with age (p = 0.010), TBI severity (p < 0.001), cognitive impairment (p = 0.010), later TBIs (p = 0.007) and alcohol abuse (p = 0.015). Mortality in the younger patient group (age at death <40 years) was higher than in the general population (SMR 4.50, 95% CI = 2.02-10.01).

CONCLUSIONS

A reduced working ability, influenced by age-, injury- and lifestyle-related factors, is associated with long-term survival after TBI. The mortality among younger patients is high, a finding which should be considered when planning the care after TBI.

A population-based study of repetitive traumatic brain injury among persons with traumatic brain injury

PRIMARY OBJECTIVE

The objective was to estimate and compare the hazards of repetitive traumatic brain injury (RTBI) events as a function of the index TBI severity, in a cohort of TBI hospital discharges include in the South Carolina Traumatic Brain Injury Follow-up Registry.

RESEARCH DESIGN

Retrospective cohort.

METHODS AND PROCEDURES

There were 4357 persons with TBI who were followed from the index hospital discharge through 31 December 2005 for RTBI events through the statewide hospital discharge (HD) and emergency department (ED) records. Prentice, Williams, Peterson total time/conditional probability model (PWP-CP) for recurrent events survival analysis was used to assess RTBI as a function of index TBI severity.

MAIN OUTCOMES AND RESULTS

Index TBI severity approached significance in its relationship with RTBI, with persons with a severe index TBI experiencing events at a higher rate than those with a mild/moderate index TBI. Among the other covariates evaluated, epilepsy/seizure disorder, race, gender, payer status, cause of injury and having a prior history of TBI were associated with RTBI.

CONCLUSIONS

While TBI severity approached significance with RTBI, other variables, such as epilepsy/seizure disorder, seem to have a more significant relationship with RTBI.

Pre-existing health conditions and repeat traumatic brain injury

OBJECTIVE

To assess and compare the effect of Pre-existing epilepsy/seizure disorder and drug/alcohol problem on the hazard of repeat traumatic brain injury (TBI) in persons with TBI who participated in a follow-up study.

DESIGN

Retrospective cohort.

SETTING

Acute care hospitals in South Carolina.

PARTICIPANTS

Participants were from the South Carolina Traumatic Brain Injury Follow-up Registry cohort of persons (N=2118) who were discharged from an acute care hospital in South Carolina and who participated in a year-1 follow-up interview.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Repeat TBI was defined by 2 isolated events of TBI in the same person at least 72 hours apart and recorded in hospital discharge or emergency department records from 1999 through 2005.

RESULTS

A Cox proportional hazards model was used to assess the associations of Pre-existing epilepsy/seizure disorder and drug/alcohol problem with time to repeat TBI, controlling for other confounding factors. There were 2099 persons with information on both Pre-existing conditions. There were 147 (7%) persons who sustained repeat TBI after recruitment to the follow-up study, and 82 (3.9%) had a previous TBI before recruitment for which they were seen in the hospital discharge or emergency department since 1996. The hazard of repeat TBI for persons with Pre-existing epilepsy/seizure disorder was 2.3 times the hazard for those without (hazard ratio, 2.3; 95% confidence interval, 1.2-4.4; P=.011). Pre-existing drug/alcohol problem was not associated with repeat TBI. Other variables significantly associated with repeat TBI were having a prior TBI, being insured under Medicaid, and having no insurance.

CONCLUSIONS

Pre-existing epilepsy/seizure disorder predisposes to repeat TBI. Appropriate management of seizure control may be an important strategy to allay the occurrence of repeat TBI.

A population-based study of risk of epilepsy after hospitalization for traumatic brain injury

PURPOSE

This study was undertaken to determine the risk of developing posttraumatic epilepsy (PTE) within 3 years after discharge among a population-based sample of older adolescents and adults hospitalized with traumatic brain injury (TBI) in South Carolina. It also identifies characteristics related to development of PTE within this population.

METHODS

A stratified random sample of persons aged 15 and older with TBI was selected from the South Carolina nonfederal hospital discharge dataset for four consecutive years. Medical records of recruits were reviewed, and they participated in up to three yearly follow-up telephone interviews.

RESULTS

The cumulative incidence of PTE in the first 3 years after discharge, after adjusting for loss to follow-up, was 4.4 per 100 persons over 3 years for hospitalized mild TBI, 7.6 for moderate, and 13.6 for severe. Those with severe TBI, posttraumatic seizures prior to discharge, and a history of depression were most at risk for PTE. This higher risk group also included persons with three or more chronic medical conditions at discharge.

DISCUSSION

These results raise the possibility that although some of the characteristics related to development of PTE are nonmodifiable, other factors, such as depression, might be altered with intervention. Further research into factors associated with developing PTE could lead to risk-reducing treatments.

Traumatic brain injury and forensic neuropsychology

As part of a special issue of The Journal of Head Trauma Rehabilitation, forensic neuropsychology is reviewed as it applies to traumatic brain injury (TBI) and other types of acquired brain injury in which clinical neuropsychologists and rehabilitation psychologists may be asked to render professional opinions about the neurobehavioral effects and outcome of a brain injury. The article introduces and overviews the topic focusing on the process of forensic neuropsychological consultation and practice as it applies to patients with TBI or other types of acquired brain injury. The emphasis is on the application of scientist-practitioner standards as they apply to legal questions about the status of a TBI patient and how best that may be achieved. This article introduces each topic area covered in this special edition.

A review of health-related quality of life in adult traumatic brain injury survivors in the context of combat veterans

Health-related quality of life (HRQOL) research in traumatic brain injury (TBI) populations is beginning to emerge in the literature. Because rehabilitation and reintegration issues are complex with TBI, especially with new combat veterans, it is critical that future HRQOL research be designed to consider these issues. Utilizing explicit definitions and a conceptual model of HRQOL can provide researchers with a holistic base on which to build interventions for successful patient outcomes. The conceptual model of HRQOL of C.E. Ferrans, J.J. Zerwic, J.E. Wilbur, and J.L. Larson (2005) is an exemplar model that presents clear definitions and encompasses domains of HRQOL relevant to TBI survivors and their families. This review was organized utilizing the model of HRQOL of Ferrans et al. The objective of this review was to identify gaps in current knowledge of HRQOL and TBI. These findings were then used to develop recommendations for future research with combat veterans who have sustained a TBI.

Predictors of psychological symptoms 1 year after traumatic brain injury: a population-based, epidemiological study

OBJECTIVE

To examine self-reported psychological symptoms 1 year after traumatic brain injury (TBI) in a population-based sample.

PARTICIPANTS

There were 1560 adults who had sustained TBI.

DESIGN

A telephone survey with questions about recent mood and anxiety symptoms, and diagnoses since TBI. Polychotomous logistic regression with 3 response levels (probable, possible, and no mood or anxiety symptoms) identified predictors of psychological symptoms.

RESULTS

Overall, 40% of participants had clinically significant mood or anxiety symptoms-12.6% with probable symptoms and 27.5% with possible symptoms. Main risk factors for probable symptoms included younger age, poor physical functioning, inadequate social support, and being a white woman. Other risk factors included being retired or unemployed, and pre-TBI psychiatric disorder or multiple concussions.

CONCLUSIONS

These findings suggest the need for careful screening of persons with TBI who are at particular risk of developing psychological symptoms, and persons who have recently sustained TBI and their families to be educated about the possibility of developing such symptoms.