Predictive Risk Factors for Early Seizures in Traumatic Brain Injury

Development of individualized risk assessment models for predicting post-traumatic epilepsy 1 and 2 years after moderate-to-severe traumatic brain injury: A traumatic brain injury model system study

OBJECTIVE

Although traumatic brain injury (TBI) and post-traumatic epilepsy (PTE) are common, there are no prospective models quantifying individual epilepsy risk after moderate-to-severe TBI (msTBI). We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2 years after msTBI.

METHODS

We used data from 6089 prospectively enrolled participants (≥16 years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2-year period post-injury. We performed a case-complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2-year seizure risk (Model 1). Then a 2-year seizure risk was assessed excluding the acute care variables (Model 2). In addition, we generated prognostic models predicting new/recurrent seizures during Year 2 post-msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity ≥.60, area under the receiver-operating characteristic curve (AUROC), and AUROC model performance through 5-fold cross-validation (CV).

RESULTS

Model 1 (73.8% men, 44.1 ± 19.7 years, 76.1% moderate TBI) had a model sensitivity = 76.00% and average AUROC = .73 ± .02 in 5-fold CV. Model 2 had a model sensitivity = 72.16% and average AUROC = .70 ± .02 in 5-fold CV. Model 3 had a sensitivity = 86.63% and average AUROC = .84 ± .03 in 5-fold CV. Model 4 had a sensitivity = 73.68% and average AUROC = .67 ± .03 in 5-fold CV. Cranial surgeries, acute care seizures, intracranial fragments, and traumatic hemorrhages were consistent predictors across all models. Demographic and mental health variables contributed to some models. Simulated, clinical examples model individual PTE predictions.

SIGNIFICANCE

Using information available, acute-care, and year-1 post-injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence-based PTE prognostication within a 2-year period. We developed interactive web-based tools for testing prediction model external validity among independent cohorts. Individualized PTE risk may inform clinical trial development/design and clinical decision support tools for this population.

Incidence and predictors of seizure-related injuries among epileptic patients undergoing follow-up treatment at public hospitals in Central Ethiopia

Seizure-related injuries represent a significant concern for both individuals with epilepsy and their caregivers. Compared to the general population, those diagnosed with epilepsy face a heightened risk of sustaining injuries. Despite this, there is a notable scarcity of data regarding seizure-related injuries among epileptic patients. This study aimed to evaluate the incidence of seizure-related injuries and identify their predictors among epileptic patients undergoing follow-up treatment at selected public hospitals in Central Ethiopia, in 2023. A prospective follow-up study was carried out in selected public hospitals in central Ethiopia. The study included epileptic patients aged ≥ 18 years who had not experienced any previous injury during follow-up treatment from January 1st, 2023, to December 31st, 2023. Data collection involved conducting interviews with participants using a structured questionnaire and reviewing patients' charts. Univariate analysis, multivariate, and regression analysis were performed to identify potential associations between variables and seizure-related injuries. Variables were deemed significantly associated with seizure-related injuries if they attained a p value of 0.05 with a 95% confidence interval. Out of the 561 participants, 265 (47.2%) experienced seizure-related injuries (95% CI 43.12, 51.38). The incidence density rate of seizure-related injuries among epileptic patients was 11.97 per 100 person-months of observation (95% CI 10.61, 13.50). In multivariate analysis, epileptic patients who had generalized tonic-clonic seizures (adjusted hazard ratio 1.4, 95% CI 1.07-1.84), comorbidities (adjusted hazard ratio 1.3, 95% CI 1.11-1.71), were on polytherapy drug regimens (adjusted hazard ratio 1.80, 95% CI 0.30-2.49), and consumed alcoholic drinks (adjusted hazard ratio 1.5, 95% CI 1.21-1.89) were identified as independent predictors of seizure-related injuries. The incidence rate of seizure-related injuries among epileptic patients was found to be significant. Risk factors identified included experiencing generalized tonic-clonic seizures, having at least one additional health condition, being on multiple medications, and consuming alcohol. To improve survival from injuries, targeted precautions for generalized tonic-clonic seizures, strict adherence to prescribed medication regimens, and avoiding alcohol consumption are recommended.

Knowledge and attitudes toward mild traumatic brain injury among patients and family members

Introduction

Mild traumatic brain injury (mTBI) is a prevalent health issue with significant effects on patients' lives. Understanding and attitudes toward mTBI among patients and their families can influence management and outcomes. This study aimed to assess knowledge and attitudes toward mTBI in these groups.

Methods

A cross-sectional study was conducted at Zhejiang Hospital from July 1, 2023, to September 30, 2023. Patients with mTBI and their family members participated. Data were collected via an online questionnaire covering demographic information and mTBI knowledge and attitudes. Knowledge scores ranged from 0 to 20 and attitude scores from 8 to 40. Multivariate logistic regression identified factors influencing these scores.

Results

A total of 573 valid questionnaires were analyzed (289 males, 50.44%; 284 females, 49.56%). Among respondents, 258 (45.03%) had experienced a concussion. Mean knowledge and attitude scores were 11.00 ± 2.75 and 27.78 ± 4.07, respectively. Monthly per capita income of 5,000-10,000 RMB was negatively associated with knowledge and attitude scores (β = 0.160, 95% CI: [3.245 to 0.210], P = 0.026). Middle school education decreased the likelihood of positive attitudes toward mTBI (OR = 0.378, 95% CI: [0.1630.874], P = 0.023). mTBI due to falls was associated with increased likelihood of positive attitudes (OR = 3.588, 95% CI: [1.274-10.111], P = 0.016).

Discussion

Significant gaps in knowledge and attitudes toward mTBI exist among patients and their families, influenced by income and education levels. Personal experience with mTBI from falls correlates with more positive attitudes. These findings highlight the need for targeted educational interventions to improve understanding and attitudes, ultimately enhancing patient care and management. Comprehensive, accessible mTBI education is crucial for fostering positive attitudes and better knowledge among patients and their families.

Medical malpractice and epidural hematomas: a retrospective analysis of 101 cases in the United States

Background

Neurosurgeons face particularly high rates of litigation compared to physicians in other fields. Malpractice claims are commonly seen after mismanagement of life-threatening medical emergencies, such as epidural haematomas. Due to the lack of legal analysis pertaining to this condition, the aim of this study is to identify risk factors associated with litigation in cases relating to the diagnosis and treatment of epidural haematomas.

Materials and methods

Westlaw Edge, an online database, was used to analyze malpractice cases related to epidural haematomas between 1986 and 2022. Information regarding plaintiff demographics, defendant specialty, reason for litigation, trial outcomes, and payouts for verdicts and settlements were recorded. Comparative analysis between cases that returned a jury verdict in favour of the plaintiff versus defendant was completed.

Results

A total of 101 cases were included in the analysis. Failure to diagnose was the most common reason for litigation (n = 64, 63.4%), followed by negligent care resulting in an epidural haematoma (n = 44, 43.6%). Spine surgery (n = 29, 28.7%), trauma (n = 28, 27.7%), and epidural injection/catheter/electrode placement (n = 21, 20.8%) were the primary causes of haematomas. Neurosurgeons (n = 18, 17.8%) and anesthesiologists (n = 17, 16.8%) were the two most common physician specialties cited as defendants. Most cases resulted in a jury verdict in favour of the defense (n = 54, 53.5%). For cases ending in plaintiff verdicts, the average payout was $3 621 590.45, while the average payment for settlements was $2 432 272.73.

Conclusion

Failure to diagnose epidural haematomas is the most common reason for malpractice litigation, with neurosurgeons and anesthesiologists being the most common physician specialties to be named as defendants. More than half of all cases returned a jury verdict in favour of the defense and, on average, settlements proved to be more cost-effective than plaintiff verdicts.

Incidence and predictors of early posttraumatic seizures among patients with moderate or severe traumatic brain injury in Northwest Ethiopia: an institution-based prospective study

BACKGROUND

Early posttraumatic seizure (PTS) is a well-known complication of traumatic brain injury (TBI) that can induce the development of secondary brain injuries, including increased intracranial pressure, brain death, and metabolic crisis which may result in worse outcomes. It is also a well-recognized risk factor for the development of late post-traumatic seizure and epilepsy. This study was aimed to assess the incidence and predictors of PTS among patients with moderate or severe TBI admitted to Debre Tabor Comprehensive Specialized Hospital, Northwest Ethiopia.

METHODS AND SETTING

An institutional-based prospective follow-up study was conducted on 402 patients with TBI admitted to the neurologic unit from June 1, 2022 to January 30, 2023. A systematic sampling technique was employed. The incidence rate of occurrence of early PTS was calculated. Both bivariable and multivariable Cox proportional hazard regression was performed. The strength of the association was measured using adjusted hazard ratios with a 95% confidence interval and p-values < 0.05.

RESULTS

The incidence rate of early PTS was 2.7 per 100 person-days observation. Early PTS was observed in 17.7% of TBI patients. Age 75 and above (AHR = 2.85, 95%CI: 1.58-5.39), severe TBI (AHR = 2.06, 95%CI: 1.03-3.71), epidural hematoma (AHR = 2.4, 95% CI: 1.28-4.57), brain contusion (AHR = 2.6, 95%CI: 1.07-4.09), surgical intervention (AHR = 1.7, 95%CI: 1.03-3.82), posttraumatic amnesia (AHR = 1.99, 95%CI: 1.08-3.48), history of comorbidities (AHR = 1.56, 95%CI: 1.08-3.86), and history of alcohol abuse (AHR = 3.1, 95%CI: 1.89-5.23) were potential predictors of early PTS.

CONCLUSION

The incidence of early PTS was high. Since, early PTS can worsen secondary brain damage, knowing the predictors helps to provide an effective management plan for patients likely to develop early PTS and improve their outcome.

Seizure Prophylaxis in Young Patients Following Traumatic Brain Injury

Introduction

Phenytoin is one of the commonly used anti.seizure medications in nontraumatic seizures. However, its utility and safety in young patients with traumatic brain injury (TBI) for the prevention of early-onset seizures (EOS) are debatable. We sought to explore the use of phenytoin as a seizure prophylaxis following TBI. We hypothesized that administering phenytoin is not effective in preventing EOS after TBI.

Methods

This was a retrospective observational study conducted on adult TBI patients. EOS was defined as a witnessed seizure within a week postinjury. Data were compared as phenytoin versus no-phenytoin use, EOS versus no-EOS, and among TBI severity groups.

Results

During 1 year, 639 TBI patients were included with a mean age of 32 years; of them, 183 received phenytoin as seizure prophylaxis, and 453 received no prophylaxis medication. EOS was documented in 13 (2.0%) patients who received phenytoin, and none had EOS among the nonphenytoin group. The phenytoin group was more likely to have a higher Marshall Score (P = 0.001), lower Glasgow Coma Scale (GCS) (P = 0.001), EOS (P = 0.001), and higher mortality (P = 0.001). Phenytoin was administrated for 15.2%, 43.2%, and 64.5% of mild, moderate, and severe TBI patients, respectively. EOS and no-EOS groups were comparable for age, gender, mechanism of injury, GCS, Marshall Score, serum phenytoin levels, liver function levels, hospital stay, and mortality. Multivariable logistic regression analysis showed that low serum albumin (odds ratio [OR] 0.81; 95% confidence interval [CI] 0.676.0.962) and toxic phenytoin level (OR 43; 95% CI 2.420.780.7) were independent predictors of EOS.

Conclusions

In this study, the prophylactic use of phenytoin in TBI was ineffective in preventing EOS. Large-scale matched studies and well-defined hospital protocols are needed for the proper utility of phenytoin post-TBI.

Establishment and validation of PTE prediction model in patients with cerebral contusion

Post-traumatic epilepsy (PTE) is an important cause of poor prognosis in patients with cerebral contusions. The primary purpose of this study is to evaluate the high-risk factors of PTE by summarizing and analyzing the baseline data, laboratory examination, and imaging features of patients with a cerebral contusion, and then developing a Nomogram prediction model and validating it. This study included 457 patients diagnosed with cerebral contusion who met the inclusion criteria from November 2016 to November 2019 at the Qinghai Provincial People's Hospital. All patients were assessed for seizure activity seven days after injury. Univariate analysis was used to determine the risk factors for PTE. Significant risk factors in univariate analysis were selected for binary logistic regression analysis. P < 0.05 was statistically significant. Based on the binary logistic regression analysis results, the prediction scoring system of PTE is established by Nomogram, and the line chart model is drawn. Finally, external validation was performed on 457 participants to assess its performance. Univariate and binary logistic regression analyses were performed using SPSS software, and the independent predictors significantly associated with PTE were screened as Contusion site, Chronic alcohol use, Contusion volume, Skull fracture, Subdural hematoma (SDH), Glasgow coma scale (GCS) score, and Non late post-traumatic seizure (Non-LPTS). Based on this, a Nomogram model was developed. The prediction accuracy of our scoring system was C-index = 98.29%. The confidence interval of the C-index was 97.28% ~ 99.30%. Internal validation showed that the calibration plot of this model was close to the ideal line. This study developed and verified a highly accurate Nomogram model, which can be used to individualize PTE prediction in patients with a cerebral contusion. It can identify individuals at high risk of PTE and help us pay attention to prevention in advance. The model has a low cost and is easy to be popularized in the clinic. This model still has some limitations and deficiencies, which need to be verified and improved by future large-sample and multicenter prospective studies.

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.

Risk Factors and Prognosis of Early Posttraumatic Seizures in Moderate to Severe Traumatic Brain Injury

IMPORTANCE

Early posttraumatic seizures (EPS) that may occur following a traumatic brain injury (TBI) are associated with poorer outcomes and development of posttraumatic epilepsy (PTE).

OBJECTIVE

To evaluate risk factors for EPS, associated morbidity and mortality, and contribution to PTE.

DESIGN, SETTING, AND PARTICIPANTS

Data were collected from an Australian registry-based cohort study of adults (age ≥18 years) with moderate to severe TBI from January 2005 to December 2019, with 2-year follow-up. The statewide trauma registry, conducted on an opt-out basis in Victoria (population 6.5 million), had 15 152 patients with moderate to severe TBI identified via Abbreviated Injury Scale (AIS) head severity score, with an opt-out rate less than 0.5% (opt-out n = 136).

MAIN OUTCOMES AND MEASURES

EPS were identified via International Statistical Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes recorded after the acute admission. Outcome measures also included in-hospital metrics, 2-year outcomes including PTE, and post-discharge mortality. Adaptive least absolute shrinkage and selection operator (LASSO) regression was used to build a prediction model for risk factors of EPS.

RESULTS

Among the 15 152 participants (10 457 [69%] male; median [IQR] age, 60 [35-79] y), 416 (2.7%) were identified with EPS, including 27 (0.2%) with status epilepticus. Significant risk factors on multivariable analysis for developing EPS were younger age, higher Charlson Comorbidity Index, TBI sustained from a low fall, subdural hemorrhage, subarachnoid hemorrhage, higher Injury Severity Score, and greater head injury severity, measured using the AIS and Glasgow Coma Score. After adjustment for confounders, EPS were associated with increased ICU admission and ICU length of stay, ventilation and duration, hospital length of stay, and discharge to inpatient rehabilitation rather than home, but not in-hospital mortality. Outcomes in TBI admission survivors at 24 months, including mortality (relative risk [RR] = 2.14; 95% CI, 1.32-3.46; P = .002), development of PTE (RR = 2.91; 95% CI, 2.22-3.81; P < .001), and use of antiseizure medications (RR = 2.44; 95% CI, 1.98-3.02; P < .001), were poorer for cases with EPS after adjustment for confounders. The prediction model for EPS had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.66-0.79), sensitivity of 66%, and specificity of 73% in the validation set.

DISCUSSION

We identified important risk factors for EPS following moderate to severe TBI. Early posttraumatic seizures were associated with longer ICU and hospital admissions, ICU ventilation, and poorer 24-month outcomes including mortality and development of PTE.

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.

The Use of Rotterdam CT Score for Prediction of Outcomes in Pediatric Traumatic Brain Injury Patients Admitted to Emergency Service

INTRODUCTION

Rotterdam CT score for prediction of outcome in traumatic brain injury is widely used for patient evaluation. The data on the assessment of pediatric traumatic brain injury patients with the Rotterdam scale in our country are still limited. In this study, we aimed to evaluate the use of the Rotterdam scale on pediatric trauma patients in our country and assess its relationship with lesion type, location and severity, trauma type, and need for surgery.

METHODS

A total of 229 pediatric patients admitted to the emergency service due to head trauma were included in our study. Patients were evaluated in terms of age, gender, Glasgow Coma Scale (GCS), initial and follow-up Rotterdam scale scores, length of stay, presence of other traumas, seizures, antiepileptic drug use, need for surgical necessity, and final outcome.

RESULTS

A total of 229 patients were included in the study, and the mean age of the patients was 95.8 months. Of the patients, 87 (38%) were girls and 142 (62%) were boys. Regarding GCS at the time of admission, 59% (n = 135) of the patients had mild (GCS = 13-15), 30.6% (n = 70) had moderate (GCS = 9-12), and 10.5% (n = 24) had severe (GCS < 9) head trauma. The mean Rotterdam scale score was calculated as 1.51 (ranging from 1 to 3) for mild, 2.22 (ranging from 1 to 4) for moderate, and 4.33 (ranging from 2 to 6) for severe head trauma patients. Rotterdam scale score increases significantly as the degree of head injury increases (p < 0.001).

DISCUSSION

With the adequate use of GCS and cerebral computed tomography imaging, pediatric patients with a higher risk of mortality and need for surgery can be predicted. We recommend the follow-up of pediatric traumatic brain injury patients with repeated CT scans to observe alterations in Rotterdam CT scores, which may be predictive for the need for surgery and intensive care.