The Application of the CRASH-CT Prognostic Model for Older Adults With Traumatic Brain Injury: A Population-Based Observational Cohort Study

Neuroworsening in the Emergency Department Is a Predictor of Traumatic Brain Injury Intervention and Outcome: A TRACK-TBI Pilot Study

INTRODUCTION

Neuroworsening may be a sign of progressive brain injury and is a factor for treatment of traumatic brain injury (TBI) in intensive care settings. The implications of neuroworsening for clinical management and long-term sequelae of TBI in the emergency department (ED) require characterization.

METHODS

Adult TBI subjects from the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot Study with ED admission and disposition Glasgow Coma Scale (GCS) scores were extracted. All patients received head computed tomography (CT) scan <24 h post-injury. Neuroworsening was defined as a decline in motor GCS at ED disposition (vs. ED admission). Clinical and CT characteristics, neurosurgical intervention, in-hospital mortality, and 3- and 6-month Glasgow Outcome Scale-Extended (GOS-E) scores were compared by neuroworsening status. Multivariable regressions were performed for neurosurgical intervention and unfavorable outcome (GOS-E ≤ 3). Multivariable odds ratios (mOR) with [95% confidence intervals] were reported.

RESULTS

In 481 subjects, 91.1% had ED admission GCS 13-15 and 3.3% had neuroworsening. All neuroworsening subjects were admitted to intensive care unit (vs. non-neuroworsening: 26.2%) and were CT-positive for structural injury (vs. 45.4%). Neuroworsening was associated with subdural (75.0%/22.2%), subarachnoid (81.3%/31.2%), and intraventricular hemorrhage (18.8%/2.2%), contusion (68.8%/20.4%), midline shift (50.0%/2.6%), cisternal compression (56.3%/5.6%), and cerebral edema (68.8%/12.3%; all p < 0.001). Neuroworsening subjects had higher likelihoods of cranial surgery (56.3%/3.5%), intracranial pressure (ICP) monitoring (62.5%/2.6%), in-hospital mortality (37.5%/0.6%), and unfavorable 3- and 6-month outcome (58.3%/4.9%; 53.8%/6.2%; all p < 0.001). On multivariable analysis, neuroworsening predicted surgery (mOR = 4.65 [1.02-21.19]), ICP monitoring (mOR = 15.48 [2.92-81.85], and unfavorable 3- and 6-month outcome (mOR = 5.36 [1.13-25.36]; mOR = 5.68 [1.18-27.35]).

CONCLUSIONS

Neuroworsening in the ED is an early indicator of TBI severity, and a predictor of neurosurgical intervention and unfavorable outcome. Clinicians must be vigilant in detecting neuroworsening, as affected patients are at increased risk for poor outcomes and may benefit from immediate therapeutic interventions.

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms

Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population’s aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

Prognosis in Moderate and Severe Traumatic Brain Injury: A Systematic Review of Contemporary Models and Validation Studies

Outcome prognostication in traumatic brain injury (TBI) is important but challenging due to heterogeneity of the disease. The aim of this systematic review is to present the current state-of-the-art on prognostic models for outcome after moderate and severe TBI and evidence on their validity. We searched for studies reporting on the development, validation or extension of prognostic models for functional outcome after TBI with Glasgow Coma Scale (GCS) ≤12 published between 2006-2018. Studies with patients age ≥14 years and evaluating a multi-variable prognostic model based on admission characteristics were included. Model discrimination was expressed with the area under the receiver operating characteristic curve (AUC), and model calibration with calibration slope and intercept. We included 58 studies describing 67 different prognostic models, comprising the development of 42 models, 149 external validations of 31 models, and 12 model extensions. The most common predictors were GCS (motor) score (n = 55), age (n = 54), and pupillary reactivity (n = 48). Model discrimination varied substantially between studies. The International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) models were developed on the largest cohorts (8509 and 10,008 patients, respectively) and were most often externally validated (n = 91), yielding AUCs ranging between 0.65-0.90 and 0.66-1.00, respectively. Model calibration was reported with a calibration intercept and slope for seven models in 53 validations, and was highly variable. In conclusion, the discriminatory validity of the IMPACT and CRASH prognostic models is supported across a range of settings. The variation in calibration, reflecting heterogeneity in reliability of predictions, motivates continuous validation and updating if clinical implementation is pursued.

Comparison of two simple models for prediction of short term mortality in patients after severe traumatic brain injury

INTRODUCTION

The subscale motor score of Glasgow Coma Scale (msGCS) and the Abbreviated Injury Score of head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim was to compare the prognostic performance of a HAIS-based prediction model including HAIS, pupil reactivity and age, and the reference prediction model including msGCS in emergency department (ED), pupil reactivity and age.

METHODS

Secondary analysis of a prospective epidemiological study including patients after severe TBI (HAIS > 3) with follow-up from the time of accident until 14 days or earlier death was performed in Switzerland. Performance of prediction, based on accuracy of discrimination [area under the receiver-operating curve (AUROC)], calibration (Hosmer-Lemeshow test) and validity (bootstrapping with 2000 repetitions to correct) for optimism of the two prediction models were investigated. A non-inferiority approach was performed and an a priori threshold for important differences was established.

RESULTS

The cohort included 808 patients [median age 56 {inter-quartile range (IQR) 33-71}, median motor part of GCS in ED 1 (1-6), abnormal pupil reactivity 29.0%] with a death rate of 29.7% at 14 days. The accuracy of discrimination was similar (AUROC HAIS-based prediction model: 0.839; AUROC msGCS-based prediction model: 0.826, difference of the 2 AUROC 0.013 (-0.007 to 0.037). A similar calibration was observed (Hosmer-Lemeshow X² 11.64, p = 0.168 vs. Hosmer-Lemeshow X² 8.66, p = 0.372). Internal validity of HAIS-based prediction model was high (optimism corrected AUROC: 0.837).

CONCLUSIONS

Performance of prediction for short-term mortality after severe TBI with HAIS-based prediction model was non-inferior to reference prediction model using msGCS as predictor.

Geriatric Traumatic Brain Injury: Epidemiology, Outcomes, Knowledge Gaps, and Future Directions

This review of the literature on traumatic brain injury (TBI) in older adults focuses on incident TBI sustained in older adulthood ("geriatric TBI") rather than on the separate, but related, topic of older adults with a history of earlier-life TBI. We describe the epidemiology of geriatric TBI, the impact of comorbidities and pre-injury function on TBI risk and outcomes, diagnostic testing, management issues, outcomes, and critical directions for future research. The highest incidence of TBI-related emergency department visits, hospitalizations, and deaths occur in older adults. Higher morbidity and mortality rates among older versus younger individuals with TBI may contribute to an assumption of futility about aggressive management of geriatric TBI. However, many older adults with TBI respond well to aggressive management and rehabilitation, suggesting that chronological age and TBI severity alone are inadequate prognostic markers. Yet there are few geriatric-specific TBI guidelines to assist with complex management decisions, and TBI prognostic models do not perform optimally in this population. Major barriers in management of geriatric TBI include under-representation of older adults in TBI research, lack of systematic measurement of pre-injury health that may be a better predictor of outcome and response to treatment than age and TBI severity alone, and lack of geriatric-specific TBI common data elements (CDEs). This review highlights the urgent need to develop more age-inclusive TBI research protocols, geriatric TBI CDEs, geriatric TBI prognostic models, and evidence-based geriatric TBI consensus management guidelines aimed at improving short- and long-term outcomes for the large and growing geriatric TBI population.