Is It Reliable to Predict the Outcome of Elderly Patients with Severe Traumatic Brain Injury Using the IMPACT Prognostic Calculator?

Predictive power of the eTBI score for 30 day outcome in elderly patients with traumatic brain Injury

Traumatic brain injury (TBI) is a common problem in elderly individuals, with significant morbidity and mortality. The elderly Traumatic Brain Injury (eTBI) score, a novel tool for predicting outcomes in elderly patients with TBI, has shown promising results in previous studies. This study aimed to validate the eTBI score in a larger cohort of elderly patients with TBI in the Middle East. We conducted a retrospective study on 337 TBI patients with a mean age of 73.04 ± 8.73, admitted to a tertiary care hospital between March 2021 and November 2022. Within 30 days of admission, the patients' conditions, including mortality and entering a vegetative state, were evaluated. The study population was split into three groups based on eTBI score: low, medium, and high risk; then patients were divided into two subgroups based on their Glasgow Outcome Scale (GOS ≤ 2, GOS > 2) in 30 days from hospital admission. Poor outcomes (mortality and entering a vegetative state) occurred in 24.3% of the study population. Within 30 days of hospital admission, 88% of low-risk patients experienced some degree of improvement, while 100% of high-risk patients died or fell into a vegetative state. In the medium-risk group, there was a significant correlation between unresponsive pupil (P = 0.006), initial GCS score (P = 0.003), need for a ventilator device (P = 0.015), need for surgical treatment (P = 0.031) and poor outcomes. Despite having a low sensitivity (21% vs. 57%), the eTBI score performed well in terms of accuracy (81% vs. 88%), specificity (100 vs. 98%), positive predictive value (100% vs. 90%), and negative predictive value (80% vs. 88%) for both eTBI ≤ 0 and eTBI ≤ 3 thresholds. The eTBI score is a reliable tool for predicting outcomes in elderly patients with TBI. This scoring system has a positive predictive value of 100% in the eTBI ≤ 0 group, which shows that 100% of the patients who are predicted by the eTBI score to have a poor outcome will indeed have a poor outcome. Patients in the high-risk group should be closely monitored and provided with intensive care, while those in the low-risk group can be reassured about their prognosis. The eTBI score can also be used in conjunction with other clinical factors to inform treatment decisions for patients in the medium-risk group.

Identifying factors predicting outcomes after major trauma in older patients: Prognostic systematic review and meta-analysis

INTRODUCTION

Trauma is the most common cause of morbidity and mortality in older people, and it is important to determine the predictors of outcomes after major trauma in older people.

METHODS

MEDLINE, Embase, and Web of Science were searched, and manual search of relevant papers since 1987 to February 2023 was performed. Random-effects meta-analyses were performed. The primary outcome of interest was mortality, and secondary outcomes were medical complications, length of stay, discharge destination, readmission, and intensive care requirement.

RESULTS

Among 6,064 studies in the search strategy, 136 studies qualified the inclusion criteria. Forty-three factors, ranging from demographics to patient factors, admission measurements, and injury factors, were identified as potential predictors. Mortality was the commonest outcome investigated, and increasing age was associated with increased risk of in-hospital mortality (odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03-1.07) along with male sex (OR, 1.40; 95% CI, 1.24-1.59). Comorbidities of heart disease (OR, 2.59; 95% CI, 1.41-4.77), renal disease (OR, 2.52; 95% CI, 1.79-3.56), respiratory disease (OR, 1.40; 95% CI, 1.09-1.81), diabetes (OR, 1.35; 95% CI, 1.03-1.77), and neurological disease (OR, 1.42; 95% CI, 0.93-2.18) were also associated with increased in-hospital mortality risk. Each point increase in the Glasgow Coma Scale lowered the risk of in-hospital mortality (OR, 0.85; 95% CI, 0.76-0.95), while each point increase in Injury Severity Score increased the risk of in-hospital mortality (OR, 1.07; 95% CI, 1.04-1.09). There were limited studies and substantial variability in secondary outcome predictors; however, medical comorbidities, frailty, and premorbid living condition appeared predictive for those outcomes.

CONCLUSION

This review was able to identify potential predictors for older trauma patients. The identification of these factors allows for future development of risk stratification tools for clinicians.

LEVEL OF EVIDENCE

Systematic Review and Meta-Analysis; Level III.

Guidelines for Neuroprognostication in Critically Ill Adults with Moderate-Severe Traumatic Brain Injury

BACKGROUND

Moderate-severe traumatic brain injury (msTBI) carries high morbidity and mortality worldwide. Accurate neuroprognostication is essential in guiding clinical decisions, including patient triage and transition to comfort measures. Here we provide recommendations regarding the reliability of major clinical predictors and prediction models commonly used in msTBI neuroprognostication, guiding clinicians in counseling surrogate decision-makers.

METHODS

Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, we conducted a systematic narrative review of the most clinically relevant predictors and prediction models cited in the literature. The review involved framing specific population/intervention/comparator/outcome/timing/setting (PICOTS) questions and employing stringent full-text screening criteria to examine the literature, focusing on four GRADE criteria: quality of evidence, desirability of outcomes, values and preferences, and resource use. Moreover, good practice recommendations addressing the key principles of neuroprognostication were drafted.

RESULTS

After screening 8125 articles, 41 met our eligibility criteria. Ten clinical variables and nine grading scales were selected. Many articles varied in defining "poor" functional outcomes. For consistency, we treated "poor" as "unfavorable". Although many clinical variables are associated with poor outcome in msTBI, only the presence of bilateral pupillary nonreactivity on admission, conditional on accurate assessment without confounding from medications or injuries, was deemed moderately reliable for counseling surrogates regarding 6-month functional outcomes or in-hospital mortality. In terms of prediction models, the Corticosteroid Randomization After Significant Head Injury (CRASH)-basic, CRASH-CT (CRASH-basic extended by computed tomography features), International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT)-core, IMPACT-extended, and IMPACT-lab models were recommended as moderately reliable in predicting 14-day to 6-month mortality and functional outcomes at 6 months and beyond. When using "moderately reliable" predictors or prediction models, the clinician must acknowledge "substantial" uncertainty in the prognosis.

CONCLUSIONS

These guidelines provide recommendations to clinicians on the formal reliability of individual predictors and prediction models of poor outcome when counseling surrogates of patients with msTBI and suggest broad principles of neuroprognostication.

Mortality of Surgically Treated Neurotrauma in Elderly Patients and the Development of a Prediction Score: Geriatric Neurotrauma Mortality Score

BACKGROUND

As the population worldwide is aging, the need for surgery in elderly patients with neurotrauma is increasing. The aim of this study was to compare the outcome of elderly patients undergoing surgery for neurotrauma with younger patients and to identify the risk factors for mortality.

METHODS

We retrospectively analyzed consecutive patients undergoing craniotomy or craniectomy for neurotrauma at our institution from 2012 to 2019. Patients were divided into two groups (≥70 years or <70 years) and compared. The primary outcome was the 30-day mortality rate. Potential risk factors for 30-day mortality were assessed in a uni- and multivariate regression model for both age groups, forming the basis of a 30-day mortality prediction score.

RESULTS

We included 163 consecutive patients (average age 57.98 ± 19.87 years); 54 patients were ≥70 years. Patients ≥70 years showed a significantly better median preoperative Glasgow Coma Scale (GCS) score compared with young patients (P < 0.001), and fewer pupil asymmetry (P = 0.001), despite having a higher Marshall score (P = 0.07) at admission. Multivariate regression analysis identified low pre- and postoperative GCS scores and the lack of prompt postoperative prophylactic low-molecular-weight heparin treatment as risk factors for 30-day mortality. Our score showed moderate accuracy in predicting 30-day mortality with an area under the curve of 0.76.

CONCLUSIONS

Elderly patients after neurotrauma present with a better GCS at admission despite having more severe radiographic injuries. Mortality and favorable outcome rates are comparable between the age groups.

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.

Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis

This review aimed to analyze the results of investigations that performed external validation or that compared prognostic models to identify the models and their variations that showed the best performance in predicting mortality, survival, and unfavorable outcome after severe traumatic brain injury. Pubmed, Embase, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, TROVE, and Open Grey databases were searched. A total of 1616 studies were identified and screened, and 15 studies were subsequently included for analysis after applying the selection criteria. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) models were the most externally validated among studies of severe traumatic brain injury. The results of the review showed that most publications encountered an area under the curve ≥ 0.70. The area under the curve meta-analysis showed similarity between the CRASH and IMPACT models and their variations for predicting mortality and unfavorable outcomes. Calibration results showed that the variations of CRASH and IMPACT models demonstrated adequate calibration in most studies for both outcomes, but without a clear indication of uncertainties in the evaluations of these models. Based on the results of this meta-analysis, the choice of prognostic models for clinical application may depend on the availability of predictors, characteristics of the population, and trauma care services.

Development and Verification of Prognostic Prediction Models for Patients with Brain Trauma Based on Coagulation Function Indexes

Objective

To assess the effect of adding coagulation indices to the currently existing prognostic prediction models of traumatic brain injury (TBI) in the prediction of outcome.

Methods

A total of 210 TBI patients from 2017 to 2019 and 131 TBI patients in 2020 were selected for development and internal verification of the new model. The primary outcomes include death at 14 days and Glasgow Outcome Score (GOS) at 6 months. The performance of each model is evaluated by means of discrimination (area under the curve (AUC)), calibration (Hosmer-Lemeshow (H-L) goodness-of-fit test), and precision (Brier score).

Results

The IMPACT Core model showed better prediction ability than the CRASH Basic model. Adding one coagulation index at a time to the IMPACT Core model, the new combined models IMPACT Core+FIB and IMPACT Core+APTT are optimal for the 6-month unfavorable outcome and 6-month mortality, respectively (AUC, 0.830 and 0.878). The new models were built based on the regression coefficients of the models. Internal verification indicated that for the prediction of 6-month unfavorable outcome and 6-month mortality, both the IMPACT Core+FIB model and the IMPACT Core+APTT model show better discrimination (AUC, 0.823 vs. 0.818 and 0.853 vs. 0.837), better calibration (HL, p = 0.114 and p = 0.317) and higher precision (Brier score, 0.148 vs. 0.141 and 0.147 vs. 0.164), respectively, than the original models.

Conclusion

Our research shows that the combination of the traumatic brain injury prognostic models and coagulation indices can improve the 6-month outcome prediction of patients with TBI.

Prediction of Outcome Based on Trauma and Injury Severity Score, IMPACT and CRASH Prognostic Models in Moderate-to-Severe Traumatic Brain Injury in the Elderly

Objectives:

This study aimed to evaluate the trauma and injury severity score (TRISS), IMPACT (international mission for prognosis and analysis of clinical trials), and CRASH (corticosteroid randomization after significant head injury) prognostic models for prediction of outcome after moderate-to-severe traumatic brain injury (TBI) in the elderly following road traffic accident.

Design:

This was a prospective observational study.

Materials and Methods:

This was a prospective observational study on 104 elderly trauma patients who were admitted to tertiary care hospital, over a consecutive period of 18 months from December 2016 to May 2018. On the day of admission, data were collected from each patient to compute the TRISS, IMPACT, and CRASH and outcome evaluation was prospectively done at discharge, 14^th day, and 6-month follow-up.

Results:

This study included 104 TBI patients with a mean age of 66.75 years and with a mortality rate of 32% and 45%, respectively, at discharge and at the end of 6 months. The predictive accuracies of the TRISS, CRASH (computed tomography), and IMPACT (core, extended, laboratory) were calculated using receiver operator characteristic (ROC) curves for the prediction of mortality. Best cutoff point for predicting mortality in elderly TBI patients using TRISS system was a score of ≤88 (sensitivity 94%, specificity of 80%, and area under ROC curve 0.95), similarly cutoff point under the CRASH at 14 days was score of >35 (100%, 80%, 0.958); for CRASH at 6 months, best cutoff point was at >84 (88%, 88%, 0.959); for IMPACT (core), it was >38 (88%, 93%, 0.976); for IMPACT (extended), it was >27 (91%, 89%, 0.968); and for IMPACT (lab), it was >41 (82%, 100%, 0.954). There were statistical differences among TRISS, CRASH (at 14 days and 6 months), and IMPACT (core, extended, lab) in terms of area under the ROC curve (P < 0.0001).

Conclusion:

IMPACT (core, extended) models were the strongest predictors of mortality in moderate-to-severe TBI when compared with the TRISS, CRASH, and IMPACT (lab) models.

Mortality and Outcome in Elderly Patients Undergoing Emergent or Elective Cranial Surgery

OBJECTIVE

Due to the aging population, the number of elderly patients in need of cranial surgery for various neurosurgical pathologies is growing. We sought to compare mortality and outcome of elderly patients undergoing cranial surgery with a younger population.

METHODS

This was a retrospective analysis of adult patients undergoing craniotomy or craniectomy for various indications. Patients were allocated to 4 age groups (<65 years, 65-74 years, 75-84 years, ≥85 years; groups 1-4, respectively). Primary outcome was 30-day mortality rate, whereas secondary outcome measurements were clinical outcome measured by the modified Rankin Scale score, morbidity (bleeding, infection, and thromboembolic complications), length of stay (LOS), and discharge location.

RESULTS

We included 838 consecutive patients. Overall, 30-day mortality was 5.0% (n = 42), showing significant difference between the groups (2.8%, 7.3%, 7.5%, and 22.7% groups 1-4, respectively; P < 0.001). Mortality remained statistically significantly different between the groups also after stratification for elective or emergent surgery. Cumulative 30-day mortality-free rate was significantly different between the groups as well (log rank test χ² = 24.58, P < 0.001). Elderly patients showed significantly greater rates of bleeding (P = 0.003), longer LOS (P < 0.001), more discharges to rehabilitation facilities (P = 0.008), and a trend toward worst modified Rankin Scale score at follow-up (P = 0.08). After multivariate regression analysis, age (≥75 years) and lower preoperative Glasgow Coma Scale score (<14) were significantly associated with greater mortality rates, whereas postoperative thrombosis prophylaxis was a protective factor for mortality.

CONCLUSIONS

In patients undergoing craniotomy or craniectomy, advanced age seems to be associated with greater mortality and bleeding rates, longer LOS, and more discharge to rehabilitation facilities.

Association of APOE ε4 with progressive hemorrhagic injury in patients with traumatic intracerebral hemorrhage

OBJECTIVE

The intracranial hematoma volume in patients with traumatic brain injury is a key parameter for the determination of the management approach and outcome. Apolipoprotein E (APOE) ε4 is reported to be a risk factor for larger hematoma volume, which might contribute to a poor outcome. However, whether APOE ε4 is related to progressive hemorrhagic injury (PHI), a common occurrence in the clinical setting, remains unclear. In this study, the authors aimed to investigate the association between the APOE genotype and occurrence of PHI.

METHODS

This prospective study included a cohort of 123 patients with traumatic intracerebral hemorrhage who initially underwent conservative treatment. These patients were assigned to the PHI or non-PHI group according to the follow-up CT scan. A polymerase chain reaction and sequencing method were carried out to determine the APOE genotype. Multivariate logistic regression analysis was applied to identify predictors of PHI.

RESULTS

The overall frequency of the alleles was as follows: E2/2, 0%; E2/3, 14.6%; E3/3, 57.8%; E2/4, 2.4%; E3/4, 22.8%; and E4/4, 2.4%. Thirty-four patients carried at least one allele of ε4. In this study 60 patients (48.8%) experienced PHI, and the distribution of the alleles was as follows: E2/2, 0%; E2/3, 5.7%; E3/3, 22.8%; E2/4, 2.4%; E3/4, 16.3%; and E4/4, 1.6%, which was significantly different from that in the non-PHI group (p = 0.008). Additionally, the late operation rate in the PHI group was significantly higher than that in the non-PHI group (24.4% vs 11.4%, p = 0.002). Multivariate logistic regression identified APOE ε4 (OR 5.14, 95% CI 2.40-11.62), an elevated international normalized ratio (OR 3.57, 95% CI 1.61-8.26), and higher glucose level (≥ 10 mmol/L) (OR 3.88, 95% CI 1.54-10.77) as independent risk factors for PHI. Moreover, APOE ε4 was not a risk factor for the coagulopathy and outcome of the patients with traumatic intracerebral hemorrhage.

CONCLUSIONS

The presence of APOE ε4, an elevated international normalized ratio, and a higher glucose level (≥ 10 mmol/L) are predictors of PHI. Additionally, APOE ε4 is not associated with traumatic coagulopathy and patient outcome.

IMPACT and CRASH prognostic models for traumatic brain injury: external validation in a South-American cohort

OBJECTIVE

To develop a robust prognostic model, the more diverse the settings in which the system is tested and found to be accurate, the more likely it will be generalisable to untested settings. This study aimed to externally validate the International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticosteroid Randomization after Significant Head Injury (CRASH) models for low-income and middle-income countries using a dataset of patients with severe traumatic brain injury (TBI) from the Benchmark Evidence from South American Trials: Treatment of Intracranial Pressure study and a simultaneously conducted observational study.

METHOD

A total of 550 patients with severe TBI were enrolled in the study, and 466 of those were included in the analysis. Patient admission characteristics were extracted to predict unfavourable outcome (Glasgow Outcome Scale: GOS<3) and mortality (GOS 1) at 14 days or 6 months.

RESULTS

There were 48% of the participants who had unfavourable outcome at 6 months and these included 38% who had died. The area under the receiver operating characteristic curve (AUC) values were 0.683-0.775 and 0.640-0.731 for the IMPACT and CRASH models respectively. The IMPACT CT model had the highest AUC for predicting unfavourable outcomes, and the IMPACT Lab model had the best discrimination for predicting 6-month mortality. The discrimination for both the IMPACT and CRASH models improved with increasing complexity of the models. Calibration revealed that there were disagreement between observed and predicted outcomes in the IMPACT and CRASH models.

CONCLUSION

The overall performance of all IMPACT and CRASH models was adequate when used to predict outcomes in the dataset. However, some disagreement in calibration suggests the necessity for updating prognostic models to maintain currency and generalisability.

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.

Mortality After Traumatic Brain Injury in Elderly Patients: A New Scoring System

BACKGROUND

Traumatic brain injury (TBI) remains a life-threatening condition characterized by growing incidence worldwide, particularly in the aging population, in which the primary goal of treatment appears to be avoidance of chronic institutionalization.

METHODS

To identify independent predictors of 30-day mortality or vegetative state in a geriatric population and calculate an intuitive scoring system, we screened 480 patients after TBI treated at a single department of neurosurgery over a 2-year period. We analyzed data of 214 consecutive patients aged ≥65 years, including demographics, medical history, cause and time of injury, neurologic state, radiologic reports, and laboratory results. A predictive model was developed using logistic regression modeling with a backward stepwise feature selection.

RESULTS

The median Glasgow Coma Scale (GCS) score on admission was 14 (interquartile range, 12-15), whereas the 30-day mortality or vegetative state rate amounted to 23.4%. Starting with 20 predefined features, the final prediction model highlighted the importance of GCS motor score (odds ratio [OR], 0.17; 95% confidence interval [CI], 0.09-0.32); presence of comorbid cardiac, pulmonary, or renal dysfunction or malignancy (OR, 2.86; 9 5% CI, 1.08-7.61); platelets ≤100 × 10⁹ cells/L (OR, 13.60; 95% CI, 3.33-55.49); and red blood cell distribution width coefficient of variation ≥14.5% (OR, 2.91; 95% CI, 1.09-7.78). The discovered coefficients were used for nomogram development. It was further simplified to facilitate clinical use. The proposed scoring system, Elderly Traumatic Brain Injury Score (eTBI Score), yielded similar performance metrics.

CONCLUSIONS

The eTBI Score is the first scoring system designed specifically for older adults. It could constitute a framework for clinical decision-making and serve as an outcome predictor. Its capability to stratify risk provides reliable criteria for assessing efficacy of TBI management.

Traumatic brain injured patients: primum non nocere

PURPOSE OF REVIEW

Traumatic brain injury (TBI) remains an unfortunately common disease with potentially devastating consequences for patients and their families. However, it is important to remember that it is a spectrum of disease and thus, a one 'treatment fits all' approach is not appropriate to achieve optimal outcomes. This review aims to inform readers about recent updates in prehospital and neurocritical care management of patients with TBI.

RECENT FINDINGS

Prehospital care teams which include a physician may reduce mortality. The commonly held value of SBP more than 90 in TBI is now being challenged. There is increasing evidence that patients do better if managed in specialized neurocritical care or trauma ICU. Repeating computed tomography brain 12 h after initial scan may be of benefit. Elderly patients with TBI appear not to want an operation if it might leave them cognitively impaired.

SUMMARY

Prehospital and neuro ICU management of TBI patients can significantly improve patient outcome. However, it is important to also consider whether these patients would actually want to be treated particularly in the elderly population.