Predictors of Hospital Mortality and the Related Burden of Disease in Severe Traumatic Brain Injury: A Prospective Multicentric Study in Brazil

Predictors of mortality and rehabilitation location in adults with prolonged coma following traumatic brain injury

INTRODUCTION

Traumatic brain injury (TBI) is a leading cause of death and disability, often resulting in prolonged coma and disordered consciousness. There are currently gaps in understanding the factors affecting rehabilitation location and outcome after TBI.

OBJECTIVE

To identify the impact of demographics, comorbidities, and complications on discharge disposition in adults with prolonged coma following TBI.

DESIGN

Retrospective cohort study.

SETTING

Tertiary care hospitals and trauma centers in the United States.

PARTICIPANTS

Patients 18 years of age or older with TBI and prolonged coma during the years 2008 to 2015.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Demographics, clinical injury data, comorbidities, and complications were collected, and odds ratios (ORs) and descriptive analysis were calculated for mortality, long-term rehabilitation, and home discharge without services.

RESULTS

A total of 6929 patients with TBI and prolonged coma were included in the final analysis; 3318 (47.9%) were discharged to rehabilitation facilities, 1859 (26.8%) died, and 1752 (25.3%) were discharged home. Older patients and those with higher injury severity scores had significantly higher ORs for mortality and rehab discharge. A total of 58.3% of patients presented with at least one comorbidity. Non-White ethnicities and self-pay/uninsured patients were significantly less likely to be discharged to a rehab facility. Furthermore, comorbidities including congestive heart failure (CHF) and diabetes were associated with a significantly increased OR for mortality and rehab discharge compared to home discharge without services.

CONCLUSIONS

Comorbidities, age, and injury severity were the most significant risk factors for increased mortality and acute rehab discharge. Maximizing the treatment of comorbidities including CHF and diabetes has the potential to decrease mortality and adverse outcomes following TBI with prolonged coma.

Which curve is better? A comparative analysis of trauma scoring systems in a South Asian country

Objectives

A diverse set of trauma scoring systems are used globally to predict outcomes and benchmark trauma systems. There is a significant potential benefit of using these scores in low and middle-income countries (LMICs); however, its standardized use based on type of injury is still limited. Our objective is to compare trauma scoring systems between neurotrauma and polytrauma patients to identify the better predictor of mortality in low-resource settings.

Methods

Data were extracted from a digital, multicenter trauma registry implemented in South Asia for a secondary analysis. Adult patients (≥18 years) presenting with a traumatic injury from December 2021 to December 2022 were included in this study. Injury Severity Score (ISS), Trauma and Injury Severity Score (TRISS), Revised Trauma Score (RTS), Mechanism/GCS/Age/Pressure score and GCS/Age/Pressure score were calculated for each patient to predict in-hospital mortality. We used receiver operating characteristic curves to derive sensitivity, specificity and area under the curve (AUC) for each score, including Glasgow Coma Scale (GCS).

Results

The mean age of 2007 patients included in this study was 41.2±17.8 years, with 49.1% patients presenting with neurotrauma. The overall in-hospital mortality rate was 17.2%. GCS and RTS proved to be the best predictors of in-hospital mortality for neurotrauma (AUC: 0.885 and 0.874, respectively), while TRISS and ISS were better predictors for polytrauma patients (AUC: 0.729 and 0.722, respectively).

Conclusion

Trauma scoring systems show differing predictability for in-hospital mortality depending on the type of trauma. Therefore, it is vital to take into account the region of body injury for provision of quality trauma care. Furthermore, context-specific and injury-specific use of these scores in LMICs can enable strengthening of their trauma systems.

Level of evidence

Level III.

Is transcranial direct current stimulation (tDCS) effective to improve cognition and functionality after severe traumatic brain injury? A perspective article and hypothesis

Severe traumatic brain injury (sTBI) is an important cause of disability and mortality and affects people of all ages. Current scientific evidence indicates that motor dysfunction and cognitive impairment are the main limiting factors in patients with sTBI. Transcranial direct current stimulation (tDCS) seems to be a good therapeutic option, but when it comes to patients with sTBI, the results are inconclusive, and some protocols have not yet been tested. In addition, there is still a lack of information on tDCS-related physiological mechanisms, especially during the acute phase. In the present study, based on current evidence on tDCS mechanisms of action, we hypothesized that performing tDCS sessions in individuals with sTBI, especially in the acute and subacute phases, together with conventional therapy sessions, could improve cognition and motor function in this population. This hypothesis presents a new possibility for treating sTBI, seeking to elucidate the extent to which early tDCS may affect long-term clinical outcomes.

Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes.

METHODS

We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies.

RESULTS

ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms.

CONCLUSION

ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.

Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients

BACKGROUND

 Pupil reactivity and the Glasgow Coma Scale (GCS) score are the most clinically relevant information to predict the survival of traumatic brain injury (TBI) patients.

OBJECTIVE

 We evaluated the accuracy of the GCS-Pupil score (GCS-P) as a prognostic index to predict hospital mortality in Brazilian patients with severe TBI and compare it with a model combining GCS and pupil response with additional clinical and radiological prognostic factors.

METHODS

 Data from 1,066 patients with severe TBI from 5 prospective studies were analyzed. We determined the association between hospital mortality and the combination of GCS, pupil reactivity, age, glucose levels, cranial computed tomography (CT), or the GCS-P score by multivariate binary logistic regression.

RESULTS

 Eighty-five percent (n = 908) of patients were men. The mean age was 35 years old, and the overall hospital mortality was 32.8%. The area under the receiver operating characteristic curve (AUROC) was 0.73 (0.70-0.77) for the model using the GCS-P score and 0.80 (0.77-0.83) for the model including clinical and radiological variables. The GCS-P score showed similar accuracy in predicting the mortality reported for the patients with severe TBI derived from the International Mission for Prognosis and Clinical Trials in TBI (IMPACT) and the Corticosteroid Randomization After Significant Head Injury (CRASH) studies.

CONCLUSION

 Our results support the external validation of the GCS-P to predict hospital mortality following a severe TBI. The predictive value of the GCS-P for long-term mortality, functional, and neuropsychiatric outcomes in Brazilian patients with mild, moderate, and severe TBI deserves further investigation.

Traumatic Brain Injury in Select Low- and Middle-Income Countries: A Narrative Review of the Literature

Low- and middle-income countries (LMICs) experience the majority of traumatic brain injuries (TBIs), yet few studies have examined the epidemiology and management strategies of TBI in LMICs. The objective of this narrative review is to discuss the epidemiology of TBI within LMICs, describe the adherence to Brain Trauma Foundation (BTF) guidelines for the management of severe TBI in LMICs, and document TBI management strategies currently used in LMICs. Articles from January 1, 2009 to September 30, 2021 that included patients with TBI greater than 18 years of age in low-, low middle-, and high middle-income countries were queried in PubMed. Search results demonstrated that TBI in LMICs mostly impacts young males involved in road traffic accidents. Within LMICs there are a myriad of approaches to managing TBI with few randomized controlled trials performed within LMICs to evaluate those interventions. More studies are needed in LMICs to establish the effectiveness and appropriateness of BTF guidelines for managing TBI and to help identify methods for managing TBI that are appropriate in low-resource settings. The problem of limited pre- and post-hospital care is a bigger challenge that needs to be considered while addressing management of TBI in LMICs.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries

BACKGROUND

Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches.

OBJECTIVE

To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings.

METHODS

We used the Duke University Medical Center National Trauma Data Bank and Mulago National Referral Hospital (MNRH) registry to predict in-hospital mortality for the HIC and LMIC settings, respectively. Six ML models were built on each data set, and the best model was chosen through nested cross-validation. The CRASH and IMPACT models were externally validated on the MNRH database.

RESULTS

ML models built on National Trauma Data Bank (n = 5393, 84 predictors) demonstrated an area under the receiver operating curve (AUROC) of 0.91 (95% CI: 0.85-0.97) while models constructed on MNRH (n = 877, 31 predictors) demonstrated an AUROC of 0.89 (95% CI: 0.81-0.97). Direct comparison with CRASH and IMPACT models showed significant improvement of the proposed LMIC models regarding AUROC (P = .038).

CONCLUSION

We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.

Survival status and predictors of mortality among traumatic brain injury patients in an Ethiopian hospital: A retrospective cohort study

INTRODUCTION

Traumatic brain injury is a major global public health problem causing substantial mortality among the adult population. Hence, this study aimed to determine the predictors of mortality among adult traumatic brain injury patients in Felegehiwot Comprehensive Specialized Hospital in Northwest Ethiopia during 2020.

METHODS

A retrospective cohort study was conducted at Felegehiwot Comprehensive Specialized Hospital using anonymized patient data obtained from chart review. Descriptive statistics were used to summarise the patient characteristics. The Kaplan-Meier survival curve and log-rank test were used to test for differences in survival status among groups. The Cox proportional hazards regression model was used at the 5% level of significance to determine the net effect of each explanatory variable on time to death.

RESULTS

In total, 338 patients aged ≥15 years and diagnosed with traumatic brain injury were included in the analysis. Among these patients, 103 (30.45%) died, giving a crude death rate of 25.53 per 1000 (95% CI: 21.05-30.98) person-days of follow-up. The overall median survival time was 44 days. The independent predictors of mortality after diagnosis of traumatic brain injury were admission Glasgow coma scale score ≤ 8 (adjusted hazard ratio (AHR): 4.85; 95% confidence interval (CI): 1.73-13.62), bilateral non-reactive pupils at admission (AHR: 2.00 (95% CI: 1.10-3.71), elevated systolic blood pressure at admission (AHR: 0.31; 95% CI:0.11-0.86), elevated diastolic blood pressure at admission (AHR: 3.54; 95% CI: 1.33-9.43), and haematoma evacuation (AHR: 0.42; 95% CI: 0.16-0.90).

DISCUSSION

The Survival status of traumatic brain injury patients was relatively low in this study. Glasgow coma scale score, bilateral non-reactive pupils, and elevated blood pressure were significant predictors of mortality. Further prospective follow-up studies that include residence and occupation are recommended.

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.

Traumatic brain injury hospital incidence in Brazil: an analysis of the past 10 years

Objetivo

Caracterizar os aspectos demográficos e sociais e o ônus econômico do traumatismo craniencefálico no sistema público de saúde brasileiro na última década.

Métodos

Analisaram-se os dados provenientes da base de dados do Departamento de Informática do Sistema Único de Saúde referentes ao período entre janeiro de 2008 e dezembro de 2019.

Resultados

Entre 2008 e 2019 ocorreram, em média, no Brasil, 131.014,83 internações por traumatismo craniencefálico ao ano, com incidência de 65,54 por 100 mil habitantes. Deve-se salientar a elevada incidência de traumatismo craniencefálico em adultos idosos (acima de 70 anos), acompanhada de altas taxas de mortalidade. Além disso, há também elevada incidência de traumatismo craniencefálico em adultos jovens (20 a 29 anos e 30 a 39 anos). Os dados aqui apresentados demonstram uma proporção de traumatismos craniencefálicos de 3,6 homens/mulheres.

Conclusão

Embora se acredite que os dados apresentados subestimem a incidência e mortalidade associadas com o traumatismo craniencefálico no Brasil, este estudo pode ajudar na implantação de futuras estratégias de promoção da saúde para a população brasileira e mundial, com o objetivo de diminuir a incidência, a mortalidade e os custos do traumatismo craniencefálico.

Polytrauma Is Associated with Increased Three- and Six-Month Disability after Traumatic Brain Injury: A TRACK-TBI Pilot Study

Polytrauma and traumatic brain injury (TBI) frequently co-occur and outcomes are routinely measured by the Glasgow Outcome Scale-Extended (GOSE). Polytrauma may confound GOSE measurement of TBI-specific outcomes. Adult patients with TBI from the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study had presented to a Level 1 trauma center after injury, received head computed tomography (CT) within 24 h, and completed the GOSE at 3 months and 6 months post-injury. Polytrauma was defined as an Abbreviated Injury Score (AIS) ≥3 in any extracranial region.

Univariate regressions were performed using known GOSE clinical cutoffs. Multi-variable regressions were performed for the 3- and 6-month GOSE, controlling for known demographic and injury predictors. Of 361 subjects (age 44.9 ± 18.9 years, 69.8% male), 69 (19.1%) suffered polytrauma. By Glasgow Coma Scale (GCS) assessment, 80.1% had mild, 5.8% moderate, and 14.1% severe TBI. On univariate logistic regression, polytrauma was associated with increased odds of moderate disability or worse (GOSE ≤6; 3 month odds ratio [OR] = 2.57 [95% confidence interval (CI): 1.50-4.41; 6 month OR = 1.70 [95% CI: 1.01-2.88]) and death/severe disability (GOSE ≤4; 3 month OR = 3.80 [95% CI: 2.03-7.11]; 6 month OR = 3.33 [95% CI: 1.71-6.46]). Compared with patients with isolated TBI, more polytrauma patients experienced a decline in GOSE from 3 to 6 months (37.7 vs. 24.7%), and fewer improved (11.6 vs. 22.6%). Polytrauma was associated with greater univariate ordinal odds for poorer GOSE (3 month OR = 2.79 [95% CI: 1.73-4.49]; 6 month OR = 1.73 [95% CI: 1.07-2.79]), which was conserved on multi-variable ordinal regression (3 month OR = 3.05 [95% CI: 1.76-5.26]; 6 month OR = 2.04 [95% CI: 1.18-3.42]). Patients with TBI with polytrauma are at greater risk for 3- and 6-month disability compared with those with isolated TBI. Methodological improvements in assessing TBI-specific disability, versus disability attributable to all systemic injuries, will generate better TBI outcomes assessment tools.

Survival and Functional Outcomes at Discharge After Traumatic Brain Injury in Children versus Adults in Resource-Poor Setting

BACKGROUND

More than 90% of trauma mortality occurs in low- and middle-income countries, especially in sub-Saharan Africa. Head injury is the primary driver of trauma mortality in the prehospital and in-hospital setting.

METHODS

An observational study was performed on patients presenting with traumatic brain injury (TBI) from October 2016 through May 2017 at Kamuzu Central Hospital, Malawi. Bivariate analysis and logistic regression were performed to determine the odds of favorable functional outcomes and mortality after controlling for significant covariates.

RESULTS

Of the 356 patients with TBI, 72 (20.2%) were children <18 years of age. Males comprised 202 (87.1%) and 46 (63.9%) of the adult and pediatric cohorts, respectively. Motor vehicle crash was the leading etiology in adults and children. There was no significant difference between adult and pediatric Glasgow Coma Scale score on admission, 10.8 ± 3.9 versus 10.9 ± 3.5, respectively (P = 0.8). More adult (n = 76, 32.3%) than pediatric (n = 13, 18.1%) patients died. On multivariable analysis, pediatric patients were more likely to have a favorable outcome defined by a Glasgow Outcome Scale of good recovery or moderate disability (odds ratio 3.70, 95% confidence interval 1.22-11.17, P = 0.02) and were less likely to die after TBI (odds ratio 0.29, 95% confidence interval 0.09-0.93, P = 0.04).

CONCLUSIONS

We show a survival advantage and better functional outcomes in children following TBI. This may be attributable to increased resiliency to TBI in children or the prioritization of children in a resource-poor environment. Investments in neurosurgical care following TBI are needed to improve outcomes.

Baseline Predictors of Survival, Neurological Recovery, Cognitive Function, Neuropsychiatric Outcomes, and Return to Work in Patients after a Severe Traumatic Brain Injury: an Updated Review

Introduction

Severe traumatic brain injury (sTBI) is a common cause of death and disability worldwide, with long-term squeal among survivors that include cognitive deficits, psychosocial and neuropsychiatric dysfunction, failure to return to pre-injury levels of work, school and inter-personal relationships, and overall reduced quality of and satisfaction with life.

Aim

The aim of this work is to review the current literature on baseline predictors of outcomes in adults post sTBI.

Method

Most of available literature on baseline predictors of outcomes in adults post sTBI were reviewed and summarized in this work.

Results

Currently, a sizeable number of composite predictors of mortality and overall function exists; however, these instruments tend to over-estimate poor outcomes and fail to address issues like cognition, psychosocial/ neuropsychiatric dysfunction, and return to work or school.

Conclusion

This article reviews currently-identified predictors of all these outcomes.