Predictors of In-Hospital Mortality and Years of Life Lost in Patients with Severe Traumatic Brain Injury: A Retrospective Study in the Metropolitan Region of Vitória, Brazil

Predictors of In-Hospital Mortality and Years of Life Lost in Patients with Severe Traumatic Brain Injury: A Retrospective Study in the Metropolitan Region of Vitória, Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of In-Hospital Mortality and Years of Life Lost in Patients with Severe Traumatic Brain Injury: A Retrospective Study in the Metropolitan Region of Vitória, Brazil WALTER GOMES DA SILVA FILHO, RODRIGO FREIRE DE ALMEIDA, JESSICA VAZ GONÇALVES, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4095458/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Traumatic Brain Injury (TBI) poses a notable incidence in both developed countries and emerging economies worldwide. Each year, approximately 70 million people are affected by TBI, and around 11% of these events are classified as severe. Data from the Global Burden of Disease (GBD) in 2019 revealed that Brazil accounts for 1.1% of all moderate/severe TBI incidents worldwide. Within this context, falls stand out as the primary cause, representing 67% of cases. Analyzing the statistics at the state level, it is observed that Espírito Santo is responsible for 14% of all TBI incidents in Brazil, with falls also being the main cause, at a percentile of 67%. The discovery of predictors of TBI mortality is crucial to support intervention and prognosis strategies, potentially guiding public health policies. Due to the scarcity of studies in this area, our objective was to identify predictors of mortality in severe TBI and analyze the burden of TBI in the metropolitan region of Vitória, Espírito Santo. We conducted a retrospective observational cohort study at a trauma reference hospital in the state, including all individuals diagnosed with severe TBI from 2019 to 2022. The dependent variable analyzed was the outcome of hospital mortality. After applying the inclusion criteria, 863 individuals were included in the study, of which 450 (52.14%) had a fatal outcome, and 413 (47.86%) survived. The mean age was 48.67 years, with 82.5% being male. Falls (55.56%) were the leading cause of death in the population. The final binomial logistic regression model indicated that age up to 59 years, time up to 10 days in orotracheal intubation (OTI), and the type of trauma from falls were variables with notable predictive power for severe TBI-related mortality in the population. The calculation of Years of Life Lost (YLLs) revealed that TBI had an accumulated impact of 10,870.23 years of life lost prematurely in the study population. This is the first retrospective study to analyze predictive variables and the burden of TBI in the Greater Vitória region. However, it is important to note that unanalyzed factors may influence outcomes, such as the sample restriction to patients from a single reference hospital in the region, potentially limiting the understanding of variations in epidemiological profiles and adjacent health services. Epidemiology Traumatic Brain Injury Predictors Mortality Years of Life Lost TBI Burden. BACKGROUND Traumatic brain injury (TBI) poses a significant challenge to global health, with notable incidences in developed countries and emerging economies worldwide (NAJEM et al., 2018). Each year, approximately 70 million people are affected by TBI, with approximately 11% of these events classified as severe (DEWAN et al., 2018). In the United States, for example, 10% of cases are considered severe (CAPIZZI et al., 2020), with a staggering estimate of up to 500 cases per 100,000 young adults under 45 years in the United States and Europe (CONDE et al., 2020). In Brazil, between 2008 and 2019, the average annual incidence of hospitalizations exceeded 131,000, with a hospital mortality rate exceeding 30% for severe cases. TBI is the fifth leading cause of death among individuals aged 15 to 29 in Brazil and the third among those aged 30 to 44; TBI is a significant concern, particularly in traffic-related incidents (ALKHAIBARY et al., 2021). The southeastern region had the highest absolute incidence, totaling 648,447 cases during the period (CARTERI et al., 2020). The economic impact of TBI is substantial. In the United States, the direct medical care cost is estimated at approximately $95 million, equivalent to $1.67 million per 100,000 people. In Europe, TBIs account for 37% of all deaths from traumatic injuries, resulting in a financial impact of €22,907 million in 2010 (MAGALHÃES et al., 2022). In Brazil, healthcare costs exceeded US$43,000,000.00, with an average cost per admission of US$327.68 (CARTERI et al., 2020). Despite the high incidence of TBI, epidemiological data are limited, especially in Brazil. The Brazilian Ministry of Health emphasizes that, like other low-income countries, morbidity and mortality from TBI are not systematically recorded. This information gap makes the epidemiological situation of TBI in Brazil even more concerning, given that this issue is one of the main causes of death and disability in the country (TÔRRES, 2021). GLOBAL BURDEN OF TBI ACCORDING TO THE GLOBAL BURDEN OF DISEASE The Global Burden of Disease (GBD), developed by the Institute for Health Metrics and Evaluation (IHME), plays a crucial role in assessing global health, providing comprehensive insight into the complexities associated with this field. The GBD employs metrics such as disability-adjusted life years (DALYs) to quantify the impact of diseases. This approach combines years lost due to premature death and years lived with disability, offering a comprehensive measure of the disease burden in a population. By utilizing DALYs, the GBD enables the global comparison of health trends, allowing for the identification of health priorities, efficient resource allocation, and the formulation of more effective public policies. The ability of the GBD Study to provide a holistic perspective of global health significantly contributes to understanding the challenges faced worldwide. The GBD plays an essential role in guiding efforts in public health promotion and mitigating the impact of diseases. Its constant updating and refinement ensure that information aligns with the changing dynamics of global health, enabling a more agile and informed response to emerging challenges. Thus, the GBD continues to be a crucial tool for healthcare professionals, researchers, and policymakers in the pursuit of effective strategies to improve health on a global scale. The GBD also addresses the impact of traumatic brain injury (TBI) globally. TBI is a significant condition that contributes to the global burden of disease and affects not only mortality but also morbidity and quality of life. GBD 2019 data highlight that Brazil is responsible for 1.1% of all moderate/severe TBIs worldwide, with falls being the leading cause of TBI, representing 67% of the total cases. Among Brazilian states, Espírito Santo is responsible for 14% of all TBIs in Brazil, with falls also being the primary cause, accounting for 67% of all TBIs. By including specific TBI data, the GBD plays a fundamental role in identifying geographic areas or populations most affected by traumatic cranial injuries. This information is valuable for guiding interventions and public health policies aimed at preventing injury, accessing emergency care, and rehabilitating affected individuals. The continuous updating and refinement of the GBD Consortium ensure that TBI-related estimates align with changes in the dynamics of global health, providing a solid foundation for the formulation of effective prevention and treatment strategies. Thus, the GBD plays an integral role not only in the overall understanding of global health challenges but also in the specific approach to conditions such as traumatic brain injury. DEFINITION, CLASSIFICATION, AND TYPES OF TRAUMATIC BRAIN INJURY Traumatic brain injury (TBI) is defined as any incidence of external force to the head resulting in anatomical injury to the skull followed by brain damage (MAGALHÃES et al., 2022). The type of TBI can predict the severity of the event. Falls, motor vehicle collisions, sports-related injuries, surgical errors resulting in brain tissue injuries through invasive procedures, and instances of physical violence are examples of traumatic events that can lead to brain injury. In high-income countries, the primary cause of TBIs is motor vehicle accidents, while in low- to middle-income countries, TBIs often involve pedestrians, cyclists, or young adult motorcyclists in traffic accidents (BRYAN-HANCOCK et al., 2010). Given the complexity of events associated with trauma, in some cases where current clinical imaging techniques may not detect trauma, a differential assessment of the degree of traumatic brain injury (TBI) is necessary to define severity and guide clinical management for each patient. Examples of differential assessments regarding physical and cognitive functions include measuring intracranial pressure, cerebral blood flow, and laboratory tests and assessing the level of consciousness using the Glasgow Coma Scale (GCS) (DIXON, 2017). PATHOPHYSIOLOGY OF TBI The pathophysiology of traumatic brain injury (TBI) is complex, making accurate diagnosis challenging. Depending on the magnitude of the trauma, whether it is penetrating or not, the location and severity of the impact, as well as the depth and extent of penetration into the brain, are significant in characterizing the underlying events of the trauma (ZASLER et al., 2012). The brain is a fragile structure; therefore, any penetration can result in disruptions to its structure. Axonal damage, for example, can interrupt neuronal circuits, and rupture of important vascular structures can lead to losses in maintaining brain activities (EAPEN et al., 2018). Hence, the effects underlying trauma can immediately cause harm to the brain, inducing cell death in the surrounding cells, along with significant morphological and functional neuronal changes (CAPIZZI et al., 2020). TBI induces significant physiological changes, such as positive regulation. This is an immediate mechanism of TBI events that occur within minutes after trauma. The inflammatory response is another local phenomenon in which astrocytes and microglia secrete proinflammatory cytokines such as tumor necrosis factor, interleukin-6, and interleukin-1b in the perilesional region in response to injury (CAPIZZI et al., 2020). Injuries resulting from traumatic brain injury (TBI) can be classified as focal or diffuse, characterizing TBI as primary or secondary. Both types of injuries can occur in the same patient. Importantly, regardless of the cause, TBI has the potential to result in severe physical, cognitive, and behavioral impairments, causing temporary dysfunction or permanent sequelae (REIS et al., 2015). DIAGNOSTIC VARIABLES AS A PROGNOSTIC MODEL FOR TBI The diagnosis of traumatic brain injury (TBI) typically relies on differential diagnosis, which uses prognostic models as a guiding framework. These prognostic models consist of two or more TBI variables that are significantly correlated with each other and are used to predict a possible clinical outcome (BUTCHER et al., 2007). To determine the variables related to TBI incidence, the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) score was used to characterize age, Glasgow Coma Scale (GCS) score, pupillary response, and lesion severity on computed tomography (CT) as independent variables for determining the prognosis of TBI patients. According to Butcher et al. (2007) and Perel et al. (2006), other variables with the potential to predict TBI severity according to inferred parameters or with the potential to directly interfere with the incidence and/or outcome of TBI include the following: Gender. The severity of lesions on the Marshall CT Scale. Associated injuries such as: Polytrauma. Open fractures. Intracranial injuries or vascular injuries. Comorbidities from Chronic Diseases such as Diabetes, Hypertension, or heart diseases. Time between TBI and medical care; and Length of Hospitalization These variables are widely recognized in the medical literature and can be found in various studies and systematic reviews on TBI in Brazil. It is important to consider these variables when assessing the prognosis of a TBI patient and take measures to minimize these risks. GLASGOW COMA SCALE The GCS is the main scale used to assess the level of consciousness in TBI patients (TEASDALE; JENNETT, 1974). The scale is based on patient data regarding eye opening, motor response, and verbal response (MARMAROU et al., 2007). As shown in Table 1, scores on the GCS range from 3 to 15, where 13 to 15 corresponds to mild TBI, 9 to 12 corresponds to moderate TBI, and 3 to 8 corresponds to severe TBI (MARMAROU et al., 2007). PUPIL RESPONSE EVALUATION The purpose of pupil response evaluation is to demonstrate an individual's responsiveness to light stimuli detected by rods (light-sensitive receptors in the retina). This stimulus induces pupillary constriction (direct pupillary reflex) in the stimulated eye, as well as a consensual pupillary reflex in the contralateral eye. These reflexes depend on the integration of afferent and efferent pathways at the brainstem level. Pupil analysis is crucial for determining the degree of direct injury to the structures of the third pair of cranial nerves (oculomotor). Pupil dilation results from direct injury to the cranial nerve due to herniation of the temporal lobe over the mesencephalic region. This event constitutes a neurological emergency because it strongly indicates severe structural brain injury, which can lead to compression of vital structures in the brainstem (MAAS, 2007). Pupillary light reflexes were tested in each pupil individually to assess the direct and consensual photomotor reflexes and the size and symmetry of the pupils. Isocoric pupils are symmetrical and react to light. Regarding pupil assessment, it is important to note that in this evaluation method, constricted pupils may indicate drug use, especially opioid use (ARÊAS, 2019). For example, the use of drugs such as cocaine and analogs can lead to bilateral mydriasis, which can be a confounding factor in comatose patients who have used adrenergic system agonist drugs, as they cause dilation of both pupils (ARÊAS, 2019). Traumatic brain injury (TBI) is a significant cause of morbidity and mortality among young adults in several countries and poses a considerable challenge in managing such traumatic events. Among traumatic events, TBI stands out as a leading cause of death, profoundly impacting affected individuals, their families, and society as a whole (WEBER et al., 2022). Epidemiological data on the disease are crucial for shaping public policies aimed at combating these accidents (AREAS, 2019). As highlighted, the annual average incidence of hospitalizations in Brazil exceeds 131,000 cases, with the southeast region having the highest absolute incidence (648,447 cases) (ALKHAIBARY et al., 2021; CARTERI et al., 2020). Another relevant point is not only discussing the incidence but also the deficiencies related to this injury. Approximately 5.3 million people in the USA (LANGLOIS et al., 2005) and approximately 7.7 million people in the European Union (TAGLIAFERRI et al., 2006) present disabilities related to this injury. It is estimated that 30-70% of survivors experience depressive symptoms or exacerbation of signs/symptoms, such as decision-making confusion and impulsive-aggressive behavior (ROOZENBEEK et al., 2013). It is further estimated that more than 1.1% of the US population lives with some type of sequelae caused by TBI, with more than 40% of patients having some form of moderate to severe or disabling injury (CAPIZZI, 2020). In addition to the sequelae themselves, problems associated with TBI can affect interpersonal relationships and contribute to unwanted or deficient community, social, and professional integration, potentially leading to the institutionalization of individuals affected by this issue. Another relevant point concerns the costs. Understanding the hospital costs of TBI is essential for public policy. According to a study published in the Brazilian Journal of Intensive Care, the average total annual costs of hospital expenses associated with TBI patients were approximately US$43,238,319.90, with an average cost per admission of US$327.68 (CARTERI et al., 2021). These costs are included in the National Cost Management Program (PNGC), which is an important public management tool created to address the lack of cost information in public health institutions (BRASIL, 2021, 2000). The costs are also part of the Health Price Bank (BPS), a system developed by the Ministry of Health for recording and consulting information on purchases of medications and health products made by public and private institutions (BRASIL, s.d.). Therefore, costs associated with permanent disability due to TBI are significantly relevant (CAPIZZI, 2020), representing high expenses for public coffers (AREAS, 2019). In other countries, such as the USA, the direct medical care cost was estimated to be approximately US$95 million, equivalent to US$1.67 million per 100,000 people. In Europe, traumatic injuries represent 37% of all deaths from traumatic injuries, resulting in a financial impact of €22,907 million in 2010 (MAGALHÃES et al., 2022). In Brazil, the financial impact of healthcare costs exceeded US$43,000,000.00, with an average cost per admission of US$327.68 (CARTERI et al., 2020). Another relevant piece of data is the increased risk for the elderly population. In the last decade, the incidence of TBI has increased worldwide due to accidents of different natures and the aging of the population, considering the risk of falls. Given the above, only two studies were conducted aiming to prospectively evaluate the hospital mortality of TBI patients—one in Santa Catarina and the other in São Paulo. In a 2019 study, Arêas et al. (2019) prospectively described that predictors of TBI can be important auxiliary tools for diagnostic and therapeutic decision-making, as these factors are based on patient prognosis. However, they concluded that the incidence of TBI determined by disability-adjusted life years still needs to be investigated in Brazil. Another prospective study, which included predictors of morbidity and mortality for patients with different TBIs, was proposed by Brazilian researchers. In this study, patients admitted to the emergency department of the University Hospital of the University of São Paulo with a low GCS score at admission and abnormal brain computed tomography (CT) (n = 1,275) between September 2003 and December 2009 were included (SILVA et al., 2009). Advanced age, lower GCS score, worse lesions evidenced on skull CT by the Marshall classification, and pupillary abnormalities were found to be the main independent predictors of hospital mortality in both studies (AREAS et al., 2019). These predictors are well described for their association with mortality in TBI patients worldwide, although the strength of the association may differ according to the income level of the country (IMPACT, 2007, MARMAROU et al., 2007, PEREL et al., 2008). Despite the data already described in the literature and the existence of a retrospective study in Brazil on TBI mortality, there is still no retrospective study aiming to identify predictors of hospital mortality and the incidence of severe TBI in hospitalized patients in Greater Vitória. Thus, the aim of this retrospective study was to identify possible predictors of hospital mortality due to severe TBI and to identify the causes of severe TBI in a trauma reference hospital in the state of Espírito Santo from 2018 to 2022. The hypothesis of this study was as follows: An association between severe TBI and variables described in the literature that are related to TBI mortality. Age, sex, length of hospital stays, GCS score at admission, and presence of intracranial lesions, for example, are possible variables that can predict the prognosis of TBI patients as well as patient outcome. To identify predictors of severe TBI, it is important to investigate the variables that may be associated with the severity of the injury. Several studies suggest that age, sex, the presence of comorbidities, the length of hospital stay, the level of consciousness at the time of admission, the presence of intracranial lesions, and the use of drugs or alcohol at the time of trauma can be predictors of severe TBI (ROOZENBEEK et al., 2013; MAJDAN et al., 2016). A study with 1,107 severe TBI patients showed that advanced age, the presence of comorbidities, and the level of consciousness at the time of admission were independent predictive factors for mortality (NÍ CHRÓINÍN D et al., 2019). TBI is a serious global health problem causing death, and in Brazil, it is no different. States in the Southeast and South regions of the country, such as São Paulo and Santa Catarina, have high incidences of severe TBI, as already described in the literature. In this context, our hypothesis becomes even stronger and more relevant. This study is justified not only by the scarcity of retrospective epidemiological studies aiming to identify predictors of incidence and death from severe TBI in the Greater Vitória-ES region but also by the fact that understanding the epidemiological characteristics of the investigated population and region, as well as the incidence, mortality, and especially the course of TBI from 2018 until now, provides invaluable support for the potential to inform public health policies for the prevention, control, and improvement of TBI outcomes. Therefore, the aim of this study was to Identify predictors of mortality due to severe TBI in the metropolitan area of Vitória-ES and describe the burden of TBI in the population. METHODS This work was analyzed and approved by the Research Ethics Committee (CEP) with Human Beings of the Federal University of Espírito Santo (opinion no. 4,222,002) and by the Health Department of Espírito Santo (SESA-ES). SAMPLE Eight hundred and sixty-three (n= 863) patients classified with severe TBI were admitted to the hospital unit in the city of Vitória-ES. All patients were of both sexes and were older than 18 years STUDY DESIGN This was a retrospective observational study on the incidence of TBI and predictors of mortality due to severe TBI. SPECIFIC PROCEDURES All patients admitted to the hospital unit in this study met the definition criteria for severe TBI at the hospital unit in the city of Vitória-ES in the state public health network. The collections were related to the years 2019, 2020, 2021, and 2022 (48 months). The database used was from the State Urgency and Emergency Hospital (HEUE), which is currently the reference hospital unit for traumatic events at the state level in Espírito Santos. This hospital mainly serves the metropolitan area of Vitória (Vitória, Vila Velha, Cariacica, Serra, and Guarapari) and some adjacent cities, mainly in the Serrana region (Domingos Martins, Viana, Marechal Floriano) and the North region (Aracruz, Linhares, and Colatina), for health interventions related to trauma in general, including TBI. The hospital is in the central region of the city of Vitória, near expressways. The state has a large territorial extension and currently has an estimated population of 4,108,508 people. The population was described using official data from the Brazilian Institute of Geography and Statistics (IBGE) from 2021. Patients were considered to have severe TBI and were included in the study if they presented a GCS score ≤ 8 at the time of admission to the hospital unit or were submitted to orotracheal intubation (OTI) at some point during hospitalization or when they underwent any neurosurgical procedure. Patients who did not meet any of the inclusion criteria were not included in this study. The protocol model was structured similarly to the models used in previous studies (MARTINS et al., 2009; ARÊAS, 2019). MORTALITY AND PREDICTOR VARIABLES The primary outcome was in-hospital mortality during hospitalization. To profile the study participants, demographic, clinical, neuroradiological, and neurosurgical characteristics were collected. The collected data included age, sex, time from trauma to health service admission, days of hospitalization, days in the ICU, days on ventilatory support, ECG scores at admission, and pupillary response to examination. TBI BURDEN The metric used to calculate the burden of TBI in years of life lost (YLLs) was as follows: YLLs per patient = average life expectancy in Brazil for the last 4 years (2019, 2020, 2021, and 2022) - age at TBI death. where YLLs represents the years of life lost due to premature mortality The life expectancy used for premature death calculation was 77.3 years, based on the life expectancy of the Brazilian population over the last 4 years , according to data from the Brazilian Institute of Geography and Statistics (IBGE, 2023). At 48 months, there were 450 deaths in the study population; all of these outcomes were used for calculating YLLs. It is important to note that the accumulated YLL value is obtained by summing the prematurely lost years of life for all patients in the analyzed population (GBD, 2019). In other words, the number of lost years can be high depending on the type of injury and the number of people affected by it. Thus, in addition to calculating the accumulated YLLs for the total study population, aiming to understand the TBI burden in the population in more detail, YLLs were also calculated by age group in three categories. The metrics used were the same as for the cumulative values but used only the specific data for each category of the outcome variable, as shown below: All TBI burden analyses were performed using Microsoft® Excel version 16.78 (2023). STATISTICAL ANALYSIS Before starting the statistical inference of the data, the Kolmogorov‒Smirnov test was conducted to check for data normality. To profile the patients in this study, descriptive statistics were calculated, using means and standard deviations (M±SD) for continuous variables and calculating percentiles for all categories of all described variables. For categorical variables, the nonparametric inferential chi-square test was used. The significance level adopted was p ≤ 0.05 according to Areas et al. (2019). This initial overview of the study population, in addition to providing a clear view of the population's characteristics, enabled us to observe the magnitude of variables associated with severe TBI, which could have some potential predictive value for death. These variables could be further evaluated and analyzed via logistic regression to determine their real predictive value for severe TBI mortality in the present study. To calculate the predictive value of variables associated with severe TBI, a binomial logistic regression test was carried out. It is important to note that the choice of the binomial logistic regression test was due to its suitability for analysis, given the sample characteristics of this study, namely, considerably large N (greater than 200), categorical dichotomous dependent variable (outcome), and type of analysis (prediction of mortality). All the statistical analyses were performed using the SPSS 20.0 program (Chicago, IL). RESULTS During the period between 2019 and 2022, a total of 863 patients were selected from the HEUE referral hospital, following the application of inclusion and exclusion criteria. Of these, 450 (52.14%) patients succumbed to their injuries, while 4513 (47.86%) were discharged following traumatic brain injury (TBI) treatment. The mean age of the study cohort was 46.13 ± 17.8 years, with 712 (82.5%) males and 151 (17.5%) females. The leading cause associated with TBI (p < 0.000) was falls (49.24%), followed by traffic accidents (p < 0.000) (32.56%). Among the study population, 55.61% (n = 480 - p < 0.000) presented with ECG scores < 8 upon admission to the emergency department. Intubation upon ICU admission (n = 742–85.97% - p < 0.000), performance of neurosurgery (n = 268–31.05% - p < 0.000), and duration of ICU intubation up to 10 days (n = 643 − 74.50% - p < 0.000) were significant factors. ICU admission for up to 10 days (n = 517 − 59.90% - p < 0.000) and hospital stay length up to 30 days (n = 662 − 76.70% - p < 0.000) were events significantly associated with TBI in the study. Additional demographic and clinical variables are presented in Table 1. <<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>> After describing and assessing the magnitude of association of variables with severe traumatic brain injury (TBI), we conducted a predictive analysis of these variables for mortality within the study. In our binomial logistic regression analysis, reference categories were established within each variable to facilitate data comparison with the outcome of death. The total number of deaths recorded was 450, and the mathematical expression describing the logistic regression model was as follows: [x²(27) = 398,066; p < 0,001, R² Nagelkerke = 0,493] We found that males exhibited a significant association with a higher likelihood of hospital mortality compared to females (Table 2). Furthermore, our analysis revealed that individuals aged up to 59 years were most prone to succumbing to their injuries during hospitalization (Table 2). However, concerning odds ratios, individuals aged 60 years or older showed a significant increase in the risk of hospital mortality compared to their younger counterparts (Table 2). Both traffic accidents and falls emerged as significant risk factors with considerable predictive value for hospital mortality compared to other causes of trauma (p < 0.000). Moreover, pre-hospital care demonstrated a strong association with a heightened risk of hospital mortality compared to patients who did not receive such care (OR: 0.279; 95% CI: 0.210–0.372, p < 0.000). Additionally, lower scores (< 8) on the Glasgow Coma Scale indicated a significant increase in the likelihood of hospital mortality (Table 2). Notably, intubation upon admission to the ICU and the performance of neurosurgery were identified as significant risk factors for hospital mortality (p < 0.000). Regarding intubation duration and length of hospital stay, both were associated with a significant increase in the risk of hospital mortality, particularly stays of up to 10 days for each variable. Global Burden of Severe TBI Calculated by Years of Life Lost Due to TBI Events The average national life expectancy for the period was 77.3 years, serving as a basis for calculating the Years of Life Lost (YLLs) in the study population. Table 3 depicts the cumulative burden of severe TBI estimated through the YLLs attributed to premature mortality in patients who experienced fatal outcomes in the study. The total accumulated YLLs during the 48-month study period amounted to 10,870.23 YLLs, averaging 2,717.55 YLLs per 12 months in the Greater Vitória region. Age-stratified analysis revealed that young adults (aged 25 to 59 years) represented the most affected population group in terms of TBI burden. <<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>> DISCUSSION Traumatic brain injury (TBI) poses a significant public health challenge globally, stemming from various traumatic events. Its repercussions are profoundly impactful, with short-, medium-, and long-term effects that can transiently disrupt survivors' lives or result in enduring disabilities (McCrea et al., 2021 ). This study aimed to discern predictors for severe TBI mortality in the Greater Vitória region and delineate the TBI burden using Years of Life Lost (YLLs) calculation. Upon retrospective analysis of selected variables, the results uncovered robust correlations between sex, age, trauma type, hospital stay duration, endotracheal intubation duration, trauma-hospital admission time, Glasgow Coma Scale (GCS) scores upon hospital admission, neurosurgeries, and hospital length of stay—all exhibiting high predictive values for mortality. As the first of its kind in the Greater Vitória region, this study sought to investigate hospital mortality in a public trauma reference institution. While ongoing observational studies aim to prospectively outline factors related to TBI, predicting patient prognosis, clinical evolution, and hospital outcomes (Silva et al., 2009; Areas et al., 2019 ), retrospective studies on TBI hospital mortality remain scarce. For instance, only one study conducted in Curitiba delineated the clinical characteristics and epidemiological profile of TBI patients from 2012 to 2019 (Réa-Neto et al., 2023 ). It's noteworthy that prospective studies conducted in Brazil were executed in states with different socioeconomic characteristics from Espírito Santo, as well as distinct healthcare resources compared to the healthcare institution in this study. Furthermore, social, cultural, pre-hospital care services, and clinical practices may vary between Brazilian regions, suggesting potential disparities in data due to population characteristics. Additionally, Réa-Neto's retrospective study did not probe into predictors for hospital mortality. Thus, this study aimed to retrospectively identify potential predictors of severe TBI hospital mortality in the Greater Vitória-ES region and delineate the TBI burden in the surveyed area. Upon retrospective analysis, it was observed that hospital mortality stood at 52.14%, significantly higher than mortality percentiles reported by other authors. Compared to other countries, mortality in Brazil exhibits higher percentages compared to North America (13%), Europe (25%), and China (20%). Several factors are associated with high TBI mortality in Espírito Santo, including injury severity, causes, intervention timing, and clinical management. This study found a predominance of male patients (82.5%), associated with a higher risk of hospital mortality, consistent with previous studies indicating higher TBI incidence and severity in males, underscoring the high predictive power of this data. Additionally, age, a well-explored factor in the literature, showed a highly significant association with hospital mortality, highlighting that patients aged 60 or older have a higher risk of severe TBI death. This relationship is supported by literature suggesting advanced age as an independent risk factor for unfavorable outcomes in TBI patients. Interestingly, young adults, despite exhibiting lower mortality rates compared to individuals over 60 years old, showed high mortality rates, warranting further investigation into the possible issues involved in this trend of elevated mortality in young adults. Traffic accidents and falls, significant problems previously described, were identified as significant risk factors for hospital mortality, consistent with other literature highlighting these causes as major contributors to severe TBI. The association of pre-hospital care with increased hospital mortality risk is concerning and may indicate the need for a more detailed analysis of pre-hospital care practices in the surveyed region. Lower scores (< 8) on the Glasgow Coma Scale, one of the primary neurological assessment tools, were associated with a significant increase in hospital mortality chances, confirming the importance of this prognostic indicator in TBI patients. Intubation during ICU admission and neurosurgery were identified as significant risk factors for hospital mortality. This association is consistent with previous evidence on the influence of these interventions on severe TBI outcomes. The analysis of severe TBI burden through YLLs underscores the substantial impact of this condition on the studied population. The average of 2,717.55 YLLs per year highlights the epidemiological relevance of TBI in the region. The distribution of YLLs by age group identifies young adults (aged 25 to 59 years) as the most affected population, aligning with population studies, providing additional comparisons to assess TBI burden in different regions. This study has the potential to significantly contribute to local epidemiology by providing detailed data on TBI epidemiology in the Greater Vitória region, significantly contributing to the understanding of risk factors and associated outcomes. This is crucial for guiding public health policies and prevention strategies. Additionally, Comprehensive Predictor Evaluation through statistical analysis, including logistic regression, allowed for the comprehensive identification of predictors of severe TBI mortality. This adds a valuable layer of evidence to existing literature and aids in a deeper understanding of this condition. Last but not least, the social and clinical impact resulting from the analysis of premature Years of Life Lost (YLLs) highlights the substantial impact of severe TBI burden on the studied population. These data can inform and guide not only healthcare professionals but also public health policymakers, aiming for more effective prevention and intervention strategies. In conclusion, this study has the potential to provide detailed data on TBI epidemiology in the Greater Vitória region, contributing to the understanding of risk factors and associated outcomes. Limiting the sample to patients from a reference hospital may affect result generalization. This study has limitations. Unfortunately, unanalyzed factors may influence outcomes. For example, extrapolating results to the entire population is hindered by the main limitation of this study, which is the sample restriction to patients from a single reference hospital in the Greater Vitória region, preventing a better understanding of variations in epidemiological profiles and healthcare services. Unexplored factors that retrospective analysis may have overlooked may be relevant in influencing outcomes. The lack of this information may limit a complete understanding of severe TBI mortality predictors. The absence of temporal characteristics, given the retrospective nature of the study, may have been predominant limitations for a better understanding of changes in medical practices and outcomes over time; for example, the evolution in TBI clinical approach over time may have impacted the results. CONCLUSION In summary, this study aimed to uncover predictors for severe Traumatic Brain Injury (TBI) mortality in the Greater Vitória-ES region and delineate the TBI burden through Years of Life Lost Prematurely (YLLs) calculation. Our findings unveiled substantial associations between hospital mortality and variables such as sex, age, type of trauma, duration of hospitalization, length of endotracheal intubation, trauma-hospital admission time, Glasgow Coma Scale scores upon hospital admission, neurosurgeries, and hospital length of stay, all of which exhibited high predictive accuracy for mortality. Hospital mortality attributable to TBI in the region stood at 52.14%, surpassing percentiles observed in other studies. YLLs analysis underscored the significant impact of TBI, particularly among young adults. This study offers valuable insights into local epidemiology, furnishing detailed data to inform public health policies and prevention strategies in the Greater Vitória-ES region. Nevertheless, it's vital to acknowledge the limitations stemming from our sample being confined to a single reference hospital, potentially affecting result generalization to the broader population. Abbreviations MVA - Mechanical Ventilation Assistance CI - Confidence Interval GCS - Glasgow Coma Scale BIGS - Brazilian Institute of Geography and Statistics UESH - Urgency and Emergency State Hospital OR - Odds Ratio SD - Standard Deviation SPSS - Statistical Package for the Social Sciences TBI - Traumatic Brain Injury ICF - Informed Consent Form ICU - Intensive Care Unit GBD - Global Burden of Disease IMPACT - International Mission for Prognosis and Analysis of Clinical Trials in TBI Declarations Acknowledgments Thanks to the State Hospital of Urgency and Emergency (HEUE) for supporting this study and providing all the resources necessary for this project. Ethics approval and consent to participate: This work received ethical consent from the Research Ethics Committee of the Federal University of Espírito Santo through term number 31705020.7.0000.5060. Declaration of interest statement The author(s) have no conflicts of interest relevant to this article. No commercial or financial relationships could be interpreted as a potential conflict of interest. Availability of data and materials: All data used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding There is no funding for this work. Author Contributions WGSF collected data, conducted statistical analysis, and interpreted the data, RFA performed statistical analysis, JVG collected data, HSV collected data, CBL collected data, LCSR collected data, TSR collected data, RSP collected data, RMG collected data, FZSA conducted statistical analysis, constructed tables, and interpreted results. References ALKHAIBARY A, et al. Traumatic brain injury: a perspective on the silent epidemic. Cureus . 2021;13(5). AREAS FZ, et al. Predictors of hospital mortality and the related burden of disease in severe traumatic brain injury: a prospective multicentric study in Brazil. Frontiers in neurology . 2019;10:432. BENI S, NÍ CHRÓINÍN D, FLETCHER T. A focus on the how of meaningful physical education in primary schools. Sport, Education and Society . 2019;24(6):624-637. BRAZIL. National Hospital Care Policy. June 4, 2021. Available at: https://www.gov.br/saude/pt-br/composicao/saes/atencao-especializada-e-hospitalar/politica-nacional-de-atencao-hospitalar. Accessed on: July 8, 2023. BRYAN-HANCOCK C, HARRISON J. The global burden of traumatic brain injury: preliminary results from the Global Burden of Disease Project. Injury Prevention . 2010;16(Suppl 1):A17-A17. CAPIZZI A, WOO J, VERDUZCO-GUTIERREZ M. Traumatic brain injury: an overview of epidemiology, pathophysiology, and medical management. Medical Clinics . 2020;104(2):213-238. CARTERI RBK, SILVA RA. Traumatic brain injury hospital incidence in Brazil: an analysis of the past 10 years. Revista Brasileira de Terapia Intensiva . 2021;33(2). Available at: https://doi.org/10.5935/0103-507x.20210036. Accessed on: July 8, 2023. CHESTNUT RM, et al. A trial of intracranial-pressure monitoring in traumatic brain injury. New England Journal of Medicine . 2015;372(25):2407-2416. CONDE V, SIEBNER HR. Brain damage by trauma. Handbook of clinical neurology . 2020;168:39-49. DEWAN MC, et al. Estimating the global incidence of traumatic brain injury. Journal of neurosurgery . 2018;130(4):1080-1097. DIXON KJ. Pathophysiology of traumatic brain injury. Physical Medicine and Rehabilitation Clinics . 2017;28(2):215-225. EAPEN BC, CIFU DX. Rehabilitation after traumatic brain injury . Elsevier Health Sciences. 2018. GAO G, et al. Clinical characteristics and outcomes in patients with traumatic brain injury in China: a prospective, multicenter, longitudinal, observational study. The Lancet Neurology . 2020;19(8):670-677. GERBER LM, Chiu YL, Carney N, Härtl R, Ghajar J. Marked reduction in mortality in patients with severe traumatic brain injury. J Neurosurg . 2013;119(6):1583-90. HAAGSMA JA, et al. The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. Injury prevention . 2016;22(1):3-18. Health Prices Database. Available at: https://www.gov.br/saude/pt-br/acesso-a-informacao/banco-de-precos. Accessed on: July 8, 2023. INSTITUTE FOR HEALTH METRICS AND EVALUATION (IHME). Global Burden of Disease Study 2019 (GBD 2019). JAMES SL, et al. (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet . 2018;392(10159):1789-1858. LANGLOIS JA, RUTLAND-BROWN W, THOMAS KE. The incidence of traumatic brain injury among children in the United States: differences by race. The Journal of head trauma rehabilitation . 2005;20(3):229-238. MAAS AI, et al. Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. The Lancet Neurology . 2017;16(12):987-1048. MAGALHÃES ALG, et al. Traumatic brain injury in Brazil: an epidemiological study and systematic review of the literature. Arquivos de Neuro-Psiquiatria . 2022;80:410-423. MAJDAN M, et al. Epidemiology of traumatic brain injuries in Europe: a cross-sectional analysis. The Lancet Public Health . 2017;2(8):e376-e383. MCCREA MA, et al. Functional outcomes over the first year after moderate to severe traumatic brain injury in the prospective, longitudinal TRACK-TBI study. JAMA neurology . 2021;78(8):982-992. MILLER GF, Daugherty J, Waltzman D, Sarmiento K. Predictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity & mortality. Injury . 2021;52(5):1138-1144. MOORE L, et al. Effectiveness of the implementation of prehospital thrombolysis in a statewide system. Circulation . 2015;131(1):82-89. MRC CRASH Trial Collaborators, et al. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ . 2008;336(7641):425-9. doi: 10.1136/bmj.39461.643438.25. MURRAY GD, et al. Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. Journal of neurotrauma . 2007;24(2):329-337. NAJEM D, et al. Traumatic brain injury: classification, models, and markers. Biochemistry and cell biology . 2018;96(4):391-406. PEREL P, et al. Comparison of treatment effects between animal experiments and clinical trials: systematic review. BMJ . 2018;337:a1682. PEREL P, et al. Systematic review of prognostic models in traumatic brain injury. BMC medical informatics and decision making . 2006;6(1):1-10. POLÍTICA Nacional de Redução da Morbimortalidade por Acidentes e Violência. Revista de Saúde Pública . 2000;34(4):427-430. Available at: https://doi.org/10.1590/s0034-89102000000400020. Accessed on: July 8, 2023. RÉA-NETO Á, et al. Epidemiological and clinical characteristics predictive of ICU mortality of patients with traumatic brain injury treated at a trauma referral hospital–a cohort study. BMC neurology . 2023;23(1):1-11. REIS C, et al. What’s new in traumatic brain injury: update on tracking, monitoring and treatment. International journal of molecular sciences . 2015;16(6):11903-11965. ROOZENBEEK B, et al. Prediction of outcome after moderate and severe traumatic brain injury: external validation of the IMPACT and CRASH prognostic models. Critical Care Medicine . 2007;41(10):2256-2257. ROOZENBEEK B, MAAS A, MENON DK. Changing patterns in the epidemiology of traumatic brain injury. Nature Reviews Neurology . 2013;9(4):231-236. STEYERBERG EW, et al. Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicenter, longitudinal, cohort study. The Lancet Neurology . 2019;18(10):923-934. STEYERBERG EW, et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Medicine . 2018;5(8):e165. TAGLIAFERRI F, et al. A systematic review of brain injury epidemiology in Europe. Acta neurochirurgica . 2006;148:255-268. TEASDALE G, JENNETT B. Assessment of coma and impaired consciousness: a practical scale. The Lancet . 1974;304(7872):81-84. TORRES SG, BALDO JHL, PROPÉRCIO AA. EPIDEMIOLOGICAL PROFILE OF TRAUMATIC BRAIN INJURY IN THE NORTHERN REGION OF BRAZIL BETWEEN 2010 AND 2020. Facit Business and Technology Journal . 2021;2(31). WEBER C, et al. Characteristics, image findings and clinical outcome of moderate and severe traumatic brain injury among severely injured children: a population-based cohort study. European journal of trauma and emergency surgery . 2022:1-8. ZASLER ND, KATZ DI, ZAFONTE RD (Ed.). Brain injury medicine: principles and practice . Demos Medical Publishing. 2012. Tables Tables 1 to 2 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Brazil\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eTraumatic brain injury (TBI) poses a significant challenge to global health, with notable incidences in developed countries and emerging economies worldwide (NAJEM et al., 2018). Each year, approximately 70 million people are affected by TBI, with approximately 11% of these events classified as severe (DEWAN et al., 2018). In the United States, for example, 10% of cases are considered severe (CAPIZZI et al., 2020), with a staggering estimate of up to 500 cases per 100,000 young adults under 45 years in the United States and Europe (CONDE et al., 2020).\u003c/p\u003e\n\u003cp\u003eIn Brazil, between 2008 and 2019, the average annual incidence of hospitalizations exceeded 131,000, with a hospital mortality rate exceeding 30% for severe cases. TBI is the fifth leading cause of death among individuals aged 15 to 29 in Brazil and the third among those aged 30 to 44; TBI is a significant concern, particularly in traffic-related incidents (ALKHAIBARY et al., 2021). The southeastern region had the highest absolute incidence, totaling 648,447 cases during the period (CARTERI et al., 2020).\u003c/p\u003e\n\u003cp\u003eThe economic impact of TBI is substantial. In the United States, the direct medical care cost is estimated at approximately $95 million, equivalent to $1.67 million per 100,000 people. In Europe, TBIs account for 37% of all deaths from traumatic injuries, resulting in a financial impact of €22,907 million in 2010 (MAGALHÃES et al., 2022). In Brazil, healthcare costs exceeded US$43,000,000.00, with an average cost per admission of US$327.68 (CARTERI et al., 2020).\u003c/p\u003e\n\u003cp\u003eDespite the high incidence of TBI, epidemiological data are limited, especially in Brazil. The Brazilian Ministry of Health emphasizes that, like other low-income countries, morbidity and mortality from TBI are not systematically recorded. This information gap makes the epidemiological situation of TBI in Brazil even more concerning, given that this issue is one of the main causes of death and disability in the country (TÔRRES, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGLOBAL BURDEN OF TBI ACCORDING TO THE GLOBAL BURDEN OF DISEASE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Global Burden of Disease (GBD), developed by the Institute for Health Metrics and Evaluation (IHME), plays a crucial role in assessing global health, providing comprehensive insight into the complexities associated with this field. The GBD employs metrics such as disability-adjusted life years (DALYs) to quantify the impact of diseases. This approach combines years lost due to premature death and years lived with disability, offering a comprehensive measure of the disease burden in a population.\u003c/p\u003e\n\u003cp\u003eBy utilizing DALYs, the GBD enables the global comparison of health trends, allowing for the identification of health priorities, efficient resource allocation, and the formulation of more effective public policies. The ability of the GBD Study to provide a holistic perspective of global health significantly contributes to understanding the challenges faced worldwide.\u003c/p\u003e\n\u003cp\u003eThe GBD plays an essential role in guiding efforts in public health promotion and mitigating the impact of diseases. Its constant updating and refinement ensure that information aligns with the changing dynamics of global health, enabling a more agile and informed response to emerging challenges. Thus, the GBD continues to be a crucial tool for healthcare professionals, researchers, and policymakers in the pursuit of effective strategies to improve health on a global scale.\u003c/p\u003e\n\u003cp\u003eThe GBD also addresses the impact of traumatic brain injury (TBI) globally. TBI is a significant condition that contributes to the global burden of disease and affects not only mortality but also morbidity and quality of life.\u003c/p\u003e\n\u003cp\u003eGBD 2019 data highlight that Brazil is responsible for 1.1% of all moderate/severe TBIs worldwide, with falls being the leading cause of TBI, representing 67% of the total cases. Among Brazilian states, Espírito Santo is responsible for 14% of all TBIs in Brazil, with falls also being the primary cause, accounting for 67% of all TBIs.\u003c/p\u003e\n\u003cp\u003eBy including specific TBI data, the GBD plays a fundamental role in identifying geographic areas or populations most affected by traumatic cranial injuries. This information is valuable for guiding interventions and public health policies aimed at preventing injury, accessing emergency care, and rehabilitating affected individuals.\u003c/p\u003e\n\u003cp\u003eThe continuous updating and refinement of the GBD Consortium ensure that TBI-related estimates align with changes in the dynamics of global health, providing a solid foundation for the formulation of effective prevention and treatment strategies. Thus, the GBD plays an integral role not only in the overall understanding of global health challenges but also in the specific approach to conditions such as traumatic brain injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEFINITION, CLASSIFICATION, AND TYPES OF TRAUMATIC BRAIN INJURY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraumatic brain injury (TBI) is defined as any incidence of external force to the head resulting in anatomical injury to the skull followed by brain damage (MAGALHÃES et al., 2022). The type of TBI can predict the severity of the event.\u003c/p\u003e\n\u003cp\u003eFalls, motor vehicle collisions, sports-related injuries, surgical errors resulting in brain tissue injuries through invasive procedures, and instances of physical violence are examples of traumatic events that can lead to brain injury. In high-income countries, the primary cause of TBIs is motor vehicle accidents, while in low- to middle-income countries, TBIs often involve pedestrians, cyclists, or young adult motorcyclists in traffic accidents (BRYAN-HANCOCK et al., 2010).\u003c/p\u003e\n\u003cp\u003eGiven the complexity of events associated with trauma, in some cases where current clinical imaging techniques may not detect trauma, a differential assessment of the degree of traumatic brain injury (TBI) is necessary to define severity and guide clinical management for each patient. Examples of differential assessments regarding physical and cognitive functions include measuring intracranial pressure, cerebral blood flow, and laboratory tests and assessing the level of consciousness using the Glasgow Coma Scale (GCS) (DIXON, 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePATHOPHYSIOLOGY OF TBI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pathophysiology of traumatic brain injury (TBI) is complex, making accurate diagnosis challenging. Depending on the magnitude of the trauma, whether it is penetrating or not, the location and severity of the impact, as well as the depth and extent of penetration into the brain, are significant in characterizing the underlying events of the trauma (ZASLER et al., 2012).\u003c/p\u003e\n\u003cp\u003eThe brain is a fragile structure; therefore, any penetration can result in disruptions to its structure. Axonal damage, for example, can interrupt neuronal circuits, and rupture of important vascular structures can lead to losses in maintaining brain activities (EAPEN et al., 2018).\u003c/p\u003e\n\u003cp\u003eHence, the effects underlying trauma can immediately cause harm to the brain, inducing cell death in the surrounding cells, along with significant morphological and functional neuronal changes (CAPIZZI et al., 2020).\u003c/p\u003e\n\u003cp\u003eTBI induces significant physiological changes, such as positive regulation. This is an immediate mechanism of TBI events that occur within minutes after trauma. The inflammatory response is another local phenomenon in which astrocytes and microglia secrete proinflammatory cytokines such as tumor necrosis factor, interleukin-6, and interleukin-1b in the perilesional region in response to injury (CAPIZZI et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInjuries resulting from traumatic brain injury (TBI) can be classified as focal or diffuse, characterizing TBI as primary or secondary. Both types of injuries can occur in the same patient. Importantly, regardless of the cause, TBI has the potential to result in severe physical, cognitive, and behavioral impairments, causing temporary dysfunction or permanent sequelae (REIS et al., 2015).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDIAGNOSTIC VARIABLES AS A PROGNOSTIC MODEL FOR TBI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnosis of traumatic brain injury (TBI) typically relies on differential diagnosis, which uses prognostic models as a guiding framework. These prognostic models consist of two or more TBI variables that are significantly correlated with each other and are used to predict a possible clinical outcome (BUTCHER et al., 2007).\u003c/p\u003e\n\u003cp\u003eTo determine the variables related to TBI incidence, the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) score was used to characterize age, Glasgow Coma Scale (GCS) score, pupillary response, and lesion severity on computed tomography (CT) as independent variables for determining the prognosis of TBI patients.\u003c/p\u003e\n\u003cp\u003eAccording to Butcher et al. (2007) and Perel et al. (2006), other variables with the potential to predict TBI severity according to inferred parameters or with the potential to directly interfere with the incidence and/or outcome of TBI include the following:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eGender.\u003c/li\u003e\n \u003cli\u003eThe severity of lesions on the Marshall CT Scale.\u003c/li\u003e\n \u003cli\u003eAssociated injuries such as:\u003c/li\u003e\n\u003c/ul\u003e\n\u003col\u003e\n \u003cli\u003ePolytrauma.\u003c/li\u003e\n \u003cli\u003eOpen fractures.\u003c/li\u003e\n \u003cli\u003eIntracranial injuries or vascular injuries.\u003c/li\u003e\n \u003cli\u003eComorbidities from Chronic Diseases such as Diabetes, Hypertension, or heart diseases.\u003c/li\u003e\n \u003cli\u003eTime between TBI and medical care; and\u003c/li\u003e\n \u003cli\u003eLength of Hospitalization\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThese variables are widely recognized in the medical literature and can be found in various studies and systematic reviews on TBI in Brazil. It is important to consider these variables when assessing the prognosis of a TBI patient and take measures to minimize these risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGLASGOW COMA SCALE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GCS is the main scale used to assess the level of consciousness in TBI patients (TEASDALE; JENNETT, 1974). The scale is based on patient data regarding eye opening, motor response, and verbal response (MARMAROU et al., 2007). As shown in Table 1, scores on the GCS range from 3 to 15, where 13 to 15 corresponds to mild TBI, 9 to 12 corresponds to moderate TBI, and 3 to 8 corresponds to severe TBI (MARMAROU et al., 2007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePUPIL RESPONSE EVALUATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of pupil response evaluation is to demonstrate an individual's responsiveness to light stimuli detected by rods (light-sensitive receptors in the retina). This stimulus induces pupillary constriction (direct pupillary reflex) in the stimulated eye, as well as a consensual pupillary reflex in the contralateral eye. These reflexes depend on the integration of afferent and efferent pathways at the brainstem level.\u003c/p\u003e\n\u003cp\u003ePupil analysis is crucial for determining the degree of direct injury to the structures of the third pair of cranial nerves (oculomotor). Pupil dilation results from direct injury to the cranial nerve due to herniation of the temporal lobe over the mesencephalic region. This event constitutes a neurological emergency because it strongly indicates severe structural brain injury, which can lead to compression of vital structures in the brainstem (MAAS, 2007).\u003c/p\u003e\n\u003cp\u003ePupillary light reflexes were tested in each pupil individually to assess the direct and consensual photomotor reflexes and the size and symmetry of the pupils. Isocoric pupils are symmetrical and react to light.\u003c/p\u003e\n\u003cp\u003eRegarding pupil assessment, it is important to note that in this evaluation method, constricted pupils may indicate drug use, especially opioid use (ARÊAS, 2019). For example, the use of drugs such as cocaine and analogs can lead to bilateral mydriasis, which can be a confounding factor in comatose patients who have used adrenergic system agonist drugs, as they cause dilation of both pupils (ARÊAS, 2019).\u003c/p\u003e\n\u003cp\u003eTraumatic brain injury (TBI) is a significant cause of morbidity and mortality among young adults in several countries and poses a considerable challenge in managing such traumatic events. Among traumatic events, TBI stands out as a leading cause of death, profoundly impacting affected individuals, their families, and society as a whole (WEBER et al., 2022). Epidemiological data on the disease are crucial for shaping public policies aimed at combating these accidents (AREAS, 2019). As highlighted, the annual average incidence of hospitalizations in Brazil exceeds 131,000 cases, with the southeast region having the highest absolute incidence (648,447 cases) (ALKHAIBARY et al., 2021; CARTERI et al., 2020).\u003c/p\u003e\n\u003cp\u003eAnother relevant point is not only discussing the incidence but also the deficiencies related to this injury. Approximately 5.3 million people in the USA (LANGLOIS et al., 2005) and approximately 7.7 million people in the European Union (TAGLIAFERRI et al., 2006) present disabilities related to this injury. It is estimated that 30-70% of survivors experience depressive symptoms or exacerbation of signs/symptoms, such as decision-making confusion and impulsive-aggressive behavior (ROOZENBEEK et al., 2013). It is further estimated that more than 1.1% of the US population lives with some type of sequelae caused by TBI, with more than 40% of patients having some form of moderate to severe or disabling injury (CAPIZZI, 2020).\u003c/p\u003e\n\u003cp\u003eIn addition to the sequelae themselves, problems associated with TBI can affect interpersonal relationships and contribute to unwanted or deficient community, social, and professional integration, potentially leading to the institutionalization of individuals affected by this issue.\u003c/p\u003e\n\u003cp\u003eAnother relevant point concerns the costs. Understanding the hospital costs of TBI is essential for public policy. According to a study published in the Brazilian Journal of Intensive Care, the average total annual costs of hospital expenses associated with TBI patients were approximately US$43,238,319.90, with an average cost per admission of US$327.68 (CARTERI et al., 2021). These costs are included in the National Cost Management Program (PNGC), which is an important public management tool created to address the lack of cost information in public health institutions (BRASIL, 2021, 2000). The costs are also part of the Health Price Bank (BPS), a system developed by the Ministry of Health for recording and consulting information on purchases of medications and health products made by public and private institutions (BRASIL, s.d.). Therefore, costs associated with permanent disability due to TBI are significantly relevant (CAPIZZI, 2020), representing high expenses for public coffers (AREAS, 2019).\u003c/p\u003e\n\u003cp\u003eIn other countries, such as the USA, the direct medical care cost was estimated to be approximately US$95 million, equivalent to US$1.67 million per 100,000 people. In Europe, traumatic injuries represent 37% of all deaths from traumatic injuries, resulting in a financial impact of €22,907 million in 2010 (MAGALHÃES et al., 2022). In Brazil, the financial impact of healthcare costs exceeded US$43,000,000.00, with an average cost per admission of US$327.68 (CARTERI et al., 2020).\u003c/p\u003e\n\u003cp\u003eAnother relevant piece of data is the increased risk for the elderly population. In the last decade, the incidence of TBI has increased worldwide due to accidents of different natures and the aging of the population, considering the risk of falls.\u003c/p\u003e\n\u003cp\u003eGiven the above, only two studies were conducted aiming to prospectively evaluate the hospital mortality of TBI patients—one in Santa Catarina and the other in São Paulo. In a 2019 study, Arêas et al. (2019) prospectively described that predictors of TBI can be important auxiliary tools for diagnostic and therapeutic decision-making, as these factors are based on patient prognosis. However, they concluded that the incidence of TBI determined by disability-adjusted life years still needs to be investigated in Brazil.\u003c/p\u003e\n\u003cp\u003eAnother prospective study, which included predictors of morbidity and mortality for patients with different TBIs, was proposed by Brazilian researchers. In this study, patients admitted to the emergency department of the University Hospital of the University of São Paulo with a low GCS score at admission and abnormal brain computed tomography (CT) (n = 1,275) between September 2003 and December 2009 were included (SILVA et al., 2009). Advanced age, lower GCS score, worse lesions evidenced on skull CT by the Marshall classification, and pupillary abnormalities were found to be the main independent predictors of hospital mortality in both studies (AREAS et al., 2019). These predictors are well described for their association with mortality in TBI patients worldwide, although the strength of the association may differ according to the income level of the country (IMPACT, 2007, MARMAROU et al., 2007, PEREL et al., 2008).\u003c/p\u003e\n\u003cp\u003eDespite the data already described in the literature and the existence of a retrospective study in Brazil on TBI mortality, there is still no retrospective study aiming to identify predictors of hospital mortality and the incidence of severe TBI in hospitalized patients in Greater Vitória. Thus, the aim of this retrospective study was to identify possible predictors of hospital mortality due to severe TBI and to identify the causes of severe TBI in a trauma reference hospital in the state of Espírito Santo from 2018 to 2022.\u003c/p\u003e\n\u003cp\u003eThe hypothesis of this study was as follows: An association between severe TBI and variables described in the literature that are related to TBI mortality. Age, sex, length of hospital stays, GCS score at admission, and presence of intracranial lesions, for example, are possible variables that can predict the prognosis of TBI patients as well as patient outcome. To identify predictors of severe TBI, it is important to investigate the variables that may be associated with the severity of the injury. Several studies suggest that age, sex, the presence of comorbidities, the length of hospital stay, the level of consciousness at the time of admission, the presence of intracranial lesions, and the use of drugs or alcohol at the time of trauma can be predictors of severe TBI (ROOZENBEEK et al., 2013; MAJDAN et al., 2016). A study with 1,107 severe TBI patients showed that advanced age, the presence of comorbidities, and the level of consciousness at the time of admission were independent predictive factors for mortality (NÍ CHRÓINÍN D et al., 2019). TBI is a serious global health problem causing death, and in Brazil, it is no different. States in the Southeast and South regions of the country, such as São Paulo and Santa Catarina, have high incidences of severe TBI, as already described in the literature. In this context, our hypothesis becomes even stronger and more relevant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is justified not only by the scarcity of retrospective epidemiological studies aiming to identify predictors of incidence and death from severe TBI in the Greater Vitória-ES region but also by the fact that understanding the epidemiological characteristics of the investigated population and region, as well as the incidence, mortality, and especially the course of TBI from 2018 until now, provides invaluable support for the potential to inform public health policies for the prevention, control, and improvement of TBI outcomes. Therefore, the aim of this study was to Identify predictors of mortality due to severe TBI in the metropolitan area of Vitória-ES and describe the burden of TBI in the population.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":" METHODS","content":"\u003cp\u003eThis work was analyzed and approved by the Research Ethics Committee (CEP) with Human Beings of the Federal University of Espírito Santo (opinion no. 4,222,002) and by the Health Department of Espírito Santo (SESA-ES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSAMPLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEight hundred and sixty-three (n= 863) patients classified with severe TBI were admitted to the hospital unit in the city of Vitória-ES. All patients were of both sexes and were older than 18 years\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTUDY DESIGN\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective observational study on the incidence of TBI and predictors of mortality due to severe TBI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPECIFIC PROCEDURES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients admitted to the hospital unit in this study met the definition criteria for severe TBI at the hospital unit in the city of Vitória-ES in the state public health network. The collections were related to the years 2019, 2020, 2021, and 2022 (48 months).\u003c/p\u003e\n\u003cp\u003eThe database used was from the State Urgency and Emergency Hospital (HEUE), which is currently the reference hospital unit for traumatic events at the state level in Espírito Santos. This hospital mainly serves the metropolitan area of Vitória (Vitória, Vila Velha, Cariacica, Serra, and Guarapari) and some adjacent cities, mainly in the Serrana region (Domingos Martins, Viana, Marechal Floriano) and the North region (Aracruz, Linhares, and Colatina), for health interventions related to trauma in general, including TBI.\u003c/p\u003e\n\u003cp\u003eThe hospital is in the central region of the city of Vitória, near expressways. The state has a large territorial extension and currently has an estimated population of 4,108,508 people. The population was described using official data from the Brazilian Institute of Geography and Statistics (IBGE) from 2021.\u003c/p\u003e\n\u003cp\u003ePatients were considered to have severe TBI and were included in the study if they presented a GCS score ≤ 8 at the time of admission to the hospital unit or were submitted to orotracheal intubation (OTI) at some point during hospitalization or when they underwent any neurosurgical procedure. Patients who did not meet any of the inclusion criteria were not included in this study. The protocol model was structured similarly to the models used in previous studies (MARTINS et al., 2009; ARÊAS, 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMORTALITY AND PREDICTOR VARIABLES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was in-hospital mortality during hospitalization. To profile the study participants, demographic, clinical, neuroradiological, and neurosurgical characteristics were collected. The collected data included age, sex, time from trauma to health service admission, days of hospitalization, days in the ICU, days on ventilatory support, ECG scores at admission, and pupillary response to examination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTBI BURDEN\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe metric used to calculate the burden of TBI in years of life lost (YLLs) was as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eYLLs per patient = average life expectancy in Brazil for the last 4 years (2019, 2020, 2021, and 2022) - age at TBI death.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ewhere YLLs represents the years of life lost due to premature mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe life expectancy used for premature death calculation was 77.3 years, based on the life expectancy of the Brazilian population over the last 4 years , according to data from the Brazilian Institute of Geography and Statistics (IBGE, 2023). At 48 months, there were 450 deaths in the study population; all of these outcomes were used for calculating YLLs.\u003c/p\u003e\n\u003cp\u003eIt is important to note that the accumulated YLL value is obtained by summing the prematurely lost years of life for all patients in the analyzed population (GBD, 2019). In other words, the number of lost years can be high depending on the type of injury and the number of people affected by it. Thus, in addition to calculating the accumulated YLLs for the total study population, aiming to understand the TBI burden in the population in more detail, YLLs were also calculated by age group in three categories. The metrics used were the same as for the cumulative values but used only the specific data for each category of the outcome variable, as shown below:\u003c/p\u003e\n\u003cp\u003eAll TBI burden analyses were performed using Microsoft® Excel version 16.78 (2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSTATISTICAL ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore starting the statistical inference of the data, the Kolmogorov‒Smirnov test was conducted to check for data normality. To profile the patients in this study, descriptive statistics were calculated, using means and standard deviations (M±SD) for continuous variables and calculating percentiles for all categories of all described variables. For categorical variables, the nonparametric inferential chi-square test was used. The significance level adopted was p ≤ 0.05 according to Areas et al. (2019).\u003c/p\u003e\n\u003cp\u003eThis initial overview of the study population, in addition to providing a clear view of the population's characteristics, enabled us to observe the magnitude of variables associated with severe TBI, which could have some potential predictive value for death. These variables could be further evaluated and analyzed via logistic regression to determine their real predictive value for severe TBI mortality in the present study.\u003c/p\u003e\n\u003cp\u003eTo calculate the predictive value of variables associated with severe TBI, a binomial logistic regression test was carried out. It is important to note that the choice of the binomial logistic regression test was due to its suitability for analysis, given the sample characteristics of this study, namely, considerably large N (greater than 200), categorical dichotomous dependent variable (outcome), and type of analysis (prediction of mortality). All the statistical analyses were performed using the SPSS 20.0 program (Chicago, IL).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDuring the period between 2019 and 2022, a total of 863 patients were selected from the HEUE referral hospital, following the application of inclusion and exclusion criteria. Of these, 450 (52.14%) patients succumbed to their injuries, while 4513 (47.86%) were discharged following traumatic brain injury (TBI) treatment.\u003c/p\u003e \u003cp\u003eThe mean age of the study cohort was 46.13\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8 years, with 712 (82.5%) males and 151 (17.5%) females. The leading cause associated with TBI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000) was falls (49.24%), followed by traffic accidents (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000) (32.56%).\u003c/p\u003e \u003cp\u003eAmong the study population, 55.61% (n\u0026thinsp;=\u0026thinsp;480 - p\u0026thinsp;\u0026lt;\u0026thinsp;0.000) presented with ECG scores\u0026thinsp;\u0026lt;\u0026thinsp;8 upon admission to the emergency department. Intubation upon ICU admission (n\u0026thinsp;=\u0026thinsp;742\u0026ndash;85.97% - p\u0026thinsp;\u0026lt;\u0026thinsp;0.000), performance of neurosurgery (n\u0026thinsp;=\u0026thinsp;268\u0026ndash;31.05% - p\u0026thinsp;\u0026lt;\u0026thinsp;0.000), and duration of ICU intubation up to 10 days (n\u0026thinsp;=\u0026thinsp;643\u0026thinsp;\u0026minus;\u0026thinsp;74.50% - p\u0026thinsp;\u0026lt;\u0026thinsp;0.000) were significant factors.\u003c/p\u003e \u003cp\u003eICU admission for up to 10 days (n\u0026thinsp;=\u0026thinsp;517\u0026thinsp;\u0026minus;\u0026thinsp;59.90% - p\u0026thinsp;\u0026lt;\u0026thinsp;0.000) and hospital stay length up to 30 days (n\u0026thinsp;=\u0026thinsp;662\u0026thinsp;\u0026minus;\u0026thinsp;76.70% - p\u0026thinsp;\u0026lt;\u0026thinsp;0.000) were events significantly associated with TBI in the study.\u003c/p\u003e \u003cp\u003eAdditional demographic and clinical variables are presented in Table\u0026nbsp;1.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;INSERT TABLE 1 NEAR HERE\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/h2\u003e \u003cp\u003eAfter describing and assessing the magnitude of association of variables with severe traumatic brain injury (TBI), we conducted a predictive analysis of these variables for mortality within the study. In our binomial logistic regression analysis, reference categories were established within each variable to facilitate data comparison with the outcome of death. The total number of deaths recorded was 450, and the mathematical expression describing the logistic regression model was as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e[x\u0026sup2;(27)\u0026thinsp;=\u0026thinsp;398,066; p\u0026thinsp;\u0026lt;\u0026thinsp;0,001, R\u0026sup2; Nagelkerke\u0026thinsp;=\u0026thinsp;0,493]\u003c/h2\u003e \u003cp\u003eWe found that males exhibited a significant association with a higher likelihood of hospital mortality compared to females (Table\u0026nbsp;2). Furthermore, our analysis revealed that individuals aged up to 59 years were most prone to succumbing to their injuries during hospitalization (Table\u0026nbsp;2). However, concerning odds ratios, individuals aged 60 years or older showed a significant increase in the risk of hospital mortality compared to their younger counterparts (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eBoth traffic accidents and falls emerged as significant risk factors with considerable predictive value for hospital mortality compared to other causes of trauma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000). Moreover, pre-hospital care demonstrated a strong association with a heightened risk of hospital mortality compared to patients who did not receive such care (OR: 0.279; 95% CI: 0.210\u0026ndash;0.372, p\u0026thinsp;\u0026lt;\u0026thinsp;0.000).\u003c/p\u003e \u003cp\u003eAdditionally, lower scores (\u0026lt;\u0026thinsp;8) on the Glasgow Coma Scale indicated a significant increase in the likelihood of hospital mortality (Table\u0026nbsp;2). Notably, intubation upon admission to the ICU and the performance of neurosurgery were identified as significant risk factors for hospital mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.000).\u003c/p\u003e \u003cp\u003eRegarding intubation duration and length of hospital stay, both were associated with a significant increase in the risk of hospital mortality, particularly stays of up to 10 days for each variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Burden of Severe TBI Calculated by Years of Life Lost Due to TBI Events\u003c/h2\u003e \u003cp\u003eThe average national life expectancy for the period was 77.3 years, serving as a basis for calculating the Years of Life Lost (YLLs) in the study population. Table\u0026nbsp;3 depicts the cumulative burden of severe TBI estimated through the YLLs attributed to premature mortality in patients who experienced fatal outcomes in the study.\u003c/p\u003e \u003cp\u003eThe total accumulated YLLs during the 48-month study period amounted to 10,870.23 YLLs, averaging 2,717.55 YLLs per 12 months in the Greater Vit\u0026oacute;ria region. Age-stratified analysis revealed that young adults (aged 25 to 59 years) represented the most affected population group in terms of TBI burden.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;\u0026lt;INSERT TABLE 2 NEAR HERE\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u0026gt;\u003c/h2\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eTraumatic brain injury (TBI) poses a significant public health challenge globally, stemming from various traumatic events. Its repercussions are profoundly impactful, with short-, medium-, and long-term effects that can transiently disrupt survivors' lives or result in enduring disabilities (McCrea et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aimed to discern predictors for severe TBI mortality in the Greater Vit\u0026oacute;ria region and delineate the TBI burden using Years of Life Lost (YLLs) calculation. Upon retrospective analysis of selected variables, the results uncovered robust correlations between sex, age, trauma type, hospital stay duration, endotracheal intubation duration, trauma-hospital admission time, Glasgow Coma Scale (GCS) scores upon hospital admission, neurosurgeries, and hospital length of stay\u0026mdash;all exhibiting high predictive values for mortality.\u003c/p\u003e \u003cp\u003eAs the first of its kind in the Greater Vit\u0026oacute;ria region, this study sought to investigate hospital mortality in a public trauma reference institution. While ongoing observational studies aim to prospectively outline factors related to TBI, predicting patient prognosis, clinical evolution, and hospital outcomes (Silva et al., 2009; Areas et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), retrospective studies on TBI hospital mortality remain scarce. For instance, only one study conducted in Curitiba delineated the clinical characteristics and epidemiological profile of TBI patients from 2012 to 2019 (R\u0026eacute;a-Neto et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt's noteworthy that prospective studies conducted in Brazil were executed in states with different socioeconomic characteristics from Esp\u0026iacute;rito Santo, as well as distinct healthcare resources compared to the healthcare institution in this study. Furthermore, social, cultural, pre-hospital care services, and clinical practices may vary between Brazilian regions, suggesting potential disparities in data due to population characteristics. Additionally, R\u0026eacute;a-Neto's retrospective study did not probe into predictors for hospital mortality.\u003c/p\u003e \u003cp\u003eThus, this study aimed to retrospectively identify potential predictors of severe TBI hospital mortality in the Greater Vit\u0026oacute;ria-ES region and delineate the TBI burden in the surveyed area. Upon retrospective analysis, it was observed that hospital mortality stood at 52.14%, significantly higher than mortality percentiles reported by other authors. Compared to other countries, mortality in Brazil exhibits higher percentages compared to North America (13%), Europe (25%), and China (20%). Several factors are associated with high TBI mortality in Esp\u0026iacute;rito Santo, including injury severity, causes, intervention timing, and clinical management.\u003c/p\u003e \u003cp\u003eThis study found a predominance of male patients (82.5%), associated with a higher risk of hospital mortality, consistent with previous studies indicating higher TBI incidence and severity in males, underscoring the high predictive power of this data. Additionally, age, a well-explored factor in the literature, showed a highly significant association with hospital mortality, highlighting that patients aged 60 or older have a higher risk of severe TBI death. This relationship is supported by literature suggesting advanced age as an independent risk factor for unfavorable outcomes in TBI patients.\u003c/p\u003e \u003cp\u003eInterestingly, young adults, despite exhibiting lower mortality rates compared to individuals over 60 years old, showed high mortality rates, warranting further investigation into the possible issues involved in this trend of elevated mortality in young adults. Traffic accidents and falls, significant problems previously described, were identified as significant risk factors for hospital mortality, consistent with other literature highlighting these causes as major contributors to severe TBI. The association of pre-hospital care with increased hospital mortality risk is concerning and may indicate the need for a more detailed analysis of pre-hospital care practices in the surveyed region.\u003c/p\u003e \u003cp\u003eLower scores (\u0026lt;\u0026thinsp;8) on the Glasgow Coma Scale, one of the primary neurological assessment tools, were associated with a significant increase in hospital mortality chances, confirming the importance of this prognostic indicator in TBI patients. Intubation during ICU admission and neurosurgery were identified as significant risk factors for hospital mortality. This association is consistent with previous evidence on the influence of these interventions on severe TBI outcomes.\u003c/p\u003e \u003cp\u003eThe analysis of severe TBI burden through YLLs underscores the substantial impact of this condition on the studied population. The average of 2,717.55 YLLs per year highlights the epidemiological relevance of TBI in the region. The distribution of YLLs by age group identifies young adults (aged 25 to 59 years) as the most affected population, aligning with population studies, providing additional comparisons to assess TBI burden in different regions.\u003c/p\u003e \u003cp\u003eThis study has the potential to significantly contribute to local epidemiology by providing detailed data on TBI epidemiology in the Greater Vit\u0026oacute;ria region, significantly contributing to the understanding of risk factors and associated outcomes. This is crucial for guiding public health policies and prevention strategies. Additionally, Comprehensive Predictor Evaluation through statistical analysis, including logistic regression, allowed for the comprehensive identification of predictors of severe TBI mortality. This adds a valuable layer of evidence to existing literature and aids in a deeper understanding of this condition.\u003c/p\u003e \u003cp\u003eLast but not least, the social and clinical impact resulting from the analysis of premature Years of Life Lost (YLLs) highlights the substantial impact of severe TBI burden on the studied population. These data can inform and guide not only healthcare professionals but also public health policymakers, aiming for more effective prevention and intervention strategies.\u003c/p\u003e \u003cp\u003eIn conclusion, this study has the potential to provide detailed data on TBI epidemiology in the Greater Vit\u0026oacute;ria region, contributing to the understanding of risk factors and associated outcomes. Limiting the sample to patients from a reference hospital may affect result generalization. This study has limitations. Unfortunately, unanalyzed factors may influence outcomes. For example, extrapolating results to the entire population is hindered by the main limitation of this study, which is the sample restriction to patients from a single reference hospital in the Greater Vit\u0026oacute;ria region, preventing a better understanding of variations in epidemiological profiles and healthcare services.\u003c/p\u003e \u003cp\u003eUnexplored factors that retrospective analysis may have overlooked may be relevant in influencing outcomes. The lack of this information may limit a complete understanding of severe TBI mortality predictors. The absence of temporal characteristics, given the retrospective nature of the study, may have been predominant limitations for a better understanding of changes in medical practices and outcomes over time; for example, the evolution in TBI clinical approach over time may have impacted the results.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, this study aimed to uncover predictors for severe Traumatic Brain Injury (TBI) mortality in the Greater Vit\u0026oacute;ria-ES region and delineate the TBI burden through Years of Life Lost Prematurely (YLLs) calculation. Our findings unveiled substantial associations between hospital mortality and variables such as sex, age, type of trauma, duration of hospitalization, length of endotracheal intubation, trauma-hospital admission time, Glasgow Coma Scale scores upon hospital admission, neurosurgeries, and hospital length of stay, all of which exhibited high predictive accuracy for mortality.\u003c/p\u003e \u003cp\u003eHospital mortality attributable to TBI in the region stood at 52.14%, surpassing percentiles observed in other studies. YLLs analysis underscored the significant impact of TBI, particularly among young adults. This study offers valuable insights into local epidemiology, furnishing detailed data to inform public health policies and prevention strategies in the Greater Vit\u0026oacute;ria-ES region.\u003c/p\u003e \u003cp\u003eNevertheless, it's vital to acknowledge the limitations stemming from our sample being confined to a single reference hospital, potentially affecting result generalization to the broader population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ch3\u003e\u003cstrong\u003eMVA - Mechanical Ventilation Assistance\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eCI - Confidence Interval\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eGCS - Glasgow Coma Scale\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eBIGS - Brazilian Institute of Geography and Statistics\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eUESH - Urgency and Emergency State Hospital\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eOR - Odds Ratio\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eSD - Standard Deviation\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eSPSS - Statistical Package for the Social Sciences\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eTBI - Traumatic Brain Injury\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eICF - Informed Consent Form\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eICU - Intensive Care Unit\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eGBD - Global Burden of Disease\u003c/strong\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eIMPACT - International Mission for Prognosis and Analysis of Clinical Trials in TBI\u003c/strong\u003e\u003c/h3\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to the State Hospital of Urgency and Emergency (HEUE) for supporting this study and providing all the resources necessary for this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received ethical consent from the Research Ethics Committee of the Federal University of Espírito Santo through term number 31705020.7.0000.5060.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) have no conflicts of interest relevant to this article. No\u0026nbsp;commercial or financial relationships could\u0026nbsp;be interpreted as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWGSF collected data, conducted statistical analysis, and interpreted the data, RFA performed statistical analysis, JVG collected data, HSV collected data, CBL collected data, LCSR collected data, TSR collected data, RSP collected data, RMG collected data, FZSA conducted statistical analysis, constructed tables, and interpreted results.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eALKHAIBARY A, et al.\u003c/strong\u003e Traumatic brain injury: a perspective on the silent epidemic. \u003cem\u003eCureus\u003c/em\u003e. 2021;13(5).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAREAS FZ, et al.\u003c/strong\u003e Predictors of hospital mortality and the related burden of disease in severe traumatic brain injury: a prospective multicentric study in Brazil. \u003cem\u003eFrontiers in neurology\u003c/em\u003e. 2019;10:432.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBENI S, N\u0026Iacute; CHR\u0026Oacute;IN\u0026Iacute;N D, FLETCHER T.\u003c/strong\u003e A focus on the how of meaningful physical education in primary schools. \u003cem\u003eSport, Education and Society\u003c/em\u003e. 2019;24(6):624-637.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBRAZIL. National Hospital Care Policy.\u0026nbsp;\u003c/strong\u003eJune 4, 2021. Available at: https://www.gov.br/saude/pt-br/composicao/saes/atencao-especializada-e-hospitalar/politica-nacional-de-atencao-hospitalar. Accessed on: July 8, 2023.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBRYAN-HANCOCK C, HARRISON J.\u003c/strong\u003e The global burden of traumatic brain injury: preliminary results from the Global Burden of Disease Project. \u003cem\u003eInjury Prevention\u003c/em\u003e. 2010;16(Suppl 1):A17-A17.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCAPIZZI A, WOO J, VERDUZCO-GUTIERREZ M.\u003c/strong\u003e Traumatic brain injury: an overview of epidemiology, pathophysiology, and medical management. \u003cem\u003eMedical Clinics\u003c/em\u003e. 2020;104(2):213-238.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCARTERI RBK, SILVA RA.\u003c/strong\u003e Traumatic brain injury hospital incidence in Brazil: an analysis of the past 10 years.\u0026nbsp;\u003cem\u003eRevista Brasileira de Terapia Intensiva\u003c/em\u003e. 2021;33(2). Available at: https://doi.org/10.5935/0103-507x.20210036. Accessed on: July 8, 2023.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCHESTNUT RM, et al.\u003c/strong\u003e A trial of intracranial-pressure monitoring in traumatic brain injury. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e. 2015;372(25):2407-2416.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCONDE V, SIEBNER HR.\u003c/strong\u003e Brain damage by trauma. \u003cem\u003eHandbook of clinical neurology\u003c/em\u003e. 2020;168:39-49.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDEWAN MC, et al.\u003c/strong\u003e Estimating the global incidence of traumatic brain injury. \u003cem\u003eJournal of neurosurgery\u003c/em\u003e. 2018;130(4):1080-1097.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDIXON KJ.\u003c/strong\u003e Pathophysiology of traumatic brain injury. \u003cem\u003ePhysical Medicine and Rehabilitation Clinics\u003c/em\u003e. 2017;28(2):215-225.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eEAPEN BC, CIFU DX.\u003c/strong\u003e \u003cem\u003eRehabilitation after traumatic brain injury\u003c/em\u003e. Elsevier Health Sciences. 2018.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGAO G, et al.\u003c/strong\u003e Clinical characteristics and outcomes in patients with traumatic brain injury in China: a prospective, multicenter, longitudinal, observational study. \u003cem\u003eThe Lancet Neurology\u003c/em\u003e. 2020;19(8):670-677.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eGERBER LM, Chiu YL, Carney N, H\u0026auml;rtl R, Ghajar J.\u003c/strong\u003e Marked reduction in mortality in patients with severe traumatic brain injury. \u003cem\u003eJ Neurosurg\u003c/em\u003e. 2013;119(6):1583-90.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHAAGSMA JA, et al.\u003c/strong\u003e The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. \u003cem\u003eInjury prevention\u003c/em\u003e. 2016;22(1):3-18.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHealth Prices Database.\u0026nbsp;\u003c/strong\u003eAvailable at: https://www.gov.br/saude/pt-br/acesso-a-informacao/banco-de-precos. Accessed on: July 8, 2023.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eINSTITUTE FOR HEALTH METRICS AND EVALUATION (IHME).\u003c/strong\u003e Global Burden of Disease Study 2019 (GBD 2019).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eJAMES SL, et al.\u003c/strong\u003e (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. \u003cem\u003eThe Lancet\u003c/em\u003e. 2018;392(10159):1789-1858.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLANGLOIS JA, RUTLAND-BROWN W, THOMAS KE.\u003c/strong\u003e The incidence of traumatic brain injury among children in the United States: differences by race. \u003cem\u003eThe Journal of head trauma rehabilitation\u003c/em\u003e. 2005;20(3):229-238.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMAAS AI, et al.\u003c/strong\u003e Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. \u003cem\u003eThe Lancet Neurology\u003c/em\u003e. 2017;16(12):987-1048.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMAGALH\u0026Atilde;ES ALG, et al.\u003c/strong\u003e Traumatic brain injury in Brazil: an epidemiological study and systematic review of the literature. \u003cem\u003eArquivos de Neuro-Psiquiatria\u003c/em\u003e. 2022;80:410-423.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMAJDAN M, et al.\u003c/strong\u003e Epidemiology of traumatic brain injuries in Europe: a cross-sectional analysis. \u003cem\u003eThe Lancet Public Health\u003c/em\u003e. 2017;2(8):e376-e383.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMCCREA MA, et al.\u003c/strong\u003e Functional outcomes over the first year after moderate to severe traumatic brain injury in the prospective, longitudinal TRACK-TBI study. \u003cem\u003eJAMA neurology\u003c/em\u003e. 2021;78(8):982-992.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMILLER GF, Daugherty J, Waltzman D, Sarmiento K.\u003c/strong\u003e Predictors of traumatic brain injury morbidity and mortality: Examination of data from the national trauma data bank: Predictors of TBI morbidity \u0026amp; mortality. \u003cem\u003eInjury\u003c/em\u003e. 2021;52(5):1138-1144.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMOORE L, et al.\u003c/strong\u003e Effectiveness of the implementation of prehospital thrombolysis in a statewide system. \u003cem\u003eCirculation\u003c/em\u003e. 2015;131(1):82-89.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMRC CRASH Trial Collaborators, et al.\u003c/strong\u003e Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. \u003cem\u003eBMJ\u003c/em\u003e. 2008;336(7641):425-9. doi: 10.1136/bmj.39461.643438.25.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMURRAY GD, et al.\u003c/strong\u003e Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. \u003cem\u003eJournal of neurotrauma\u003c/em\u003e. 2007;24(2):329-337.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNAJEM D, et al.\u003c/strong\u003e Traumatic brain injury: classification, models, and markers. \u003cem\u003eBiochemistry and cell biology\u003c/em\u003e. 2018;96(4):391-406.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePEREL P, et al.\u003c/strong\u003e Comparison of treatment effects between animal experiments and clinical trials: systematic review. \u003cem\u003eBMJ\u003c/em\u003e. 2018;337:a1682.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePEREL P, et al.\u003c/strong\u003e Systematic review of prognostic models in traumatic brain injury.\u0026nbsp;\u003cem\u003eBMC medical informatics and decision making\u003c/em\u003e. 2006;6(1):1-10.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePOL\u0026Iacute;TICA Nacional de Redu\u0026ccedil;\u0026atilde;o da Morbimortalidade por Acidentes e Viol\u0026ecirc;ncia.\u003c/strong\u003e \u003cem\u003eRevista de Sa\u0026uacute;de P\u0026uacute;blica\u003c/em\u003e. 2000;34(4):427-430. Available at: https://doi.org/10.1590/s0034-89102000000400020. Accessed on: July 8, 2023.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eR\u0026Eacute;A-NETO \u0026Aacute;, et al.\u003c/strong\u003e Epidemiological and clinical characteristics predictive of ICU mortality of patients with traumatic brain injury treated at a trauma referral hospital\u0026ndash;a cohort study. \u003cem\u003eBMC neurology\u003c/em\u003e. 2023;23(1):1-11.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eREIS C, et al.\u003c/strong\u003e What\u0026rsquo;s new in traumatic brain injury: update on tracking, monitoring and treatment. \u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e. 2015;16(6):11903-11965.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eROOZENBEEK B, et al.\u003c/strong\u003e Prediction of outcome after moderate and severe traumatic brain injury: external validation of the IMPACT and CRASH prognostic models. \u003cem\u003eCritical Care Medicine\u003c/em\u003e. 2007;41(10):2256-2257.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eROOZENBEEK B, MAAS A, MENON DK.\u003c/strong\u003e Changing patterns in the epidemiology of traumatic brain injury. \u003cem\u003eNature Reviews Neurology\u003c/em\u003e. 2013;9(4):231-236.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSTEYERBERG EW, et al.\u003c/strong\u003e Case-mix, care pathways, and outcomes in patients with traumatic brain injury in CENTER-TBI: a European prospective, multicenter, longitudinal, cohort study. \u003cem\u003eThe Lancet Neurology\u003c/em\u003e. 2019;18(10):923-934.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSTEYERBERG EW, et al.\u003c/strong\u003e Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. \u003cem\u003ePLoS Medicine\u003c/em\u003e. 2018;5(8):e165.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTAGLIAFERRI F, et al.\u003c/strong\u003e A systematic review of brain injury epidemiology in Europe. \u003cem\u003eActa neurochirurgica\u003c/em\u003e. 2006;148:255-268.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTEASDALE G, JENNETT B.\u003c/strong\u003e Assessment of coma and impaired consciousness: a practical scale. \u003cem\u003eThe Lancet\u003c/em\u003e. 1974;304(7872):81-84.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTORRES SG, BALDO JHL, PROP\u0026Eacute;RCIO AA.\u003c/strong\u003e EPIDEMIOLOGICAL PROFILE OF TRAUMATIC BRAIN INJURY IN THE NORTHERN REGION OF BRAZIL BETWEEN 2010 AND 2020. \u003cem\u003eFacit Business and Technology Journal\u003c/em\u003e. 2021;2(31).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWEBER C, et al.\u003c/strong\u003e Characteristics, image findings and clinical outcome of moderate and severe traumatic brain injury among severely injured children: a population-based cohort study. \u003cem\u003eEuropean journal of trauma and emergency surgery\u003c/em\u003e. 2022:1-8.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eZASLER ND, KATZ DI, ZAFONTE RD (Ed.).\u003c/strong\u003e \u003cem\u003eBrain injury medicine: principles and practice\u003c/em\u003e. Demos Medical Publishing. 2012.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 2 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidade Federal do Espírito Santo","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Traumatic Brain Injury, Predictors, Mortality, Years of Life Lost, TBI Burden.","lastPublishedDoi":"10.21203/rs.3.rs-4095458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4095458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraumatic Brain Injury (TBI) poses a notable incidence in both developed countries and emerging economies worldwide. Each year, approximately 70 million people are affected by TBI, and around 11% of these events are classified as severe. Data from the Global Burden of Disease (GBD) in 2019 revealed that Brazil accounts for 1.1% of all moderate/severe TBI incidents worldwide. Within this context, falls stand out as the primary cause, representing 67% of cases. Analyzing the statistics at the state level, it is observed that Espírito Santo is responsible for 14% of all TBI incidents in Brazil, with falls also being the main cause, at a percentile of 67%. The discovery of predictors of TBI mortality is crucial to support intervention and prognosis strategies, potentially guiding public health policies. Due to the scarcity of studies in this area, our objective was to identify predictors of mortality in severe TBI and analyze the burden of TBI in the metropolitan region of Vitória, Espírito Santo. We conducted a retrospective observational cohort study at a trauma reference hospital in the state, including all individuals diagnosed with severe TBI from 2019 to 2022. The dependent variable analyzed was the outcome of hospital mortality. After applying the inclusion criteria, 863 individuals were included in the study, of which 450 (52.14%) had a fatal outcome, and 413 (47.86%) survived. The mean age was 48.67 years, with 82.5% being male. Falls (55.56%) were the leading cause of death in the population. The final binomial logistic regression model indicated that age up to 59 years, time up to 10 days in orotracheal intubation (OTI), and the type of trauma from falls were variables with notable predictive power for severe TBI-related mortality in the population. The calculation of Years of Life Lost (YLLs) revealed that TBI had an accumulated impact of 10,870.23 years of life lost prematurely in the study population. This is the first retrospective study to analyze predictive variables and the burden of TBI in the Greater Vitória region. However, it is important to note that unanalyzed factors may influence outcomes, such as the sample restriction to patients from a single reference hospital in the region, potentially limiting the understanding of variations in epidemiological profiles and adjacent health services.\u003c/p\u003e","manuscriptTitle":"Predictors of In-Hospital Mortality and Years of Life Lost in Patients with Severe Traumatic Brain Injury: A Retrospective Study in the Metropolitan Region of Vitória, Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 14:40:31","doi":"10.21203/rs.3.rs-4095458/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ad4b365-df47-4f57-b06d-59f8872f5a91","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29418616,"name":"Epidemiology"}],"tags":[],"updatedAt":"2024-03-28T09:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-14 14:40:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4095458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4095458","identity":"rs-4095458","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}