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

doi:10.21203/rs.3.rs-4095458/v1

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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

https://doi.org/10.21203/rs.3.rs-4095458/v1

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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.

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.

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).

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.

<<<<<<<<<<<<<<<<<<<INSERT TABLE 1 NEAR HERE>>>>>>>>>>>>>>>>>>>

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.

<<<<<<<<<<<<<<<<<<<INSERT TABLE 2 NEAR HERE>>>>>>>>>>>>>>>>>>>

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.

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.

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

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.

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Tables 1 to 2 are available in the Supplementary Files section

The authors declare no competing interests.

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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

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Abstract

BACKGROUND

METHODS

RESULTS

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[x²(27) = 398,066; p < 0,001, R² Nagelkerke = 0,493]

Global Burden of Severe TBI Calculated by Years of Life Lost Due to TBI Events

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DISCUSSION

CONCLUSION

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

References

Tables

Additional Declarations

Supplementary Files

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Version 1