Assessing the Geriatric Trauma Outcome Score: A National Analysis

Abstract

Background

To assess the strength of the Geriatric Trauma Outcome Score (GTOS) in predicting mortality in geriatric trauma patients using a nationally representative sample.

Methods

Data from the National Trauma Data Bank were collected retrospectively from 2017 and 2018 for patients aged 65 and older (N = 487,317). Age, injury severity score (ISS), transfusion status, and hospital discharge status (survived vs deceased) were extracted. GTOS was then calculated for each patient. Simple logistic regression models were used to model hospital discharge status with GTOS and each component. Receiver-operating characteristic (ROC) curves were created using the predicted probabilities from the logistic models, and the area under the curve (AUC) for each model was calculated.

Results

Patients had a mean (SD) GTOS of 101.85 (19.53), age of 77.06 (7.20) years, and ISS of 9.75 (6.90). Very few (2%) patients had a blood transfusion within 24 hours of admission, and the overall survival rate was 96%. All models showed statistical significance in predicting discharge status (P < 0.0001) with AUCs of 0.5436 (age), 0.5727 (receipt of blood), 0.7979 (ISS), and 0.8145 (GTOS). When comparing models from each component to that of GTOS, GTOS remained more predictive than each individual component (P < 0.0001).

Discussion

After analyzing our models based on a nationally representative trauma data bank, GTOS predicted mortality better than each of its individual components. Therefore, GTOS is an appropriate tool to predict mortality among geriatric trauma patients and should be considered for applications such as informing goals-of-care or trauma transfer decision-making.

Keywords

geriatrics trauma acute care acute care surgery critical care

Key Takeaways

• This study suggests that the Geriatric Trauma Outcome Score (GTOS) outperforms any of its individual components in predicting mortality among the geriatric trauma population

• The findings from this study across a nationally representative sample underscores the utility of the Geriatric Trauma Outcome Score (GTOS) as a robust predictor of mortality and hospital discharge outcomes among geriatric trauma patients

• By quantifying the likelihood of mortality, the Geriatric Trauma Outcome Score (GTOS) provides an evidence-based framework to guide discussions about prognosis, advanced directives, and goals of care

Introduction

Geriatric trauma is a critical public health issue that accounts for a significant proportion of trauma-related morbidity and mortality.¹ As the population ages, the incidence of trauma among older adults continues to rise, presenting unique challenges to health care systems. Older adults are more vulnerable to poor outcomes following trauma due to physiological changes associated with aging, including reduced cardiovascular and pulmonary reserves, polypharmacy, and an increased prevalence of comorbidities such as osteoporosis and cognitive decline.² These factors not only complicate the clinical management of geriatric trauma patients but also necessitate specialized tools to guide prognosis and decision-making.

In 2015, the Geriatric Trauma Outcome Score (GTOS) was first presented to predict mortality in geriatric trauma patients, which could be utilized to better prognosticate and provide more information to patients to make informed decisions about their own care.³ To establish this score, Zhao et al retrospectively collected geriatric trauma data from a single Level 1 trauma center over thirteen years and analyzed age, injury severity score (ISS), whether units of packed red blood cells were transfused in the first 24 hours of admission, and mortality status; an equation with these variables was then developed to calculate the GTOS, which would be associated with a certain likelihood of mortality/survival. Since its introduction in 2015, the GTOS has been expanded to estimate the probability of unfavorable discharge (skilled nursing facility, hospice, etc.) (GTOS II), adjusted based on Glascow Coma Score (GCS) to predict 1-year mortality after hospitalization (GTOS III), and extended to predict neurological performance outcomes.^4-6 It also has been validated in a variety of settings both within and outside the United States, established the potential for wide utilization and adoption of this tool.^7-12

Despite the validation of the GTOS in several settings, individual homogenous samples may lack external validity due to disparities at baseline. As a result, there is an unmet need to assess the GTOS’ capability in a nationally representative sample to allow for broader application across the United States. The purpose of this study was to utilize data from a nationally representative trauma bank to determine whether individual components of the GTOS could be used as a replacement for the GTOS overall. We hypothesize the GTOS will more accurately predict and discriminate between survival and mortality compared to its individual components—age, ISS, and transfusion status—across a nationally representative sample. Furthermore, we explore the implications of GTOS in broader applications, such as resource allocation and the standardization of care protocols for geriatric trauma patients.

Methods

This study was approved by the Institutional Review Board (IRB) at Ballad Health as minimal risk to participants and non-human subjects’ research. Consent was not obtained from participants due to the secondary, de-identified nature of the data set. Retrospective data from the National Trauma Data Bank (NTDB) from 2017 and 2018 were utilized for this project. The NTDB is the largest ongoing (since 1989) compilation of U.S. trauma registry data ever constructed. The database contains records from more than 900 trauma centers that voluntarily contribute data, with the database currently being comprised of more than 7.5 million individual electronic records.¹³

From the NTDB, variables of interest including age (years), injury severity score (ISS), transfusion status (receipt of red blood cell transfusion within 24 hours of admission vs not), and mortality (deceased vs survived to discharge) were extracted. Age and injury severity scores were reported as continuous variables, while transfusion status and mortality status were reported as binary, categorical variables with values of 0 for no transfusion or for survived status and 1 for received transfusion or for a deceased. Data were then subset to include only senior adults (age ≥65 years), and records with missing data for any of the extracted components were excluded from the analysis. Geriatric Trauma Outcome Score (GTOS) is a continuous variable that was calculated for each record as follows:³

G T O S = A g e + 2.5 (I S S) + 22 (r e c e i p t o f b l o o d t r a n s f u s i o n w i t h i n 24 h o u r s)

Once computed, descriptive statistics of the study population were calculated, with means and standard deviations reported for continuous variables and frequencies and percentages reported for categorical variables. Then, individual, simple logistic regression models were used to model mortality (outcome) with each component of the GTOS as well as with the total GTOS. Odds ratios (OR) and 95% confidence intervals (95% CIs) were calculated for each model. Receiver-operating characteristic (ROC) curves were created using the predicted probabilities from each model, and the area under the curve (AUC) was calculated for each. Subsequently, the AUC for each individual predictor model was then compared to the GTOS model (reference model) using a Bonferroni-adjusted chi-square test for significance to determine whether individual components of the GTOS were better predictors of mortality than the calculated GTOS. An a priori α = 0.05 two-sided level of significance was used for all modeling (Version 9.4, SAS for Windows, StataNOW/SE 18.5 for Windows).

Results

Records from the 2017 NTDB (N = 997,970) and the 2018 NTDB (N = 1,043,736) were combined after selecting records with age 65+ years and removing records with missing components. The final analytic data set was comprised of 487,317 records (Figure 1).

Patients ranged in age from 65 to 89 years [mean (SD)] = 77.06 (7.20)], with a mean (SD) ISS of 9.75 (6.90), and mean (SD) GTOS of 101.85 (19.53)]. The majority of patients were alive at time of discharge (96%), with only 2% receiving blood transfusion within 24 hours of arrival to the trauma center (Table 1).

Table 1.

Descriptive Statistics of Study Population.

	Total (N = 487,317)	2017 (N = 229,173)	2018 (N = 258,144)
	Mean ± SD	Mean ± SD	Mean ± SD
Age	77.06 ± 7.20	77.08 ± 7.23	77.04 ± 7.18
ISS	9.72 ± 6.90	9.76 ± 6.95	9.68 ± 6.85
GTOS	101.85 ± 19.53	101.99 ± 19.66	101.73 ± 19.41

	n (%)	n (%)	n (%)
Mortality status at discharge
Survived	466155 (96)	218993 (96)	247162 (96)
Deceased	21162 (4)	10180 (4)	10982 (4)
Receipt of red blood cells within 24 hours	11017 (2)	5247 (2)	5770 (2)

All simple logistic regression models demonstrated statistical significance at the α = 0.05 level in predicting discharge status (P < 0.001), with the largest effect size observed for blood transfusion, followed by ISS then age (Table 2). The odds of mortality are 11.64 times higher for individuals that received a red blood cell transfusion within 24 hours of admission compared to those who did not receive a blood transfusion (OR = 11.64, 95% CI: 11.15 to 12.15, P < 0.0001). The odds of mortality were 3.95 times higher (95% CI: 3.89 to 4.01, P < 0.0001) for every 10-point increase in ISS.

Table 2.

Simple Logistic Regression Modeling Mortality.

Models	Odds ratio	95% CI	P-value
GTOS (10 points)	1.69	1.68 - 1.70	<0.0001
Age (5 years)	1.11	1.10 - 1.12	<0.0001
ISS (10 points)	3.95	3.89 - 4.01	<0.0001
Receipt of red blood cells^a	11.64	11.15 - 12.15	<0.0001

^aReference group is individuals that did not receive red cell blood transfusion.

When evaluating AUC of the ROC curves, both GTOS and ISS were similarly predictive (0.81 and 0.80, respectively) and higher than those for blood transfusion (AUC = 0.57) and age (AUC = 0.54) (Table 3). When comparing the individual component models to the GTOS model, GTOS was statistically different from all other models and remained the most predictive of mortality (P < 0.0001) (Figure 2).

Table 3.

Areas Under the Curve and Gold Standard Comparison for Logistic Models.

Models	ROC area	Std. error	P-value^a
GTOS	0.8145	0.0017
Age	0.5436	0.0020	<0.0001
ISS	0.7979	0.0018	<0.0001
Receipt of red blood cells	0.5727	0.0013	<0.0001

^aCompares AUC from GTOS model to AUC for individual component models.

Discussion

With the proportion of adults aged 65 and above increasing to a predicted 22% of the population by 2040, it is paramount that there are validated screening tools to predict the prognosis of geriatric trauma patients.¹⁴ Frailty has been shown to have adverse effects in this population, including increased mortality, higher complication rates, and unfavorable discharge disposition.^15-17 In response to this unmet need, Geriatric Trauma Outcome Score (GTOS) was developed as robust predictor of mortality among geriatric trauma patients that has been developed and validated in multiple single-center studies, but to date has not been studied with nationally representative data. A study such as this enables researchers to detect significant differences across a complete data set demonstrating that the GTOS outperforms its individual components—age, Injury Severity Score (ISS), and need for early blood transfusion—in predicting mortality, emphasizing its comprehensive nature. These results align with previous individual center and multicenter studies validating the GTOS in various clinical settings, further establishing its role as a valuable tool in geriatric trauma care.^7-12 Most importantly, the novelty of our study stems from utilizing data from the NTDB to validate the GTOS with a nationally representative sample.

The GTOS’ primary strength is its ability to provide a standardized metric that simplifies complex clinical data into actionable insights. This can be particularly beneficial in resource-constrained settings, such as resource limited hospitals and health care centers, where health care providers may lack access to advanced diagnostic tools or specialized personnel. To date, there have been multiple studies examining how predictive the GTOS is compared to other known scores, such as the Trauma-Specific Frailty Index (TSFI) and the Trauma and Injury Severity Score (TRISS). When compared to TSFI, GTOS was superior in predicting mortality.¹⁸ However, when compared to TRISS, the results are more variable with studies suggesting that they have similar predictive ability, while another suggests that TRISS is more predictive when compared to GTOS.^19,20 However, as the GTOS has fewer variables compared to both TSFI and TRISS, GTOS may be more easily implemented in settings where either resources or time are limited. By offering a reliable means of prognostication, the GTOS can aid in triaging patients, optimizing resource allocation, and informing decisions regarding trauma transfers. For instance, a high score might prompt earlier discussions about goals of care between patient/family and provider, potentially reducing the prevalence of nontherapeutic transfers and ensuring that interventions align with patient and family preferences.

Furthermore, the application of the GTOS in this study highlights its potential to enhance communication between health care providers, patients, and families. When having a goals-of-care discussion with a select patient, the provider can quickly calculate the GTOS based on age, ISS, and their transfusion status and correlate that score to a certain chance of mortality. ISS can either be personally calculated or obtained from their registry/research team, and challenges could be addressed through consultation with higher level referral trauma centers. In more advanced systems, the GTOS and respective chance of mortality could be automatically calculated in the electronic medical record for each trauma patient. Best practice advisories could be designed to activate if someone has not had a goals-of-care discussion and their chance of mortality has passed a certain pre-set threshold. Given the relative simplicity of calculating the GTOS compared to other scoring modalities, the GTOS allows for an evidence-based, data driven discussion between providers, patients, and family members to guide discussions about prognosis and care planning. This is especially crucial in the geriatric population, where advanced directives and clear goals of care are often underutilized, and where other scoring systems may not apply. Early identification of patients with a high probability of poor outcomes can empower families to make informed decisions, ultimately improving patient satisfaction and aligning care with individual values.

Despite its strengths, the study also identifies several limitations that warrant consideration. The retrospective nature of the analysis may introduce selection bias, and the reliance on registry data could lead to incomplete or incorrect information. Additionally, while the study utilized a nationally representative sample, the findings may not be generalizable to all health care settings, particularly those with significantly different patient demographics or trauma care protocols. Future research should aim to validate the GTOS across diverse populations and explore its integration into electronic health records to automate calculations and enhance clinical utility.

Despite the limitations, this study across a nationally representative sample underscores the value of the GTOS as a comprehensive prognostic tool in geriatric trauma care. Its ability to integrate multiple clinical variables into a single predictive score makes it a practical resource for improving outcomes and standardizing care in both urban and rural settings. As healthcare systems continue to adapt to the growing volume of geriatric trauma, tools like the GTOS will be instrumental in guiding evidence-based decision-making and ensuring that care remains patient-centered.

Footnotes

ORCID iDs

Henrik A. Hahamyan

Matthew A. Heard

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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