ABSTRACT
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Purpose
Traumatic brain injury (TBI) is a leading cause of morbidity and mortality, particularly in older adults, in whom age-related physiological changes influence injury response and recovery. Smaller-volume TBIs, including subdural hematomas (≤8 mm), epidural hematomas (≤8 mm), and contusions (≤2 cm), are generally considered less severe; however, their clinical impact varies with age. This study aims to assess the effect of age on clinical outcomes, specifically midline shift and neurosurgical interventions, in patients with isolated, smaller-volume blunt TBIs.
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Methods
This retrospective registry-based cohort study analyzed American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) Participant Use File data from 2017 to 2022. Patients aged ≥40 years with isolated smaller-volume TBIs were categorized into middle-aged (40–65 years) and older adult (≥65 years) groups. Multivariate logistic regression assessed associations between age, TBI type, midline shift, and neurosurgical interventions, adjusting for demographic, clinical, and injury-related variables.
-
Results
Among 135,343 patients, older adults with small contusions were 68% lower odds to experience a midline shift (adjusted odds ratio [OR], 0.32; 95% CI, 0.23–0.43; P<0.01). They also had significantly lower odds of undergoing craniotomy (epidural hematomas: adjusted OR, 0.60 [95% CI, 0.37–0.95], P=0.03; contusions: adjusted OR, 0.11 [95% CI, 0.05–0.23], P<0.01) or intracranial pressure monitoring (contusions: adjusted OR, 0.36; 95% CI, 0.18–0.75; P<0.01) compared to middle-aged patients.
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Conclusion
Older adults with smaller TBIs are less likely to experience midline shift or undergo neurosurgical intervention. These findings emphasize the need for age-specific management strategies and suggest that a less aggressive intervention approach may be appropriate for older adults with smaller TBIs. Existing guidelines may require age-specific revisions. Further research is needed to explore long-term outcomes.
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Keywords: Contusions; Logistic models; Morbidity; Subdural hematoma; Traumatic brain injuries
Graphical abstract
INTRODUCTION
Background
Traumatic brain injury (TBI) remains a leading cause of morbidity and mortality, with about 2.8 million cases annually in the United States, and falls account for nearly half of all cases [
1]. Conventional severity measures such as the Glasgow Coma Scale (GCS), Abbreviated Injury Scale (AIS), and Injury Severity Score (ISS) are widely used, but they may not fully capture the risk associated with smaller hemorrhagic lesions [
2].
Smaller TBIs, including subdural hematoma (SDH; ≤8 mm), epidural hematomas (EDH; ≤8 mm), and contusions (≤2 cm), are generally considered less severe, but emerging evidence indicates that they can still lead to meaningful neurological decline [
3,
4]. Earlier studies highlighted the vulnerability of older adults due to cerebral atrophy, reduced compliance, and anticoagulation use [
5,
6], whereas more recent work suggests that middle-aged patients may also face risks of adverse outcomes, particularly when brain volume and reserve are preserved, increasing susceptibility to mass effect [
7,
8]. Current guidelines and classification systems, such as the Parkland modified Berne-Norwood criteria, do not fully account for these age-specific differences, underscoring the need for improved management strategies [
9]. Age-specific risk of midline shift and interventions in small TBIs remains incompletely characterized.
The objective of this study was to quantify age-specific risks of midline shift and neurosurgical interventions among adults with isolated small-volume blunt traumatic brain injuries using a multicenter trauma registry. Primary outcomes were midline shift on early computed tomography (CT) and neurosurgical interventions, including craniotomy and intracranial pressure (ICP) monitoring. We hypothesized that, among patients with small hemorrhagic TBIs, middle-aged patients (40–64 years) would have a higher risk of midline shift and neurosurgical intervention than older adults (≥65 years), after adjustment for demographic, clinical, and injury-related factors.
METHODS
Ethics statement
This study was deemed exempt from oversight by the Institutional Review Board of Marshfield Clinic Research Institute (No. RHO10224), in compliance with current regulations. Results were reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines.
Study design and setting
This was a retrospective registry-based cohort study, conducted using data from the American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) Participant Use File (PUF), which includes prospectively collected trauma registry data from participating level I–III trauma centers across the United States. We analyzed admissions recorded between January 1, 2017, and December 31, 2022. Outcomes were restricted to events occurring during the index hospitalization, as no follow-up data after discharge were available.
Participants
The study population consisted of adult patients (≥40 years) who had an isolated TBI (
Fig. 1). Patients were excluded if they had a skull fracture. Patients were defined as having an isolated TBI (by size and type) using AIS predot codes, which determine the presentation and size of hemorrhage or hematoma based on the CT scans obtained closest to the first 24 hours [
10]. Patients were also excluded if they had an ISS region 2–6 (face, chest, abdominal or pelvic, extremities or pelvic girdle, external) with an AIS score >2, indicating moderate to severe injury. This exclusion was applied to ensure that TBI injuries were isolated to mild injury in other body regions. The coding language used to define TBI type and size risk groups was based on AIS predot language in the ACS-TQIP-PUF (
Material S1). The TBI population was further restricted to blunt trauma only, with smaller isolated SDH (≤8 mm), EDH (≤8 mm), and contusions (≤2 cm). There were six exposure groups based on adult (40–64 years) and older adult (≥65 years) age categories and TBI type. The TBI size and type groupings were aligned with the modified Berne-Norwood criteria.
Participants were followed only during the index hospital admission; outcomes were assessed from admission through discharge, and no information on events after discharge was available.
Variables
The primary outcomes of interest in this study were midline shift and neurosurgical interventions among patients with smaller isolated TBIs. Midline shift, an important radiological marker of ICP and brain herniation risk, was evaluated as a binary outcome to assess its association with different TBI types and sizes in middle-aged (40–64 years) and older adult (≥65 years) patients. Neurosurgical interventions, including craniotomy and ICP monitoring, were analyzed to determine the likelihood of requiring surgical management based on TBI classification. Because smaller hemorrhagic TBIs (SDH ≤8 mm, EDH ≤8 mm, and contusion ≤2 cm) are often considered less severe, this study sought to determine whether they nonetheless carry meaningful risks for neurological deterioration requiring intervention.
Data source and measurement
This retrospective cohort study utilized the ACS-TQIP-PUF from 2017 to 2022. The ACS-TQIP-PUF dataset contains anonymized research data from more than 700 trauma centers across the United States, including level I–V or undesignated centers, and comprises all records transmitted to the National Trauma Data Bank [
11]. The study involved a retrospective review of the dataset to gather information on multiple factors, including TBI type and size, demographics (age, sex, race, ethnicity, insurance, center characteristics, and mode of transportation), injury categories (midline shift, injury type, ISS and AIS body regions, GCS, and injury mechanism), venous thrombosis prophylaxis (VTEp), comorbidities, and neurosurgical interventions (ICP, craniotomy).
In ACS-TQIP, radiologic variables such as hemorrhage type, hemorrhage size, and midline shift are abstracted from radiology reports using standardized registry protocols. Comorbidities and preinjury medication use, including anticoagulant and antiplatelet agents, are recorded using predefined registry fields. These standardized procedures were applied across all participating centers, reducing differential misclassification between age groups and TBI subtypes.
Bias
To reduce selection bias, inclusion and exclusion criteria were applied uniformly to all registry records from participating centers. Standardized ACS-TQIP definitions and coding procedures were used to minimize information bias related to misclassification of injury type, hemorrhage size, and outcomes. Multivariable logistic regression models that included clinically relevant covariates were used to mitigate confounding by baseline demographic, clinical, and injury-related factors.
Study size
All patients in ACS-TQIP from 2017 to 2022 who met the inclusion and exclusion criteria were included in the analytic cohort. No formal a priori sample size calculation was performed, because the study was designed to use the entire eligible registry population to maximize the precision of estimates and allow for robust subgroup analyses.
Statistical methods
Descriptive statistics for the full sample were reported, including mean, median, and frequencies. Continuous variables were compared using the Kruskal-Wallis test, while categorical variables were compared using Pearson chi-square test of proportions, as appropriate, in R ver. 4.4.1 (R Foundation for Statistical Computing). Predictor variables for the multivariate regression model were selected using multicollinearity testing for tolerance, ensuring that values greater than 0.5 met the goodness-of-fit criterion. All variables chosen for the model were found to be independent of each other and adequately powered. Statistical significance was defined as P<0.05 for all models. Multivariate logistic regression models were constructed with midline shift and neurosurgical interventions as the dependent variables, respectively. Logistic regression analyses were performed using IBM SPSS ver. 28 (IBM Corp). The primary predictor effects included size and type of TBI stratified by adult and older adult age groups. Secondary controlled effects (covariates) included sex, race, ethnicity, injury mechanism, mode of transportation, GCS, facility level, venous thrombosis prophylaxis with low-molecular-weight heparin, and comorbidities.
Statistical analyses incorporated nonparametric and chi-square tests to evaluate associations between patient characteristics and mortality outcomes. Due to the non-normal distribution of continuous variables, the Kruskal-Wallis test was used, whereas Pearson chi-square test assessed categorical variable distributions. These procedures identified significant differences across TBI subtypes prior to conducting logistic regression analysis.
RESULTS
Patient description
The final study population included 135,343 middle-aged and older adult patients with moderate-to-critical TBI. Patients were categorized into six exposure groups based on TBI type and age: SDH ≤8 mm (24.7% in 40–64 years, 68.5% in ≥65 years), EDH ≤8 mm (0.6% in 40–64 years, 1.4% in ≥65 years), and isolated contusion ≤2 cm (1.9% in 40–64 years, 2.8% in ≥65 years). The median age of the total sample was 74 years (interquartile range [IQR], 64.5–83.5 years) (
Table S1). Most patients were Caucasian (81.4%) and male (52.3%), with Medicare (63.4%) as the primary insurance provider. Most patients arrived at the hospital via ground ambulance (79.8%), and 41.6% were transported to a level I trauma center. Among the sample, 24.2% were receiving preexisting anticoagulation therapy (
Table S2). Additional comorbidities associated with increased risk of bleeding or intracranial hemorrhage included renal failure (3.5%), hypertension (64.3%), congestive heart failure (8.1%), cerebrovascular accident (6.9%), and alcohol use disorder (8.8%). The median ISS for the total sample was 14 (IQR, 10.5–17.5; moderate injury), while the maximum head AIS score was 3 (IQR, 2.5–3.5; serious TBI) (
Table S3). The mean GCS score was 15.0±1.8 (mild TBI), and most injuries (82.4%) resulted from falls. Among the total sample, 6.0% exhibited midline shift, 0.8% required ICP monitoring, and 2.3% underwent craniotomy.
Among older adults with smaller EDH, the median age was 78 years (IQR, 72–84 years; P<0.01). This group was more likely to have preexisting anticoagulation therapy (29.6%, P<0.01) and renal failure (3.7%, P<0.01). Older adults with smaller EDH who underwent craniotomy (1.9%) typically received the procedure within a mean of 41.9±57.0 hours after hospital arrival (P<0.01). Those who received an ICP monitor (0.4%) typically underwent the procedure within a mean of 11.4±12.5 hours (P<0.01). Older adults with smaller contusions had a median age of 78 years (IQR, 72–84 years; P<0.01) and were most commonly covered by Medicare (79.5%, P<0.01), with 81.0% arriving via ground transportation (P<0.01). This group was also more likely to have preexisting anticoagulation therapy (29.6%, P<0.01), dementia (18.2%, P<0.01), and functionally dependent health status (23.9%, P<0.01). Among older adult contusion patients who received ICP monitoring (0.2%), the procedure was generally performed within a mean of 15.6±11.5 hours after arrival (P<0.01).
Associations of age group and clinical factors with midline shift
The logistic regression model assessing midline shift was statistically significant (P<0.01) (
Table 1). Older adults with smaller contusions (≤2 cm) had 68% lower odds of experiencing a midline shift compared to the middle-aged cohort (odds ratio [OR], 0.32; 95% CI, 0.23–0.43; P<0.01). Additionally, patients with an advanced directive (OR, 1.18; 95% CI, 1.05–1.32; P<0.01), anticoagulation therapy (OR, 1.12; 95% CI, 1.04–1.20; P<0.01), and cerebrovascular accident (OR, 1.21; 95% CI, 1.09–1.34; P<0.01) were more likely to experience a midline shift.
The logistic regression model predicting craniotomy was statistically significant (P<0.01) (
Table 2). Older adults with smaller contusions (≤2 cm) had 89% lower odds of undergoing craniotomy compared to the middle-aged cohort (OR, 0.11; 95% CI, 0.05–0.23; P<0.01). Older adults with smaller EDH (≤8 mm) had 40% lower odds of undergoing craniotomy compared to the adult cohort (OR, 0.60; 95% CI, 0.37–0.95; P=0.03).
The logistic regression model assessing ICP monitoring was statistically significant (P<0.01) (
Table 3). Older adults with smaller contusions (≤2 cm) had 64% lower odds of receiving ICP monitoring compared to the middle-aged cohort (OR, 0.36; 95% CI, 0.18–0.75; P<0.01). Male TBI patients (OR, 1.28; 95% CI, 1.10–1.49; P<0.01), those administered low-molecular-weight heparin (OR, 1.59; 95% CI, 1.36–1.87; P<0.01), and those with preexisting congestive heart failure (OR, 1.31; 95% CI, 1.01–1.59; P=0.03) had higher odds of receiving ICP monitoring.
DISCUSSION
Key results
This study offers new insights into the clinical importance of smaller isolated TBIs among middle-aged and older adult populations. Our findings challenge the common belief that older adults represent the highest-risk group for deterioration after minor hemorrhagic lesions. Instead, middle-aged patients (40–64 years) showed significantly higher odds of midline shift, craniotomy, and ICP monitoring compared to older adults, despite the latter having more baseline health issues and more frequent anticoagulant use. These results suggest that age influences the physiological response to small-volume hemorrhages and should be considered when planning surveillance and treatment strategies.
Interpretation and comparison with previous studies
One possible explanation for these age-specific differences is the structural change associated with cerebral atrophy in older adults. Progressive loss of brain volume increases intracranial compliance, allowing for greater tolerance of hemorrhage before mass effect or midline shift develops [
5,
6]. In contrast, middle-aged patients tend to have relatively preserved brain volume and reserve, which may render them more susceptible to mass effect even with small hemorrhagic lesions [
7]. This paradox may explain why older adults, despite higher rates of anticoagulation and multiple comorbidities, demonstrated lower rates of urgent neurosurgical intervention in our study.
Current classification systems, such as the modified Berne-Norwood criteria, categorize TBI risk primarily by lesion size and type, without adequately incorporating age-related vulnerability [
9]. Our results indicate that middle-aged patients with small hemorrhages may require lower thresholds for neurosurgical consultation, repeat imaging, or intensive care unit (ICU)-level monitoring compared to older adults. The latter may benefit from a more individualized evaluation that focuses on anticoagulation reversal, management of comorbidities, and functional recovery [
8].
Our findings also contribute to the growing literature questioning the adequacy of size-based definitions of “minor” or “low-risk” hemorrhagic TBI. Although smaller subdural hematomas, epidural hematomas, and contusions are often viewed as clinically stable, this study shows that their progression varies by age and can still result in severe outcomes, particularly in middle-aged patients. This highlights the need to update guideline recommendations not only for older adults but also for middle-aged patients who may otherwise be overlooked in existing triage and monitoring protocols.
Overall, this study highlights a critical and often underrecognized clinical insight: middle-aged patients with smaller TBIs are at higher risk for midline shift and neurosurgical intervention than older adults, even though the latter group carries a greater burden of comorbidities and anticoagulation. These findings underscore the importance of integrating age considerations into TBI classification systems, adjusting guideline thresholds for monitoring and intervention, and tailoring management strategies to improve outcomes across the adult lifespan.
Clinical implications
The findings of this study suggest that existing guideline frameworks for smaller TBIs, which often emphasize risk in older adults, may require revision to recognize the vulnerability of middle-aged patients. Although older adults remain a key clinical concern due to anticoagulation management and long-term disability, our data show that middle-aged adults have a higher immediate risk of midline shift and neurosurgical intervention. Current protocols, such as the modified Berne-Norwood criteria, should incorporate age-specific thresholds for intervention. In particular, middle-aged patients with smaller hemorrhagic lesions may require lower thresholds for ICU admission, early repeat imaging, and prompt neurosurgical consultation. Conversely, older adults may benefit from more personalized management plans emphasizing anticoagulation reversal, stabilization of comorbidities, and functional recovery. Recognizing these distinct trajectories may enhance triage decisions, improve monitoring strategies, and support the development of more accurate age-stratified TBI management guidelines.
Limitations
First, as a retrospective cohort study utilizing the ACS-TQIP database, the study is inherently subject to selection and information bias. The dataset relies on standardized trauma registry coding, and although efforts were made to ensure data accuracy, the potential for misclassification of TBI size and type remains. Additionally, because the study relied on AIS predot codes, subtle variations in hemorrhagic injury progression that could influence clinical outcomes may not have been captured. Second, the study is limited by the absence of long-term functional outcomes. Although we analyzed in-hospital metrics such as midline shift and neurosurgical interventions, we were unable to evaluate cognitive, neurological, or quality-of-life outcomes after discharge. Given the increasing recognition of TBI-related cognitive impairment and disability, future studies should incorporate longitudinal follow-up to better assess the long-term impact of smaller TBIs in aging populations. Lastly, although we adjusted for important demographic and clinical variables, residual confounding remains a possibility. Provider decision-making, differences in hospital neurosurgical capabilities, and variations in clinical management strategies may influence the likelihood of surgical versus conservative treatment.
Generalizability
The generalizability of our findings applies primarily to adult patients treated at ACS-TQIP–participating trauma centers in the United States, which are predominantly high-resource hospitals with established trauma systems. Applicability to non-ACS centers, lower-resource settings, or regions with different prehospital and in-hospital care patterns may therefore be limited. Additionally, our cohort focused on isolated blunt TBIs with small hemorrhagic lesions, so extrapolation to penetrating trauma, larger-volume hemorrhages, or patients with significant extracranial injuries should be approached cautiously.
Suggestions for further studies
Future research should focus on prospective validation of these findings and the inclusion of long-term outcomes. Although our study concentrated on acute neurological deterioration, middle-aged patients may face unique challenges related to returning to work and maintaining independence, whereas older adults may be more vulnerable to long-term disability and institutionalization [
12]. Advanced risk stratification tools, including predictive modeling and machine learning, could enhance patient-specific outcome predictions by integrating lesion characteristics, comorbidities, and dynamic physiological data. Future investigations should build upon these findings to optimize clinical decision-making, improve patient outcomes, and refine TBI management guidelines for this at-risk population.
This study highlights the underappreciated risks associated with smaller hemorrhagic TBIs, particularly in aging populations with comorbidities. Our findings suggest that lesion size alone is insufficient to predict neurological deterioration or the need for surgical intervention; instead, age, anticoagulation status, and injury mechanism play essential roles. These insights reinforce the need for updated risk stratification models, enhanced surveillance strategies, and tailored management protocols for middle-aged and older TBI patients.
ARTICLE INFORMATION
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Author contributions
Conceptualization: all authors; Data curation: HXRL, DLM; Formal analysis: HXRL, DLM; Funding acquisition: HXRL; Methodology: all authors; Writing–original draft: all authors; Writing–review & editing: all authors. All authors read and approved the final manuscript.
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Conflicts of interest
The authors have no conflicts of interest to declare.
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Funding
This study was supported in part through philanthropic support of the Marshfield Clinic Research Institute, led by the Marshfield Clinic Health System Foundation (No. 255800-00-RES SUPPT TRAUMA). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Data availability
Data analyzed in this study were obtained from the American College of Surgeons (ACS) TQP PUF Admission 2017 to 2022 Version. The data are not publicly accessible and were used under license for this study. They may be obtained from the ACS by qualified investigators under a data-use agreement or from the corresponding author upon reasonable request, with permission from the ACS.
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Additional information
This study was presented at the 2025 American College of Surgeons Trauma Quality and Safety Conference (QSCC25) as an abstract (No. 2046178) on July 17–20, 2025, in San Diego, CA, USA.
Supplementary materials
Material S1.
The coding language used to group traumatic brain injury type and size was determined using AIS predot language in the American College of Surgeons Trauma Quality Improvement Program Participant Use File.
jacs-2025-0007-Material-S1.xlsx
Table S1.
Patient demographics among middle-aged and older adults (≥40 years) who had smaller isolated blunt traumatic brain injury (SDH, EDH, contusion) without a skull fracture
jacs-2025-0007-Table-S1.pdf
Table S2.
Patient injury characteristics among middle-aged and older adult patients (≥40 years) with smaller isolated blunt traumatic brain injury (SDH, EDH, contusion) without a skull fracture
jacs-2025-0007-Table-S2.pdf
Table S3.
Patient comorbidities among middle-aged and older adults (≥40 years) who had smaller isolated blunt traumatic brain injury (SDH, EDH, contusion) without a skull fracture
jacs-2025-0007-Table-S3.pdf
Fig. 1.Participant flowchart among middle-aged to older adults (≥40 years) who had smaller isolated blunt traumatic brain injury (TBI; subdural hemorrhage [SDH], subarachnoid hemorrhage, epidural hemorrhage [EDH]) without a skull fracture. ACS, American College of Surgeons; TQIP, Trauma Quality Improvement Program; AIS, Abbreviated Injury Scale; ISS, Injury Severity Score; NFS, not further specified.
Table 1.Logistic regression analysis of factors associated with a midline shift among middle-aged to older adults (≥ 40 years) who had smaller isolated blunt traumatic brain injury (SDH, EDH, contusion) without a skull fracture
|
Factor |
SE |
OR (95% CI) |
P-value |
|
SDH ≤8 mm |
|
|
0.15 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.03 |
1.05 (0.98–1.14) |
|
|
EDH ≤8 mm |
|
|
0.77 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.13 |
0.96 (0.74–1.25) |
|
|
Contusion ≤2 cm |
|
|
<0.01 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.16 |
0.32 (0.23–0.43) |
|
|
Controlling covariate |
|
|
|
|
Sex (male) |
0.03 |
1.02 (0.96–1.08) |
0.47 |
|
Race (Caucasian) |
0.03 |
0.96 (0.89–1.03) |
0.29 |
|
Ethnicity (Hispanic or Latino) |
0.05 |
0.97 (0.87–1.08) |
0.61 |
|
Fall mechanism |
0.04 |
1.33 (1.22–1.45) |
<0.01 |
|
Ground ambulance |
0.03 |
0.73 (0.68–0.78) |
<0.01 |
|
GCS score |
0.00 |
0.88 (0.87–0.89) |
<0.01 |
|
Level I trauma center |
0.03 |
0.97 (0.91–1.03) |
0.32 |
|
Low-molecular-weight heparin |
0.03 |
0.95 (0.88–1.02) |
0.20 |
|
Neurosurgical interventions |
|
|
|
|
ICP monitoring |
0.09 |
4.50 (3.76–5.39) |
<0.01 |
|
Craniotomy |
0.05 |
17.27 (15.65–19.05) |
<0.01 |
|
Comorbidity |
|
|
|
|
Advanced directive |
0.05 |
1.18 (1.05–1.32) |
<0.01 |
|
Alcohol use disorder |
0.05 |
1.10 (0.99–1.22) |
0.05 |
|
Anticoagulation |
0.03 |
1.12 (1.04–1.20) |
<0.01 |
|
Congestive heart failure |
0.05 |
0.97 (0.87–1.08) |
0.61 |
|
Cerebral vascular accident |
0.05 |
1.21 (1.09–1.34) |
<0.01 |
|
COPD |
0.05 |
1.09 (0.99–1.20) |
0.07 |
|
Dementia |
0.04 |
1.09 (1.00–1.19) |
0.04 |
|
Diabetes |
0.03 |
0.93 (0.87–1.00) |
0.05 |
|
Functional dependent health status |
0.04 |
0.78 (0.72–0.85) |
<0.01 |
|
Hypertension |
0.03 |
1.05 (0.98–1.12) |
0.14 |
|
Mental health status |
0.04 |
0.96 (0.89–1.05) |
0.46 |
|
Renal failure |
0.07 |
1.05 (0.90–1.23) |
0.51 |
|
Current smoker |
0.04 |
1.01 (0.92–1.11) |
0.74 |
Table 2.Logistic regression analysis of factors associated with a craniotomy among middle-aged to older adults (≥40 years) who had smaller isolated blunt traumatic brain injury (SDH, EDH, contusion) without a skull fracture
|
Factor |
SE |
OR (95% CI) |
P-value |
|
SDH ≤8 mm |
|
|
0.94 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.05 |
0.99 (0.89–1.11) |
|
|
EDH ≤8 mm |
|
|
0.03 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.23 |
0.60 (0.37–0.95) |
|
|
Contusion ≤2 cm |
|
|
<0.01 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.38 |
0.11 (0.05–0.23) |
|
|
Controlling covariate |
|
|
|
|
Sex (male) |
0.04 |
1.27 (1.16–1.39) |
<0.01 |
|
Race (Caucasian) |
0.05 |
0.72 (0.65–0.80) |
<0.01 |
|
Ethnicity (Hispanic or Latino) |
0.07 |
1.09 (0.94–1.26) |
0.20 |
|
Fall mechanism |
0.06 |
1.67 (1.47–1.90) |
<0.01 |
|
Ground ambulance |
0.04 |
0.60 (0.55–0.66) |
<0.01 |
|
GCS score |
0.00 |
0.85 (0.84–0.86) |
<0.01 |
|
Level I trauma center |
0.04 |
0.94 (0.86–1.03) |
0.21 |
|
Low-molecular-weight heparin |
0.05 |
1.68 (1.52–1.80) |
<0.01 |
|
Comorbidity |
|
|
|
|
Advanced directive |
0.09 |
1.02 (0.84–1.24) |
0.78 |
|
Alcohol use disorder |
0.07 |
0.93 (0.80–1.08) |
0.36 |
|
Anticoagulation |
0.05 |
1.04 (0.93–1.15) |
0.44 |
|
Congestive heart failure |
0.08 |
0.93 (0.79–1.10) |
0.44 |
|
Cerebral vascular accident |
0.08 |
1.20 (1.02–1.41) |
0.02 |
|
COPD |
0.07 |
0.96 (0.82–1.11) |
0.59 |
|
Dementia |
0.08 |
0.58 (0.49–0.68) |
<0.01 |
|
Diabetes |
0.05 |
1.00 (0.90–1.10) |
0.95 |
|
Functional dependent health status |
0.06 |
0.70 (0.62–0.80) |
<0.01 |
|
Hypertension |
0.04 |
1.14 (1.04–1.26) |
<0.01 |
|
Mental health status |
0.06 |
1.05 (0.92–1.18) |
0.43 |
|
Renal failure |
0.12 |
0.86 (0.67–1.10) |
0.23 |
|
Current smoker |
0.06 |
1.13 (0.99–1.28) |
0.05 |
Table 3.Logistic regression analysis of factors associated with intracranial pressure monitoring among middle-aged to older adults (≥40 years) who had smaller isolated blunt traumatic brain injury (SDH, EDH, contusion) without a skull fracture
|
Factor |
SE |
OR (95% CI) |
P-value |
|
SDH ≤8 mm |
|
|
0.75 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.08 |
0.97 (0.81–1.15) |
|
|
EDH ≤8 mm |
|
|
0.16 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.41 |
0.56 (0.24–1.27) |
|
|
Contusion ≤2 cm |
|
|
<0.01 |
|
40–64 yr |
- |
1 (Reference) |
|
|
≥65 yr |
0.36 |
0.36 (0.18–0.75) |
|
|
Controlling covariate |
|
|
|
|
Sex (male) |
0.07 |
1.28 (1.10–1.49) |
<0.01 |
|
Race (Caucasian) |
0.08 |
0.74 (0.63–0.88) |
<0.01 |
|
Ethnicity (Hispanic or Latino) |
0.11 |
1.22 (0.98–1.53) |
0.07 |
|
Fall mechanism |
0.10 |
1.54 (1.26–1.88) |
<0.01 |
|
Ground ambulance |
0.08 |
0.69 (0.59–0.81) |
<0.01 |
|
GCS score |
0.00 |
0.80 (0.79–0.81) |
<0.01 |
|
Level I trauma center |
0.07 |
1.30 (1.12–1.51) |
<0.01 |
|
Low-molecular-weight heparin |
0.08 |
1.59 (1.36–1.87) |
<0.01 |
|
Comorbidity |
|
|
|
|
Advanced directive |
0.18 |
0.74 (0.51–1.06) |
0.10 |
|
Alcohol use disorder |
0.12 |
0.86 (0.68–1.09) |
0.23 |
|
Anticoagulation |
0.09 |
0.91 (0.76–1.09) |
0.34 |
|
Congestive heart failure |
0.13 |
1.31 (1.01–1.59) |
0.03 |
|
Cerebral vascular accident |
0.14 |
1.10 (0.83–1.45) |
0.48 |
|
COPD |
0.13 |
0.88 (0.68–1.14) |
0.36 |
|
Dementia |
0.13 |
0.60 (0.46–0.79) |
<0.01 |
|
Diabetes |
0.08 |
1.00 (0.85–1.18) |
0.92 |
|
Functional dependent health status |
0.10 |
0.72 (0.58–0.90) |
<0.01 |
|
Hypertension |
0.07 |
0.89 (0.77–1.04) |
0.17 |
|
Mental health status |
0.10 |
0.89 (0.72–1.11) |
0.33 |
|
Renal failure |
0.19 |
0.96 (0.65–1.42) |
0.86 |
|
Current smoker |
0.10 |
1.04 (0.84–1.28) |
0.68 |
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