Abstract
Objective
Beta-blockers have been studied for their impact on traumatic brain injury (TBI). We aimed to examine the association of pre-injury beta-blocker exposure with early brain injury biomarker levels and outcomes following TBI.
Methods
We retrospectively studied adults (≥40 years) participating in the Transforming Clinical Research and Knowledge in TBI (TRACK-TBI) study. The exposure was pre-injury beta-blocker utilization. Primary outcome was blood-based brain injury biomarker levels on day 1 following injury. Secondary outcomes included biomarkers on days 3 and 5, hospital mortality, and the 6-month Glasgow Outcome Scale – Extended. Inverse probability-weighted models assessed the association between early beta-blocker exposure, biomarker levels, and outcomes, stratified by TBI severity.
Results
A total of 1,185 patients were included, with 101 on pre-injury beta-blockers (BB+): 21 in the moderate/severe group and 80 in the mild TBI group. BB+ patients were older than BB− in both mild (67 vs. 57 years, P < 0.001) and moderate/severe TBI (64 vs. 56 years, P = 0.003). Hypertension was more common in BB+ patients (78% mild, 67% moderate/severe, P < 0.001). Pre-injury beta-blocker use was not associated with day 1 biomarker levels. The 6-month GOSE scores in the BB+ moderate/severe TBI were lower, but the effect was marginal (B = −1.20, 95% CI [−2.39, −0.01], P = 0.049).
Conclusion
Our study did not find a clear association between pre-injury beta-blocker exposure and day 1 blood-based brain injury biomarkers or clinical outcomes. These findings warrant confirmation in future studies with larger cohorts.
Keywords: Traumatic Brain Injury, Beta-blockers, Biomarkers, Critical Care Outcomes
Introduction
Traumatic brain injury (TBI) is a global health problem, with over 27 million new cases annually.1 It often leads to significant disability and long-term care needs. The heterogeneous pathology of TBI complicates standardized treatment, but key features include disruption of cerebral autoregulation and autonomic dysfunction, resulting in sympathetic activation, hemodynamic instability, and ultimately secondary brain injury.
Beta-blockers have been studied in TBI for their potential to counteract the post-injury hyperadrenergic state and catecholamine surge. Ding et al. suggested that beta-blockers may reduce in-hospital mortality and improve long-term outcomes.2 However, their effectiveness remains inconsistent, perhaps related to the difference in timing of exposure, types, dose, duration, and outcome measures selected. While blood-based biomarkers are increasingly used for TBI diagnosis, monitoring, and prognosis3, the association between pre-injury beta-blocker exposure and biomarker levels is not well established. To address this gap, we examine the association between pre-injury beta-blocker exposure, brain injury biomarkers, and clinical outcomes in TBI patients.
Methods
Study Design and Database
We conducted a retrospective cohort study using data from TRACK-TBI (ClinicalTrials.gov#NCT02119182) study, which enrolled TBI patients at 18 U.S. Level 1 trauma centers from 2014-2018.4 Detailed inclusion and exclusion criteria have been described previously.5 Participants presented to the emergency department (ED) within 24 hours of injury. Collected data included demographics, clinical variables, blood samples (aliquoted and frozen at −80°C within 1 hour per TBI-CDE protocol4), and clinical outcomes over the following year.
Trained staff collected data using structured tools consistent with the National Institute of Neurological Disorders and Stroke (NINDS) TBI-CDE6, following ethical standards and the 1975 Helsinki Declaration. The Duke University Institutional Review Board approved our retrospective analysis of TRACK-TBI data on August 16th, 2022
Population, Exposure, Outcomes, and Covariates
We included TRACK-TBI participants aged ≥ 40 years due to the low prevalence of pre-injury beta-blocker use in younger patients. Patients without Glasgow Coma Scale (GCS) data were excluded. TBI severity was classified as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS 3-8). The primary exposure was pre-injury beta-blocker use, identified from medication lists at the time of injury. Cardio-selective (acebutolol, atenolol, bisoprolol, metoprolol, and nebivolol) and non-selective (nadolol, propranolol) beta-blockers were included. The primary outcome was day 1 brain injury biomarker levels: glial fibrillary acidic protein (GFAP; pg/mL)7, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1; pg/mL)8, S100 calcium-binding protein B ( S100B; μg/L)9, Neuron Specific Enolase (NSE; ng/mL)10, and high-sensitivity C-reactive protein (hs-CRP; mg/L).8 Details are provided in Supplemental Digital Content 1 - Table showing common traumatic brain injury biomarkers. Secondary outcomes included biomarker levels on days 3 and 5, hospital mortality, and the 6-month Glasgow Outcome Scale Extended (GOSE) score.11 Covariates included: hospital site, age, sex, race, ethnicity, educational levels, injury causes, GCS at Emergency Department (ED) arrival, systolic blood pressure and mean arterial pressure at ED, injury mechanism, non-head/neck Injury Severity Score (ISS), Abbreviated Injury Scale (AIS) head score, initial head computed tomography (CT) finding, Rotterdam Score, blood transfusion in the ED, hyperosmolar therapy in the ED and the history of transient ischemic attack (TIA). All covariates were included in propensity score weighting.
Statistical analysis
Differences in patient and injury characteristics by pre-injury beta-blocker use were assessed using standardized mean differences (SMD). Statistical significance was determined by Kruskal-Wallis tests for continuous/ordinal variables and Fisher’s exact tests for categorical variables. Biomarker levels were log-transformed to achieve a Gaussian distribution for parametric analysis. S100B, which remained non-normal after transformation, was analyzed using a rank-based non-parametric method. Day 1 biomarker levels differences were evaluated using linear regression on the transformed values. Analyses were complete-case without accounting for potential survivorship bias due to a lack of outcome assessment. Cross-sectional and change-score analysis of the Day 3 & Day 5 biomarkers was performed using a mixed-effect regression model for each biomarker type, incorporating all values at days 1, 3, and 5, fitting a random intercept for each subject. All analyses used inverse probability weighting and covariate adjustment to account for potential differences in baseline characteristics due to the non-random group allocation. The weights were derived using a boosted regression algorithm to generate predicted probabilities of being in the beta-blockers cohort based on all baseline characteristics, and standardizing so that individuals resembling the opposite group were weighted higher. Age and hypertension were directly adjusted for due to residual imbalance after weighting. Group-by-time interaction terms tested differences in biomarker change over time. Effect sizes are expressed as differences in log-transformed biomarker values (B) and ratios of untransformed values (Exp(B)), with B representing unstandardized coefficients. Clinical outcomes were analyzed similarly, using logistic regression for discharged alive and linear regression for 6-month GOSE. A two-sided p-value < 0.05 defined statistical significance. Primary analysis was interpreted using Benjamini-Hochberg correction for multiple comparisons (m=10) at a 5% false discovery rate. Inverse probability weighting models was implemented using the Toolkit for Weighting and Analysis of Nonequivalent Groups (TWANG) application by Rand Corporation. Other analysis was conducted using SPSS version 26 and SAS version 9.4.
Results
A total of 1185 patients were included in the study, with 223 (18.8%) classified as having moderate/severe TBI and 962 (81.2%) as having mild TBI (Figure 1). Twenty-one patients (9.4%) in the moderate/severe TBI group and 80 patients (8.3%) in the mild TBI group were exposed to pre-injury beta-blockers (BB+).
Figure 1.
Study Flow Diagram
Demographic and Clinical Characteristics of Cohort
Demographic characteristics are summarized in Table 1. The mean age (standard deviation; SD) was 58(12) years in the mild TBI group and 56(11) years in the moderate/severe group. In both severity groups, patients with pre-injury beta-blocker (BB+) exposure were older: 67(12) vs. 57(11) years in mild TBI, and 64 (11) vs. 56(11) years in moderate/severe TBI.
Table 1.
Demographic Data
| Moderate/Severe | Mild | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Total | Pre-Injury Beta Blockers | Total | Pre-Injury Beta Blockers | |||||||
| Column %’s | Row %’s | Column %’s | Row %’s | |||||||
| No | Yes | SMD | P unwt | No | Yes | SMD | P unwt | |||
| Subjects | 223 | 202 | 21 | 962 | 882 | 80 | ||||
| Age Mean (SD) | 56 (11) | 56 (11) | 64 (11) | 0.7 | 0.003 | 58 (12) | 57(11) | 67 (12) | 0.84 | <.001 |
| Sex | ||||||||||
| A) Male | 181 (81%) | 167 (83%) | 14 (67%) | −0.41 | 0.083 | 606 (63%) | 559 (63%) | 47 (59%) | −0.1 | 0.468 |
| B) Female | 42 (19%) | 35 (17%) | 7 (33%) | 0.41 | 356 (37%) | 323 (37%) | 33 (41%) | 0.1 | ||
| Race | ||||||||||
| A) White | 178 (83%) | 159 (82%) | 19 (95%) | 0.4 | 0.492 | 765 (81%) | 692 (80%) | 73 (92%) | 0.45 | 0.017 |
| B) Black | 24 (11%) | 23 (12%) | 1 (5%) | −0.31 | 138 (15%) | 133 (15%) | 5 (6%) | −0.37 | ||
| C) Other | 12 (6%) | 12 (6%) | 0 (0%) | --- | 45 (5%) | 44 (5%) | 1 (1%) | −0.34 | ||
| Hispanic | 41 (19%) | 40 (21%) | 1 (5%) | −0.4 | 0.134 | 142 (15%) | 131 (15%) | 11 (14%) | −0.03 | 0.87 |
| Education Years | ||||||||||
| Mean (SD) | 13 (3) | 13 (3) | 13 (2) | 0.08 | 0.951 | 14 (3) | 14 (3) | 14(3) | 0.09 | 0.811 |
| Injury Cause | ||||||||||
| A) MVC Occupant | 26 (12%) | 26 (13%) | 0 (0%) | --- | 0.408 | 232 (24%) | 212 (24%) | 20 (25%) | 0.02 | 0.004 |
| B) MCC | 33 (15%) | 30 (15%) | 3 (14%) | −0.02 | 66 (7%) | 61 (7%) | 5 (6%) | −0.03 | ||
| C) MVC (cyclist or ped.) | 35 (16%) | 33 (16%) | 2 (10%) | −0.23 | 144 (15%) | 139 (16%) | 5 (6%) | −0.39 | ||
| D) Fall | 84 (38%) | 73 (36%) | 11 (52%) | 0.33 | 385 (40%) | 340 (39%) | 45 (56%) | 0.36 | ||
| E) Assault | 17 (8%) | 15 (7%) | 2 (10%) | 0.07 | 52 (5%) | 52 (6%) | 0 (0%) | --- | ||
| F) Other/Unknown | 28 (13%) | 25 (12%) | 3 (14%) | 0.06 | 83 (9%) | 78 (9%) | 5 (6%) | −0.11 | ||
| ED GCS | ||||||||||
| Mean (SD) | 5.9 (3.2) | 6.0 (3.2) | 4.9 (2.8) | −0.36 | 0.105 | 14.7 (0.5) | 14.7 (0.5) | 14.8 (0.4) | 0.21 | 0.099 |
| Median (IQR) | 5 (3-9) | 6 (3-9) | 3 (3-6) | 15 (15-15) | 15 (15-15) | 15 (15-15) | ||||
| 3-8 | 164 (74%) | 147 (73%) | 17 (81%) | 0.23 | 0.604 | --- | --- | --- | --- | --- |
| 9-12 | 59 (26%) | 55 (27%) | 4 (19%) | −0.23 | --- | --- | --- | --- | ||
| 13-15 | --- | --- | --- | --- | 962 (100%) | 882 (100%) | 80 (100%) | |||
| ISS Non-Head/Neck | ||||||||||
| Mean (SD) | 7.0 (7.5) | 7.3 (7.6) | 4.2 (5.9) | −0.41 | 0.089 | 5.5 (6.2) | 5.6 (6.3) | 4.6 (5.0) | −0.17 | 0.391 |
| Median (IQR) | 4 (1-12) | 5 (1-13) | 1 (1-5) | 4 (1-9) | 4 (1-9) | 3 (1-7.5) | ||||
| AIS Head/Neck | ||||||||||
| Mean (SD) | 3.8 (1.3) | 3.8 (1.3) | 4.1 (1.1) | 0.26 | 0.26 | 2.3 (1.4) | 2.3 (1.4) | 2.1 (1.5) | −0.19 | 0.283 |
| Median (IQR) | 4 (3-5) | 4 (3-5) | 5 (3-5) | 2 (2-3) | 2 (2-3) | 2 (1-3) | ||||
| ED SBP Mean (SD) | 145 (35) | 144 (34) | 157 (38) | 0.38 | 0.152 | 146 (24) | 145 (24) | 149 (28) | 0.15 | 0.196 |
| ED MAP Mean (SD) | 109 (26) | 108 (26) | 118 (22) | 0.39 | 0.081 | 105 (17) | 105 (17) | 106 (19) | 0 | 0.817 |
| Initial CT | ||||||||||
| Negative | 9 (4%) | 8 (4%) | 1 (5%) | 0.04 | 0.597 | 484 (53%) | 447 (53%) | 37 (49%) | −0.07 | 0.63 |
| Positive | 193 (96%) | 175 (96%) | 18 (95%) | −0.04 | 436 (47%) | 398 (47%) | 38 (51%) | 0.07 | ||
| ED Blood Transfusion | 50 (23%) | 48 (24%) | 2 (10%) | −0.35 | 0.173 | 32 (3%) | 30 (3%) | 2 (3%) | −0.05 | 1 |
| Rotterdam Score | ||||||||||
| Mean (SD) | 3.6 (1.3) | 3.5 (1.3) | 4.3 (1.4) | 0.6 | 0.023 | 2.3 (0.6) | 2.3 (0.6) | 2.4 (0.5) | 0.08 | 0.424 |
| Median (IQR) | 3 (3-5) | 3 (3-4) | 4.5 (3-6) | 2 (2-3) | 2 (2-3) | 2 (2-3) | ||||
| History of Hypertension | 66 (32%) | 52 (28%) | 14 (67%) | 0.82 | 0.001 | 312 (33%) | 250 (29%) | 62 (78%) | 1.03 | <.001 |
| History of TIA’s | 0 (0%) | 0 (0%) | 0 (0%) | --- | --- | 17 (2%) | 15 (2%) | 2 (3%) | 0.06 | 0.649 |
| ED Mannitol /Hypertonic Saline | 53 (24%) | 47 (23%) | 6 (29%) | 0.12 | 0.594 | 27 (3%) | 26 (3%) | 1 (1%) | −0.1 | 0.72 |
Statistical significance was assessed using Kruskal-Wallis and Fisher’s exact tests,
SMD = standardized mean difference; Punwt = Unweighted P value; SD = Standard Deviation; IQR = Interquartile Range, MVC = Motor Vehicle Collision, ped.= pedestrian, MCC = Motorcycle Crash; AIS = Abbreviated Injury Scale; CT = Computed Tomography; ED = Emergency Department; GCS = Glasgow Coma Scale; ICP = Intracranial Pressure; ISS = Injury Severity Score; MAP = Mean Arterial Pressure; TIA = Transient Ischemic Attack; SBP = Systolic Blood Pressure, Initial CT; positive = positive for intracranial hemorrhage,
Median (IQR) ED GCS was 5 (3-9) in the moderate/severe group and 15 (15-15) in the mild TBI group, with no significant difference between BB− and BB+ groups. The median (IQR) Rotterdam score was higher in the BB+ group for moderate/severe TBI patients (4.5 [3-6] vs 3 [3-4] in the BB− group). Both moderate/severe and mild TBI BB+ groups had a higher prevalence of hypertension (67% and 78%, respectively). Other covariates were similar between the BB+ and BB− groups in both TBI severity categories.
Association of Early Beta-blocker Exposure and Day 1 Biomarkers
None of the biomarkers on day 1 differed significantly between BB+ and BB− subgroups in either the moderate/severe or mild TBI groups after adjusting for multiple comparisons (Table 2). However, there were trends toward higher S100B (modeled ratio 1.32, 95% CI [1.06,1.66], P = 0.038) and lower hs-CRP (modeled ratio 0.46, 95% CI [0.23,0.90], P = 0.023) in the BB+ group within the moderate/severe TBI cohort, and lower GFAP levels (modeled ratio 0.62, 95% CI [0.41, 0.93, P = 0.021) in the BB+ within the mild TBI cohort.
Table 2.
Day 1 Biomarker Levels
| Preinjury | Modeled Estimate | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Beta-Blockers | No Covariate Adjustment | Propensity-Weighted and Covariate-Adjusted | |||||||
| No | Yes | B | Exp(B) | P | B | Exp(B) | P | PMC | |
| (95% CI) | (95% CI) | (95% CI) | (95% CI) | ||||||
| Moderate/Severe | |||||||||
| GFAP | N=195 | N=21 | −0.13 | 0.88 | 0.72 | −0.21 | 0.81 | 0.535 | 0.767 |
| Log-transformed | 8.22 (1.51) | 8.09 (1.96) | (−0.83, 0.58) | (0.43, 1.78) | (−0.89, 0.46) | (0.41, 1.59) | |||
| UCH-L1 | N=195 | N=21 | 0.04 | 1.04 | 0.887 | 0.11 | 1.11 | 0.682 | 0.767 |
| Log-transformed | 6.77 (1.12) | 6.81 (1.21) | (−0.47, 0.55) | (0.62, 1.73) | (−0.40, 0.62) | (0.67, 1.85) | |||
| NSE | N=161 | N=17 | −0.1 | 0.91 | 0.603 | 0.1 | 1.1 | 0.602 | 0.767 |
| Log-transformed | 3.58 (0.72) | 3.48 (0.64) | (−0.45, 0.26) | (0.63, 1.30) | (−0.26, 0.45) | (0.77, 1.58) | |||
| S100B * | N=161 | N=17 | 0.043 | 0.038 | 0.127 | ||||
| Log-transformed | 0.47 (0.44) | 0.81 (0.88) | |||||||
| hs-CRP | N=144 | N=16 | −0.66 | 0.52 | 0.058 | −0.78 | 0.46 | 0.023 | 0.115 |
| Log-transformed | 3.77 (1.28) | 3.11 (1.49) | (−1.34, 0.02) | (0.26, 1.02) | (−1.46, −0.11) | (0.23, 0.90) | |||
| Mild | |||||||||
| GFAP | N=835 | N=74 | −0.4 | 0.67 | 0.058 | −0.48 | 0.62 | 0.021 | 0.115 |
| Log-transformed | 5.78 (1.77) | 5.37 (1.59) | (−0.82, 0.01) | (0.44, 1.01) | (−0.89, −0.07) | (0.41, 0.93) | |||
| UCH-L1 | N=835 | N=74 | −0.01 | 0.99 | 0.928 | 0 | 1 | 0.996 | 0.996 |
| Log-transformed | 5.33 (0.91) | 5.32 (0.78) | (−0.22, 0.20) | (0.80, 1.23) | (−0.21, 0.21) | (0.81, 1.24) | |||
| NSE | N=766 | N=71 | −0.12 | 0.89 | 0.205 | −0.04 | 0.96 | 0.646 | 0.767 |
| Log-transformed | 3.06 (0.74) | 2.95 (0.64) | (−0.29, 0.06) | (0.74, 1.07) | (−0.22, 0.13) | (0.81, 1.14) | |||
| S100B * | N=765 | N=71 | 0.131 | 0.312 | 0.767 | ||||
| Log-transformed | 0.16 (0.20) | 0.13 (0.09) | |||||||
| hs-CRP | N=728 | N=68 | 0.09 | 1.09 | 0.601 | −0.07 | 0.93 | 0.69 | 0.767 |
| Log-transformed | 2.47 (1.30) | 2.56 (1.31) | (−0.24, 0.41) | (0.79, 1.51) | (−0.40, 0.27) | (0.67, 1.30) | |||
Statistical significance was assessed using mixed-effects linear regression on the day 1/3/5 log-transformed values.
PMC = multiple comparisons (Benjamini-Hochberg, m=10, 5% FDR).
Log(S100B) is not Gaussian, thus an analagous rank-based approach was used. All rank-based effects sizes are in the same direction as their parametric analog
All covariates are used in propensity-weighted presented in Table 1.
Age and hypertension are used in the covariate-adjusted models.
B represents the unstandardized regression coefficient from models using log-transformed biomarker values.
Exp(B) indicates the corresponding ratio of untransformed biomarker levels of the beta-blocker group to the non-beta-blocker group, providing an interpretable estimate of relative change between groups
CI = confidence interval, GFAP = Glial Fibrillary Acidic Protein; UCH-L1 = Ubiquitin Carboxy-terminal Hydrolase L1; NSE = Neuron Specific Enolase; S100B = S100 calcium-binding protein B; hs-CRP = High-sensitivity C-reactive Protein, GFAP and UCH-L1 are expressed in pg/mL; NSE in ng/mL; S100B in μg/L; and hs-CRP in mg/L.
Association of Early Beta-blocker Exposure and Day 3 and 5 Biomarkers (Exploratory analyses)
In the moderate/severe TBI group, pre-injury beta-blocker exposure was not associated with brain injury biomarker levels on either day 3 or day 5.
In the mild TBI group, GFAP levels on day 3 were significantly lower in the BB+ group (modeled ratio 0.57, 95% CI [0.34, 0.94, P = 0.027), along with a significantly lower level of S100B (P = 0.042). No significant biomarker differences were observed on day 5 (Supplemental Digital Content 2 - Table showing day 3 and day 5 biomarker levels).
In the mild TBI, hs-CRP was significantly increased from day 1 to day 3 in the BB+ group (modeled ratio 1.54, 95% CI [1.04, 2.03], P = 0.033). No significant changes were found for other biomarkers from day 1 to day 3 or day 1 to 5 across BB+ and BB− groups in either TBI severity group (Supplemental Digital Content 3 - Figure showing biomarker levels on days 1, 3, and 5 and Supplemental Digital Content 4 - Table showing changes in biomarker levels).
Association of Early Beta-blocker Exposure and the Clinical Outcome
There was no significant difference in alive at discharge between the BB+ and BB− groups in either TBI cohort. In the moderate/severe group, 62% of BB+ patients were discharged alive compared to 76% of BB− patients (OR 0.47, 95% CI [0.17, 1.26], P = 0.132). In the mild group, 99% of both BB+ and BB− patients survived to discharge (OR 1.04, 95% CI [0.15, 7.29], P = 0.969).
At 6 months, the mean GOSE score (SD) in the mild TBI group was similar between BB+ (6[2]) and BB− (7[2]) patients (B = −0.32, 95% CI [−0.72, 0.07], P = 0.11). In the moderate/severe group, BB+ patients had a significantly lower GOSE score (2[2]) compared to BB− patients (4[2]), (B = −1.20, 95% CI [−2.39, −0.01], P = 0.049) (Supplemental Digital Content 5 - Table showing clinical outcomes and Supplemental Digital Content 6 - Figure showing 6-month GOSE distribution among the moderate/severe and mild TBI group).
Discussion
Pre-injury beta-blocker exposure was not significantly associated with day 1 biomarker levels. Although GFAP and S100B levels at day 3 were significantly lower in the BB+ mild TBI group, these associations were not observed in any cohorts on day 5. Survival to discharge did not differ by beta-blocker exposure. In the BB+ moderate/severe TBI, the 6-month GOSE score was lower, though statistical significance was marginal.
Our study aims to examine how beta-blocker exposure might affect blood-brain biomarkers. We found no association with day 1 biomarker levels, possibly due to the absence of direct effects on neuronal cells or a delayed impact beyond the acute phase. This is supported by our exploratory finding of reduced day 3 GFAP and S100B levels in the BB+ mild TBI group, which may suggest a potential protective effect on secondary brain injury. Variability in beta-blocker type, dose, and patient characteristics, as well as factors like TBI type and contusion volume, may also influence biomarker levels. However, interpretation of the day 3 biomarker findings should be made with caution, as they are secondary outcomes, not corrected for multiple comparisons, and are hypothesis-generating.
Beta-blockers have been proposed to reduce proinflammatory cytokine release12, potentially leading to a greater decline in hs-CRP levels in the BB+ group. However, we observed a greater change in hs-CRP from day 1 to day 3 in the BB+ mild TBI group. Additionally, GOSE scores were lower in the BB+ moderate/severe TBI group. Although this effect was marginal, it raises the possibility that some subgroups may experience harm from pre-injury beta-blocker exposure. Given the small sample size, our findings are exploratory, and a larger cohort is needed for confirmation. These observations may also be influenced by underlying cardiovascular conditions, subgroup effects, or the higher Rotterdam score in the BB+ moderate/severe group, indicating more severe injury. Thus, the potential benefits of beta-blockers in TBI remain uncertain and warrant further investigation.
Mohseni et al. reported a protective effect of pre-injury beta-blockers in severe TBI,13 while Hart et al. found no mortality benefit.14 In our study, beta-blockers were not associated with improved survival and showed only a marginal reduction in 6-month GOSE scores. The wide confidence interval for discharge rates and borderline p-value suggests a larger cohort is needed for more precise estimates. Overall, the benefits of pre-injury beta-blockers remain inconclusive. Although Chen et al. and Hart et al. reported reduced mortality, they also noted increased risks of infection, prolonged ventilator use, and longer ICU and hospital stays.14,15Our cohort lacked data on these complications.
This study has several limitations. First, its retrospective observational design limits the ability to establish a causal relationship between beta-blocker exposure and brain injury biomarker levels. A primary concern is potential confounding by indication. While we applied propensity weighting and adjusted for relevant covariates, residual imbalances between groups may still impact the robustness of our conclusions. The heterogeneity of the beta-blocker subtype (cardioselective vs. non-cardioselective; lipophilic vs. hydrophilic) may affect outcomes. Additionally, limited data on indication, dose, timing, and frequency in the database restricted our ability to draw firm conclusions. The small sample size, the missing biomarker data, and potential variability in patient compliance further limit our ability to detect group differences and reduce the generalizability of our findings. We also acknowledge that combining moderate and severe TBI groups may have influenced the results, as the small sample size in each category prevented separation analyses with adequate statistical power.
In conclusion, our study did not find a clear association between pre-injury beta-blocker exposure and day 1 blood-based brain injury biomarkers or clinical outcomes. The potential benefits of beta-blockers in TBI are still subject to debate and warrant further investigation in larger cohorts.
Supplementary Material
Source of funding:
NIH grant numbers K23NS109274 and R01NS130832
*. The TRACK-TBI Investigators:
Shawn Eagle, PhD, University of Pittsburgh; Raquel Gardner, MD, Sheba Medical Center, Ramat Gan, Israel; Shankar Gopinath, MD, Baylor College of Medicine; Christine Mac Donald, PhD, University Washington; Michael McCrea, PhD, Medical College of Wisconsin; Randall Merchant, PhD, Virginia Commonwealth University; Pratik Mukherjee, MD PhD, University of California, San Francisco; Laura B. Ngwenya, MD, PhD, University of Cincinnati; David Okonkwo, MD PhD, University of Pittsburgh; Claudia Robertson, MD, Baylor College of Medicine; Richard B Rodgers, MD, Goodman Campbell Brain and Spine; David Schnyer, PhD, UT Austin; Murray Stein, MD MPH, University of California, San Diego; Sabrina R. Taylor, PhD, University of California, San Francisco; John K. Yue, MD, University of California, San Francisco
Footnotes
Conflicts of Interest: All authors report no conflict of interest related to the work published
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