Surgical Versus Medical Management in Older Adults with Traumatic Brain Injury: A Systematic Review and Meta-analysis

Surgical Versus Medical Management in Older Adults with Traumatic Brain Injury: A Systematic Review and Meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Surgical Versus Medical Management in Older Adults with Traumatic Brain Injury: A Systematic Review and Meta-analysis Luis E. Cueva-Cañola, Andrea C. Beltran-De la Fuente, Mael S. Ayala-Alban, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9362369/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Traumatic brain injury (TBI) in older adults is associated with high mortality and poor functional outcomes. However, optimal management remains uncertain, as evidence comparing surgical and medical strategies is limited, heterogeneous, and extrapolated from younger populations. We conducted a systematic review and meta-analysis to compare outcomes between surgical and medical management in this population. Methods PubMed, Embase, and Web of Science were searched from database inception to December 8, 2025. Studies including adults aged ≥60 years with TBI comparing surgical versus medical management were included. The primary outcome was favorable neurological outcome, while secondary outcomes included hospital length of stay (LOS) and mortality. Pooled estimates were calculated as risk ratios (RR) and mean differences (MD) using random-effects models with restricted maximum likelihood and Hartung–Knapp adjustment. Results Sixteen cohort studies comprising 132,823 patients were included. Surgical management was not associated with improved favorable neurological outcomes at discharge, 3, 6, or 12 months. However, it was associated with longer LOS (MD = 6.35 days, 95% CI: 2.55 to 10.14; p < 0.01). No differences were observed in in-hospital, 30-day, 3-month, 12-month, or 24-month mortality. Notably, surgical management was associated with a reduction in 6-month mortality (RR = 0.68, 95% CI: 0.51–0.92; p = 0.02). Conclusions In older adults with TBI, surgical management was associated with reduced 6-month mortality and longer hospital LOS, but not with improved functional outcomes, highlighting a dissociation between survival and recovery. This dissociation has implications for clinical decision-making and patient and family counseling. Individualized decision-making that incorporates frailty, comorbidities, and potential reversibility of injury is essential, rather than relying solely on age. While this study represents the best available comparative evidence to date on surgical versus non-surgical management in older adults with TBI, findings should be interpreted with caution due to heterogeneity and low to very low certainty of the evidence. High-quality randomized controlled trials are needed to better define the role of surgery in this population. Brain Injuries Traumatic Aged Neurosurgical Procedures Conservative Treatment Treatment Outcome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Traumatic brain injury (TBI) in older adults represents a growing global health challenge driven by population aging and associated with substantial mortality and long-term disability. Among individuals aged 60 years and older, in-hospital mortality reaches 16% and increases sharply with injury severity, from 5% in mild cases to 18% in moderate injury and up to 65% in severe TBI. 1 Poor functional outcomes are common in this population, affecting 65.2% of patients, with institutionalization rates of 28.5% and dependency ranging from 16.9% to 74.0%. 2 Despite this considerable burden, optimal management strategies in this population remain uncertain. Current evidence guiding surgical management in TBI is largely derived from randomized controlled trials (RCTs) such as the DECRA trial and RESCUEicp trial, which form the basis of contemporary clinical practice. 3 These studies included patients aged 15 to 59 years and 10 to 65 years, respectively. They showed that early decompressive craniectomy (DC) reduces intracranial pressure (ICP) and length of stay (LOS) but is associated with worse functional outcomes, 4 whereas delayed DC used as a rescue strategy reduces mortality and improves long term neurological outcomes. 5 However, the limited inclusion of older adults in these trials restricts the external validity of their findings in a population with the highest disease burden, where differences in baseline characteristics and recovery potential may influence treatment effects. Older adults represent a distinct clinical population in whom both the response to TBI and the effects of its management may differ from those observed in younger patients. 6,7 Age related physiological changes, including reduced brain resilience, impaired neuroplasticity, and decreased tolerance to high ICP, may adversely affect recovery. 8 In addition, the higher prevalence of comorbidities, baseline functional limitations, and the widespread use of antithrombotic therapies increase the risk of complications and may modify treatment effects. 9–11 Frailty further increases vulnerability and is consistently associated with higher mortality and worse functional outcomes. 12 In clinical practice, older patients with TBI are less likely to receive aggressive management, including advanced ICP directed therapies such as osmotic therapy, cerebrospinal fluid (CSF) drainage, hypothermia, barbiturate coma, and neurosurgical interventions. This pattern may reflect not only individualized clinical decision making but also therapeutic uncertainty in the absence of robust evidence specific to this population. Importantly, although advanced age remains an independent predictor of poor prognosis, emerging evidence suggests that outcomes may be influenced by the intensity and type of care delivered. 13 The lack of robust, population specific evidence continues to hinder evidence-based decision making regarding surgical versus medical management in older adults with TBI. Defining the balance between potential benefit and harm is critical to avoid both underuse and inappropriate use of invasive interventions, and to better inform prognosis, treatment selection, and resource utilization in this high risk and rapidly growing population. Therefore, we conducted a systematic review and meta-analysis to compare surgical and medical management in older adults with TBI, with the primary aim of evaluating favorable neurological outcomes, and secondary outcomes including hospital LOS, and mortality. Methods Study Design and Reporting Standards This study was designed as a systematic review and meta-analysis and conducted in accordance with the methodological recommendations of the Cochrane Handbook for Systematic Reviews of Interventions. 14 The reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines to ensure transparency, methodological rigor, and reproducibility. 15 Protocol and Registration The study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) on November 21, 2025 under the registration number CRD420251236960, prior to study initiation, to enhance methodological transparency and reduce the risk of selective reporting. 16 Eligibility Criteria Studies were included if they met all of the following criteria: (1) included adult patients aged 60 years and older with TBI, with diagnosis confirmed by clinical examination and neuroimaging using computed tomography (CT) or magnetic resonance imaging; no restrictions were applied regarding injury severity; (2) evaluated surgical management, defined as any invasive neurosurgical intervention performed to evacuate intracranial lesions or control ICP, including DC, craniotomy for hematoma evacuation, burr hole procedures, and other decompressive interventions; (3) included a comparator group receiving medical management, defined as non-surgical treatment consisting of hemodynamic stabilization and neuroprotective strategies, including osmotic therapy with mannitol or hypertonic saline, sedation and analgesia, mechanical ventilation, temperature control strategies, barbiturate coma, and comprehensive critical care support; (4) reported at least one of the following outcomes: favorable neurological outcome, hospital LOS, or mortality; and (5) were RCTs or observational studies, including prospective or retrospective cohort designs, with no restrictions on language. Studies were excluded if they met any of the following criteria: (1) did not include patients aged 60 years and older or did not provide extractable data for this subgroup; (2) were case reports, case series without a comparator group, narrative reviews, systematic reviews, editorials, letters, or conference abstracts without full text; (3) included animal studies or experimental models; or (4) did not provide sufficient data for outcome extraction or analysis. Information Sources and Search Strategy A comprehensive literature search was conducted in PubMed, Embase, and Web of Science from database inception to December 8, 2025. The search strategy incorporated controlled vocabulary and free text terms related to traumatic brain injury, neurosurgical interventions, medical management, and older adult populations. The complete search strategies for each database are provided in Supplementary Table 1 . Study Selection All retrieved records were imported into Zotero, where duplicate records were identified and removed. The deduplicated dataset was subsequently uploaded to Rayyan for study selection. Screening was conducted in two sequential stages. First, two reviewers (M.S.A.A. and S.M.A.) independently screened titles and abstracts. Second, the full texts of potentially eligible studies were independently assessed by the same reviewers. Discrepancies at any stage were resolved through discussion, and when consensus could not be reached, a third reviewer (A.C.B.D.L.F.) was consulted. Data Extraction Data were extracted independently by two reviewers (O.N.P.P. and R.G.R.) using a standardized and piloted data extraction form. Extracted variables included: (1) study characteristics, (2) demographic characteristics, (3) pre-injury status, (4) injury characteristics, (5) injury severity, (6) initial clinical presentation, (7) neuroimaging findings, and (8) management and intervention variables. When required, additional information was sought from study authors. Discrepancies between reviewers were resolved through discussion and, when necessary, consultation with a third reviewer (A.G.C.C.). Risk of Bias Assessment The risk of bias of included studies was independently assessed by two reviewers (M.E.L.G. and R.G.R.) using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS I) tool. This tool evaluates bias across the following domains: confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported results. 17 Each domain was rated as low, moderate, serious, or critical risk of bias, and an overall risk of bias judgment was assigned to each study accordingly. Discrepancies were resolved through discussion, and when necessary, consultation with a third reviewer (A.C.B.D.L.F.). Outcomes The primary outcome was favorable neurological outcome, defined using validated functional scales and operationalized as a Glasgow Outcome Scale score of 4 to 5 or a Glasgow Outcome Scale Extended score of 5 to 8. 18,19 Favorable neurological outcome was analyzed according to the timing of assessment, including at hospital discharge, 3 months, 6 months, and 12 months. Secondary outcomes included hospital LOS, defined as the total number of days from hospital admission to discharge; and all-cause mortality, defined as death from any cause during follow-up. Mortality was analyzed according to the timing of assessment, including in hospital mortality, 30-day mortality, and mortality at 3-months, 6-months, 12-months, and 24-months. Data Synthesis and Statistical Analysis All statistical analyses were conducted in RStudio (R Foundation for Statistical Computing, Vienna, Austria) using the meta and metafor packages. Effect estimates were synthesized using random-effects models, accounting for expected clinical and methodological heterogeneity across studies. For dichotomous outcomes, pooled estimates were calculated as risk ratios (RR) with 95% confidence intervals (CI), while mean differences (MD) were used for continuous outcomes. Between-study variance (τ²) was estimated using the restricted maximum likelihood (REML) method. 20 To account for uncertainty, particularly in the context of a small number of studies and anticipated heterogeneity, the Hartung–Knapp adjustment (HKSJ) was applied to all models, providing more conservative estimates. 21–23 Statistical heterogeneity was assessed using Cochran’s Q test (QE) and quantified with the I² statistic and τ². Heterogeneity was interpreted as low (I² 75%), with p < 0.10 indicating statistically significant heterogeneity. In addition, prediction intervals (PI) were calculated to estimate the range within which the true effect of a future study is expected to lie. 24 Inter reviewer agreement was evaluated using Cohen’s kappa (κ) and interpreted as follows: κ 0.80 excellent agreement. 25,26 Subgroup Analyses Pre-specified subgroup analyses were conducted to explore potential sources of heterogeneity and to assess the consistency of treatment effects across clinically relevant populations. These included comparisons according to study design (unmatched versus matched studies), age-stratified analyses (≥65, ≥70, and ≥80 years), and clinically defined subgroups such as subdural hematoma (SDH) and isolated TBI. Additionally, analyses were stratified by injury severity based on the Glasgow Coma Scale (GCS), categorized as mild (GCS 13 to 15), moderate (GCS 9 to 12), and severe TBI (GCS ≤8). For each subgroup, pooled estimates were calculated using the same random-effects model (REML with HKSJ) applied in the primary analysis. Subgroup findings were interpreted with caution, considering the potential for residual confounding, multiple comparisons, and limited statistical power. Additional clinically relevant subgroups were pre-specified but could not be analyzed due to insufficient or inconsistent reporting across included studies. These comprised frailty status, baseline functional status, comorbidity burden, and key radiological variables such as midline shift (MLS), hematoma volume, cerebral edema, and ICP parameters, as well as time to intervention and type of surgical technique. Influence Analysis An influence analysis was performed using a leave-one-out approach, in which each study was sequentially removed and the model refitted. The number of studies (n) and the number of model parameters (p) were defined. Studentized residuals (rstudent), defined as residuals standardized by their estimated variance, were used to detect outliers (|rstudent| > 2; >3 considered extreme). Difference in fits (DFFITS), reflecting the change in fitted values after study removal, was considered influential if |DFFITS| > . Cook’s distance (Cook’s d), measuring the overall influence of each study on model estimates, was considered relevant when > 4/n. The covariance ratio (cov.r), which evaluates the impact on the precision of the variance–covariance matrix, was considered influential when outside 1 ± 3p/n. Hat values, defined as the diagonal elements of the projection matrix, were considered high when > . The standardized difference in coefficients (DFBETAS), measuring the change in each coefficient after study removal, was considered relevant if |DFBETAS| > 1. Study weights, representing the relative contribution of each study to the pooled estimate under the random-effects model, were examined to identify disproportionately influential studies; unusually large weights compared to the average were considered indicative of potential influence. Additionally, changes in τ² and heterogeneity (Q statistic) were assessed after exclusion of each study. 27–29 Publication Bias Assessment Publication bias and small-study effects were assessed using the Doi plot in combination with the Luis Furuya-Kanamori (LFK) index, as proposed by Furuya et al. 30 The Doi plot represents effect sizes against a Z-score–based transformation of study precision, providing an alternative graphical approach to the conventional funnel plot. While funnel plots and Egger’s regression remain widely used and informative tools, the Doi plot offers a complementary visualization with improved interpretability of asymmetry patterns, particularly when assessing small-study effects. 31 Asymmetry was quantified using the LFK index, which measures the deviation between both sides of the Doi plot relative to the point of minimum absolute Z-score. Interpretation followed predefined thresholds: no asymmetry (|LFK| ≤ 1), minor asymmetry (|LFK| between 1 and 2), and major asymmetry (|LFK| > 2). Certainty of Evidence Assessment The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. 32 Evaluations were performed independently by two reviewers (L.E.C.C. and L.R.C.), both with extensive experience in evidence synthesis and certainty assessment. Any discrepancies were resolved through discussion, and when consensus was not achieved, a third reviewer (A.C.B.D.L.F.) was consulted for adjudication. Final certainty ratings were categorized as high, moderate, low, or very low, reflecting the overall confidence in the estimated effect. Results Study Selection The screening process identified 925 records. After removal of duplicates, 750 studies underwent title and abstract screening, with an agreement of 92.0% and excellent inter-rater reliability (κ = 0.84). A total of 144 articles were considered potentially eligible and advanced to full-text assessment. During full-text review, agreement increased to 95.1%, with excellent reliability (κ = 0.90). Following eligibility assessment, 16 cohort studies were included. 33–48 The PRISMA flow diagram is presented in Fig. 1 . Characteristics of Included Studies A total of 16 cohort studies were included, comprising 2 matched studies using propensity score matching (PSM), 33,34 and 14 unmatched observational cohorts. 35–48 In the two matched studies, PSM was performed using 1:1 nearest neighbor approaches with predefined calipers, adjusting for demographic characteristics, clinical severity measures, radiological findings, and treatment related factors, with post matching assessment to ensure adequate balance between groups. Overall, most studies used a retrospective design (n = 15, 93.8%), 33–47 while only one was prospective (n = 1, 6.3%). 48 Study periods ranged from 1998 to 2024, and median year of publication was 2023 (range: 2014 to 2025). India contributed the largest number of studies (n = 5, 31.3%), 34,41,42,45,48 followed by the United States (n = 3, 18.8%), 36,38,40 whereas the United Kingdom, 35 Spain, 33 Germany, 37 Belgium, 39 Iran, 43 Japan, 44 Italy, 46 and China 47 each contributed one study (n = 1, 6.3%). This distribution corresponded to three continents, including Asia (n = 8, 50.0%), 34,41–45,47,48 Europe (n = 5, 31.3%), 33,35,37,39,46 and North America (n = 3, 18.8%). 36,38,40 Regarding follow-up, five studies (31.3%) reported in hospital follow-up only, 36,37,40,43,45 whereas the remaining 11 studies (68.8%) included post discharge follow-up; among these, the median follow-up duration was 6 months (range: 3 to 24 months). 33–35,38,39,41,42,44,46–48 Definitions of older adults varied across studies, with the most commonly used threshold being ≥65 years (n = 5, 31.3%), 33,39,44,47,48 followed by ≥60 years (n = 3, 18.8%), 34,42,45 ≥80 years (n = 3, 18.8%), 37,38,40 and ≥70 years (n = 2, 12.5%), 35,46 while other cutoffs such as >60 years, 41 >65 years, 48 and ≥90 years, 36 were each reported in one study (n = 1, 6.3%). Characteristics of Included Population A total of 132,823 older adults with TBI were included. Females slightly predominated, accounting for 72,071 patients (54.3%), compared with 60,747 males (45.7%). The mean age was 84.0 ± 3.7 years (n = 130,746). 33,36,38–48 Comorbidities were common, particularly hypertension (HTN) (79,052/129,071, 61.2%), 34,38,40–42,45,48 and diabetes mellitus (26,872/129,220, 20.8%). 34,38–42,45,48 Functional dependency or high frailty was reported in 11,104/128,515 patients (8.6%), 33,35,36,39,40,45 as assessed by instruments such as the World Health Organization (WHO) Performance Status, 35 Clinical Frailty Scale, 33 and Activities of Daily Living, 39 or equivalent definitions across studies. 36,40,45 Anticoagulant or antiplatelet use was documented in 618/1,856 patients (33.3%). 33,35,36,38,41,45,46,48 Falls were the leading mechanism of injury, occurring in 85,898/132,584 cases (64.8%), 33,35–37,39–48 followed by road traffic accidents in 11,696/132,318 (8.8%). 33,35,37,40–48 Regarding injury severity, mild TBI was reported in 1,123/2,385 patients (47.1%), 33–35,42,45–48 moderate in 588/2,385 (24.7%), 33–35,42,45–48 and severe in 2,099/4,966 (42.3%). 33–35,37,44–48 The mean GCS score was 13.6 ± 3.1 (n = 129,502). 36,38–43,45,46 Patients most commonly presented with loss of consciousness (669/878, 76.2%), 34,41,42 followed by vomiting (413/878, 47.0%), 34,41,42 and seizures (53/1,091, 4.9%). 34,41,42,46 Other reported clinical features included altered sensorium, 42 motor deficits, 33,41,42 pupillary abnormalities, 33,37,41,42,45–47 otorrhagia/epistaxis, 34,42,43 CSF leakage, 42 hypotension, 44 HTN, 45 and hypoxia. 44 The patient characteristics, including demographic data, pre-injury status, and injury-related variables, are summarized in Table 1 . Additional radiological findings included a mean MLS of 5.6 ± 5.7 mm (n = 773), 33,36,39,41,46,47 effaced basal cisterns in 289/476 cases (60.7%), 33,41,47,48 a mean Marshall score of 2.6 ± 1.2 (n = 985), 39,42,48 and skull fractures in 885/3,680 patients (24.0%). 34,37,39,41,42,45,48 SDH was the most frequently reported lesion, present in 2,725/4,568 patients (59.7%). 33–39,41,42,45–48 Neuroimaging findings and management characteristics are summarized in Table 2 . Management Surgical management was performed in 8,168 patients (6.1%), whereas 124,655 (93.9%) received medical treatment. The proportion of surgical treatment varied across continents: 45.6% in Asia, 32.5% in Europe, and 4.7% in North America. Among surgically treated patients, craniotomy was performed in 599/1,047 (57.2%), 35,36,38,39,41–43,45,47 whereas DC was reported in 412/1,530 (26.9%). 34–39,41–43,45,47 The distribution of treatment is shown in Fig. 2 . Favorable Neurological Outcome at Discharge This analysis included 3,384 older adults, of whom 1,109 (32.8%) underwent surgical management and 2,275 (67.2%) received medical management. 35,37,43,45,46,48 Favorable neurological outcomes at discharge were observed in 505 (45.5%) surgically treated patients and 1,160 (51.0%) of those managed medically. There was no statistically significant difference between groups (RR = 0.72, 95% CI: 0.45–1.13; p = 0.12). However, heterogeneity was high (I² = 92.0%). Favorable Neurological Outcome at 3 Months Favorable neurological outcome at 3 months was reported in a single study by Jyoti et al., 41 including 120 older adults. Of these, 91 (75.8%) underwent surgical management and 29 (24.2%) received medical management. There was no statistically significant difference between groups (RR = 0.64, 95% CI: 0.26–1.55; p = 0.32). Favorable Neurological Outcome at 6 Months This analysis included 2,313 older adults, of whom 1,167 (50.5%) underwent surgical management and 1,146 (49.5%) received medical management. 33–35,42,44,46,47 Favorable neurological outcomes at 6 months were observed in 304 (26.0%) surgically treated patients and 265 (23.1%) of those managed medically. There was no statistically significant difference between groups (RR = 1.30, 95% CI: 0.55–3.08; p = 0.48). However, heterogeneity was high (I² = 90.5%). Favorable Neurological Outcome at 12 Months Favorable neurological outcome at 12 months was reported in a single study by Castaño et al., 33 including 62 older adults. Of these, 31 (50.0%) underwent surgical management and 31 (50.0%) received medical management. There was no statistically significant difference between groups (RR = 1.86, 95% CI: 0.86–4.02; p = 0.12). Hospital LOS This analysis included 127,888 older adults, of whom 6,268 (4.9%) underwent surgical management and 121,620 (95.1%) received medical management. 35,36,39,40,46 The mean hospital LOS was 13.0 ± 10.9 days in the surgical group and 5.4 ± 6.2 days in the medical group. Patients who underwent surgical management had a longer hospital stay compared with those managed medically (MD = 6.35 days, 95% CI: 2.55 to 10.14; p < 0.01). However, heterogeneity was high (I² = 87.4%). In-Hospital Mortality This analysis included 130,552 older adults, of whom 7,058 (5.4%) underwent surgical management and 123,494 (94.6%) received medical management. 35–38,40,43,45,46 In-hospital mortality was observed in 1,743 (24.7%) surgically treated patients and 12,899 (10.4%) of those managed medically. There was no statistically significant difference between groups (RR = 1.33, 95% CI: 0.75–2.36; p = 0.28). However, heterogeneity was high (I² = 97.6%). 30-Day Mortality This analysis included 315 older adults, of whom 131 (41.6%) underwent surgical management and 184 (58.4%) received medical management. 33,38,39 Mortality at 30 days was observed in 35 (26.7%) surgically treated patients and 78 (42.4%) of those managed medically. There was no statistically significant difference between groups (RR = 0.67, 95% CI: 0.31–1.46; p = 0.16). However, heterogeneity was moderate (I² = 23.3%). 3-Month Mortality 3-month mortality was reported in a single study by Duehr et al., 38 including 104 older adults. Of these, 35 (33.7%) underwent surgical management and 69 (66.3%) received medical management. There was no statistically significant difference between groups (RR = 0.88, 95% CI: 0.63–1.22; p = 0.44). 6-Month Mortality This analysis included 1,912 older adults, of whom 982 (51.4%) underwent surgical management and 930 (48.6%) received medical management. 33–35,38,39,44,46,47 Mortality at 6 months was observed in 431 (43.9%) surgically treated patients and 568 (61.1%) of those managed medically. Patients who underwent surgical management had significantly lower rates of 6-month mortality compared with those managed medically (RR = 0.68, 95% CI: 0.51–0.92; p = 0.02). However, heterogeneity was high (I² = 67.2%). 12-Month Mortality This analysis included 166 older adults, of whom 66 (39.8%) underwent surgical management and 100 (60.2%) received medical management. 33,38 Mortality at 12 months was observed in 32 (48.5%) surgically treated patients and 69 (69.0%) of those managed medically. There was no statistically significant difference between groups (RR = 0.69, 95% CI: 0.02–24.31; p = 0.41). However, heterogeneity was high (I² = 69.5%). 24-Month Mortality 24-month mortality was reported in a single study by Duehr et al., 38 including 104 older adults. Of these, 35 (33.7%) underwent surgical management and 69 (66.3%) received medical management. There was no statistically significant difference between groups (RR = 0.86, 95% CI: 0.63–1.18; p = 0.35). Subgroup Analysis We conducted subgroup analyses according to predefined clinical and methodological variables. No statistically significant subgroup differences were observed for favorable neurological outcomes, and results were consistent with the main analysis across most subgroups. For hospital length of stay, findings were also consistent across subgroups, although the effect was not significant in patients aged ≥80 years (MD = 5.12 days, 95% CI: −19.65 to 29.88; p = 0.23; I² = 96.4%). Regarding mortality, no consistent subgroup differences were identified. For 6-month mortality, matched studies showed no significant differences with high heterogeneity (RR = 0.70, 95% CI: 0.00–337.17; p = 0.60; I² = 80.8%), whereas a significant reduction was observed in patients with SDH (RR = 0.80, 95% CI: 0.70–0.92; p = 0.02; I² = 0.0%). No significant differences were found in isolated TBI (RR = 0.63, 95% CI: 0.00–734.28; p = 0.56; I² = 90.3%), moderate TBI (RR = 0.85, 95% CI: 0.00–17687.35; p = 0.87; I² = 85.3%), or severe TBI (RR = 0.83, 95% CI: 0.38–1.82; p = 0.20; I² = 0.0%). Full subgroup analyses are presented in Table 3 . Influence Analysis Influence analyses identified a limited number of studies exerting a disproportionate effect on pooled estimates across outcomes. For favorable neurological outcome at discharge, Trevisi et al. 2020 was clearly influential, with multiple metrics exceeding thresholds and a marked reduction in heterogeneity after exclusion, 46 whereas Singh et al. 2025 showed only moderate influence. 45 For favorable neurological outcome at 6 months, both Wan et al. 2016 and Trevisi et al. 2020 demonstrated notable influence, each associated with outlying behavior and reductions in heterogeneity upon exclusion, while other studies showed minimal impact. 46,47 For hospital LOS, Cook et al. 2025 was highly influential, with extreme outlying behavior and complete resolution of heterogeneity after exclusion. Other studies showed only moderate or negligible influence. 36 For in-hospital mortality, Haddad et al. 2021 was identified as clearly influential across multiple metrics, with reduced heterogeneity following exclusion, while the remaining studies had limited impact. 40 For 30-day mortality, Duehr et al. 2022 and Castaño et al. 2024 showed relevant influence, 33,38 whereas no meaningful influence was observed for other studies. For 6-month mortality, Wan et al. 2016 was the most influential study, with extreme outlying behavior and elimination of heterogeneity upon exclusion. 47 For the outcome of 12-month mortality, only two studies were available. Castaño et al. 2024 showed a significant reduction in mortality favoring the surgical group (RR = 0.50, 95% CI 0.30–0.85), 33 whereas Duehr et al. 2022 did not demonstrate a significant effect (RR = 0.88, 95% CI 0.64–1.21). 38 Publication Bias Overall, no major asymmetry was detected across outcomes, and the risk of publication bias appeared to be low. neurological outcome at discharge (LFK = −0.211), favorable neurological outcome at 6 months (LFK = −0.005), (Fig. 3) hospital LOS (LFK = 0.294), (Fig. 4) in-hospital mortality (LFK = −0.193), and 6-month mortality (LFK = −0.446), (Fig. 5) values were within the range of no asymmetry, suggesting no evidence of publication bias. In contrast, 30-day mortality (LFK = −1.578), and 12-month mortality (LFK = −1.414) showed minor asymmetry, indicating a low likelihood of small-study effects. Publication bias could not be assessed for favorable neurological outcome at 3 months, favorable neurological outcome at 12 months, 3-month mortality, and 24-month mortality, as these outcomes were informed by single studies. Risk of Bias Overall agreement was 87.5% (112/128 decisions), with a κ = 0.72. Across studies, 2/16 (12.5%) were judged as low risk of bias overall, whereas 14/16 (87.5%) were rated as moderate risk (Fig. 6) . Most studies were rated as low risk of bias in the domains of selection of participants (10/16, 62.5%), classification of interventions (15/16, 93.75%), deviations from intended interventions (14/16, 87.5%), missing data (12/16, 75.0%), measurement of outcomes (9/16, 56.25%), and selection of the reported result (16/16, 100%), whereas bias due to confounding was predominantly rated as moderate (14/16, 87.5%). Certainty of Evidence Agreement in the GRADE assessment was 94.8%, with a κ = 0.93. Overall, the certainty of evidence ranged from low to very low across all evaluated outcomes (Table 4) . Certainty was rated as very low for favorable neurological outcome at discharge, favorable neurological outcome at 6 months, in-hospital mortality, and 12-month mortality, whereas the remaining outcomes, including favorable neurological outcome at 3 months, favorable neurological outcome at 12 months, hospital LOS, 30-day mortality, 3-month mortality, 6-month mortality, and 24-month mortality, were rated as low certainty. Discussion This systematic review and meta-analysis synthesizes the available evidence comparing surgical versus medical management in 132,823 older adults with TBI. The main findings suggest that: (1) surgical management was not consistently associated with improved favorable neurological outcomes across different time points (discharge, 3, 6, and 12 months); (2) it increased hospital LOS by a mean of 6.35 days; and (3) although a significant 32% reduction in 6-month mortality was observed, no significant differences were found for in-hospital, 30-day, 3-month, 12-month, or 24-month mortality. Nevertheless, the certainty of the evidence was rated as very low to low; therefore, these findings should be interpreted with caution. The relatively low proportion of patients undergoing surgical management, 6.1%, likely reflects careful patient selection rather than underuse. In older adults, surgery is typically reserved for cases with clearly identifiable and potentially reversible structural lesions. 33,49–51 Diffuse injury patterns, small hemorrhages, and limited mass effect often favor conservative management, as surgery may not meaningfully alter the underlying pathophysiology. 52 This is further influenced by advanced age, high comorbidity burden, and reduced physiological reserve, which increase vulnerability to perioperative stress. 53,54 Frailty and functional dependency, although inconsistently reported, are well-established determinants of outcomes and likely play a central role in decision-making. 55,56 The frequent use of anticoagulant and antiplatelet therapy further complicates surgical timing and increases bleeding risk. 57,58 In addition, early goals-of-care discussions in severely injured older patients may limit the use of aggressive interventions when expected quality of life (QoL) is uncertain. 59,60 Regional variation in surgical rates suggests that treatment decisions are also shaped by healthcare systems and sociocultural factors. The higher proportion of surgically treated patients in Asia (45.6%) compared with Europe (32.5%) and North America (4.7%) may reflect differences in thresholds for intervention, family involvement in decision-making, and cultural attitudes toward life-sustaining treatment. 61,62 In contrast, practice patterns in North America may place greater emphasis on advance directives, shared decision-making, and quality-of-life considerations. 63–65 Resource availability, including intensive care capacity and access to neurosurgical care, may further influence patient selection. 66–68 These differences in practice patterns are consistent with international expert consensus. A Delphi study by Lagares et al. defined older age as a multidimensional construct and reported that the median expert threshold decreased from 75 years (IQR 70–80) in patients without comorbidities to 65 years (IQR 65–70) in those with comorbidities. In surgical decision-making, no absolute age cutoff was established for craniotomy; however, for DC, the median threshold was 70 years (IQR 70–80) without comorbidities and 65 years (IQR 65–70) with comorbidities for primary procedures, and 70 years (IQR 65–75) versus 65 years (IQR 60–70) for secondary procedures, supporting a more individualized approach. 69 The absence of consistent improvement in favorable neurological outcomes should be interpreted in the context of age-related biological vulnerability. Aging is associated with amplified and prolonged neuroinflammatory responses, increased oxidative stress, mitochondrial dysfunction, and reduced synaptic plasticity, all of which limit neuronal recovery after injury. 70–73 Impaired cerebrovascular autoregulation and reduced capacity to maintain stable cerebral perfusion pressure (CPP) contribute to ongoing secondary injury despite adequate structural intervention. 74,75 In addition, cerebral atrophy may delay the clinical detection of expanding lesions, leading to intervention at more advanced stages when damage is less reversible. 76,77 Patient assessment in older adults also presents important limitations. The GCS may underestimate injury severity in this population due to cerebral atrophy, which allows greater intracranial compensation and may mask the clinical impact of hemorrhagic lesions. 78–81 In addition, baseline cognitive impairment may complicate interpretation, making it difficult to distinguish between chronic deficits and acute deterioration, potentially contributing to under triage and delayed recognition of clinically significant injury. 82,83 The longer hospital stay observed in surgically treated patients likely reflects a combination of survivorship bias and increased clinical complexity. Patients who survive the acute phase after surgery remain hospitalized longer, whereas medically managed patients may experience earlier mortality. Surgical care is also associated with more intensive monitoring, delayed neurological recovery, and the need for rehabilitation planning. 84 Postoperative complications such as delirium, infections, and functional decline are more frequent in older adults and may prolong hospitalization. 34,84 Injury severity and selection factors also contribute, as surgically treated patients often present with more severe but potentially reversible lesions requiring extended inpatient care. 85 Discharge delays related to placement in rehabilitation or long-term care facilities may further increase LOS. 86 The observed reduction in 6-month mortality likely reflects a time-limited benefit in carefully selected patients. Surgical intervention can relieve mass effect, reduce ICP, and restore CPP, thereby mitigating secondary injury processes such as ischemia and excitotoxicity. However, this early survival advantage does not appear to persist over time. 87 Long-term outcomes in older adults are strongly influenced by limited neuroplasticity, systemic vulnerability, and a high burden of complications, including infections, cardiovascular events, and functional decline, which may offset initial gains. 88 The absence of a clear benefit in matched analyses raises concern that the associations observed in unadjusted analyses may be driven by selection bias, particularly confounding by indication, rather than a true treatment effect. Patients selected for surgery are more likely to have anatomically accessible and potentially reversible lesions, preserved brainstem function, and a baseline condition compatible with recovery. 89,90 In contrast, patients with diffuse injury, advanced frailty, or significant comorbidity are less frequently offered surgery. 33,91,92 As a result, apparent benefits observed in unadjusted analyses may partially reflect baseline differences rather than a true treatment effect. In addition, the consistent benefit observed in SDH likely reflects its focal and surgically reversible nature, as evacuation directly relieves mass effect and restores CPP. 93,94 Clinical Implications In the absence of robust, population-specific recommendations for older adults with TBI, these findings represent the best available comparative evidence to date and may help inform decision-making in clinical practice. They support an early, structured, and individualized assessment that goes beyond neurological scores and imaging, incorporating premorbid functional status, frailty, comorbidity burden, and medication exposure as key determinants of prognosis. Radiological severity or traditional thresholds should not automatically prompt escalation, but rather guide a broader evaluation of reversibility and physiological reserve. A key implication is the need to distinguish between patients with potentially reversible intracranial pathology, who may benefit from timely intervention, and those in whom outcomes are driven by systemic vulnerability or diffuse injury, where a more cautious approach is appropriate. Importantly, prognostic uncertainty should not lead to unwarranted therapeutic nihilism, such as premature limitation of care based solely on age or assumptions of poor outcomes. This supports individualized decision-making regarding surgery, ICP monitoring, and intensive care escalation based on clinical evolution, imaging progression, and patient-specific factors, while also supporting decisions not to escalate when expected benefit is limited. Early communication with patients and families is essential to align treatment with expected outcomes, QoL, and patient values. Future Research In this context, ethical challenges specific to older adults with TBI should be explicitly addressed according to study design. In RCTs, structured consent frameworks including surrogate consent, deferred consent, and, where appropriate, waiver of consent should be incorporated to allow timely enrolment in emergency settings without delaying life-saving interventions, while preserving ethical standards and regulatory compliance. Pragmatic and registry-based trial designs may further enhance feasibility and external validity in this population. In prospective observational studies, standardized documentation of goals-of-care decisions, treatment limitations, and decision-making processes should be systematically incorporated to reduce confounding by indication and selection bias. These strategies are essential to improve internal validity, ensure ethical rigor, and enhance the interpretability and generalizability of findings. Additionally, future studies should incorporate variables beyond traditional injury severity, including frailty, comorbidity burden, polypharmacy, and pre-injury functional status, to better define biological age and improve patient selection. Further research is needed to refine surgical indications and compare outcomes between craniotomy and DC, as well as to determine optimal ICP and CPP targets for this population. In addition, the prognostic value of traditional clinical predictors such as the GCS should be reassessed, and new models integrating clinical, radiological, and patient-related variables should be developed. Evidence-based protocols are also needed for antithrombotic management, repeat CT imaging, and the timing of delayed intervention in lesions at risk of progression. Finally, future studies should prioritize multidisciplinary care strategies and long-term patient-centered outcomes, including functional recovery, independence, and QoL. Limitations While this study represents the best available comparative evidence to date on surgical versus non-surgical management in older adults with TBI, several limitations should be considered when interpreting these findings. First, the certainty of evidence was low to very low, and most data were derived from observational, predominantly retrospective studies, making results susceptible to residual confounding, selection bias, and confounding by indication. Treatment allocation was not randomized and likely influenced by injury severity, radiological features, frailty, comorbidities, and goals of care, many of which were inconsistently reported. Second, several outcomes were informed by a limited number of studies, with some based on a single study, reducing robustness and generalizability, while variability in follow-up duration and incomplete long-term data may introduce attrition bias. Third, there was substantial clinical and methodological heterogeneity, including variability in definitions of older adults, injury severity, and thresholds for intervention, as well as differences in surgical and medical management strategies. Because treatment choices are closely linked to patient severity and prognosis, groups were likely non-comparable, introducing residual confounding and contributing to heterogeneity in effect estimates. Fourth, key clinical variables such as frailty, baseline function, comorbidities, detailed radiological parameters, and treatment-related factors were incompletely and inconsistently reported across the included studies, limiting the ability to explore their impact through subgroup or meta-regression analyses and increasing the risk of residual confounding, including from factors such as goals-of-care decisions. Fifth, outcome measures have inherent limitations: mortality may be influenced by non-neurological risks, functional scales may not fully capture QoL, and hospital LOS may reflect system-level factors and survivorship bias. Finally, differences in healthcare systems, institutional practices, and temporal changes in care may affect external validity, and although major publication bias was not detected, small study effects cannot be excluded. Conclusion In older adults with TBI, surgical management appears to be associated with reduced mortality but not with improved functional recovery, highlighting a clinically relevant mismatch between survival and neurological outcomes. This dissociation warrants deeper consideration, as it has direct implications for surgical decision-making and for counseling patients and families, particularly regarding expectations of independence, QoL, and potential long-term care needs, and highlights the need to prioritize outcomes that reflect meaningful recovery rather than survival alone. Another important consideration is that individualized decision-making, incorporating frailty, comorbidities, and the potential reversibility of injury, is essential rather than relying solely on chronological age. Although prognostic uncertainty remains high, decisions should avoid unfounded therapeutic nihilism and instead be guided by a balanced, individualized, and multidisciplinary approach that incorporates patient values and goals of care. Because the available evidence is predominantly observational, heterogeneous, and of low to very low certainty, these findings should be interpreted with caution. Further high-quality, age-specific RCTs are needed to better define the role of surgical management and to identify which patients are most likely to benefit in terms of meaningful, patient-centered outcomes. Abbreviations ADL Activities of Daily Living AF Atrial Fibrillation AIS Abbreviated Injury Scale CAD Coronary Artery Disease CCI Charlson Comorbidity Index CFS Clinical Frailty Scale CHF Congestive Heart Failure CI Confidence Interval CKD Chronic Kidney Disease COPD Chronic Obstructive Pulmonary Disease CPP Cerebral Perfusion Pressure CSF Cerebrospinal Fluid CT Computed Tomography CVA Cerebrovascular Accident DC Decompressive Craniectomy DFBETAS Difference in Betas DFFITS Difference in Fits DM Diabetes Mellitus EDH Epidural Hematoma ENT Ear,Nose,and Throat EVD External Ventricular Drain GCS Glasgow Coma Scale GRADE Grading of Recommendations Assessment,Development and Evaluation HKSJ Hartung–Knapp–Sidik–Jonkman adjustment HLD Hyperlipidemia HTN Hypertension ICH Intracerebral Hemorrhage ICP Intracranial Pressure IHD Ischemic Heart Disease ISS Injury Severity Score IVH Intraventricular Hemorrhage I² I-squared statistic IQR Interquartile Range κ(Kappa) Cohen’s Kappa LFK index Luis Furuya-Kanamori index LOS Length of Stay MD Mean Difference MI Myocardial Infarction MLS Midline Shift PI Prediction Interval PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PROSPERO International Prospective Register of Systematic Reviews PSM Propensity Score Matching PTB Pulmonary Tuberculosis QE Cochran’s Q statistic QoL Quality of Life RA Rheumatoid Arthritis RCTs Randomized Controlled Trials REML Restricted Maximum Likelihood ROBINS-I Risk Of Bias In Non-randomized Studies of Interventions RR Risk Ratio RTA Road Traffic Accident SAH Subarachnoid Hemorrhage SBP Systolic Blood Pressure SDH Subdural Hematoma TBI Traumatic Brain Injury TIA Transient Ischemic Attack WHO World Health Organization τ² Between-study variance. Declarations 1. Compliance with Instructions to Authors The authors confirm that the manuscript fully conforms to the journal’s instructions for authors, including all formatting, structural, and submission specifications. 2. Author Contributions Luis E. Cueva-Cañola: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Software, Validation, Writing – original draft, Writing – review & editing, Project administration. Andrea C. Beltran-De la Fuente: Data curation, Validation, Writing – review & editing. Mael S. Ayala-Alban: Data curation, Validation. Sergio Morales Acosta: Data curation, Validation. Astrid G. Carrión-Cuéllar: Data curation, Validation, Writing – review & editing. Olga N. Polania-Pérez: Data curation, Validation, Writing – review & editing. Rupesh G. Rathod: Data curation, Validation, Writing – review & editing. Manuel E. Lopez-Gonzales: Data curation, Validation. Ana Karina La Madrid Barreto: Conceptualization, Validation, Writing – review & editing. Leonardo Rangel-Castilla: Conceptualization, Validation, Writing – review & editing. 3. Authorship Criteria and Final Approval All authors meet the authorship requirements set by the International Committee of Medical Journal Editors (ICMJE), have reviewed and approved the final version of the manuscript, and agree to take full responsibility for all aspects of the study. 4. Originality and Prior Publication This manuscript represents original work that has not been previously published and is not under consideration for publication in another journal. 5. Ethical Approval and Informed Consent Ethical approval and informed consent were deemed unnecessary because this study is a systematic review and meta-analysis based exclusively on previously published data, with no direct involvement of human subjects or use of identifiable individual-level information. 6. Conflicts of Interest The authors declare that no conflicts of interest exist with respect to this work. 7. Reporting Guidelines This systematic review and meta-analysis was performed and reported in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. 8. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Credit Author Statement Luis E. Cueva-Cañola : Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Software, Validation, Writing – original draft, Writing – review & editing, Project administration. Andrea C. Beltran-De la Fuente: Data curation, Validation, Writing – review & editing. Mael S. Ayala-Alban: Data curation, Validation. Sergio Morales Acosta: Data curation, Validation. Astrid G. Carrión-Cuéllar: Data curation, Validation, Writing – review & editing. Olga N. Polania-Pérez: Data curation, Validation, Writing – review & editing. Rupesh G. Rathod: Data curation, Validation, Writing – review & editing. Manuel E. Lopez-Gonzales: Data curation, Validation. Ana Karina La Madrid Barreto: Conceptualization, Validation, Writing – review & editing. Leonardo Rangel-Castilla: Conceptualization, Validation, Writing – review & editing. Acknowledgment The authors express their sincere gratitude to their families and mentors for their unwavering support, encouragement, and guidance throughout the development of this work. Special acknowledgment is extended to Pavel P.R., Luis C.J., Santos C.C., Carmen C.S., and Pamela Z.M, Tara R., Guru R., R.B.L., S.K.D.L.F.B., Alexandra R.A., Clara A. T., Hipolito A.C., Marilyn B. T., Sergio M.L., Angelica A.C., and Emmanuel M.A., for their valuable contributions, insightful feedback, and continuous support that significantly enriched this study. Conflict of Interest The authors declare that they have no conflicts of interest relevant to this study. Use of Artificial Intelligence Artificial intelligence tools were used solely to assist with language editing and clarity of expression. No AI tools were used in data extraction, data analysis, study selection, or interpretation of results. All scientific content, analyses, and conclusions are the sole responsibility of the authors. References Ma Z, He Z, Li Z, et al. Traumatic brain injury in elderly population: A global systematic review and meta-analysis of in-hospital mortality and risk factors among 2.22 million individuals. Ageing Res Rev. 2024;99(102376):102376. 10.1016/j.arr.2024.102376 . Gavrila Laic RA, Bogaert L, Vander Sloten J, Depreitere B. Functional outcome, dependency and well-being after traumatic brain injury in the elderly population: A systematic review and meta-analysis. Brain Spine. 2021;1(100849):100849. 10.1016/j.bas.2021.100849 . Patel PP, Egodage T, Martin MJ. Decompressive craniectomy for traumatic brain injury: a review of recent landmark trials. Trauma Surg Acute Care Open. 2025;10(Suppl 1):e001784. 10.1136/tsaco-2025-001784 . Cooper DJ, Rosenfeld JV, Murray L, et al. Decompressive craniectomy in diffuse traumatic brain injury. N Engl J Med. 2011;364(16):1493–502. 10.1056/NEJMoa1102077 . Hutchinson PJ, Kolias AG, Timofeev IS, et al. Trial of decompressive craniectomy for traumatic intracranial hypertension. N Engl J Med. 2016;375(12):1119–30. 10.1056/NEJMoa1605215 . Yan J, Wang C, Sun B. Global, regional, and national burdens of traumatic brain injury from 1990 to 2021. Front Public Health. 2025;13:1556147. 10.3389/fpubh.2025.1556147 . Downing MG, Carty M, Olver J, et al. The impact of age on outcome 2 years after traumatic brain injury: Case control study. Ann Phys Rehabil Med. 2024;67(5):101834. 10.1016/j.rehab.2024.101834 . Maas AIR, Menon DK, Manley GT, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol. 2022;21(11):1004–60. 10.1016/S1474-4422(22)00309-X . Glass NE, Vadlamani A, Hwang F, et al. Bleeding and thromboembolism after traumatic brain injury in the elderly: A real conundrum. J Surg Res. 2019;235:615–20. 10.1016/j.jss.2018.10.021 . Wong ZKH, Mengistu TS, Raith E, Thornton N, Hiskens M. The effect of antithrombotic agents on the incidence of intracranial hemorrhage in elderly patients with traumatic brain injury. Trauma Care (Basel). 2026;6(1):3. 10.3390/traumacare6010003 . Gardner RC, Dams-O’Connor K, Morrissey MR, Manley GT. Geriatric traumatic brain injury: Epidemiology, outcomes, knowledge gaps, and future directions. J Neurotrauma. 2018;35(7):889–906. 10.1089/neu.2017.5371 . Roohollahi F, Molavi S, Mohammadi M, et al. Prognostic value of frailty for outcome following traumatic brain injury: A systematic review and meta-analysis. J Neurotrauma. 2024;41(3–4):331–48. 10.1089/neu.2023.0176 . Corriero A, Fornaciari A, Terrazzino S, et al. The impact of age and intensity of treatment on the outcome of traumatic brain injury. Front Neurol. 2024;15:1471209. 10.3389/fneur.2024.1471209 . Higgins JPT. In: Higgins J, Thomas J, editors. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Wiley-Blackwell; 2019. 10.1002/9781119536604 . Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. 10.1136/bmj.n71 . Schiavo JH. PROSPERO: An international register of systematic review protocols. Med Ref Serv Q. 2019;38(2):171–80. 10.1080/02763869.2019.1588072 . Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919. 10.1136/bmj.i4919 . Wilson L, Boase K, Nelson LD, et al. A manual for the Glasgow Outcome Scale-Extended interview. J Neurotrauma. 2021;38(17):2435–46. 10.1089/neu.2020.7527 . Zuckerman DA, Giacino JT, Bodien YG. Traumatic brain injury: What is a favorable outcome? J Neurotrauma. 2022;39(13–14):1010–2. 10.1089/neu.2021.0356 . Langan D, Higgins JPT, Jackson D, et al. A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods. 2019;10(1):83–98. 10.1002/jrsm.1316 . van Aert RCM, Jackson D. A new justification of the Hartung-Knapp method for random-effects meta-analysis based on weighted least squares regression. Res Synth Methods. 2019;10(4):515–27. 10.1002/jrsm.1356 . Röver C, Knapp G, Friede T. Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Med Res Methodol. 2015;15(1):99. 10.1186/s12874-015-0091-1 . IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14(1):25. 10.1186/1471-2288-14-25 . Partlett C, Riley RD. Random effects meta-analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation: RANDOM EFFECTS META-ANALYSIS: COVERAGE PERFORMANCE OF 95%CI AND PI. Stat Med. 2017;36(2):301–17. 10.1002/sim.7140 . Mitani AA, Freer PE, Nelson KP. Summary measures of agreement and association between many raters’ ordinal classifications. Ann Epidemiol. 2017;27(10):677–e6854. 10.1016/j.annepidem.2017.09.001 . Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74. 10.2307/2529310 . Mao Y, Shen Y, Yang Q, Shi Q, Li S. A leave-one-out algorithm for contribution analysis in component network meta-analysis. BMC Med Res Methodol. 2025;25(1):191. 10.1186/s12874-025-02619-w . Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112–25. 10.1002/jrsm.11 . Habib M, Amin M, Aljeddani SMA. Influence analysis in the lognormal regression model with fitted and quantile residuals. Axioms. 2025;14(6):464. 10.3390/axioms14060464 . Furuya-Kanamori L, Barendregt JJ, Doi SAR. A new improved graphical and quantitative method for detecting bias in meta-analysis. Int J Evid Based Healthc. 2018;16(4):195–203. 10.1097/XEB.0000000000000141 . Furuya-Kanamori L, Rousou X, Kostoulas P, Doi SAR. Examining and interpreting Doi plot asymmetry in meta-analyses of randomized controlled trials. J Evid Based Med. 2025;18(3):e70063. 10.1111/jebm.70063 . Brozek JL, Canelo-Aybar C, Akl EA, et al. GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making. J Clin Epidemiol. 2021;129:138–50. 10.1016/j.jclinepi.2020.09.018 . Castaño-Leon AM, Gomez PA, Jimenez-Roldan L, et al. The impact of early surgery on mortality and functional recovery in older adults with traumatic intracranial lesions: a propensity score-based analysis. Acta Neurochir (Wien). 2024;166(1):443. 10.1007/s00701-024-06324-3 . Rajendran G, Mahalingam S, Ramkumar A, Tamilarasu KP, Kannan R. Surgical versus conservative management for traumatic brain injury in elderly patients: A propensity-matched cohort study. Turk J Emerg Med. 2025;25(2):92–9. 10.4103/tjem.tjem_133_24 . Aziz N, O’Halloran PJ, McKenna GS. Subdural haematoma in the elderly: management and outcomes of a UK Major Trauma Centre. Br J Neurosurg. 2023;37(6):1604–12. 10.1080/02688697.2022.2122402 . Cook R, Zima L, Kitagawa R. Acute subdural hematomas in nonagenarians and centenarians. J Clin Neurosci. 2025;132(110997):110997. 10.1016/j.jocn.2024.110997 . Czorlich P, Mader MMD, Emami P, Westphal M, Lefering R, Hoffmann M. Operative versus non-operative treatment of traumatic brain injuries in patients 80 years of age or older. Neurosurg Rev. 2020;43(5):1305–14. 10.1007/s10143-019-01159-4 . Duehr J, Rodriguez-Torres S, Njoku-Austin C, et al. Superiority of craniotomy over supportive care for octogenarians and nonagenarians in operable acute traumatic subdural hematoma. Clin Neurol Neurosurg. 2022;212(107069):107069. 10.1016/j.clineuro.2021.107069 . Gavrila Laic RA, Sloten JV, Depreitere B. Neurosurgical treatment in elderly patients with Traumatic brain injury: A 20-year follow-up study. Brain Spine. 2023;3(101723):101723. 10.1016/j.bas.2023.101723 . Haddad AF, DiGiorgio AM, Lee YM, et al. The morbidity and mortality of surgery for Traumatic Brain Injury in geriatric patients: A study of over 100 000 patient cases: A study of over 100 000 patient cases. Neurosurgery. 2021;89(6):1062–70. 10.1093/neuros/nyab359 . Jyoti Mahakul D, Sharma T, Bhandarkar D, Jagetia A, Singh D, Agarwal J. Treatment outcome in elderly traumatic brain injury patients at a level 2 trauma care facility in a low-middle-income country. World Neurosurg. 2025;201(124203):124203. 10.1016/j.wneu.2025.124203 . Kumar A, Jaiswal S, Ojha BK, et al. Retrospective outcome analysis of geriatric traumatic brain injury treated at a tertiary care center in India. Ind J Neurosurg. 2025;14(01):034–43. 10.1055/s-0044-1788254 . Shahrokh Z, Seyed Mohsen Aghapour B, Alijani E, Bijani S. The Outcome of Surgical versus Conservative Management in Old Patients with Traumatic Brain Injury. Iran J Neurosurg. 2022;8:26–26. Shimoda K, Maeda T, Tado M, Yoshino A, Katayama Y, Bullock MR. Outcome and surgical management for geriatric traumatic brain injury: analysis of 888 cases registered in the Japan Neurotrauma Data Bank. World Neurosurg. 2014;82(6):1300–6. 10.1016/j.wneu.2014.08.014 . Singh SP, Mohindra S, Kumar N, Tadakamalla SS. Retrospective evaluation of outcomes in geriatric traumatic brain injury. Int J Med Public Health. 2025;15(3). 10.70034/ijmedph.2025.3.308 . Trevisi G, Sturiale CL, Scerrati A, et al. Acute subdural hematoma in the elderly: outcome analysis in a retrospective multicentric series of 213 patients. Neurosurg Focus. 2020;49(4):E21. 10.3171/2020.7.focus20437 . Wan X, Liu S, Wang S, et al. Elderly patients with severe traumatic brain injury could benefit from surgical treatment. World Neurosurg. 2016;89:147–52. 10.1016/j.wneu.2016.01.084 . Bharanidharan M, Jayasree S, Rajarajan P, Venkatesan R. A prospective study of traumatic Brain Injury in geriatric patients. Int J Pharm Qual Assur. 2025;16(1):41–55. 10.25258/ijpqa.16.1.6 . Obasa AA, Olopade FE, Juliano SL, Olopade JO. Traumatic brain injury or traumatic brain disease: A scientific commentary. Brain Multiphys. 2024;6(100092):100092. 10.1016/j.brain.2024.100092 . Mckee AC, Daneshvar DH. The neuropathology of traumatic brain injury. Handb Clin Neurol. 2015;127:45–66. 10.1016/B978-0-444-52892-6.00004-0 . Lenell S, Nyholm L, Lewén A, Enblad P. Clinical outcome and prognostic factors in elderly traumatic brain injury patients receiving neurointensive care. Acta Neurochir (Wien). 2019;161(6):1243–54. 10.1007/s00701-019-03893-6 . Rauchman SH, Zubair A, Jacob B, et al. Traumatic brain injury: Mechanisms, manifestations, and visual sequelae. Front Neurosci. 2023;17:1090672. 10.3389/fnins.2023.1090672 . Canales C, Wann L, Blitz J, Whittington R. The older adult surgical patient: a review of optimization and gaps in clinical practice. Perioper Med (Lond). 2025;14(1):104. 10.1186/s13741-025-00593-x . Zhang X, Fu Y. Age-related mortality risk assessment and prognostic factors in postsurgical traumatic brain injury patients: A retrospective cohort study based on the mimic database. Curr Probl Surg. 2025;72(101882):101882. 10.1016/j.cpsurg.2025.101882 . Lou B, Yu J. Frailty evaluation for short-term mortality prediction of patients with traumatic brain injury: A meta-analysis. J Head Trauma Rehabil Published online August. 2025;25. 10.1097/HTR.0000000000001100 . Cray E, Hayat NJ, Song CH, Western M, Edlmann E. The effectiveness of clinical frailty assessment in older patients with Traumatic brain injury in predicting outcome and quality of life: a systematic review. Neurosurg Rev. 2025;48(1):511. 10.1007/s10143-025-03659-y . Vehviläinen J, Virta JJ, Skrifvars MB, et al. Effect of antiplatelet and anticoagulant medication use on injury severity and mortality in patients with traumatic brain injury treated in the intensive care unit. Acta Neurochir (Wien). 2023;165(12):4003–12. 10.1007/s00701-023-05850-w . Scotti P, Séguin C, Lo BWY, de Guise E, Troquet JM, Marcoux J. Antithrombotic agents and traumatic brain injury in the elderly population: hemorrhage patterns and outcomes. J Neurosurg. 2020;133(2):486–95. 10.3171/2019.4.JNS19252 . Hwang F, Son J, Ensor K, et al. Initiating advance care planning at admission: a brief intervention to increase goals of care discussions in geriatric trauma patients in an urban level I trauma center. Trauma Surg Acute Care Open. 2023;8(1):e001058. 10.1136/tsaco-2022-001058 . Hunt C, Zahid S, Ennis N, et al. Quality of life measures in older adults after traumatic brain injury: a systematic review. Qual Life Res. 2019;28(12):3137–51. 10.1007/s11136-019-02297-4 . van Essen TA, den Boogert HF, Cnossen MC, et al. Variation in neurosurgical management of traumatic brain injury: a survey in 68 centers participating in the CENTER-TBI study. Acta Neurochir (Wien). 2019;161(3):435–49. 10.1007/s00701-018-3761-z . Leal-Méndez F, Lewén A, Gu A, et al. Regional variation in traumatic brain injury patterns, management and mortality: a nationwide Swedish cohort study. Acta Neurochir (Wien). 2025;167(1):134. 10.1007/s00701-025-06557-w . Hukkelhoven CWPM, Steyerberg EW, Farace E, Habbema JDF, Marshall LF, Maas AIR. Regional differences in patient characteristics, case management, and outcomes in traumatic brain injury: experience from the tirilazad trials. J Neurosurg. 2002;97(3):549–57. 10.3171/jns.2002.97.3.0549 . Manley GT, Dams-O’Connor K, Alosco ML, et al. A new characterisation of acute traumatic brain injury: the NIH-NINDS TBI Classification and Nomenclature Initiative. Lancet Neurol. 2025;24(6):512–23. 10.1016/S1474-4422(25)00154-1 . Watson JD, Perrin PB, Arango-Lasprilla JC. Disparities between native americans and white individuals in global outcome trajectories over the 5 years after traumatic brain injury: A model systems study. PLoS ONE. 2025;20(4):e0321279. 10.1371/journal.pone.0321279 . Stocker RA. Intensive care in traumatic brain injury including multi-modal monitoring and neuroprotection. Med Sci (Basel). 2019;7(3):37. 10.3390/medsci7030037 . Roach CS, Shawwa JJ, Nee C, Lu VM. Neurosurgical care for traumatic brain injury in low-resource settings: A multinational review evaluating the influence of health systems framework on patient outcomes. J Neurotrauma. 2025;0897715125140625310.1177/08977151251406253. Depreitere B, Becker C, Ganau M, et al. Unique considerations in the assessment and management of traumatic brain injury in older adults. Lancet Neurol. 2025;24(2):152–65. 10.1016/S1474-4422(24)00454-X . Lagares A, Depreitere B, Marklund N, et al. Consensus on the management of traumatic brain injury in older adults: Results from a Delphi study. Brain Spine. 2025;5(104319):104319. 10.1016/j.bas.2025.104319 . Thapak P, Gomez-Pinilla F. The bioenergetics of traumatic brain injury and its long-term impact for brain plasticity and function. Pharmacol Res. 2024;208(107389):107389. 10.1016/j.phrs.2024.107389 . Wenzhe L, Boyang X, Yuchao G, Bimcle R, Yue Y. Mitochondrial and ER stress crosstalk in TBI: mechanistic insights and therapeutic opportunities. Front Cell Neurosci. 2025;19(1697060):1697060. 10.3389/fncel.2025.1697060 . Hostiuc S, Rusu MC. The dynamics of neuroinflammation in traumatic brain injury: Molecular markers useful for establishing the post-traumatic interval in forensic practice. Int J Mol Sci. 2026;27(4):2049. 10.3390/ijms27042049 . Rajan RK. A comprehensive review on adaptive plasticity and recovery mechanisms post-acquired brain injury. Neuroprotection. 2025;3(3):226–52. 10.1002/nep3.70006 . Serban NL, Ungureanu G, Florian IS, Ionescu D. Cerebral vascular disturbances following traumatic brain injury: Pathophysiology, diagnosis, and therapeutic perspectives-A narrative review. Life (Basel). 2025;15(9):1470. 10.3390/life15091470 . Deimantavicius M, Chaleckas E, Boere K, et al. Feasibility of the optimal cerebral perfusion pressure value identification without a delay that is too long. Sci Rep. 2022;12(1):17724. 10.1038/s41598-022-22566-6 . Karibe H, Hayashi T, Narisawa A, Kameyama M, Nakagawa A, Tominaga T. Clinical characteristics and outcome in elderly patients with traumatic brain injury: For establishment of management strategy. Neurol Med Chir (Tokyo). 2017;57(8):418–25. 10.2176/nmc.st.2017-0058 . Suehiro E, Tanaka T, Matsuno A. Minimally invasive and proactive approaches for treatment of acute traumatic brain injury in elderly patients. J Clin Med. 2025;14(14):5028. 10.3390/jcm14145028 . Bodien YG, Barra A, Temkin NR, et al. Diagnosing level of consciousness: The limits of the Glasgow Coma Scale total score. J Neurotrauma. 2021;38(23):3295–305. 10.1089/neu.2021.0199 . Andraos C, Siddiqi A, Brazdzionis J, Siddiqi J. Limitations of the Glasgow Coma Scale: Challenges and considerations. Cureus. 2025;17(2):e78900. 10.7759/cureus.78900 . Kehoe A, Rennie S, Smith JE. Glasgow Coma Scale is unreliable for the prediction of severe head injury in elderly trauma patients. Emerg Med J. 2015;32(8):613–5. 10.1136/emermed-2013-203488 . Benhamed A, Isaac CJ, Boucher V, et al. Effect of age on the association between the Glasgow Coma Scale and the anatomical brain lesion severity: a retrospective multicentre study. Eur J Emerg Med. 2023;30(4):271–9. 10.1097/MEJ.0000000000001041 . Shorland J, Douglas J, O’Halloran R. Cognitive-communication difficulties following traumatic brain injury sustained in older adulthood: a scoping review. Int J Lang Commun Disord. 2020;55(6):821–36. 10.1111/1460-6984.12560 . Johnson LW, Hall KD. A scoping review of cognitive assessment in adults with acute traumatic brain injury. Am J Speech Lang Pathol. 2022;31(2):739–56. 10.1044/2021_AJSLP-21-00132 . Tardif PA, Moore L, Boutin A, et al. Hospital length of stay following admission for traumatic brain injury in a Canadian integrated trauma system: A retrospective multicenter cohort study. Injury. 2017;48(1):94–100. 10.1016/j.injury.2016.10.042 . van Dijck JTJM, Dijkman MD, Ophuis RH, de Ruiter GCW, Peul WC, Polinder S. In-hospital costs after severe traumatic brain injury: A systematic review and quality assessment. PLoS ONE. 2019;14(5):e0216743. 10.1371/journal.pone.0216743 . Mirian C, Ovesen T, Jensen LR, Scheike T, Springborg JB. Length of stay as a predictor of long-term mortality in patients surviving a traumatic brain injury: A nationwide TBI cohort study of 153 177 adults. J Head Trauma Rehabil. 2026;41(2):141–51. 10.1097/HTR.0000000000001086 . Naylor RM, Henry KA, Peters PA, Bauman MMJ, Lakomkin N, Van Gompel JJ. High long-term mortality rate in elderly patients with mild traumatic brain injury and subdural hematoma due to ground-level fall: Neurosurgery’s hip fracture? World Neurosurg. 2022;167:e1122–7. 10.1016/j.wneu.2022.08.140 . Tommiska P, Knuutinen O, Lönnrot K, et al. Mortality and causes of death after surgery for chronic subdural hematoma: a post hoc study of the FINISH randomized trial. Acta Neurochir (Wien). 2025;167(1):310. 10.1007/s00701-025-06728-9 . Bramlett HM, Dietrich WD. Long-term consequences of traumatic brain injury: Current status of potential mechanisms of injury and neurological outcomes. J Neurotrauma. 2015;32(23):1834–48. 10.1089/neu.2014.3352 . Fan XT, Zhao HY, Wu CF, et al. Neurosurgical management of geriatric patients with traumatic brain injury in a medium-developed Chinese city: a recent-years overview. Front Neurol. 2025;16(1691924):1691924. 10.3389/fneur.2025.1691924 . Nia A, Leitgeb J, Widhalm HK, et al. Mortality in moderate to severe traumatic brain injury in elderly polytrauma patients at a European Level 1 trauma centre-A retrospective cohort study. J Clin Med. 2025;14(11):3843. 10.3390/jcm14113843 . Adebola O. Do we need a neurosurgical frailty index? Surg Neurol Int. 2024;15:134. 10.25259/SNI_50_2024 . Winkler J, Piedade GS, Rubbert C, Hofmann BB, Kamp MA, Slotty PJ. Cerebral perfusion changes in acute subdural hematoma. Acta Neurochir (Wien). 2023;165(9):2381–7. 10.1007/s00701-023-05703-6 . Kuhn EN, Erwood MS, Oster RA, et al. Outcomes of subdural hematoma in the elderly with a history of minor or no previous trauma. World Neurosurg. 2018;119:e374–82. 10.1016/j.wneu.2018.07.168 . Tables Tables are available in the Supplementary Files section. Supplementary Files SupplementaryMaterial.docx PRISMA2020checklist.docx Tableslegends.docx Table1.docx Table2.docx Table3.docx Table4.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 May, 2026 Reviewers invited by journal 22 Apr, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 09 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9362369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627982117,"identity":"026916c6-4ffe-43ae-8cd4-5490c45e58b2","order_by":0,"name":"Luis E. Cueva-Cañola","email":"","orcid":"","institution":"Universidad Nacional de Piura Facultad de Medicina Humana","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"E.","lastName":"Cueva-Cañola","suffix":""},{"id":627982118,"identity":"5ae61068-f9c2-4d30-a4ee-bba0aac9c1a7","order_by":1,"name":"Andrea C. Beltran-De la Fuente","email":"","orcid":"","institution":"Instituto Politecnico Nacional","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"C. Beltran-De la","lastName":"Fuente","suffix":""},{"id":627982119,"identity":"888e1501-4586-41fd-97e8-6984ea64d402","order_by":2,"name":"Mael S. Ayala-Alban","email":"","orcid":"","institution":"National Hospital Edgardo Rebagliati Martins: Hospital Nacional Edgardo Rebagliati Martins","correspondingAuthor":false,"prefix":"","firstName":"Mael","middleName":"S.","lastName":"Ayala-Alban","suffix":""},{"id":627982120,"identity":"7df49627-ae39-4c56-9a38-c2602225e821","order_by":3,"name":"Sergio Morales Acosta","email":"","orcid":"","institution":"Universidad Autonóma de Guadalajara - Facultad de Medicina: Universidad Autonoma de Guadalajara - Facultad de Medicina","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"Morales","lastName":"Acosta","suffix":""},{"id":627982121,"identity":"f70e6db3-502f-4c65-8bd6-a512fd9468b6","order_by":4,"name":"Astrid G. Carrión-Cuéllar","email":"","orcid":"","institution":"Universidad Autónoma de Barcelona Facultad de Medicina: Universitat Autonoma de Barcelona Facultat de Medicina","correspondingAuthor":false,"prefix":"","firstName":"Astrid","middleName":"G.","lastName":"Carrión-Cuéllar","suffix":""},{"id":627982122,"identity":"9f6530af-97a4-4b88-9f22-529a86f6570b","order_by":5,"name":"Olga N. Polania Pérez","email":"","orcid":"","institution":"Universidad de Santander","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"N. Polania","lastName":"Pérez","suffix":""},{"id":627982123,"identity":"34265aa3-ded4-44c7-85d6-5f983bf97a2f","order_by":6,"name":"Rupesh G. Rathod","email":"","orcid":"","institution":"Savitribai Phule Pune University","correspondingAuthor":false,"prefix":"","firstName":"Rupesh","middleName":"G.","lastName":"Rathod","suffix":""},{"id":627982124,"identity":"29fbe728-8829-460a-bbef-8fb9e3de202a","order_by":7,"name":"Manuel E. Lopez-Gonzales","email":"","orcid":"","institution":"Universidad Nacional de Piura Facultad de Medicina Humana","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"E.","lastName":"Lopez-Gonzales","suffix":""},{"id":627982125,"identity":"3c237cfa-d954-4f99-b4c8-c262487f7ec4","order_by":8,"name":"Ana Karina La Madrid Barreto","email":"","orcid":"","institution":"Universidad Nacional de Piura Facultad de Medicina Humana","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Karina La Madrid","lastName":"Barreto","suffix":""},{"id":627982126,"identity":"2eaa92ce-bb4c-417c-96c0-9cdc7bec6a46","order_by":9,"name":"Leonardo Rangel-Castilla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPgglIcPHwHwMzGRjJ6CFDUwmSPCwMbClARlAEWbitDAAtfCYgbUwENQiffjhx58/LHjY2M98e/DxxzZ5PmYGxg8fc/Bo4UszluYBOYwnd7vhjITbhm3MDMySM7fh0cLDwyAN8UvuNqDe24xALWzMvPi1MP/8AdLC/+YZSIs9MVrYJMAOk8hhA2lJJEILm5k1TxpIyzMzyRlpt5PbmBmb8fqFn4f58c0fNnVy/PzJzyQ+2Ny2nd/efPDDRzxasAHGBtLUj4JRMApGwSjAAABeGTuuKAwcsgAAAABJRU5ErkJggg==","orcid":"","institution":"Mayo Clinic Department of Neurosurgery","correspondingAuthor":true,"prefix":"","firstName":"Leonardo","middleName":"","lastName":"Rangel-Castilla","suffix":""}],"badges":[],"createdAt":"2026-04-09 02:56:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9362369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9362369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108808187,"identity":"21cf8adf-510b-4c55-8097-c0fbed13a729","added_by":"auto","created_at":"2026-05-08 15:40:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":900659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 flow diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.PRISMA2020flowdiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/82be0a836f4e4794aef4d3f2.png"},{"id":108808196,"identity":"24e75e1a-021d-488a-8050-b4d6574502f1","added_by":"auto","created_at":"2026-05-08 15:40:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1723505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographic distribution of patients across included studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Geographic distribution of included patients, with the highest contributions from the United States, India, and China. (B) Country-level treatment patterns demonstrated marked variability in the proportion of surgical versus medical management. Surgical rates ranged from 5% in the United States to 69% in Italy (median 44%, range 5–69%), while medical management ranged from 31% in Italy to 95% in the United States (median 56%, range 31–95%). Balanced approaches were observed in Iran and Spain (50% surgical vs 50% medical). (C) Continental analysis showed a decreasing gradient in surgical utilization from Asia (45.6%) to Europe (32.5%) and North America (4.7%). (D) Overall, treatment distribution was heavily weighted toward conservative care, with 93.9% of patients managed medically and 6.1% undergoing surgical intervention.\u003c/p\u003e","description":"","filename":"Figure2.Geographicdistribution.png","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/fe36153b7ea0702f3ea366e4.png"},{"id":108808148,"identity":"5d9602c9-2aa1-4212-8038-b79d1e584905","added_by":"auto","created_at":"2026-05-08 15:40:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1477444,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePublication bias for favorable neurological outcome\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.Publicationbiasforneurological.png","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/39dc5ef1ea2c089840158c79.png"},{"id":108808197,"identity":"e2a2fcc7-5c63-4f46-a6e8-8a89d0585e4c","added_by":"auto","created_at":"2026-05-08 15:40:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":210585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePublication bias for hospital length of stay\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.PublicationbiasforLOS.png","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/74f17d50865d2829c20e2d84.png"},{"id":108808195,"identity":"82696fb8-f56b-4b77-b04e-c9c7b2936236","added_by":"auto","created_at":"2026-05-08 15:40:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1564242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePublication bias for mortality\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.Publicationbiasformortality.png","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/62cf877165f3e9ddead0f44b.png"},{"id":108808039,"identity":"4d21f399-2571-41e1-a9a2-f0300dbd3d74","added_by":"auto","created_at":"2026-05-08 15:39:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5431022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk of bias\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.Riskofbias.png","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/5a412a702d385bc6b5087949.png"},{"id":109205981,"identity":"61c5ef65-c49e-4a24-9dbd-7a5a66548024","added_by":"auto","created_at":"2026-05-13 15:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11454447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/25bd9da8-2ab8-4d1e-b491-9239afeeb908.pdf"},{"id":108808147,"identity":"75957d6c-e1d3-4bce-9e94-3c94f719e0ce","added_by":"auto","created_at":"2026-05-08 15:40:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4632469,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/dcac6430bd1580b5e9504e13.docx"},{"id":108808144,"identity":"f539a4b1-2500-49df-92b6-62fe2cd60ceb","added_by":"auto","created_at":"2026-05-08 15:40:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32506,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMA2020checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/7d2175a9f161772184471cfa.docx"},{"id":108808139,"identity":"eb20096c-0b28-40ea-bf9e-df08d217d27c","added_by":"auto","created_at":"2026-05-08 15:40:03","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15969,"visible":true,"origin":"","legend":"","description":"","filename":"Tableslegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/913e521402d895d75246c859.docx"},{"id":109203815,"identity":"6534eca6-8a4d-455e-a1e7-df3cc3e879ca","added_by":"auto","created_at":"2026-05-13 14:47:20","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1681378,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/028c203914a1f0b7a4a598ba.docx"},{"id":108808149,"identity":"4e5cc38a-4f0d-485b-a199-74a127cce6c1","added_by":"auto","created_at":"2026-05-08 15:40:06","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1672307,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/6dfbfbbeb097066594694ea5.docx"},{"id":108808145,"identity":"f4809832-4996-4b33-ac6e-2cf547c06c16","added_by":"auto","created_at":"2026-05-08 15:40:05","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1671203,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/6d05690b7acb1943fdd58713.docx"},{"id":108808038,"identity":"441223ea-f7c5-47bf-b838-80ed8af07e57","added_by":"auto","created_at":"2026-05-08 15:39:24","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2770464,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.docx","url":"https://assets-eu.researchsquare.com/files/rs-9362369/v1/ef4dd6300559381b95eeee1a.docx"}],"financialInterests":"","formattedTitle":"Surgical Versus Medical Management in Older Adults with Traumatic Brain Injury: A Systematic Review and Meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraumatic brain injury (TBI) in older adults represents a growing global health challenge driven by population aging and associated with substantial mortality and long-term disability. Among individuals aged 60 years and older, in-hospital mortality reaches 16% and increases sharply with injury severity, from 5% in mild cases to 18% in moderate injury and up to 65% in severe TBI.\u003csup\u003e1\u003c/sup\u003e Poor functional outcomes are common in this population, affecting 65.2% of patients, with institutionalization rates of 28.5% and dependency ranging from 16.9% to 74.0%.\u003csup\u003e2\u003c/sup\u003e Despite this considerable burden, optimal management strategies in this population remain uncertain.\u003c/p\u003e\n\u003cp\u003eCurrent evidence guiding surgical management in TBI is largely derived from randomized controlled trials (RCTs) such as the DECRA trial and RESCUEicp trial, which form the basis of contemporary clinical practice.\u003csup\u003e3\u003c/sup\u003e These studies included patients aged 15 to 59 years and 10 to 65 years, respectively. They showed that early decompressive craniectomy (DC) reduces intracranial pressure (ICP) and length of stay (LOS) but is associated with worse functional outcomes,\u003csup\u003e4\u003c/sup\u003e whereas delayed DC used as a rescue strategy reduces mortality and improves long term neurological outcomes.\u003csup\u003e5\u003c/sup\u003e However, the limited inclusion of older adults in these trials restricts the external validity of their findings in a population with the highest disease burden, where differences in baseline characteristics and recovery potential may influence treatment effects.\u003c/p\u003e\n\u003cp\u003eOlder adults represent a distinct clinical population in whom both the response to TBI and the effects of its management may differ from those observed in younger patients.\u003csup\u003e6,7\u003c/sup\u003e Age related physiological changes, including reduced brain resilience, impaired neuroplasticity, and decreased tolerance to high ICP, may adversely affect recovery.\u003csup\u003e8\u003c/sup\u003e In addition, the higher prevalence of comorbidities, baseline functional limitations, and the widespread use of antithrombotic therapies increase the risk of complications and may modify treatment effects.\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e Frailty further increases vulnerability and is consistently associated with higher mortality and worse functional outcomes.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn clinical practice, older patients with TBI are less likely to receive aggressive management, including advanced ICP directed therapies such as osmotic therapy, cerebrospinal fluid (CSF) drainage, hypothermia, barbiturate coma, and neurosurgical interventions. This pattern may reflect not only individualized clinical decision making but also therapeutic uncertainty in the absence of robust evidence specific to this population. Importantly, although advanced age remains an independent predictor of poor prognosis, emerging evidence suggests that outcomes may be influenced by the intensity and type of care delivered.\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe lack of robust, population specific evidence continues to hinder evidence-based decision making regarding surgical versus medical management in older adults with TBI. Defining the balance between potential benefit and harm is critical to avoid both underuse and inappropriate use of invasive interventions, and to better inform prognosis, treatment selection, and resource utilization in this high risk and rapidly growing population. \u003c/p\u003e\n\u003cp\u003eTherefore, we conducted a systematic review and meta-analysis to compare surgical and medical management in older adults with TBI, with the primary aim of evaluating favorable neurological outcomes, and secondary outcomes including hospital LOS, and mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Reporting Standards\u003c/p\u003e\n\u003cp\u003eThis study was designed as a systematic review and meta-analysis and conducted in accordance with the methodological recommendations of the Cochrane Handbook for Systematic Reviews of Interventions.\u003csup\u003e14\u003c/sup\u003e The reporting adhered to the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines to ensure transparency, methodological rigor, and reproducibility.\u003csup\u003e15\u003c/sup\u003e \u003c/p\u003e\n\u003cp\u003eProtocol and Registration\u003c/p\u003e\n\u003cp\u003eThe study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) on November 21, 2025 under the registration number CRD420251236960, prior to study initiation, to enhance methodological transparency and reduce the risk of selective reporting.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eEligibility Criteria\u003c/p\u003e\n\u003cp\u003eStudies were included if they met all of the following criteria: (1) included adult patients aged 60 years and older with TBI, with diagnosis confirmed by clinical examination and neuroimaging using computed tomography (CT) or magnetic resonance imaging; no restrictions were applied regarding injury severity; (2) evaluated surgical management, defined as any invasive neurosurgical intervention performed to evacuate intracranial lesions or control ICP, including DC, craniotomy for hematoma evacuation, burr hole procedures, and other decompressive interventions; (3) included a comparator group receiving medical management, defined as non-surgical treatment consisting of hemodynamic stabilization and neuroprotective strategies, including osmotic therapy with mannitol or hypertonic saline, sedation and analgesia, mechanical ventilation, temperature control strategies, barbiturate coma, and comprehensive critical care support; (4) reported at least one of the following outcomes: favorable neurological outcome, hospital LOS, or mortality; and (5) were RCTs or observational studies, including prospective or retrospective cohort designs, with no restrictions on language.\u003c/p\u003e\n\u003cp\u003eStudies were excluded if they met any of the following criteria: (1) did not include patients aged 60 years and older or did not provide extractable data for this subgroup; (2) were case reports, case series without a comparator group, narrative reviews, systematic reviews, editorials, letters, or conference abstracts without full text; (3) included animal studies or experimental models; or (4) did not provide sufficient data for outcome extraction or analysis.\u003c/p\u003e\n\u003cp\u003eInformation Sources and Search Strategy\u003c/p\u003e\n\u003cp\u003eA comprehensive literature search was conducted in PubMed, Embase, and Web of Science from database inception to December 8, 2025. The search strategy incorporated controlled vocabulary and free text terms related to traumatic brain injury, neurosurgical interventions, medical management, and older adult populations. The complete search strategies for each database are provided in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eStudy Selection\u003c/p\u003e\n\u003cp\u003eAll retrieved records were imported into Zotero, where duplicate records were identified and removed. The deduplicated dataset was subsequently uploaded to Rayyan for study selection. Screening was conducted in two sequential stages. First, two reviewers (M.S.A.A. and S.M.A.) independently screened titles and abstracts. Second, the full texts of potentially eligible studies were independently assessed by the same reviewers. Discrepancies at any stage were resolved through discussion, and when consensus could not be reached, a third reviewer (A.C.B.D.L.F.) was consulted.\u003c/p\u003e\n\u003cp\u003eData Extraction\u003c/p\u003e\n\u003cp\u003eData were extracted independently by two reviewers (O.N.P.P. and R.G.R.) using a standardized and piloted data extraction form. Extracted variables included: (1) study characteristics, (2) demographic characteristics, (3) pre-injury status, (4) injury characteristics, (5) injury severity, (6) initial clinical presentation, (7) neuroimaging findings, and (8) management and intervention variables. When required, additional information was sought from study authors. Discrepancies between reviewers were resolved through discussion and, when necessary, consultation with a third reviewer (A.G.C.C.).\u003c/p\u003e\n\u003cp\u003eRisk of Bias Assessment\u003c/p\u003e\n\u003cp\u003eThe risk of bias of included studies was independently assessed by two reviewers (M.E.L.G. and R.G.R.) using the Risk of Bias in Non-randomized Studies of Interventions (ROBINS I) tool. This tool evaluates bias across the following domains: confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported results.\u003csup\u003e17\u003c/sup\u003e Each domain was rated as low, moderate, serious, or critical risk of bias, and an overall risk of bias judgment was assigned to each study accordingly. Discrepancies were resolved through discussion, and when necessary, consultation with a third reviewer (A.C.B.D.L.F.).\u003c/p\u003e\n\u003cp\u003eOutcomes \u003c/p\u003e\n\u003cp\u003eThe primary outcome was favorable neurological outcome, defined using validated functional scales and operationalized as a Glasgow Outcome Scale score of 4 to 5 or a Glasgow Outcome Scale Extended score of 5 to 8.\u003csup\u003e18,19\u003c/sup\u003e Favorable neurological outcome was analyzed according to the timing of assessment, including at hospital discharge, 3 months, 6 months, and 12 months. Secondary outcomes included hospital LOS, defined as the total number of days from hospital admission to discharge; and all-cause mortality, defined as death from any cause during follow-up. Mortality was analyzed according to the timing of assessment, including in hospital mortality, 30-day mortality, and mortality at 3-months, 6-months, 12-months, and 24-months.\u003c/p\u003e\n\u003cp\u003eData Synthesis and Statistical Analysis\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted in RStudio (R Foundation for Statistical Computing, Vienna, Austria) using the meta and metafor packages. Effect estimates were synthesized using random-effects models, accounting for expected clinical and methodological heterogeneity across studies.\u003c/p\u003e\n\u003cp\u003eFor dichotomous outcomes, pooled estimates were calculated as risk ratios (RR) with 95% confidence intervals (CI), while mean differences (MD) were used for continuous outcomes. Between-study variance (\u0026tau;\u0026sup2;) was estimated using the restricted maximum likelihood (REML) method.\u003csup\u003e20\u003c/sup\u003e To account for uncertainty, particularly in the context of a small number of studies and anticipated heterogeneity, the Hartung\u0026ndash;Knapp adjustment (HKSJ) was applied to all models, providing more conservative estimates.\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eStatistical heterogeneity was assessed using Cochran\u0026rsquo;s Q test (QE) and quantified with the I\u0026sup2; statistic and \u0026tau;\u0026sup2;. Heterogeneity was interpreted as low (I\u0026sup2; \u0026lt; 25%), moderate (25\u0026ndash;50%), substantial (50\u0026ndash;75%), or considerable (\u0026gt;75%), with p \u0026lt; 0.10 indicating statistically significant heterogeneity. In addition, prediction intervals (PI) were calculated to estimate the range within which the true effect of a future study is expected to lie.\u003csup\u003e24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eInter reviewer agreement was evaluated using Cohen\u0026rsquo;s kappa (\u0026kappa;) and interpreted as follows: \u0026kappa; \u0026lt; 0.20 poor, \u0026kappa; = 0.21\u0026ndash;0.40 fair, \u0026kappa; = 0.41\u0026ndash;0.60 moderate, \u0026kappa; = 0.61\u0026ndash;0.80 substantial, and \u0026kappa; \u0026gt; 0.80 excellent agreement.\u003csup\u003e25,26\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup Analyses\u003c/p\u003e\n\u003cp\u003ePre-specified subgroup analyses were conducted to explore potential sources of heterogeneity and to assess the consistency of treatment effects across clinically relevant populations. These included comparisons according to study design (unmatched versus matched studies), age-stratified analyses (\u0026ge;65, \u0026ge;70, and \u0026ge;80 years), and clinically defined subgroups such as subdural hematoma (SDH) and isolated TBI. Additionally, analyses were stratified by injury severity based on the Glasgow Coma Scale (GCS), categorized as mild (GCS 13 to 15), moderate (GCS 9 to 12), and severe TBI (GCS \u0026le;8). For each subgroup, pooled estimates were calculated using the same random-effects model (REML with HKSJ) applied in the primary analysis. Subgroup findings were interpreted with caution, considering the potential for residual confounding, multiple comparisons, and limited statistical power. Additional clinically relevant subgroups were pre-specified but could not be analyzed due to insufficient or inconsistent reporting across included studies. These comprised frailty status, baseline functional status, comorbidity burden, and key radiological variables such as midline shift (MLS), hematoma volume, cerebral edema, and ICP parameters, as well as time to intervention and type of surgical technique.\u003c/p\u003e\n\u003cp\u003eInfluence Analysis\u003c/p\u003e\n\u003cp\u003eAn influence analysis was performed using a leave-one-out approach, in which each study was sequentially removed and the model refitted. The number of studies (n) and the number of model parameters (p) were defined. Studentized residuals (rstudent), defined as residuals standardized by their estimated variance, were used to detect outliers (|rstudent| \u0026gt; 2; \u0026gt;3 considered extreme). Difference in fits (DFFITS), reflecting the change in fitted values after study removal, was considered influential if |DFFITS| \u0026gt; \u003cimg width=\"39\" height=\"22\" src=\"data:image/png;base64,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\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e. Cook\u0026rsquo;s distance (Cook\u0026rsquo;s d), measuring the overall influence of each study on model estimates, was considered relevant when \u0026gt; 4/n. The covariance ratio (cov.r), which evaluates the impact on the precision of the variance\u0026ndash;covariance matrix, was considered influential when outside 1 \u0026plusmn; 3p/n. Hat values, defined as the diagonal elements of the projection matrix, were considered high when \u0026gt; \u003cimg width=\"22\" height=\"18\" src=\"data:image/png;base64,R0lGODlhIQAbAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABQAhABIAhAAAAAAAAAAAOgAAZgA6ZgA6kABmtjoAADo6ZjqQ22YAAGa222a2/5A6AJA6ZpBmOpBmZpC2/5Db/7ZmALZmOraQOraQZrbb/7b//9uQOtv///+2Zv/bkP/btv//tv//2wWuICCOZDlmA2auLPs1RivPmyDN+DoVWu6LHkXC97EcbB9KQMAwoVSA4lGSXEZEmQXnsIBcXinSK4bVcr1B3miLgE5spE2gSWKrgmTsfPS+8dUkGXsAW0N/PVENYUBCJhOLNX6JeVt5ABlwYjB8i4QHhgB9I2MmhSKkI3IGGh8VgyJbdHGDeB0OKhMEDwEBBJIij1AkwSNKBBeTLUGgP8stkT+pryWo0Ze9v2sHstEhADs=\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e. The standardized difference in coefficients (DFBETAS), measuring the change in each coefficient after study removal, was considered relevant if |DFBETAS| \u0026gt; 1. Study weights, representing the relative contribution of each study to the pooled estimate under the random-effects model, were examined to identify disproportionately influential studies; unusually large weights compared to the average were considered indicative of potential influence. Additionally, changes in \u0026tau;\u0026sup2; and heterogeneity (Q statistic) were assessed after exclusion of each study.\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePublication Bias Assessment\u003c/p\u003e\n\u003cp\u003ePublication bias and small-study effects were assessed using the Doi plot in combination with the Luis Furuya-Kanamori (LFK) index, as proposed by Furuya et al.\u003csup\u003e30\u003c/sup\u003e The Doi plot represents effect sizes against a Z-score\u0026ndash;based transformation of study precision, providing an alternative graphical approach to the conventional funnel plot. While funnel plots and Egger\u0026rsquo;s regression remain widely used and informative tools, the Doi plot offers a complementary visualization with improved interpretability of asymmetry patterns, particularly when assessing small-study effects.\u003csup\u003e31\u003c/sup\u003e Asymmetry was quantified using the LFK index, which measures the deviation between both sides of the Doi plot relative to the point of minimum absolute Z-score. Interpretation followed predefined thresholds: no asymmetry (|LFK| \u0026le; 1), minor asymmetry (|LFK| between 1 and 2), and major asymmetry (|LFK| \u0026gt; 2).\u003c/p\u003e\n\u003cp\u003eCertainty of Evidence Assessment\u003c/p\u003e\n\u003cp\u003eThe certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.\u003csup\u003e32\u003c/sup\u003e Evaluations were performed independently by two reviewers (L.E.C.C. and L.R.C.), both with extensive experience in evidence synthesis and certainty assessment. Any discrepancies were resolved through discussion, and when consensus was not achieved, a third reviewer (A.C.B.D.L.F.) was consulted for adjudication. Final certainty ratings were categorized as high, moderate, low, or very low, reflecting the overall confidence in the estimated effect.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eStudy Selection\u003c/h2\u003e\n\u003cp\u003eThe screening process identified 925 records. After removal of duplicates, 750 studies underwent title and abstract screening, with an agreement of 92.0% and excellent inter-rater reliability (\u0026kappa; = 0.84). A total of 144 articles were considered potentially eligible and advanced to full-text assessment. During full-text review, agreement increased to 95.1%, with excellent reliability (\u0026kappa; = 0.90). Following eligibility assessment, 16 cohort studies were included.\u003csup\u003e33\u0026ndash;48\u003c/sup\u003e The PRISMA flow diagram is presented in \u003cstrong\u003eFig. 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 16 cohort studies were included, comprising 2 matched studies using propensity score matching (PSM),\u003csup\u003e33,34\u003c/sup\u003e and 14 unmatched observational cohorts.\u003csup\u003e35\u0026ndash;48\u003c/sup\u003e In the two matched studies, PSM was performed using 1:1 nearest neighbor approaches with predefined calipers, adjusting for demographic characteristics, clinical severity measures, radiological findings, and treatment related factors, with post matching assessment to ensure adequate balance between groups. Overall, most studies used a retrospective design (n = 15, 93.8%),\u003csup\u003e33\u0026ndash;47\u003c/sup\u003e while only one was prospective (n = 1, 6.3%).\u003csup\u003e48\u003c/sup\u003e Study periods ranged from 1998 to 2024, and median year of publication was 2023 (range: 2014 to 2025). India contributed the largest number of studies (n = 5, 31.3%),\u003csup\u003e34,41,42,45,48\u003c/sup\u003e followed by the United States (n = 3, 18.8%),\u003csup\u003e36,38,40\u003c/sup\u003e whereas the United Kingdom,\u003csup\u003e35\u003c/sup\u003e Spain,\u003csup\u003e33\u003c/sup\u003e Germany,\u003csup\u003e37\u003c/sup\u003e Belgium,\u003csup\u003e39\u003c/sup\u003e Iran,\u003csup\u003e43\u003c/sup\u003e Japan,\u003csup\u003e44\u003c/sup\u003e Italy,\u003csup\u003e46\u003c/sup\u003e and China\u003csup\u003e47\u003c/sup\u003e each contributed one study (n = 1, 6.3%). This distribution corresponded to three continents, including Asia (n = 8, 50.0%),\u003csup\u003e34,41\u0026ndash;45,47,48\u003c/sup\u003e Europe (n = 5, 31.3%),\u003csup\u003e33,35,37,39,46\u003c/sup\u003e and North America (n = 3, 18.8%).\u003csup\u003e36,38,40\u003c/sup\u003e Regarding follow-up, five studies (31.3%) reported in hospital follow-up only,\u003csup\u003e36,37,40,43,45\u003c/sup\u003e whereas the remaining 11 studies (68.8%) included post discharge follow-up; among these, the median follow-up duration was 6 months (range: 3 to 24 months).\u003csup\u003e33\u0026ndash;35,38,39,41,42,44,46\u0026ndash;48\u003c/sup\u003e Definitions of older adults varied across studies, with the most commonly used threshold being \u0026ge;65 years (n = 5, 31.3%),\u003csup\u003e33,39,44,47,48\u003c/sup\u003e followed by \u0026ge;60 years (n = 3, 18.8%),\u003csup\u003e34,42,45\u003c/sup\u003e \u0026ge;80 years (n = 3, 18.8%),\u003csup\u003e37,38,40\u003c/sup\u003e and \u0026ge;70 years (n = 2, 12.5%),\u003csup\u003e35,46\u003c/sup\u003e while other cutoffs such as \u0026gt;60 years,\u003csup\u003e41\u003c/sup\u003e \u0026gt;65 years,\u003csup\u003e48\u003c/sup\u003e and \u0026ge;90 years,\u003csup\u003e36\u003c/sup\u003e were each reported in one study (n = 1, 6.3%).\u003c/p\u003e\n\u003ch2\u003eCharacteristics of Included Population\u003c/h2\u003e\n\u003cp\u003eA total of 132,823 older adults with TBI were included. Females slightly predominated, accounting for 72,071 patients (54.3%), compared with 60,747 males (45.7%). The mean age was 84.0 \u0026plusmn; 3.7 years (n = 130,746).\u003csup\u003e33,36,38\u0026ndash;48\u003c/sup\u003e Comorbidities were common, particularly hypertension (HTN) (79,052/129,071, 61.2%),\u003csup\u003e34,38,40\u0026ndash;42,45,48\u003c/sup\u003e and diabetes mellitus (26,872/129,220, 20.8%).\u003csup\u003e34,38\u0026ndash;42,45,48\u003c/sup\u003e Functional dependency or high frailty was reported in 11,104/128,515 patients (8.6%),\u003csup\u003e33,35,36,39,40,45\u003c/sup\u003e as assessed by instruments such as the World Health Organization (WHO) Performance Status,\u003csup\u003e35\u003c/sup\u003e Clinical Frailty Scale,\u003csup\u003e33\u003c/sup\u003e and Activities of Daily Living,\u003csup\u003e39\u003c/sup\u003e or equivalent definitions across studies.\u003csup\u003e36,40,45\u003c/sup\u003e Anticoagulant or antiplatelet use was documented in 618/1,856 patients (33.3%).\u003csup\u003e33,35,36,38,41,45,46,48\u003c/sup\u003e Falls were the leading mechanism of injury, occurring in 85,898/132,584 cases (64.8%),\u003csup\u003e33,35\u0026ndash;37,39\u0026ndash;48\u003c/sup\u003e followed by road traffic accidents in 11,696/132,318 (8.8%).\u003csup\u003e33,35,37,40\u0026ndash;48\u003c/sup\u003e Regarding injury severity, mild TBI was reported in 1,123/2,385 patients (47.1%),\u003csup\u003e33\u0026ndash;35,42,45\u0026ndash;48\u003c/sup\u003e moderate in 588/2,385 (24.7%),\u003csup\u003e33\u0026ndash;35,42,45\u0026ndash;48\u003c/sup\u003e and severe in 2,099/4,966 (42.3%).\u003csup\u003e33\u0026ndash;35,37,44\u0026ndash;48\u003c/sup\u003e The mean GCS score was 13.6 \u0026plusmn; 3.1 (n = 129,502).\u003csup\u003e36,38\u0026ndash;43,45,46\u003c/sup\u003e Patients most commonly presented with loss of consciousness (669/878, 76.2%),\u003csup\u003e34,41,42\u003c/sup\u003e followed by vomiting (413/878, 47.0%),\u003csup\u003e34,41,42\u003c/sup\u003e and seizures (53/1,091, 4.9%).\u003csup\u003e34,41,42,46\u003c/sup\u003e Other reported clinical features included altered sensorium,\u003csup\u003e42\u003c/sup\u003e motor deficits,\u003csup\u003e33,41,42\u003c/sup\u003e pupillary abnormalities,\u003csup\u003e33,37,41,42,45\u0026ndash;47\u003c/sup\u003e otorrhagia/epistaxis,\u003csup\u003e34,42,43\u003c/sup\u003e CSF leakage,\u003csup\u003e42\u003c/sup\u003e hypotension,\u003csup\u003e44\u003c/sup\u003e HTN,\u003csup\u003e45\u003c/sup\u003e and hypoxia.\u003csup\u003e44\u003c/sup\u003e The patient characteristics, including demographic data, pre-injury status, and injury-related variables, are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAdditional radiological findings included a mean MLS of 5.6 \u0026plusmn; 5.7 mm (n = 773),\u003csup\u003e33,36,39,41,46,47\u003c/sup\u003e effaced basal cisterns in 289/476 cases (60.7%),\u003csup\u003e33,41,47,48\u003c/sup\u003e a mean Marshall score of 2.6 \u0026plusmn; 1.2 (n = 985),\u003csup\u003e39,42,48\u003c/sup\u003e and skull fractures in 885/3,680 patients (24.0%).\u003csup\u003e34,37,39,41,42,45,48\u003c/sup\u003e SDH was the most frequently reported lesion, present in 2,725/4,568 patients (59.7%).\u003csup\u003e33\u0026ndash;39,41,42,45\u0026ndash;48\u003c/sup\u003e Neuroimaging findings and management characteristics are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eManagement\u003c/h2\u003e\n\u003cp\u003eSurgical management was performed in 8,168 patients (6.1%), whereas 124,655 (93.9%) received medical treatment. The proportion of surgical treatment varied across continents: 45.6% in Asia, 32.5% in Europe, and 4.7% in North America. Among surgically treated patients, craniotomy was performed in 599/1,047 (57.2%),\u003csup\u003e35,36,38,39,41\u0026ndash;43,45,47\u003c/sup\u003e whereas DC was reported in 412/1,530 (26.9%).\u003csup\u003e34\u0026ndash;39,41\u0026ndash;43,45,47\u003c/sup\u003e The distribution of treatment is shown in \u003cstrong\u003eFig. 2\u003c/strong\u003e. \u003c/p\u003e\n\u003ch2\u003eFavorable Neurological Outcome at Discharge\u003c/h2\u003e\n\u003cp\u003eThis analysis included 3,384 older adults, of whom 1,109 (32.8%) underwent surgical management and 2,275 (67.2%) received medical management.\u003csup\u003e35,37,43,45,46,48\u003c/sup\u003e Favorable neurological outcomes at discharge were observed in 505 (45.5%) surgically treated patients and 1,160 (51.0%) of those managed medically. There was no statistically significant difference between groups (RR = 0.72, 95% CI: 0.45\u0026ndash;1.13; p = 0.12). However, heterogeneity was high (I\u0026sup2; = 92.0%).\u003c/p\u003e\n\u003ch2\u003eFavorable Neurological Outcome at 3 Months\u003c/h2\u003e\n\u003cp\u003eFavorable neurological outcome at 3 months was reported in a single study by Jyoti et al.,\u003csup\u003e41\u003c/sup\u003e including 120 older adults. Of these, 91 (75.8%) underwent surgical management and 29 (24.2%) received medical management. There was no statistically significant difference between groups (RR = 0.64, 95% CI: 0.26\u0026ndash;1.55; p = 0.32).\u003c/p\u003e\n\u003ch2\u003eFavorable Neurological Outcome at 6 Months\u003c/h2\u003e\n\u003cp\u003eThis analysis included 2,313 older adults, of whom 1,167 (50.5%) underwent surgical management and 1,146 (49.5%) received medical management.\u003csup\u003e33\u0026ndash;35,42,44,46,47\u003c/sup\u003e Favorable neurological outcomes at 6 months were observed in 304 (26.0%) surgically treated patients and 265 (23.1%) of those managed medically. There was no statistically significant difference between groups (RR = 1.30, 95% CI: 0.55\u0026ndash;3.08; p = 0.48). However, heterogeneity was high (I\u0026sup2; = 90.5%).\u003c/p\u003e\n\u003ch2\u003eFavorable Neurological Outcome at 12 Months\u003c/h2\u003e\n\u003cp\u003eFavorable neurological outcome at 12 months was reported in a single study by Casta\u0026ntilde;o et al.,\u003csup\u003e33\u003c/sup\u003e including 62 older adults. Of these, 31 (50.0%) underwent surgical management and 31 (50.0%) received medical management. There was no statistically significant difference between groups (RR = 1.86, 95% CI: 0.86\u0026ndash;4.02; p = 0.12).\u003c/p\u003e\n\u003ch2\u003eHospital LOS\u003c/h2\u003e\n\u003cp\u003eThis analysis included 127,888 older adults, of whom 6,268 (4.9%) underwent surgical management and 121,620 (95.1%) received medical management.\u003csup\u003e35,36,39,40,46\u003c/sup\u003e The mean hospital LOS was 13.0 \u0026plusmn; 10.9 days in the surgical group and 5.4 \u0026plusmn; 6.2 days in the medical group. Patients who underwent surgical management had a longer hospital stay compared with those managed medically (MD = 6.35 days, 95% CI: 2.55 to 10.14; p \u0026lt; 0.01). However, heterogeneity was high (I\u0026sup2; = 87.4%).\u003c/p\u003e\n\u003ch2\u003eIn-Hospital Mortality\u003c/h2\u003e\n\u003cp\u003eThis analysis included 130,552 older adults, of whom 7,058 (5.4%) underwent surgical management and 123,494 (94.6%) received medical management.\u003csup\u003e35\u0026ndash;38,40,43,45,46\u003c/sup\u003e In-hospital mortality was observed in 1,743 (24.7%) surgically treated patients and 12,899 (10.4%) of those managed medically. There was no statistically significant difference between groups (RR = 1.33, 95% CI: 0.75\u0026ndash;2.36; p = 0.28). However, heterogeneity was high (I\u0026sup2; = 97.6%).\u003c/p\u003e\n\u003ch2\u003e30-Day Mortality\u003c/h2\u003e\n\u003cp\u003eThis analysis included 315 older adults, of whom 131 (41.6%) underwent surgical management and 184 (58.4%) received medical management.\u003csup\u003e33,38,39\u003c/sup\u003e Mortality at 30 days was observed in 35 (26.7%) surgically treated patients and 78 (42.4%) of those managed medically. There was no statistically significant difference between groups (RR = 0.67, 95% CI: 0.31\u0026ndash;1.46; p = 0.16). However, heterogeneity was moderate (I\u0026sup2; = 23.3%).\u003c/p\u003e\n\u003ch2\u003e3-Month Mortality\u003c/h2\u003e\n\u003cp\u003e3-month mortality was reported in a single study by Duehr et al.,\u003csup\u003e38\u003c/sup\u003e including 104 older adults. Of these, 35 (33.7%) underwent surgical management and 69 (66.3%) received medical management. There was no statistically significant difference between groups (RR = 0.88, 95% CI: 0.63\u0026ndash;1.22; p = 0.44).\u003c/p\u003e\n\u003ch2\u003e6-Month Mortality\u003c/h2\u003e\n\u003cp\u003eThis analysis included 1,912 older adults, of whom 982 (51.4%) underwent surgical management and 930 (48.6%) received medical management.\u003csup\u003e33\u0026ndash;35,38,39,44,46,47\u003c/sup\u003e Mortality at 6 months was observed in 431 (43.9%) surgically treated patients and 568 (61.1%) of those managed medically. Patients who underwent surgical management had significantly lower rates of 6-month mortality compared with those managed medically (RR = 0.68, 95% CI: 0.51\u0026ndash;0.92; p = 0.02). However, heterogeneity was high (I\u0026sup2; = 67.2%).\u003c/p\u003e\n\u003ch2\u003e12-Month Mortality\u003c/h2\u003e\n\u003cp\u003eThis analysis included 166 older adults, of whom 66 (39.8%) underwent surgical management and 100 (60.2%) received medical management.\u003csup\u003e33,38\u003c/sup\u003e Mortality at 12 months was observed in 32 (48.5%) surgically treated patients and 69 (69.0%) of those managed medically. There was no statistically significant difference between groups (RR = 0.69, 95% CI: 0.02\u0026ndash;24.31; p = 0.41). However, heterogeneity was high (I\u0026sup2; = 69.5%).\u003c/p\u003e\n\u003ch2\u003e24-Month Mortality\u003c/h2\u003e\n\u003cp\u003e24-month mortality was reported in a single study by Duehr et al.,\u003csup\u003e38\u003c/sup\u003e including 104 older adults. Of these, 35 (33.7%) underwent surgical management and 69 (66.3%) received medical management. There was no statistically significant difference between groups (RR = 0.86, 95% CI: 0.63\u0026ndash;1.18; p = 0.35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted subgroup analyses according to predefined clinical and methodological variables. No statistically significant subgroup differences were observed for favorable neurological outcomes, and results were consistent with the main analysis across most subgroups.\u003c/p\u003e\n\u003cp\u003eFor hospital length of stay, findings were also consistent across subgroups, although the effect was not significant in patients aged \u0026ge;80 years (MD = 5.12 days, 95% CI: \u0026minus;19.65 to 29.88; p = 0.23; I\u0026sup2; = 96.4%).\u003c/p\u003e\n\u003cp\u003eRegarding mortality, no consistent subgroup differences were identified. For 6-month mortality, matched studies showed no significant differences with high heterogeneity (RR = 0.70, 95% CI: 0.00\u0026ndash;337.17; p = 0.60; I\u0026sup2; = 80.8%), whereas a significant reduction was observed in patients with SDH (RR = 0.80, 95% CI: 0.70\u0026ndash;0.92; p = 0.02; I\u0026sup2; = 0.0%). No significant differences were found in isolated TBI (RR = 0.63, 95% CI: 0.00\u0026ndash;734.28; p = 0.56; I\u0026sup2; = 90.3%), moderate TBI (RR = 0.85, 95% CI: 0.00\u0026ndash;17687.35; p = 0.87; I\u0026sup2; = 85.3%), or severe TBI (RR = 0.83, 95% CI: 0.38\u0026ndash;1.82; p = 0.20; I\u0026sup2; = 0.0%). Full subgroup analyses are presented in \u003cstrong\u003eTable 3\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eInfluence Analysis\u003c/h2\u003e\n\u003cp\u003eInfluence analyses identified a limited number of studies exerting a disproportionate effect on pooled estimates across outcomes. \u003c/p\u003e\n\u003cp\u003eFor favorable neurological outcome at discharge, Trevisi et al. 2020 was clearly influential, with multiple metrics exceeding thresholds and a marked reduction in heterogeneity after exclusion,\u003csup\u003e46\u003c/sup\u003e whereas Singh et al. 2025 showed only moderate influence.\u003csup\u003e45\u003c/sup\u003e For favorable neurological outcome at 6 months, both Wan et al. 2016 and Trevisi et al. 2020 demonstrated notable influence, each associated with outlying behavior and reductions in heterogeneity upon exclusion, while other studies showed minimal impact.\u003csup\u003e46,47\u003c/sup\u003e \u003c/p\u003e\n\u003cp\u003eFor hospital LOS, Cook et al. 2025 was highly influential, with extreme outlying behavior and complete resolution of heterogeneity after exclusion. Other studies showed only moderate or negligible influence.\u003csup\u003e36\u003c/sup\u003e \u003c/p\u003e\n\u003cp\u003eFor in-hospital mortality, Haddad et al. 2021 was identified as clearly influential across multiple metrics, with reduced heterogeneity following exclusion, while the remaining studies had limited impact.\u003csup\u003e40\u003c/sup\u003e For 30-day mortality, Duehr et al. 2022 and Casta\u0026ntilde;o et al. 2024 showed relevant influence,\u003csup\u003e33,38\u003c/sup\u003e whereas no meaningful influence was observed for other studies. For 6-month mortality, Wan et al. 2016 was the most influential study, with extreme outlying behavior and elimination of heterogeneity upon exclusion.\u003csup\u003e47\u003c/sup\u003e For the outcome of 12-month mortality, only two studies were available. Casta\u0026ntilde;o et al. 2024 showed a significant reduction in mortality favoring the surgical group (RR = 0.50, 95% CI 0.30\u0026ndash;0.85),\u003csup\u003e33\u003c/sup\u003e whereas Duehr et al. 2022 did not demonstrate a significant effect (RR = 0.88, 95% CI 0.64\u0026ndash;1.21).\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n\u003ch2\u003ePublication Bias\u003c/h2\u003e\n\u003cp\u003eOverall, no major asymmetry was detected across outcomes, and the risk of publication bias appeared to be low. neurological outcome at discharge (LFK = \u0026minus;0.211), favorable neurological outcome at 6 months (LFK = \u0026minus;0.005), \u003cstrong\u003e(Fig. 3)\u003c/strong\u003e hospital LOS (LFK = 0.294), \u003cstrong\u003e(Fig. 4)\u003c/strong\u003e in-hospital mortality (LFK = \u0026minus;0.193), and 6-month mortality (LFK = \u0026minus;0.446), \u003cstrong\u003e(Fig. 5)\u003c/strong\u003e values were within the range of no asymmetry, suggesting no evidence of publication bias. In contrast, 30-day mortality (LFK = \u0026minus;1.578), and 12-month mortality (LFK = \u0026minus;1.414) showed minor asymmetry, indicating a low likelihood of small-study effects. Publication bias could not be assessed for favorable neurological outcome at 3 months, favorable neurological outcome at 12 months, 3-month mortality, and 24-month mortality, as these outcomes were informed by single studies. \u003c/p\u003e\n\u003ch2\u003eRisk of Bias \u003c/h2\u003e\n\u003cp\u003eOverall agreement was 87.5% (112/128 decisions), with a \u0026kappa; = 0.72. Across studies, 2/16 (12.5%) were judged as low risk of bias overall, whereas 14/16 (87.5%) were rated as moderate risk \u003cstrong\u003e(Fig. 6)\u003c/strong\u003e. Most studies were rated as low risk of bias in the domains of selection of participants (10/16, 62.5%), classification of interventions (15/16, 93.75%), deviations from intended interventions (14/16, 87.5%), missing data (12/16, 75.0%), measurement of outcomes (9/16, 56.25%), and selection of the reported result (16/16, 100%), whereas bias due to confounding was predominantly rated as moderate (14/16, 87.5%).\u003c/p\u003e\n\u003ch2\u003eCertainty of Evidence \u003c/h2\u003e\n\u003cp\u003eAgreement in the GRADE assessment was 94.8%, with a \u0026kappa; = 0.93. Overall, the certainty of evidence ranged from low to very low across all evaluated outcomes \u003cstrong\u003e(Table 4)\u003c/strong\u003e. Certainty was rated as very low for favorable neurological outcome at discharge, favorable neurological outcome at 6 months, in-hospital mortality, and 12-month mortality, whereas the remaining outcomes, including favorable neurological outcome at 3 months, favorable neurological outcome at 12 months, hospital LOS, 30-day mortality, 3-month mortality, 6-month mortality, and 24-month mortality, were rated as low certainty. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis synthesizes the available evidence comparing surgical versus medical management in 132,823 older adults with TBI. The main findings suggest that: (1) surgical management was not consistently associated with improved favorable neurological outcomes across different time points (discharge, 3, 6, and 12 months); (2) it increased hospital LOS by a mean of 6.35 days; and (3) although a significant 32% reduction in 6-month mortality was observed, no significant differences were found for in-hospital, 30-day, 3-month, 12-month, or 24-month mortality. Nevertheless, the certainty of the evidence was rated as very low to low; therefore, these findings should be interpreted with caution.\u003c/p\u003e\n\u003cp\u003eThe relatively low proportion of patients undergoing surgical management, 6.1%, likely reflects careful patient selection rather than underuse. In older adults, surgery is typically reserved for cases with clearly identifiable and potentially reversible structural lesions.\u003csup\u003e33,49\u0026ndash;51\u003c/sup\u003e Diffuse injury patterns, small hemorrhages, and limited mass effect often favor conservative management, as surgery may not meaningfully alter the underlying pathophysiology.\u003csup\u003e52\u003c/sup\u003e This is further influenced by advanced age, high comorbidity burden, and reduced physiological reserve, which increase vulnerability to perioperative stress.\u003csup\u003e53,54\u003c/sup\u003e Frailty and functional dependency, although inconsistently reported, are well-established determinants of outcomes and likely play a central role in decision-making.\u003csup\u003e55,56\u003c/sup\u003e The frequent use of anticoagulant and antiplatelet therapy further complicates surgical timing and increases bleeding risk.\u003csup\u003e57,58\u003c/sup\u003e In addition, early goals-of-care discussions in severely injured older patients may limit the use of aggressive interventions when expected quality of life (QoL) is uncertain.\u003csup\u003e59,60\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eRegional variation in surgical rates suggests that treatment decisions are also shaped by healthcare systems and sociocultural factors. The higher proportion of surgically treated patients in Asia (45.6%) compared with Europe (32.5%) and North America (4.7%) may reflect differences in thresholds for intervention, family involvement in decision-making, and cultural attitudes toward life-sustaining treatment.\u003csup\u003e61,62\u003c/sup\u003e In contrast, practice patterns in North America may place greater emphasis on advance directives, shared decision-making, and quality-of-life considerations.\u003csup\u003e63\u0026ndash;65\u003c/sup\u003e Resource availability, including intensive care capacity and access to neurosurgical care, may further influence patient selection.\u003csup\u003e66\u0026ndash;68\u003c/sup\u003e These differences in practice patterns are consistent with international expert consensus. A Delphi study by Lagares et al. defined older age as a multidimensional construct and reported that the median expert threshold decreased from 75 years (IQR 70\u0026ndash;80) in patients without comorbidities to 65 years (IQR 65\u0026ndash;70) in those with comorbidities. In surgical decision-making, no absolute age cutoff was established for craniotomy; however, for DC, the median threshold was 70 years (IQR 70\u0026ndash;80) without comorbidities and 65 years (IQR 65\u0026ndash;70) with comorbidities for primary procedures, and 70 years (IQR 65\u0026ndash;75) versus 65 years (IQR 60\u0026ndash;70) for secondary procedures, supporting a more individualized approach.\u003csup\u003e69\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe absence of consistent improvement in favorable neurological outcomes should be interpreted in the context of age-related biological vulnerability. Aging is associated with amplified and prolonged neuroinflammatory responses, increased oxidative stress, mitochondrial dysfunction, and reduced synaptic plasticity, all of which limit neuronal recovery after injury.\u003csup\u003e70\u0026ndash;73\u003c/sup\u003e Impaired cerebrovascular autoregulation and reduced capacity to maintain stable cerebral perfusion pressure (CPP) contribute to ongoing secondary injury despite adequate structural intervention.\u003csup\u003e74,75\u003c/sup\u003e In addition, cerebral atrophy may delay the clinical detection of expanding lesions, leading to intervention at more advanced stages when damage is less reversible.\u003csup\u003e76,77\u003c/sup\u003e Patient assessment in older adults also presents important limitations. The GCS may underestimate injury severity in this population due to cerebral atrophy, which allows greater intracranial compensation and may mask the clinical impact of hemorrhagic lesions.\u003csup\u003e78\u0026ndash;81\u003c/sup\u003e In addition, baseline cognitive impairment may complicate interpretation, making it difficult to distinguish between chronic deficits and acute deterioration, potentially contributing to under triage and delayed recognition of clinically significant injury.\u003csup\u003e82,83\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe longer hospital stay observed in surgically treated patients likely reflects a combination of survivorship bias and increased clinical complexity. Patients who survive the acute phase after surgery remain hospitalized longer, whereas medically managed patients may experience earlier mortality. Surgical care is also associated with more intensive monitoring, delayed neurological recovery, and the need for rehabilitation planning.\u003csup\u003e84\u003c/sup\u003e Postoperative complications such as delirium, infections, and functional decline are more frequent in older adults and may prolong hospitalization.\u003csup\u003e34,84\u003c/sup\u003e Injury severity and selection factors also contribute, as surgically treated patients often present with more severe but potentially reversible lesions requiring extended inpatient care.\u003csup\u003e85\u003c/sup\u003e Discharge delays related to placement in rehabilitation or long-term care facilities may further increase LOS.\u003csup\u003e86\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe observed reduction in 6-month mortality likely reflects a time-limited benefit in carefully selected patients. Surgical intervention can relieve mass effect, reduce ICP, and restore CPP, thereby mitigating secondary injury processes such as ischemia and excitotoxicity. However, this early survival advantage does not appear to persist over time.\u003csup\u003e87\u003c/sup\u003e Long-term outcomes in older adults are strongly influenced by limited neuroplasticity, systemic vulnerability, and a high burden of complications, including infections, cardiovascular events, and functional decline, which may offset initial gains.\u003csup\u003e88\u003c/sup\u003e The absence of a clear benefit in matched analyses raises concern that the associations observed in unadjusted analyses may be driven by selection bias, particularly confounding by indication, rather than a true treatment effect. Patients selected for surgery are more likely to have anatomically accessible and potentially reversible lesions, preserved brainstem function, and a baseline condition compatible with recovery.\u003csup\u003e89,90\u003c/sup\u003e In contrast, patients with diffuse injury, advanced frailty, or significant comorbidity are less frequently offered surgery.\u003csup\u003e33,91,92\u003c/sup\u003e As a result, apparent benefits observed in unadjusted analyses may partially reflect baseline differences rather than a true treatment effect. In addition, the consistent benefit observed in SDH likely reflects its focal and surgically reversible nature, as evacuation directly relieves mass effect and restores CPP.\u003csup\u003e93,94\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the absence of robust, population-specific recommendations for older adults with TBI, these findings represent the best available comparative evidence to date and may help inform decision-making in clinical practice. They support an early, structured, and individualized assessment that goes beyond neurological scores and imaging, incorporating premorbid functional status, frailty, comorbidity burden, and medication exposure as key determinants of prognosis. Radiological severity or traditional thresholds should not automatically prompt escalation, but rather guide a broader evaluation of reversibility and physiological reserve. A key implication is the need to distinguish between patients with potentially reversible intracranial pathology, who may benefit from timely intervention, and those in whom outcomes are driven by systemic vulnerability or diffuse injury, where a more cautious approach is appropriate. Importantly, prognostic uncertainty should not lead to unwarranted therapeutic nihilism, such as premature limitation of care based solely on age or assumptions of poor outcomes. This supports individualized decision-making regarding surgery, ICP monitoring, and intensive care escalation based on clinical evolution, imaging progression, and patient-specific factors, while also supporting decisions not to escalate when expected benefit is limited. Early communication with patients and families is essential to align treatment with expected outcomes, QoL, and patient values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this context, ethical challenges specific to older adults with TBI should be explicitly addressed according to study design. In RCTs, structured consent frameworks including surrogate consent, deferred consent, and, where appropriate, waiver of consent should be incorporated to allow timely enrolment in emergency settings without delaying life-saving interventions, while preserving ethical standards and regulatory compliance. Pragmatic and registry-based trial designs may further enhance feasibility and external validity in this population. In prospective observational studies, standardized documentation of goals-of-care decisions, treatment limitations, and decision-making processes should be systematically incorporated to reduce confounding by indication and selection bias. These strategies are essential to improve internal validity, ensure ethical rigor, and enhance the interpretability and generalizability of findings. \u003c/p\u003e\n\u003cp\u003eAdditionally, future studies should incorporate variables beyond traditional injury severity, including frailty, comorbidity burden, polypharmacy, and pre-injury functional status, to better define biological age and improve patient selection. Further research is needed to refine surgical indications and compare outcomes between craniotomy and DC, as well as to determine optimal ICP and CPP targets for this population. In addition, the prognostic value of traditional clinical predictors such as the GCS should be reassessed, and new models integrating clinical, radiological, and patient-related variables should be developed. Evidence-based protocols are also needed for antithrombotic management, repeat CT imaging, and the timing of delayed intervention in lesions at risk of progression. Finally, future studies should prioritize multidisciplinary care strategies and long-term patient-centered outcomes, including functional recovery, independence, and QoL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile this study represents the best available comparative evidence to date on surgical versus non-surgical management in older adults with TBI, several limitations should be considered when interpreting these findings. First, the certainty of evidence was low to very low, and most data were derived from observational, predominantly retrospective studies, making results susceptible to residual confounding, selection bias, and confounding by indication. Treatment allocation was not randomized and likely influenced by injury severity, radiological features, frailty, comorbidities, and goals of care, many of which were inconsistently reported. Second, several outcomes were informed by a limited number of studies, with some based on a single study, reducing robustness and generalizability, while variability in follow-up duration and incomplete long-term data may introduce attrition bias. Third, there was substantial clinical and methodological heterogeneity, including variability in definitions of older adults, injury severity, and thresholds for intervention, as well as differences in surgical and medical management strategies. Because treatment choices are closely linked to patient severity and prognosis, groups were likely non-comparable, introducing residual confounding and contributing to heterogeneity in effect estimates. Fourth, key clinical variables such as frailty, baseline function, comorbidities, detailed radiological parameters, and treatment-related factors were incompletely and inconsistently reported across the included studies, limiting the ability to explore their impact through subgroup or meta-regression analyses and increasing the risk of residual confounding, including from factors such as goals-of-care decisions. Fifth, outcome measures have inherent limitations: mortality may be influenced by non-neurological risks, functional scales may not fully capture QoL, and hospital LOS may reflect system-level factors and survivorship bias. Finally, differences in healthcare systems, institutional practices, and temporal changes in care may affect external validity, and although major publication bias was not detected, small study effects cannot be excluded.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn older adults with TBI, surgical management appears to be associated with reduced mortality but not with improved functional recovery, highlighting a clinically relevant mismatch between survival and neurological outcomes. This dissociation warrants deeper consideration, as it has direct implications for surgical decision-making and for counseling patients and families, particularly regarding expectations of independence, QoL, and potential long-term care needs, and highlights the need to prioritize outcomes that reflect meaningful recovery rather than survival alone.\u003c/p\u003e\n\u003cp\u003eAnother important consideration is that individualized decision-making, incorporating frailty, comorbidities, and the potential reversibility of injury, is essential rather than relying solely on chronological age. Although prognostic uncertainty remains high, decisions should avoid unfounded therapeutic nihilism and instead be guided by a balanced, individualized, and multidisciplinary approach that incorporates patient values and goals of care.\u003c/p\u003e\n\u003cp\u003eBecause the available evidence is predominantly observational, heterogeneous, and of low to very low certainty, these findings should be interpreted with caution. Further high-quality, age-specific RCTs are needed to better define the role of surgical management and to identify which patients are most likely to benefit in terms of meaningful, patient-centered outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActivities of Daily Living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAtrial Fibrillation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbbreviated Injury Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronary Artery Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Frailty Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebral Perfusion Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebrospinal Fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputed Tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebrovascular Accident\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecompressive Craniectomy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFBETAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifference in Betas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFFITS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifference in Fits\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEpidural Hematoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eENT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEar,Nose,and Throat\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExternal Ventricular Drain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlasgow Coma Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRADE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrading of Recommendations Assessment,Development and Evaluation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHKSJ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHartung\u0026ndash;Knapp\u0026ndash;Sidik\u0026ndash;Jonkman adjustment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHTN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntracerebral Hemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntracranial Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIschemic Heart Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInjury Severity Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraventricular Hemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eI\u0026sup2;\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eI-squared statistic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eκ(Kappa)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCohen\u0026rsquo;s Kappa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLFK index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLuis Furuya-Kanamori index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of Stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyocardial Infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMidline Shift\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrediction Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRISMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic Reviews and Meta-Analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePROSPERO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Prospective Register of Systematic Reviews\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePulmonary Tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q statistic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQoL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality of Life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRheumatoid Arthritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCTs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Controlled Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Maximum Likelihood\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROBINS-I\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRisk Of Bias In Non-randomized Studies of Interventions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRisk Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRoad Traffic Accident\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubarachnoid Hemorrhage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic Blood Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubdural Hematoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTraumatic Brain Injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransient Ischemic Attack\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eτ\u0026sup2;\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBetween-study variance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1. Compliance with Instructions to Authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the manuscript fully conforms to the journal\u0026rsquo;s instructions for authors, including all formatting, structural, and submission specifications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Author Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuis E. Cueva-Ca\u0026ntilde;ola: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Software, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Project administration. Andrea C. Beltran-De la Fuente: Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. Mael S. Ayala-Alban: Data curation, Validation. Sergio Morales Acosta: Data curation, Validation. Astrid G. Carri\u0026oacute;n-Cu\u0026eacute;llar: Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. Olga N. Polania-P\u0026eacute;rez: Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. Rupesh G. Rathod: Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. Manuel E. Lopez-Gonzales: Data curation, Validation. Ana Karina La Madrid Barreto: Conceptualization, Validation, Writing \u0026ndash; review \u0026amp; editing. Leonardo Rangel-Castilla: Conceptualization, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Authorship Criteria and Final Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors meet the authorship requirements set by the International Committee of Medical Journal Editors (ICMJE), have reviewed and approved the final version of the manuscript, and agree to take full responsibility for all aspects of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Originality and Prior Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript represents original work that has not been previously published and is not under consideration for publication in another journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Ethical Approval and Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval and informed consent were deemed unnecessary because this study is a systematic review and meta-analysis based exclusively on previously published data, with no direct involvement of human subjects or use of identifiable individual-level information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Conflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no conflicts of interest exist with respect to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Reporting Guidelines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review and meta-analysis was performed and reported in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003cp\u003eCredit Author Statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLuis E. Cueva-Ca\u0026ntilde;ola\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Software, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Project administration. \u003cstrong\u003eAndrea C. Beltran-De la Fuente:\u003c/strong\u003e Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eMael S. Ayala-Alban:\u0026nbsp;\u003c/strong\u003eData curation, Validation. \u003cstrong\u003eSergio Morales Acosta:\u0026nbsp;\u003c/strong\u003eData curation, Validation. \u003cstrong\u003eAstrid G. Carri\u0026oacute;n-Cu\u0026eacute;llar:\u003c/strong\u003e Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eOlga N. Polania-P\u0026eacute;rez:\u003c/strong\u003e Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eRupesh G. Rathod:\u003c/strong\u003e Data curation, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eManuel E. Lopez-Gonzales:\u003c/strong\u003e Data curation, Validation. \u003cstrong\u003eAna Karina La Madrid Barreto:\u003c/strong\u003e Conceptualization, Validation, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eLeonardo Rangel-Castilla:\u0026nbsp;\u003c/strong\u003eConceptualization, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAcknowledgment\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to their families and mentors for their unwavering support, encouragement, and guidance throughout the development of this work. Special acknowledgment is extended to Pavel P.R., Luis C.J., Santos C.C., Carmen C.S., and Pamela Z.M, Tara R., Guru R., R.B.L., S.K.D.L.F.B., Alexandra R.A., Clara A. T., Hipolito A.C., Marilyn B. T., Sergio M.L., Angelica A.C., and Emmanuel M.A., for their valuable contributions, insightful feedback, and continuous support that significantly enriched this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest relevant to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUse of Artificial Intelligence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial intelligence tools were used solely to assist with language editing and clarity of expression. No AI tools were used in data extraction, data analysis, study selection, or interpretation of results. All scientific content, analyses, and conclusions are the sole responsibility of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMa Z, He Z, Li Z, et al. Traumatic brain injury in elderly population: A global systematic review and meta-analysis of in-hospital mortality and risk factors among 2.22 million individuals. Ageing Res Rev. 2024;99(102376):102376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.arr.2024.102376\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2024.102376\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavrila Laic RA, Bogaert L, Vander Sloten J, Depreitere B. Functional outcome, dependency and well-being after traumatic brain injury in the elderly population: A systematic review and meta-analysis. Brain Spine. 2021;1(100849):100849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bas.2021.100849\u003c/span\u003e\u003cspan address=\"10.1016/j.bas.2021.100849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel PP, Egodage T, Martin MJ. Decompressive craniectomy for traumatic brain injury: a review of recent landmark trials. Trauma Surg Acute Care Open. 2025;10(Suppl 1):e001784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/tsaco-2025-001784\u003c/span\u003e\u003cspan address=\"10.1136/tsaco-2025-001784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper DJ, Rosenfeld JV, Murray L, et al. Decompressive craniectomy in diffuse traumatic brain injury. N Engl J Med. 2011;364(16):1493\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1102077\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1102077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutchinson PJ, Kolias AG, Timofeev IS, et al. Trial of decompressive craniectomy for traumatic intracranial hypertension. N Engl J Med. 2016;375(12):1119\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1605215\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1605215\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan J, Wang C, Sun B. Global, regional, and national burdens of traumatic brain injury from 1990 to 2021. Front Public Health. 2025;13:1556147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpubh.2025.1556147\u003c/span\u003e\u003cspan address=\"10.3389/fpubh.2025.1556147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowning MG, Carty M, Olver J, et al. The impact of age on outcome 2 years after traumatic brain injury: Case control study. Ann Phys Rehabil Med. 2024;67(5):101834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rehab.2024.101834\u003c/span\u003e\u003cspan address=\"10.1016/j.rehab.2024.101834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaas AIR, Menon DK, Manley GT, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research. Lancet Neurol. 2022;21(11):1004\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(22)00309-X\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(22)00309-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlass NE, Vadlamani A, Hwang F, et al. Bleeding and thromboembolism after traumatic brain injury in the elderly: A real conundrum. J Surg Res. 2019;235:615\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jss.2018.10.021\u003c/span\u003e\u003cspan address=\"10.1016/j.jss.2018.10.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong ZKH, Mengistu TS, Raith E, Thornton N, Hiskens M. The effect of antithrombotic agents on the incidence of intracranial hemorrhage in elderly patients with traumatic brain injury. Trauma Care (Basel). 2026;6(1):3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/traumacare6010003\u003c/span\u003e\u003cspan address=\"10.3390/traumacare6010003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGardner RC, Dams-O\u0026rsquo;Connor K, Morrissey MR, Manley GT. Geriatric traumatic brain injury: Epidemiology, outcomes, knowledge gaps, and future directions. J Neurotrauma. 2018;35(7):889\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2017.5371\u003c/span\u003e\u003cspan address=\"10.1089/neu.2017.5371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoohollahi F, Molavi S, Mohammadi M, et al. Prognostic value of frailty for outcome following traumatic brain injury: A systematic review and meta-analysis. J Neurotrauma. 2024;41(3\u0026ndash;4):331\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2023.0176\u003c/span\u003e\u003cspan address=\"10.1089/neu.2023.0176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorriero A, Fornaciari A, Terrazzino S, et al. The impact of age and intensity of treatment on the outcome of traumatic brain injury. Front Neurol. 2024;15:1471209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2024.1471209\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2024.1471209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins JPT. In: Higgins J, Thomas J, editors. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Wiley-Blackwell; 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/9781119536604\u003c/span\u003e\u003cspan address=\"10.1002/9781119536604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.n71\u003c/span\u003e\u003cspan address=\"10.1136/bmj.n71\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchiavo JH. PROSPERO: An international register of systematic review protocols. Med Ref Serv Q. 2019;38(2):171\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/02763869.2019.1588072\u003c/span\u003e\u003cspan address=\"10.1080/02763869.2019.1588072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSterne JA, Hern\u0026aacute;n MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.i4919\u003c/span\u003e\u003cspan address=\"10.1136/bmj.i4919\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson L, Boase K, Nelson LD, et al. A manual for the Glasgow Outcome Scale-Extended interview. J Neurotrauma. 2021;38(17):2435\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2020.7527\u003c/span\u003e\u003cspan address=\"10.1089/neu.2020.7527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuckerman DA, Giacino JT, Bodien YG. Traumatic brain injury: What is a favorable outcome? J Neurotrauma. 2022;39(13\u0026ndash;14):1010\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2021.0356\u003c/span\u003e\u003cspan address=\"10.1089/neu.2021.0356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangan D, Higgins JPT, Jackson D, et al. A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods. 2019;10(1):83\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jrsm.1316\u003c/span\u003e\u003cspan address=\"10.1002/jrsm.1316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Aert RCM, Jackson D. A new justification of the Hartung-Knapp method for random-effects meta-analysis based on weighted least squares regression. Res Synth Methods. 2019;10(4):515\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jrsm.1356\u003c/span\u003e\u003cspan address=\"10.1002/jrsm.1356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;ver C, Knapp G, Friede T. Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Med Res Methodol. 2015;15(1):99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12874-015-0091-1\u003c/span\u003e\u003cspan address=\"10.1186/s12874-015-0091-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol. 2014;14(1):25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2288-14-25\u003c/span\u003e\u003cspan address=\"10.1186/1471-2288-14-25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePartlett C, Riley RD. Random effects meta-analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation: RANDOM EFFECTS META-ANALYSIS: COVERAGE PERFORMANCE OF 95%CI AND PI. Stat Med. 2017;36(2):301\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sim.7140\u003c/span\u003e\u003cspan address=\"10.1002/sim.7140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitani AA, Freer PE, Nelson KP. Summary measures of agreement and association between many raters\u0026rsquo; ordinal classifications. Ann Epidemiol. 2017;27(10):677\u0026ndash;e6854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.annepidem.2017.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.annepidem.2017.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/2529310\u003c/span\u003e\u003cspan address=\"10.2307/2529310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao Y, Shen Y, Yang Q, Shi Q, Li S. A leave-one-out algorithm for contribution analysis in component network meta-analysis. BMC Med Res Methodol. 2025;25(1):191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12874-025-02619-w\u003c/span\u003e\u003cspan address=\"10.1186/s12874-025-02619-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. 2010;1(2):112\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jrsm.11\u003c/span\u003e\u003cspan address=\"10.1002/jrsm.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabib M, Amin M, Aljeddani SMA. Influence analysis in the lognormal regression model with fitted and quantile residuals. Axioms. 2025;14(6):464. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/axioms14060464\u003c/span\u003e\u003cspan address=\"10.3390/axioms14060464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuruya-Kanamori L, Barendregt JJ, Doi SAR. A new improved graphical and quantitative method for detecting bias in meta-analysis. Int J Evid Based Healthc. 2018;16(4):195\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/XEB.0000000000000141\u003c/span\u003e\u003cspan address=\"10.1097/XEB.0000000000000141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuruya-Kanamori L, Rousou X, Kostoulas P, Doi SAR. Examining and interpreting Doi plot asymmetry in meta-analyses of randomized controlled trials. J Evid Based Med. 2025;18(3):e70063. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jebm.70063\u003c/span\u003e\u003cspan address=\"10.1111/jebm.70063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrozek JL, Canelo-Aybar C, Akl EA, et al. GRADE Guidelines 30: the GRADE approach to assessing the certainty of modeled evidence-An overview in the context of health decision-making. J Clin Epidemiol. 2021;129:138\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jclinepi.2020.09.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jclinepi.2020.09.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasta\u0026ntilde;o-Leon AM, Gomez PA, Jimenez-Roldan L, et al. The impact of early surgery on mortality and functional recovery in older adults with traumatic intracranial lesions: a propensity score-based analysis. Acta Neurochir (Wien). 2024;166(1):443. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-024-06324-3\u003c/span\u003e\u003cspan address=\"10.1007/s00701-024-06324-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajendran G, Mahalingam S, Ramkumar A, Tamilarasu KP, Kannan R. Surgical versus conservative management for traumatic brain injury in elderly patients: A propensity-matched cohort study. Turk J Emerg Med. 2025;25(2):92\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/tjem.tjem_133_24\u003c/span\u003e\u003cspan address=\"10.4103/tjem.tjem_133_24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAziz N, O\u0026rsquo;Halloran PJ, McKenna GS. Subdural haematoma in the elderly: management and outcomes of a UK Major Trauma Centre. Br J Neurosurg. 2023;37(6):1604\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/02688697.2022.2122402\u003c/span\u003e\u003cspan address=\"10.1080/02688697.2022.2122402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook R, Zima L, Kitagawa R. Acute subdural hematomas in nonagenarians and centenarians. J Clin Neurosci. 2025;132(110997):110997. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jocn.2024.110997\u003c/span\u003e\u003cspan address=\"10.1016/j.jocn.2024.110997\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzorlich P, Mader MMD, Emami P, Westphal M, Lefering R, Hoffmann M. Operative versus non-operative treatment of traumatic brain injuries in patients 80 years of age or older. Neurosurg Rev. 2020;43(5):1305\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10143-019-01159-4\u003c/span\u003e\u003cspan address=\"10.1007/s10143-019-01159-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuehr J, Rodriguez-Torres S, Njoku-Austin C, et al. Superiority of craniotomy over supportive care for octogenarians and nonagenarians in operable acute traumatic subdural hematoma. Clin Neurol Neurosurg. 2022;212(107069):107069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clineuro.2021.107069\u003c/span\u003e\u003cspan address=\"10.1016/j.clineuro.2021.107069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGavrila Laic RA, Sloten JV, Depreitere B. Neurosurgical treatment in elderly patients with Traumatic brain injury: A 20-year follow-up study. Brain Spine. 2023;3(101723):101723. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bas.2023.101723\u003c/span\u003e\u003cspan address=\"10.1016/j.bas.2023.101723\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaddad AF, DiGiorgio AM, Lee YM, et al. The morbidity and mortality of surgery for Traumatic Brain Injury in geriatric patients: A study of over 100 000 patient cases: A study of over 100 000 patient cases. Neurosurgery. 2021;89(6):1062\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/neuros/nyab359\u003c/span\u003e\u003cspan address=\"10.1093/neuros/nyab359\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJyoti Mahakul D, Sharma T, Bhandarkar D, Jagetia A, Singh D, Agarwal J. Treatment outcome in elderly traumatic brain injury patients at a level 2 trauma care facility in a low-middle-income country. World Neurosurg. 2025;201(124203):124203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2025.124203\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2025.124203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Jaiswal S, Ojha BK, et al. Retrospective outcome analysis of geriatric traumatic brain injury treated at a tertiary care center in India. Ind J Neurosurg. 2025;14(01):034\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-0044-1788254\u003c/span\u003e\u003cspan address=\"10.1055/s-0044-1788254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahrokh Z, Seyed Mohsen Aghapour B, Alijani E, Bijani S. The Outcome of Surgical versus Conservative Management in Old Patients with Traumatic Brain Injury. Iran J Neurosurg. 2022;8:26\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimoda K, Maeda T, Tado M, Yoshino A, Katayama Y, Bullock MR. Outcome and surgical management for geriatric traumatic brain injury: analysis of 888 cases registered in the Japan Neurotrauma Data Bank. World Neurosurg. 2014;82(6):1300\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2014.08.014\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2014.08.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh SP, Mohindra S, Kumar N, Tadakamalla SS. Retrospective evaluation of outcomes in geriatric traumatic brain injury. Int J Med Public Health. 2025;15(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.70034/ijmedph.2025.3.308\u003c/span\u003e\u003cspan address=\"10.70034/ijmedph.2025.3.308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrevisi G, Sturiale CL, Scerrati A, et al. Acute subdural hematoma in the elderly: outcome analysis in a retrospective multicentric series of 213 patients. Neurosurg Focus. 2020;49(4):E21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/2020.7.focus20437\u003c/span\u003e\u003cspan address=\"10.3171/2020.7.focus20437\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan X, Liu S, Wang S, et al. Elderly patients with severe traumatic brain injury could benefit from surgical treatment. World Neurosurg. 2016;89:147\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2016.01.084\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2016.01.084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBharanidharan M, Jayasree S, Rajarajan P, Venkatesan R. A prospective study of traumatic Brain Injury in geriatric patients. Int J Pharm Qual Assur. 2025;16(1):41\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25258/ijpqa.16.1.6\u003c/span\u003e\u003cspan address=\"10.25258/ijpqa.16.1.6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObasa AA, Olopade FE, Juliano SL, Olopade JO. Traumatic brain injury or traumatic brain disease: A scientific commentary. Brain Multiphys. 2024;6(100092):100092. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.brain.2024.100092\u003c/span\u003e\u003cspan address=\"10.1016/j.brain.2024.100092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMckee AC, Daneshvar DH. The neuropathology of traumatic brain injury. Handb Clin Neurol. 2015;127:45\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-444-52892-6.00004-0\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-444-52892-6.00004-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenell S, Nyholm L, Lew\u0026eacute;n A, Enblad P. Clinical outcome and prognostic factors in elderly traumatic brain injury patients receiving neurointensive care. Acta Neurochir (Wien). 2019;161(6):1243\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-019-03893-6\u003c/span\u003e\u003cspan address=\"10.1007/s00701-019-03893-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauchman SH, Zubair A, Jacob B, et al. Traumatic brain injury: Mechanisms, manifestations, and visual sequelae. Front Neurosci. 2023;17:1090672. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnins.2023.1090672\u003c/span\u003e\u003cspan address=\"10.3389/fnins.2023.1090672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanales C, Wann L, Blitz J, Whittington R. The older adult surgical patient: a review of optimization and gaps in clinical practice. Perioper Med (Lond). 2025;14(1):104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13741-025-00593-x\u003c/span\u003e\u003cspan address=\"10.1186/s13741-025-00593-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Fu Y. Age-related mortality risk assessment and prognostic factors in postsurgical traumatic brain injury patients: A retrospective cohort study based on the mimic database. Curr Probl Surg. 2025;72(101882):101882. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cpsurg.2025.101882\u003c/span\u003e\u003cspan address=\"10.1016/j.cpsurg.2025.101882\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou B, Yu J. Frailty evaluation for short-term mortality prediction of patients with traumatic brain injury: A meta-analysis. J Head Trauma Rehabil Published online August. 2025;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HTR.0000000000001100\u003c/span\u003e\u003cspan address=\"10.1097/HTR.0000000000001100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCray E, Hayat NJ, Song CH, Western M, Edlmann E. The effectiveness of clinical frailty assessment in older patients with Traumatic brain injury in predicting outcome and quality of life: a systematic review. Neurosurg Rev. 2025;48(1):511. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10143-025-03659-y\u003c/span\u003e\u003cspan address=\"10.1007/s10143-025-03659-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVehvil\u0026auml;inen J, Virta JJ, Skrifvars MB, et al. Effect of antiplatelet and anticoagulant medication use on injury severity and mortality in patients with traumatic brain injury treated in the intensive care unit. Acta Neurochir (Wien). 2023;165(12):4003\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-023-05850-w\u003c/span\u003e\u003cspan address=\"10.1007/s00701-023-05850-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScotti P, S\u0026eacute;guin C, Lo BWY, de Guise E, Troquet JM, Marcoux J. Antithrombotic agents and traumatic brain injury in the elderly population: hemorrhage patterns and outcomes. J Neurosurg. 2020;133(2):486\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/2019.4.JNS19252\u003c/span\u003e\u003cspan address=\"10.3171/2019.4.JNS19252\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang F, Son J, Ensor K, et al. Initiating advance care planning at admission: a brief intervention to increase goals of care discussions in geriatric trauma patients in an urban level I trauma center. Trauma Surg Acute Care Open. 2023;8(1):e001058. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/tsaco-2022-001058\u003c/span\u003e\u003cspan address=\"10.1136/tsaco-2022-001058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHunt C, Zahid S, Ennis N, et al. Quality of life measures in older adults after traumatic brain injury: a systematic review. Qual Life Res. 2019;28(12):3137\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11136-019-02297-4\u003c/span\u003e\u003cspan address=\"10.1007/s11136-019-02297-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Essen TA, den Boogert HF, Cnossen MC, et al. Variation in neurosurgical management of traumatic brain injury: a survey in 68 centers participating in the CENTER-TBI study. Acta Neurochir (Wien). 2019;161(3):435\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-018-3761-z\u003c/span\u003e\u003cspan address=\"10.1007/s00701-018-3761-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeal-M\u0026eacute;ndez F, Lew\u0026eacute;n A, Gu A, et al. Regional variation in traumatic brain injury patterns, management and mortality: a nationwide Swedish cohort study. Acta Neurochir (Wien). 2025;167(1):134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-025-06557-w\u003c/span\u003e\u003cspan address=\"10.1007/s00701-025-06557-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHukkelhoven CWPM, Steyerberg EW, Farace E, Habbema JDF, Marshall LF, Maas AIR. Regional differences in patient characteristics, case management, and outcomes in traumatic brain injury: experience from the tirilazad trials. J Neurosurg. 2002;97(3):549\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3171/jns.2002.97.3.0549\u003c/span\u003e\u003cspan address=\"10.3171/jns.2002.97.3.0549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManley GT, Dams-O\u0026rsquo;Connor K, Alosco ML, et al. A new characterisation of acute traumatic brain injury: the NIH-NINDS TBI Classification and Nomenclature Initiative. Lancet Neurol. 2025;24(6):512\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(25)00154-1\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(25)00154-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson JD, Perrin PB, Arango-Lasprilla JC. Disparities between native americans and white individuals in global outcome trajectories over the 5 years after traumatic brain injury: A model systems study. PLoS ONE. 2025;20(4):e0321279. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0321279\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0321279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStocker RA. Intensive care in traumatic brain injury including multi-modal monitoring and neuroprotection. Med Sci (Basel). 2019;7(3):37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medsci7030037\u003c/span\u003e\u003cspan address=\"10.3390/medsci7030037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoach CS, Shawwa JJ, Nee C, Lu VM. Neurosurgical care for traumatic brain injury in low-resource settings: A multinational review evaluating the influence of health systems framework on patient outcomes. J Neurotrauma. 2025;0897715125140625310.1177/08977151251406253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepreitere B, Becker C, Ganau M, et al. Unique considerations in the assessment and management of traumatic brain injury in older adults. Lancet Neurol. 2025;24(2):152\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(24)00454-X\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(24)00454-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagares A, Depreitere B, Marklund N, et al. Consensus on the management of traumatic brain injury in older adults: Results from a Delphi study. Brain Spine. 2025;5(104319):104319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bas.2025.104319\u003c/span\u003e\u003cspan address=\"10.1016/j.bas.2025.104319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapak P, Gomez-Pinilla F. The bioenergetics of traumatic brain injury and its long-term impact for brain plasticity and function. Pharmacol Res. 2024;208(107389):107389. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.phrs.2024.107389\u003c/span\u003e\u003cspan address=\"10.1016/j.phrs.2024.107389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenzhe L, Boyang X, Yuchao G, Bimcle R, Yue Y. Mitochondrial and ER stress crosstalk in TBI: mechanistic insights and therapeutic opportunities. Front Cell Neurosci. 2025;19(1697060):1697060. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fncel.2025.1697060\u003c/span\u003e\u003cspan address=\"10.3389/fncel.2025.1697060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHostiuc S, Rusu MC. The dynamics of neuroinflammation in traumatic brain injury: Molecular markers useful for establishing the post-traumatic interval in forensic practice. Int J Mol Sci. 2026;27(4):2049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms27042049\u003c/span\u003e\u003cspan address=\"10.3390/ijms27042049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajan RK. A comprehensive review on adaptive plasticity and recovery mechanisms post-acquired brain injury. Neuroprotection. 2025;3(3):226\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/nep3.70006\u003c/span\u003e\u003cspan address=\"10.1002/nep3.70006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerban NL, Ungureanu G, Florian IS, Ionescu D. Cerebral vascular disturbances following traumatic brain injury: Pathophysiology, diagnosis, and therapeutic perspectives-A narrative review. Life (Basel). 2025;15(9):1470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/life15091470\u003c/span\u003e\u003cspan address=\"10.3390/life15091470\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeimantavicius M, Chaleckas E, Boere K, et al. Feasibility of the optimal cerebral perfusion pressure value identification without a delay that is too long. Sci Rep. 2022;12(1):17724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-22566-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-22566-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaribe H, Hayashi T, Narisawa A, Kameyama M, Nakagawa A, Tominaga T. Clinical characteristics and outcome in elderly patients with traumatic brain injury: For establishment of management strategy. Neurol Med Chir (Tokyo). 2017;57(8):418\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2176/nmc.st.2017-0058\u003c/span\u003e\u003cspan address=\"10.2176/nmc.st.2017-0058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuehiro E, Tanaka T, Matsuno A. Minimally invasive and proactive approaches for treatment of acute traumatic brain injury in elderly patients. J Clin Med. 2025;14(14):5028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm14145028\u003c/span\u003e\u003cspan address=\"10.3390/jcm14145028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodien YG, Barra A, Temkin NR, et al. Diagnosing level of consciousness: The limits of the Glasgow Coma Scale total score. J Neurotrauma. 2021;38(23):3295\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2021.0199\u003c/span\u003e\u003cspan address=\"10.1089/neu.2021.0199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndraos C, Siddiqi A, Brazdzionis J, Siddiqi J. Limitations of the Glasgow Coma Scale: Challenges and considerations. Cureus. 2025;17(2):e78900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.78900\u003c/span\u003e\u003cspan address=\"10.7759/cureus.78900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKehoe A, Rennie S, Smith JE. Glasgow Coma Scale is unreliable for the prediction of severe head injury in elderly trauma patients. Emerg Med J. 2015;32(8):613\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/emermed-2013-203488\u003c/span\u003e\u003cspan address=\"10.1136/emermed-2013-203488\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenhamed A, Isaac CJ, Boucher V, et al. Effect of age on the association between the Glasgow Coma Scale and the anatomical brain lesion severity: a retrospective multicentre study. Eur J Emerg Med. 2023;30(4):271\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MEJ.0000000000001041\u003c/span\u003e\u003cspan address=\"10.1097/MEJ.0000000000001041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShorland J, Douglas J, O\u0026rsquo;Halloran R. Cognitive-communication difficulties following traumatic brain injury sustained in older adulthood: a scoping review. Int J Lang Commun Disord. 2020;55(6):821\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1460-6984.12560\u003c/span\u003e\u003cspan address=\"10.1111/1460-6984.12560\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson LW, Hall KD. A scoping review of cognitive assessment in adults with acute traumatic brain injury. Am J Speech Lang Pathol. 2022;31(2):739\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1044/2021_AJSLP-21-00132\u003c/span\u003e\u003cspan address=\"10.1044/2021_AJSLP-21-00132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTardif PA, Moore L, Boutin A, et al. Hospital length of stay following admission for traumatic brain injury in a Canadian integrated trauma system: A retrospective multicenter cohort study. Injury. 2017;48(1):94\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.injury.2016.10.042\u003c/span\u003e\u003cspan address=\"10.1016/j.injury.2016.10.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dijck JTJM, Dijkman MD, Ophuis RH, de Ruiter GCW, Peul WC, Polinder S. In-hospital costs after severe traumatic brain injury: A systematic review and quality assessment. PLoS ONE. 2019;14(5):e0216743. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0216743\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0216743\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirian C, Ovesen T, Jensen LR, Scheike T, Springborg JB. Length of stay as a predictor of long-term mortality in patients surviving a traumatic brain injury: A nationwide TBI cohort study of 153 177 adults. J Head Trauma Rehabil. 2026;41(2):141\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HTR.0000000000001086\u003c/span\u003e\u003cspan address=\"10.1097/HTR.0000000000001086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaylor RM, Henry KA, Peters PA, Bauman MMJ, Lakomkin N, Van Gompel JJ. High long-term mortality rate in elderly patients with mild traumatic brain injury and subdural hematoma due to ground-level fall: Neurosurgery\u0026rsquo;s hip fracture? World Neurosurg. 2022;167:e1122\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2022.08.140\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2022.08.140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTommiska P, Knuutinen O, L\u0026ouml;nnrot K, et al. Mortality and causes of death after surgery for chronic subdural hematoma: a post hoc study of the FINISH randomized trial. Acta Neurochir (Wien). 2025;167(1):310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-025-06728-9\u003c/span\u003e\u003cspan address=\"10.1007/s00701-025-06728-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBramlett HM, Dietrich WD. Long-term consequences of traumatic brain injury: Current status of potential mechanisms of injury and neurological outcomes. J Neurotrauma. 2015;32(23):1834\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/neu.2014.3352\u003c/span\u003e\u003cspan address=\"10.1089/neu.2014.3352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan XT, Zhao HY, Wu CF, et al. Neurosurgical management of geriatric patients with traumatic brain injury in a medium-developed Chinese city: a recent-years overview. Front Neurol. 2025;16(1691924):1691924. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2025.1691924\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2025.1691924\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNia A, Leitgeb J, Widhalm HK, et al. Mortality in moderate to severe traumatic brain injury in elderly polytrauma patients at a European Level 1 trauma centre-A retrospective cohort study. J Clin Med. 2025;14(11):3843. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm14113843\u003c/span\u003e\u003cspan address=\"10.3390/jcm14113843\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdebola O. Do we need a neurosurgical frailty index? Surg Neurol Int. 2024;15:134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25259/SNI_50_2024\u003c/span\u003e\u003cspan address=\"10.25259/SNI_50_2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkler J, Piedade GS, Rubbert C, Hofmann BB, Kamp MA, Slotty PJ. Cerebral perfusion changes in acute subdural hematoma. Acta Neurochir (Wien). 2023;165(9):2381\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00701-023-05703-6\u003c/span\u003e\u003cspan address=\"10.1007/s00701-023-05703-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhn EN, Erwood MS, Oster RA, et al. Outcomes of subdural hematoma in the elderly with a history of minor or no previous trauma. World Neurosurg. 2018;119:e374\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2018.07.168\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2018.07.168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Brain Injuries, Traumatic, Aged, Neurosurgical Procedures, Conservative Treatment, Treatment Outcome","lastPublishedDoi":"10.21203/rs.3.rs-9362369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9362369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraumatic brain injury (TBI) in older adults is associated with high mortality and poor functional outcomes. However, optimal management remains uncertain, as evidence comparing surgical and medical strategies is limited, heterogeneous, and extrapolated from younger populations. We conducted a systematic review and meta-analysis to compare outcomes between surgical and medical management in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePubMed, Embase, and Web of Science were searched from database inception to December 8, 2025. Studies including adults aged ≥60 years with TBI comparing surgical versus medical management were included. The primary outcome was favorable neurological outcome, while secondary outcomes included hospital length of stay (LOS) and mortality. Pooled estimates were calculated as risk ratios (RR) and mean differences (MD) using random-effects models with restricted maximum likelihood and Hartung–Knapp adjustment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSixteen cohort studies comprising 132,823 patients were included. Surgical management was not associated with improved favorable neurological outcomes at discharge, 3, 6, or 12 months. However, it was associated with longer LOS (MD = 6.35 days, 95% CI: 2.55 to 10.14; p \u0026lt; 0.01). No differences were observed in in-hospital, 30-day, 3-month, 12-month, or 24-month mortality. Notably, surgical management was associated with a reduction in 6-month mortality (RR = 0.68, 95% CI: 0.51–0.92; p = 0.02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn older adults with TBI, surgical management was associated with reduced 6-month mortality and longer hospital LOS, but not with improved functional outcomes, highlighting a dissociation between survival and recovery. This dissociation has implications for clinical decision-making and patient and family counseling. Individualized decision-making that incorporates frailty, comorbidities, and potential reversibility of injury is essential, rather than relying solely on age. While this study represents the best available comparative evidence to date on surgical versus non-surgical management in older adults with TBI, findings should be interpreted with caution due to heterogeneity and low to very low certainty of the evidence. High-quality randomized controlled trials are needed to better define the role of surgery in this population.\u003c/p\u003e","manuscriptTitle":"Surgical Versus Medical Management in Older Adults with Traumatic Brain Injury: A Systematic Review and Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 16:55:36","doi":"10.21203/rs.3.rs-9362369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-02T16:08:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T20:24:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Neurocritical Care","date":"2026-04-13T22:59:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T19:04:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurocritical Care","date":"2026-04-09T08:29:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0cdbdaac-0a3b-4dab-83ac-df5e223a7190","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-02T16:08:27+00:00","index":0,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T16:55:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 16:55:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9362369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9362369","identity":"rs-9362369","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}