Use of Extracorporeal Membrane Oxygenation in Traumatic Injuries With Acute Respiratory Distress Syndrome: A Systematic Review And Meta-analysis

Use of Extracorporeal Membrane Oxygenation in Traumatic Injuries With Acute Respiratory Distress Syndrome: 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 Systematic Review Use of Extracorporeal Membrane Oxygenation in Traumatic Injuries With Acute Respiratory Distress Syndrome: A Systematic Review And Meta-analysis Fatemeh Akbarpoor, Jonathan Mokhtar, Dario Madera, Marcelo Albuquerque Barbosa Martins, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6951549/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Extracorporeal membrane oxygenation (ECMO) is increasingly used in severe acute respiratory distress syndrome (ARDS) when conventional mechanical ventilation (CMV) fails. While large trials such as CESAR and EOLIA have demonstrated ECMO’s benefit in general ARDS, trauma-induced ARDS remains underrepresented. This systematic review and meta-analysis aimed to assess ECMO’s efficacy and safety compared to CMV in adult trauma patients with ARDS. Methods: We systematically searched PubMed, Embase, and Cochrane Central up to March 2025 following PRISMA guidelines. Eligible studies included adult trauma patients with ARDS treated with ECMO (venovenous [VV] or venoarterial [VA]) versus CMV. The primary outcome was mortality; secondary outcomes included complications, ventilator-associated pneumonia (VAP), duration of mechanical ventilation, hospital length of stay (LOS), and intensive care unit (ICU) LOS. Risk of bias was assessed using the ROBINS-I tool. Results: Four observational cohort studies (n = 1,526 patients) were included. ECMO was associated with significantly lower mortality (OR 0.29; 95% CI [0.14–0.62]; p = 0.001), with an even greater benefit in the VV ECMO subgroup (OR 0.19; 95% CI [0.07–0.53]; p = 0.002). ECMO recipients had significantly longer ICU stays (SMD 1.55; 95% CI [1.00–2.10]; p < 0.01) but no significant differences in total complications, VAP, or hospital LOS. Substantial heterogeneity was present across secondary outcomes, and sensitivity analyses identified specific studies contributing to variability. Conclusion: ECMO significantly reduces mortality in adult trauma-induced ARDS but is associated with prolonged ICU stay and notable resource demands. Further prospective, trauma-focused studies are needed to refine patient selection, optimize management, and improve long-term outcomes in this complex population. ECMO ARDS Trauma Mechanical ventilation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Extracorporeal membrane oxygenation (ECMO) is increasingly used in patients with severe acute respiratory distress syndrome (ARDS) when conventional mechanical ventilation (MV) fails. According to the Extracorporeal Life Support Organization (ELSO), approximately 7,000 to 8,000 adult patients are supported each year on ECMO for respiratory failure, predominantly using venovenous (VV) ECMO [1]. While large studies like the LUNG SAFE study [2] (Bellani et al., 2016) and randomized trials such as EOLIA [3] (Combes et al., 2018) have shaped modern ARDS management, these have focused largely on pneumonia - and sepsis-related ARDS, often overlooking trauma-specific cases. In severe ARDS, diffuse alveolar damage causes pulmonary edema, decreased compliance, ventilation-perfusion mismatch, and severe hypoxemia [4]. While mechanical ventilation (MV) stabilizes gas exchange, it often requires high pressures and oxygen levels that risk further lung injury (barotrauma, volutrauma, biotrauma). ECMO bypasses the lungs to oxygenate blood and remove CO₂, enabling “lung rest” with ultraprotective settings and reducing ventilator-induced injury [3]. This approach is especially useful in trauma-induced ARDS, where chest injury, bleeding, and systemic inflammation complicate conventional ventilation. Despite its theoretical advantages, ECMO’s clinical benefit over MV remains debated. The CESAR trial showed improved six-month survival in ECMO-referred patients [5] (Peek et al., 2009), while the EOLIA trial found no significant 60-day mortality difference, though Bayesian analyses suggested a possible benefit [3]. Both trials largely excluded trauma patients. For trauma-related ARDS, especially lung trauma, venovenous (VV) ECMO is typically used to support gas exchange, whereas venoarterial (VA) ECMO provides additional cardiac support when needed [6]. Trauma-induced ARDS represents a distinct subgroup, often resulting from blunt or penetrating chest trauma, massive transfusion, or fat embolism, with coexisting hemorrhage and coagulopathy that may influence both ECMO’s efficacy and risk profile [7]. Only recently has ECMO been trialed for trauma patients, and current evidence remains limited, drawn mainly from small retrospective cohorts and trauma registries [7–11]. This study aims to systematically review and analyze the available evidence comparing ECMO and conventional MV in adult trauma patients with ARDS, focusing on mortality, complications, and resource utilization, to better define better ECMO’s role in this complex and high-risk population. 2. Material and Methods This systematic review with meta-analysis was registered in the International Prospective Register of systematic reviews and clinical trials (PROSPERO; protocol CRD420251026604, available online), aiming to ensure transparency and reduce bias risk [12]. The study was designed following the Cochrane Collaboration Handbook for Systematic Review of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13]. 2.1 Eligibility criteria Studies were included if they met the following criteria: (I) adult trauma patients with acute respiratory distress syndrome (ARDS); (II) intervention group receiving extracorporeal membrane oxygenation (ECMO), including venovenous ECMO (VV-ECMO) or venoarterial ECMO (VA-ECMO) in cases requiring circulatory support; (III) comparator group treated with conventional mechanical ventilation (CMV); and (IV) randomized controlled trials or observational cohort studies with a control group. The exclusion criteria were: (I) studies focused solely on ECMO for cardiac failure; (II) absence of a comparator group; (III) case reports, case series, reviews, conference abstracts, or meta-analyses; (IV) non-English publications; and (V) inability to obtain original data. Notably, in some included studies, patients in the ECMO group continued to receive simultaneous CMV at adjusted (lung-protective) settings as part of the overall treatment approach. 2.2 Search strategy and data extraction Systematic searches were conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception to March 2025, using combinations of terms such as “extracorporeal membrane oxygenation,” “thoracic trauma,” and “acute respiratory distress syndrome.” The detailed search strategy is provided in Supplementary Appendix 1. Reference lists of included articles and prior reviews were also screened. Data on study characteristics, patient populations, interventions, comparators, and outcomes were independently extracted by two authors (F.A. and D.M.) using Rayyan [14], with disagreements resolved through consensus or senior author input. 2.3 Endpoints and subgroup analyses The primary endpoint was mortality. Secondary endpoints included total complications, ventilator-associated pneumonia (VAP), duration of mechanical ventilation, hospital length of stay (LOS), and intensive care unit (ICU) length of stay. Complications encompassed hemorrhagic events (including gastrointestinal bleeding, cannulation site bleeding, epistaxis, disseminated intravascular coagulation), thromboembolic events (such as deep vein thrombosis, pulmonary embolism, circuit thrombosis), pulmonary complications excluding VAP (pneumothorax, pulmonary hemorrhage), and renal complications (acute kidney injury, need for renal replacement therapy). Where reported, subgroup analyses were performed based on ECMO modality; the venovenous (VV) ECMO subgroup only. 2.4 Sensitivity analysis Sensitivity analyses were conducted using leave-one-out (L1O) methods to assess the impact of individual studies on pooled effect estimates and heterogeneity levels. Outcomes demonstrating high heterogeneity were reanalyzed by sequentially omitting each study to determine if a single study disproportionately influenced the overall results. 2.5 Risk of bias and quality assessment Risk of bias was evaluated using the Cochrane Collaboration’s Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool. Two independent reviewers (F.A. and J.M.) assessed each study, resolving disagreements through discussion or senior author input. Studies were rated as having low, moderate, serious, or critical risk of bias according to ROBINS-I domain criteria [15]. 2.6 Statistical analysis Pooled analyses were conducted using odds ratios (OR), mean differences (MD), or standardized mean differences (SMD), each with corresponding 95% confidence intervals (CIs). A random-effects model with the inverse variance method was used to account for between-study variability. Heterogeneity was assessed using the I² statistic and Cochran’s Q test, with I² > 40% and p < 0.1 considered indicative of significant heterogeneity. The DerSimonian and Laird method was used to estimate between-study variance. All statistical analyses were conducted using R version 4.4.0 [16]. 3. Results 3.1 Study selection and characteristics As detailed in Supplementary Figure 1 , the initial search yielded 880 results. After removal of duplicate records and ineligible studies, 30 remained and were fully reviewed based on inclusion criteria. Of these, a total of 4 studies were included, comprising 1526 patients from 4 non-randomized cohorts. A total of 179 (11.73%) patients received ECMO, and 1347 (88.27%) received CMV. The baseline information of patients included in each study is summarized in Table 1. Table 1 - Baseline characteristics of included studies. First author, year Study design Period Location Sample size, ECMO | CMV Male, ECMO | CMV Age, Years ECMO | CMV ECMO type VV/VA P/F ratio Murray Score (points) ECMO | CMV Allam, 2023 [8] Prospective September 2020 to February 2022 Saudi Arabia 50 | 50 42 | 43 NA VA only NA > 3 points Bosarge, 2016 [17] Retrospective March 2012 to November 2014 United States of America 15 | 14 15 | 13 36.0 (25.0-47.0) | 40.0 (23.0-47.0)* VV & VA 46 (43.8) | 70.1 (22.5) 3.6 (0.2) | 3.5 (0.4) Guirand, 2014 [10] Retrospective January 2001 to December 2009 United States of America 17 | 17 12 | 15 30.9 (11.4) | 34.1 (10.7) VV only 52.2 (10.8) | 51.1 (9.3) ≥ 3 points Henry, 2021 [9] Retrospective 2013 to 2016 United States of America 97 | 1266 79 | 1000 35 (22–51) | 56 (39–69)* VV only NA NA 3.2 Pooled analysis of all studies 3.2.1 Mortality Patients who received ECMO demonstrated a significantly lower risk of mortality compared to those who did not receive ECMO support. Pooled analysis revealed a 71% reduction in odds of death (OR 0.29; 95% CI [0.14, 0.62]; p = 0.001; Figure 1 ), underscoring the potential life-saving benefit of ECMO in critically ill populations. Moderate heterogeneity was observed across studies (I 2 = 43.7%), suggesting reasonable consistency in the survival advantage associated with ECMO. 3.2.2 Total Complications Patients receiving ECMO were associated with a higher risk of total complications compared to those who did not undergo ECMO support (OR 2.13; 95% CI [0.16, 27.68]; p = 0.562; Figure 2 ). However, this finding was not statistically significant and was accompanied by substantial heterogeneity (I 2 = 88%), indicating considerable variability across studies. 3.2.3 Ventilator-Associated Pneumonia The incidence of VAP was higher in patients receiving ECMO compared to those who did not (OR 3.70; 95% CI [0.10, 137.00]; p = 0.478; Figure 3 ). Although the direction of effect suggests an increased risk, this finding did not reach statistical significance and exhibited substantial heterogeneity (I 2 = 84.6%). 3.2.4 Duration of Mechanical Ventilation The duration of mechanical ventilation was not significantly different between the two cohorts. The pooled analysis revealed (SMD = 0.44; 95% CI [-1.66, 2.54]; p = 0.68; Figure 4 ) a statistically insignificant trend towards longer ventilator days in ECMO patients. However, the analysis was marked by substantial heterogeneity (I 2 = 97.1%), suggesting high variability in patient populations, ventilation protocols, or ECMO practices across the included studies. 3.2.5 Hospital Length of Stay LOS was higher in patients who received ECMO compared to those who did not; however, the pooled standardized mean difference (SMD = 0.28; 95% CI [-1.99, 2.55]; p = 0.81; Figure 5 ) was not statistically significant. The analysis also exhibited considerable heterogeneity (I 2 = 96.8%), reflecting wide variability across studies in the population and the need for sensitivity analysis. 3.2.6 Intensive Care Unit Length of Stay ICU length of stay was significantly longer in patients who received ECMO compared to those who did not. The pooled analysis reflected statistical significance (SMD = 1.55; 95% CI [1.00, 2.10]; p < 0.01; Figure 6 ). Moderate heterogeneity was observed across the included studies (I 2 = 66.4%), suggesting some variability in ICU management practices on patient severity, but overall consistency in the direction of effect. 3.3 Subgroup analysis A subgroup analysis was performed within the ECMO cohort, focusing specifically on patients who received venovenous (VV) ECMO. The outcomes assessed included: (1) mortality rate, (2) overall complication rate, and (3) incidence of ventilator-associated pneumonia (VAP). Patients treated with VV ECMO demonstrated a significantly lower mortality rate compared to those receiving conventional management (OR 0.19; 95% CI [0.07, 0.53]; p = 0.002; I² = 43.5%; Supplementary Figure 2a ). Although the overall complication rate was higher among VV ECMO recipients, the difference did not reach statistical significance, and substantial heterogeneity was observed across studies (OR 2.44; 95% CI [0.05, 129.23]; p = 0.662; I² = 87.2% ; Supplementary Figure 2b ). Similarly, the rate of VAP was moderately elevated in the VV ECMO group; however, this finding was also not statistically significant and demonstrated high inter-study variability (OR 2.95; 95% CI [0.07, 123.84]; p = 0.570; I² = 85.9%; Supplementary Figure 2c ). 3.4 Sensitivity analysis A leave-one-out (L1O) analysis was conducted for outcomes that exhibited fatal heterogeneity to assess the influence of individual studies on the overall effect estimates and between-study variability. In the ventilator days outcome, removing Guirand et al. markedly reduces heterogeneity from 97.1% to 51.2% (Supplementary Figure 3), indicating that this study may disproportionately contribute to variability. Similarly, in the hospital length of stay analysis, omitting Guirand et al. substantially reduced heterogeneity from 96.8% to 0.0% (Supplementary Figure 4) , while the exclusion of other studies did not have the same effect. For mortality, the L1O analysis revealed stable effect sizes with consistent directions favoring the non-ECMO cohort across all iterations. Notably, the exclusion of Allam et al. reduced heterogeneity to 0% (Supplementary Figure 5) . Similarly, the ICU length of stay outcome, the effect size remained consistent across all iterations, while heterogeneity decreased substantially from 66.4% to 0% when Henry et al. was excluded (Supplementary Figure 6) , highlighting its influence on study differences. However, for the total complication rates, exclusion of individual studies did not meaningfully reduce heterogeneity, which remained consistent across all iterations (I 2 ranged from 75.1% to 91.6%) (Supplementary Figure 7) and suggested no single study was responsible for the observed inconsistency. 3.4 Quality assessment Table 2 summarizes the ROBINS-I assessment for the four included studies. Allam et al. and Guirand et al. were judged to have a moderate overall risk of bias, mainly driven by unadjusted confounding and potential deviations from intended interventions. Bosarge et al. showed a serious overall risk of bias due to its historical control design, introducing serious selection and temporal bias. Henry et al. , which applied propensity score matching, was assessed as having low risk of bias in confounding and selection but retained moderate overall risk due to the retrospective nature of the analysis and potential deviations from standardized care. Table 2 – ROBINS-I assessment for all included studies. 4. Discussion This systematic review and meta-analysis of 4 non-randomized studies, including 1526 adult trauma patients with acute respiratory failure, offers preliminary yet important evidence supporting the use of ECMO in this high-risk population. The pooled analysis demonstrated a significant reduction in mortality among patients who received ECMO compared to those managed with CMV, with 71% lower odds of death. This survival benefit was further supported in the VV ECMO subgroup, where the effect size was even more pronounced. These findings suggest that in the context of trauma-induced ARDS, where associated injuries and bleeding risks often limit lung protective ventilation strategies, ECMO may provide a vital bridge to recovery by enabling gas exchange while minimizing ventilator-associated injuries. Of note , the included studies varied in how patients were selected for ECMO versus conventional mechanical ventilation (CMV). In Allam et al. , ECMO was initiated in patients with severe pulmonary contusions who failed to improve after 10 days of CMV, with high Murray, sequential organ failure assessment (SOFA), and CPIS scores guiding eligibility [8]. In contrast, Bosarge et al. retrospectively compared patients treated with ECMO for severe ARDS (PaO₂/FiO₂ < 100) and persistent hypoxia despite maximal support to a historical CMV cohort, with selection based on failure to respond to adjunctive measures [17]. Guirand et al. applied VV ECMO only in patients unresponsive to rescue therapies across two Level I trauma centers, using Murray scores ≥3 as part of selection [10]. Henry et al. employed a propensity-matched design using TQIP data but acknowledged that ECMO patients tended to be younger, have fewer comorbidities, and sustain more severe thoracic trauma, suggesting that clinician judgment and institutional capability also shaped patient selection [9]. In comparison to other studies, our findings reinforce and extend prior evidence on ECMO use in ARDS. While the CESAR and EOLIA trials and an individual patient data meta-analysis established ECMO’s mortality benefit in severe ARDS, these trials excluded trauma patients [3,5,18]. Our meta-analysis fills this gap by focusing on trauma-induced ARDS, demonstrating a 71% mortality reduction overall, with an even stronger effect in the VV ECMO subgroup. Notably, several retrospective studies in trauma populations similarly report favorable outcomes, though patient selection criteria varied [7,11,19,20]. For example, Miller et al. noted 71% survival to discharge in a Level 1 trauma center cohort despite high ISS and bleeding risk, while Akutsu et al. found a reduced mortality risk in ECMO-treated patients even after adjusting for baseline severity. Similarly, case series have shown that VV ECMO can be safely used in trauma patients, even in those with bleeding risks, when individualized anticoagulation is applied. Early initiation, before multi-organ dysfunction sets in, may further improve survival, particularly in patients with lower baseline SOFA or Murray scores [21,22]. Importantly, whereas the Combes et al. meta-analysis reported shorter durations on organ support, we observed significantly longer ICU stays among ECMO-treated trauma patients, likely reflecting surgical interventions and bleeding-related complications unique to trauma care [18]. Notably, our results align with and complement the findings of Zhang et al. , who recently published a large single-arm trauma-specific ECMO meta-analysis including 36 studies and 1,822 patients [23]. They reported an overall survival rate of 66.4%, with significantly better survival among VV ECMO patients (72.3%) compared to VA ECMO (39%), highlighting the critical importance of selecting the appropriate ECMO modality in trauma settings. These findings are further supported by an earlier systematic review that synthesized 58 retrospective reports and case series, reporting a pooled survival rate of nearly 70% and reinforcing the growing feasibility of ECMO in trauma patients, including those with traumatic brain injury (TBI) [24]. Zhang et al. also demonstrated that trauma patients with TBI had comparable survival rates to non-TBI trauma ECMO patients, suggesting that neurological injury alone should not automatically exclude patients from ECMO consideration, a nuance particularly relevant to trauma cohorts [23]. For secondary outcomes, we found no significant differences in total complications, VAP rates, or LOS between ECMO and non-ECMO groups, though trends favored ECMO. These findings are consistent with prior ECMO studies where survival gains often come without parallel reductions in complications or healthcare resource use. As Zhang et al. similarly observed, while ECMO can be lifesaving, it carries high complication rates, particularly bleeding and thrombotic events, underscoring the importance of center experience and careful patient selection [23]. A key strength of this study is its targeted focus on trauma-induced ARDS, an underrepresented group in prior ECMO research. By applying robust systematic review methodology, risk of bias assessment (ROBINS-I), and sensitivity analyses, we provide a nuanced synthesis of available evidence. However, several limitations must be acknowledged. All included studies were observational, introducing inherent risks of selection bias and residual confounding. The small number of included studies (n = 4) and substantial heterogeneity across secondary outcomes limit the generalizability of our pooled estimates. Additionally, variability in trauma severity, ECMO protocols, and institutional expertise likely contributed to interstudy differences, as also highlighted in the multicenter cohorts analyzed by Zhang et al [23] . Clinically, our findings support the use of VV ECMO as a rescue strategy in selected adult trauma patients with severe ARDS, particularly when conventional ventilation fails. This aligns with broader ARDS management recommendations and echoes the conclusions of the prior single-arm meta-analysis, which emphasized the importance of modality selection and early referral to experienced centers. Future research should prioritize prospective trauma-specific ECMO registries, pragmatic trials, and long-term outcome studies to better define optimal patient selection, timing, and management strategies, including the nuanced role of ECMO in patients with concomitant brain injury. 5. Conclusion ECMO reduced mortality in severe post-traumatic lung injury patients and presented a longer time in the ICU. However, no significant difference was observed in VAP incidence between groups. These promising findings need confirmation from further studies to guide the selection of the best clinical choice when it comes to patients with trauma-induced ARDS. Declarations Disclosures : All authors report no relationships that could be construed as a conflict of interest. All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation. Funding statement : No funding has been received for this study. Ethics, Consent to Participate, and Consent to Publish declarations : Not applicable. Author statements: F.A. and J.M. contributed equally to this work and share first authorship. F.A. led the methodology, formal analysis, investigation, data curation, original draft writing, visualization, and project administration. J.M. contributed to methodology, formal analysis, investigation, validation, and critical revision of the manuscript. D.M. assisted with data curation, investigation, resource gathering, and manuscript review. M.B.M. provided supervision and contributed to manuscript review and editing. A.D.N. supported with resources and critical review. 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Trivedi JR, Alotaibi A, Sweeney JC, Fox MP, Van Berkel V, Adkins K, et al. Use of Extracorporeal Membrane Oxygenation in Blunt Traumatic Injury Patients with Acute Respiratory Distress Syndrome. ASAIO Journal. 2022;68:e60–1. Zhang Y, Zhang L, Huang X, Ma N, Wang P, Li L, et al. ECMO in adult patients with severe trauma: a systematic review and meta-analysis. Eur J Med Res. 2023;28:412. Wang C, Zhang L, Qin T, Xi Z, Sun L, Wu H, et al. Extracorporeal membrane oxygenation in trauma patients: a systematic review. World J Emerg Surg. 2020;15:51. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.pptx SupplementaryFigure2.pdf SupplementaryFigure3L1OVentDays.pdf SupplementaryFigure4L1OHOSPDAYS.pdf SupplementaryFigure5L1OMortality.pdf SupplementaryFigure6L1OICU.pdf SupplementaryFigure7L1OCOMP.pdf SupplementaryAppendix1SearchStrategy.docx Cite Share Download PDF Status: Posted Version 1 posted 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-6951549","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":479159343,"identity":"3dd2de68-b04e-45ef-91c9-a8a736c52e49","order_by":0,"name":"Fatemeh Akbarpoor","email":"data:image/png;base64,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","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Akbarpoor","suffix":""},{"id":479159344,"identity":"45db8f97-3461-4914-b153-780801874766","order_by":1,"name":"Jonathan Mokhtar","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Mokhtar","suffix":""},{"id":479159345,"identity":"0c03d869-2b32-4f5e-a0f8-def36dfb2c77","order_by":2,"name":"Dario Madera","email":"","orcid":"","institution":"Pasteur Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dario","middleName":"","lastName":"Madera","suffix":""},{"id":479159346,"identity":"3d1accb0-0682-4420-a57a-9d34e82d24ba","order_by":3,"name":"Marcelo Albuquerque Barbosa Martins","email":"","orcid":"","institution":"Federal University of Ouro Preto","correspondingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"Albuquerque Barbosa","lastName":"Martins","suffix":""},{"id":479159347,"identity":"e323f04f-655d-4ac2-8aa8-ebd3173b3422","order_by":4,"name":"Amorim D. 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1","display":"","copyAsset":false,"role":"figure","size":75751,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing the odds ratio (OR) for mortality in ECMO versus non-ECMO trauma patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6951549/v1/f1f4c3d2c9a798d6c0f565d3.png"},{"id":85917685,"identity":"a2d895a4-cfcc-40c2-85b1-9ab420872374","added_by":"auto","created_at":"2025-07-03 07:11:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78281,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot comparing complication rates between ECMO and non-ECMO groups in trauma patients.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6951549/v1/96f54a5001714fd1553a455e.png"},{"id":85917686,"identity":"187013ba-ae20-4427-9857-41901ee22fde","added_by":"auto","created_at":"2025-07-03 07:11:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70846,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating the odds ratio for ventilator-associated pneumonia (VAP) between ECMO and non-ECMO groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6951549/v1/97c489c1ec31ed647b0be683.png"},{"id":85917694,"identity":"13c6b9c5-08ac-4715-b426-d73bc32d4214","added_by":"auto","created_at":"2025-07-03 07:11:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89197,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized mean difference (SMD) in duration of mechanical ventilation between ECMO and non-ECMO groups.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6951549/v1/6d9673358110bb222d8cd22a.png"},{"id":85917697,"identity":"8ede3e31-0abf-4359-a8ef-c448eae75690","added_by":"auto","created_at":"2025-07-03 07:11:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85789,"visible":true,"origin":"","legend":"\u003cp\u003eSMD comparing hospital length of stay (LOS) between ECMO and non-ECMO patients.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6951549/v1/47319d4d73cda3094d1d02ee.png"},{"id":85918085,"identity":"c7ae325e-1e6d-4c54-85a1-e886c2583221","added_by":"auto","created_at":"2025-07-03 07:19:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88457,"visible":true,"origin":"","legend":"\u003cp\u003eSMD comparing ICU length of stay (LOS) between ECMO and non-ECMO 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Introduction","content":"\u003cp\u003eExtracorporeal membrane oxygenation (ECMO) is increasingly used in patients with severe acute respiratory distress syndrome (ARDS) when conventional mechanical ventilation (MV) fails. According to the Extracorporeal Life Support Organization (ELSO), approximately 7,000 to 8,000 adult patients are supported each year on ECMO for respiratory failure, predominantly using venovenous (VV) ECMO [1]. While large studies like the LUNG SAFE study [2] (Bellani et al., 2016) and randomized trials such as EOLIA [3] (Combes et al., 2018) have shaped modern ARDS management, these have focused largely on pneumonia - and sepsis-related ARDS, often overlooking trauma-specific cases.\u003c/p\u003e\n\u003cp\u003eIn severe ARDS, diffuse alveolar damage causes pulmonary edema, decreased compliance, ventilation-perfusion mismatch, and severe hypoxemia [4]. While mechanical ventilation (MV) stabilizes gas exchange, it often requires high pressures and oxygen levels that risk further lung injury (barotrauma, volutrauma, biotrauma). ECMO bypasses the lungs to oxygenate blood and remove CO₂, enabling \u0026ldquo;lung rest\u0026rdquo; with ultraprotective settings and reducing ventilator-induced injury [3]. This approach is especially useful in trauma-induced ARDS, where chest injury, bleeding, and systemic inflammation complicate conventional ventilation.\u003c/p\u003e\n\u003cp\u003eDespite its theoretical advantages, ECMO\u0026rsquo;s clinical benefit over MV remains debated. The CESAR trial showed improved six-month survival in ECMO-referred patients [5] (Peek et al., 2009), while the EOLIA trial found no significant 60-day mortality difference, though Bayesian analyses suggested a possible benefit [3]. Both trials largely excluded trauma patients. For trauma-related ARDS, especially lung trauma, venovenous (VV) ECMO is typically used to support gas exchange, whereas venoarterial (VA) ECMO provides additional cardiac support when needed [6]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTrauma-induced ARDS represents a distinct subgroup, often resulting from blunt or penetrating chest trauma, massive transfusion, or fat embolism, with coexisting hemorrhage and coagulopathy that may influence both ECMO\u0026rsquo;s efficacy and risk profile [7]. Only recently has ECMO been trialed for trauma patients, and current evidence remains limited, drawn mainly from small retrospective cohorts and trauma registries [7\u0026ndash;11].\u003c/p\u003e\n\u003cp\u003eThis study aims to systematically review and analyze the available evidence comparing ECMO and conventional MV in adult trauma patients with ARDS, focusing on mortality, complications, and resource utilization, to better define better ECMO\u0026rsquo;s role in this complex and high-risk population.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cp\u003eThis systematic review with meta-analysis was registered in the International Prospective Register of systematic reviews and clinical trials (PROSPERO; protocol CRD420251026604, available online), aiming to ensure transparency and reduce bias risk [12]. The study was designed following the Cochrane Collaboration Handbook for Systematic Review of Interventions and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13].\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.1 Eligibility criteria\u003c/h3\u003e\n\u003cp\u003eStudies were included if they met the following criteria: (I) adult trauma patients with acute respiratory distress syndrome (ARDS); (II) intervention group receiving extracorporeal membrane oxygenation (ECMO), including venovenous ECMO (VV-ECMO) or venoarterial ECMO (VA-ECMO) in cases requiring circulatory support; (III) comparator group treated with conventional mechanical ventilation (CMV); and (IV) randomized controlled trials or observational cohort studies with a control group. The exclusion criteria were: (I) studies focused solely on ECMO for cardiac failure; (II) absence of a comparator group; (III) case reports, case series, reviews, conference abstracts, or meta-analyses; (IV) non-English publications; and (V) inability to obtain original data. Notably, in some included studies, patients in the ECMO group continued to receive simultaneous CMV at adjusted (lung-protective) settings as part of the overall treatment approach.\u003c/p\u003e\n\u003ch3\u003e2.2 Search strategy and data extraction\u003c/h3\u003e\n\u003cp\u003eSystematic searches were conducted in PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception to March 2025, using combinations of terms such as \u0026ldquo;extracorporeal membrane oxygenation,\u0026rdquo; \u0026ldquo;thoracic trauma,\u0026rdquo; and \u0026ldquo;acute respiratory distress syndrome.\u0026rdquo; The detailed search strategy is provided in Supplementary Appendix 1. Reference lists of included articles and prior reviews were also screened. Data on study characteristics, patient populations, interventions, comparators, and outcomes were independently extracted by two authors (F.A. and D.M.) using Rayyan [14], with disagreements resolved through consensus or senior author input.\u003c/p\u003e\n\u003ch3\u003e2.3 Endpoints and subgroup analyses\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was mortality. Secondary endpoints included total complications, ventilator-associated pneumonia (VAP), duration of mechanical ventilation, hospital length of stay (LOS), and intensive care unit (ICU) length of stay. Complications encompassed hemorrhagic events (including gastrointestinal bleeding, cannulation site bleeding, epistaxis, disseminated intravascular coagulation), thromboembolic events (such as deep vein thrombosis, pulmonary embolism, circuit thrombosis), pulmonary complications excluding VAP (pneumothorax, pulmonary hemorrhage), and renal complications (acute kidney injury, need for renal replacement therapy). Where reported, subgroup analyses were performed based on ECMO modality; the venovenous (VV) ECMO subgroup only.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analyses were conducted using leave-one-out (L1O) methods to assess the impact of individual studies on pooled effect estimates and heterogeneity levels. Outcomes demonstrating high heterogeneity were reanalyzed by sequentially omitting each study to determine if a single study disproportionately influenced the overall results.\u003c/p\u003e\n\u003ch3\u003e2.5 Risk of bias and quality assessment\u003c/h3\u003e\n\u003cp\u003eRisk of bias was evaluated using the Cochrane Collaboration\u0026rsquo;s Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool. Two independent reviewers (F.A. and J.M.) assessed each study, resolving disagreements through discussion or senior author input. Studies were rated as having low, moderate, serious, or critical risk of bias according to ROBINS-I domain criteria [15].\u003c/p\u003e\n\u003ch3\u003e2.6 Statistical analysis\u003c/h3\u003e\n\u003cp\u003ePooled analyses were conducted using odds ratios (OR), mean differences (MD), or standardized mean differences (SMD), each with corresponding 95% confidence intervals (CIs). A random-effects model with the inverse variance method was used to account for between-study variability. Heterogeneity was assessed using the I\u0026sup2; statistic and Cochran\u0026rsquo;s Q test, with I\u0026sup2; \u0026gt; 40% and p \u0026lt; 0.1 considered indicative of significant heterogeneity. The DerSimonian and Laird method was used to estimate between-study variance. All statistical analyses were conducted using R version 4.4.0 [16].\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Study selection and characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs detailed in\u003cstrong\u003e\u0026nbsp;Supplementary Figure 1\u003c/strong\u003e, the initial search yielded 880 results. After removal of duplicate records and ineligible studies, 30 remained and were fully reviewed based on inclusion criteria. Of these, a total of 4 studies were included, comprising 1526 patients from 4 non-randomized cohorts. \u0026nbsp;A total of 179 (11.73%) patients received ECMO, and 1347 (88.27%) received CMV. \u0026nbsp;The baseline information of patients included in each study is summarized in \u003cstrong\u003eTable 1.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 -\u0026nbsp;\u003c/strong\u003eBaseline characteristics of included studies.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFirst author, year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSample size,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eECMO | CMV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eECMO | CMV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge, Years\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eECMO | CMV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eECMO type VV/VA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP/F ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMurray Score (points)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eECMO | CMV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAllam, 2023\u003csup\u003e[8]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProspective\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSeptember 2020 to February 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 \u003cstrong\u003e|\u003c/strong\u003e 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42 \u003cstrong\u003e|\u003c/strong\u003e 43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVA only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt; 3 points\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBosarge, 2016\u003csup\u003e[17]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRetrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMarch 2012 to November 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnited States of America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 \u003cstrong\u003e|\u003c/strong\u003e 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 \u003cstrong\u003e|\u003c/strong\u003e 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.0 (25.0-47.0) \u003cstrong\u003e|\u003c/strong\u003e 40.0 (23.0-47.0)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVV \u0026amp; VA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46 (43.8) \u003cstrong\u003e|\u003c/strong\u003e 70.1 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.6 (0.2) \u003cstrong\u003e|\u003c/strong\u003e 3.5 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGuirand, 2014\u003csup\u003e[10]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRetrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJanuary 2001 to December 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnited States of America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17 \u003cstrong\u003e|\u003c/strong\u003e 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 \u003cstrong\u003e|\u003c/strong\u003e 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.9 (11.4) \u003cstrong\u003e|\u003c/strong\u003e 34.1 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVV only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.2 (10.8) \u003cstrong\u003e|\u003c/strong\u003e 51.1 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge; 3 points\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHenry, 2021\u003csup\u003e[9]\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRetrospective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2013 to 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnited States of America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97 \u003cstrong\u003e|\u003c/strong\u003e 1266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79 | 1000 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;35 (22\u0026ndash;51) \u003cstrong\u003e|\u0026nbsp;\u003c/strong\u003e56 (39\u0026ndash;69)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVV only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Pooled analysis of all studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.1 Mortality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients who received ECMO demonstrated a significantly lower risk of mortality compared to those who did not receive ECMO support. Pooled analysis revealed a 71% reduction in odds of death (OR 0.29; 95% CI [0.14, 0.62]; p = 0.001; \u003cstrong\u003eFigure 1\u003c/strong\u003e), underscoring the potential life-saving benefit of ECMO in critically ill populations. Moderate heterogeneity was observed across studies (I\u003csup\u003e2\u003c/sup\u003e = 43.7%), suggesting reasonable consistency in the survival advantage associated with ECMO.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.2 Total Complications\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients receiving ECMO were associated with a higher risk of total complications compared to those who did not undergo ECMO support (OR 2.13; 95% CI [0.16, 27.68]; p = 0.562; \u003cstrong\u003eFigure 2\u003c/strong\u003e). However, this finding was not statistically significant and was accompanied by substantial heterogeneity (I\u003csup\u003e2\u003c/sup\u003e = 88%), indicating considerable variability across studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.3 Ventilator-Associated Pneumonia\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe incidence of VAP was higher in patients receiving ECMO compared to those who did not (OR 3.70; 95% CI [0.10, 137.00]; p = 0.478; \u003cstrong\u003eFigure 3\u003c/strong\u003e). Although the direction of effect suggests an increased risk, this finding did not reach statistical significance and exhibited substantial heterogeneity (I\u003csup\u003e2\u003c/sup\u003e = 84.6%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.4 Duration of Mechanical Ventilation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe duration of mechanical ventilation was not significantly different between the two cohorts. The pooled analysis revealed (SMD = 0.44; 95% CI [-1.66, 2.54]; p = 0.68; \u003cstrong\u003eFigure 4\u003c/strong\u003e) a statistically insignificant trend towards longer ventilator days in ECMO patients. However, the analysis was marked by substantial heterogeneity (I\u003csup\u003e2\u003c/sup\u003e = 97.1%), suggesting high variability in patient populations, ventilation protocols, or ECMO practices across the included studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.5 Hospital Length of Stay\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLOS was higher in patients who received ECMO compared to those who did not; however, the pooled standardized mean difference (SMD = 0.28; 95% CI [-1.99, 2.55]; p = 0.81;\u003cstrong\u003e\u0026nbsp;Figure 5\u003c/strong\u003e) was not statistically significant. The analysis also exhibited considerable heterogeneity (I\u003csup\u003e2\u003c/sup\u003e = 96.8%), reflecting wide variability across studies in the population and the need for sensitivity analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2.6 Intensive Care Unit Length of Stay\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eICU length of stay was significantly longer in patients who received ECMO compared to those who did not. The pooled analysis reflected statistical significance (SMD = 1.55; 95% CI [1.00, 2.10]; p \u0026lt; 0.01; \u003cstrong\u003eFigure 6\u003c/strong\u003e). Moderate heterogeneity was observed across the included studies (I\u003csup\u003e2\u003c/sup\u003e = 66.4%), suggesting some variability in ICU management practices on patient severity, but overall consistency in the direction of effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Subgroup analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA subgroup analysis was performed within the ECMO cohort, focusing specifically on patients who received venovenous (VV) ECMO. The outcomes assessed included: (1) mortality rate, (2) overall complication rate, and (3) incidence of ventilator-associated pneumonia (VAP).\u003c/p\u003e\n\u003cp\u003ePatients treated with VV ECMO demonstrated a significantly lower mortality rate compared to those receiving conventional management (OR 0.19; 95% CI [0.07, 0.53]; p = 0.002; I\u0026sup2; = 43.5%; \u003cstrong\u003eSupplementary Figure 2a\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAlthough the overall complication rate was higher among VV ECMO recipients, the difference did not reach statistical significance, and substantial heterogeneity was observed across studies (OR 2.44; 95% CI [0.05, 129.23]; p = 0.662; I\u0026sup2; = 87.2%\u003cstrong\u003e; Supplementary Figure 2b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSimilarly, the rate of VAP was moderately elevated in the VV ECMO group; however, this finding was also not statistically significant and demonstrated high inter-study variability (OR 2.95; 95% CI [0.07, 123.84]; p = 0.570; I\u0026sup2; = 85.9%; \u003cstrong\u003eSupplementary Figure 2c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA leave-one-out (L1O) analysis was conducted for outcomes that exhibited fatal heterogeneity to assess the influence of individual studies on the overall effect estimates and between-study variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the ventilator days outcome, removing \u003cem\u003eGuirand et al.\u0026nbsp;\u003c/em\u003emarkedly reduces heterogeneity from 97.1% to 51.2% \u003cstrong\u003e(Supplementary Figure 3),\u0026nbsp;\u003c/strong\u003eindicating that this study may disproportionately contribute to variability. Similarly, in the hospital length of stay analysis, omitting \u003cem\u003eGuirand et al.\u0026nbsp;\u003c/em\u003esubstantially reduced heterogeneity from 96.8% to 0.0% \u003cstrong\u003e(Supplementary Figure 4)\u003c/strong\u003e, while the exclusion of other studies did not have the same effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor mortality, the L1O analysis revealed stable effect sizes with consistent directions favoring the non-ECMO cohort across all iterations. Notably, the exclusion of \u003cem\u003eAllam et al.\u0026nbsp;\u003c/em\u003ereduced heterogeneity to 0% \u003cstrong\u003e(Supplementary Figure 5)\u003c/strong\u003e. Similarly, the ICU length of stay outcome, the effect size remained consistent across all iterations, while heterogeneity decreased substantially from 66.4% to 0% when \u003cem\u003eHenry et al.\u003c/em\u003e was excluded \u003cstrong\u003e(Supplementary Figure 6)\u003c/strong\u003e, highlighting its influence on study differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, for the total complication rates, exclusion of individual studies did not meaningfully reduce heterogeneity, which remained consistent across all iterations (I\u003csup\u003e2\u003c/sup\u003e ranged from 75.1% to 91.6%) \u003cstrong\u003e(Supplementary Figure 7)\u0026nbsp;\u003c/strong\u003eand suggested no single study was responsible for the observed inconsistency.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Quality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e summarizes the ROBINS-I assessment for the four included studies. \u003cem\u003eAllam et al.\u003c/em\u003e and \u003cem\u003eGuirand et al.\u003c/em\u003e were judged to have a moderate overall risk of bias, mainly driven by unadjusted confounding and potential deviations from intended interventions. \u003cem\u003eBosarge et al.\u003c/em\u003e showed a serious overall risk of bias due to its historical control design, introducing serious selection and temporal bias. \u003cem\u003eHenry et al.\u003c/em\u003e, which applied propensity score matching, was assessed as having low risk of bias in confounding and selection but retained moderate overall risk due to the retrospective nature of the analysis and potential deviations from standardized care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash;\u0026nbsp;\u003c/strong\u003eROBINS-I assessment for all included studies.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"660\" height=\"392\"\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis of 4 non-randomized studies, including 1526 adult trauma patients with acute respiratory failure, offers preliminary yet important evidence supporting the use of ECMO in this high-risk population. The pooled analysis demonstrated a significant reduction in mortality among patients who received ECMO compared to those managed with CMV, with 71% lower odds of death. This survival benefit was further supported in the VV ECMO subgroup, where the effect size was even more pronounced. These findings suggest that in the context of trauma-induced ARDS, where associated injuries and bleeding risks often limit lung protective ventilation strategies, ECMO may provide a vital bridge to recovery by enabling gas exchange while minimizing ventilator-associated injuries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOf note\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e the included studies varied in how patients were selected for ECMO versus conventional mechanical ventilation (CMV). In \u003cem\u003eAllam et al.\u003c/em\u003e, ECMO was initiated in patients with severe pulmonary contusions who failed to improve after 10 days of CMV, with high Murray, sequential organ failure assessment (SOFA), and CPIS scores guiding eligibility [8]. In contrast, \u003cem\u003eBosarge et al.\u003c/em\u003e retrospectively compared patients treated with ECMO for severe ARDS (PaO₂/FiO₂ \u0026lt; 100) and persistent hypoxia despite maximal support to a historical CMV cohort, with selection based on failure to respond to adjunctive measures [17]. \u003cem\u003eGuirand et al.\u0026nbsp;\u003c/em\u003eapplied VV ECMO only in patients unresponsive to rescue therapies across two Level I trauma centers, using Murray scores \u0026ge;3 as part of selection [10]. \u003cem\u003eHenry et al.\u003c/em\u003e employed a propensity-matched design using TQIP data but acknowledged that ECMO patients tended to be younger, have fewer comorbidities, and sustain more severe thoracic trauma, suggesting that clinician judgment and institutional capability also shaped patient selection [9].\u003c/p\u003e\n\u003cp\u003eIn comparison to other studies, our findings reinforce and extend prior evidence on ECMO use in ARDS. While the CESAR and EOLIA trials and an individual patient data meta-analysis established ECMO\u0026rsquo;s mortality benefit in severe ARDS, these trials excluded trauma patients [3,5,18]. Our meta-analysis fills this gap by focusing on trauma-induced ARDS, demonstrating a 71% mortality reduction overall, with an even stronger effect in the VV ECMO subgroup. Notably, several retrospective studies in trauma populations similarly report favorable outcomes, though patient selection criteria varied [7,11,19,20]. For example, \u003cem\u003eMiller et al.\u003c/em\u003e noted 71% survival to discharge in a Level 1 trauma center cohort despite high ISS and bleeding risk, while \u003cem\u003eAkutsu et al.\u003c/em\u003e found a reduced mortality risk in ECMO-treated patients even after adjusting for baseline severity. Similarly, case series have shown that VV ECMO can be safely used in trauma patients, even in those with bleeding risks, when individualized anticoagulation is applied. Early initiation, before multi-organ dysfunction sets in, may further improve survival, particularly in patients with lower baseline SOFA or Murray scores [21,22]. Importantly, whereas the \u003cem\u003eCombes et al.\u003c/em\u003e meta-analysis reported shorter durations on organ support, we observed significantly \u003cstrong\u003elonger ICU stays\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eamong ECMO-treated trauma patients, likely reflecting surgical interventions and bleeding-related complications unique to trauma care [18].\u003c/p\u003e\n\u003cp\u003eNotably, our results align with and complement the findings of \u003cem\u003eZhang et al.\u003c/em\u003e, who recently published a large single-arm trauma-specific ECMO meta-analysis including 36 studies and 1,822 patients [23]. They reported an overall survival rate of 66.4%, with significantly better survival among VV ECMO patients (72.3%) compared to VA ECMO (39%), highlighting the critical importance of selecting the appropriate ECMO modality in trauma settings. These findings are further supported by an earlier systematic review that synthesized 58 retrospective reports and case series, reporting a pooled survival rate of nearly 70% and reinforcing the growing feasibility of ECMO in trauma patients, including those with traumatic brain injury (TBI) [24]. \u003cem\u003eZhang et al.\u003c/em\u003e also demonstrated that trauma patients with TBI had comparable survival rates to non-TBI trauma ECMO patients, suggesting that neurological injury alone should not automatically exclude patients from ECMO consideration, a nuance particularly relevant to trauma cohorts [23].\u003c/p\u003e\n\u003cp\u003eFor secondary outcomes, we found no significant differences in total complications, VAP rates, or LOS between ECMO and non-ECMO groups, though trends favored ECMO. These findings are consistent with prior ECMO studies where survival gains often come without parallel reductions in complications or healthcare resource use. As \u003cem\u003eZhang et al.\u003c/em\u003e similarly observed, while ECMO can be lifesaving, it carries high complication rates, particularly bleeding and thrombotic events, underscoring the importance of center experience and careful patient selection [23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA key strength of this study is its targeted focus on trauma-induced ARDS, an underrepresented group in prior ECMO research. By applying robust systematic review methodology, risk of bias assessment (ROBINS-I), and sensitivity analyses, we provide a nuanced synthesis of available evidence. However, several limitations must be acknowledged. All included studies were observational, introducing inherent risks of selection bias and residual confounding. The small number of included studies (n = 4) and substantial heterogeneity across secondary outcomes limit the generalizability of our pooled estimates. Additionally, variability in trauma severity, ECMO protocols, and institutional expertise likely contributed to interstudy differences, as also highlighted in the multicenter cohorts analyzed by \u003cem\u003eZhang et al\u0026nbsp;\u003c/em\u003e[23]\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eClinically, our findings support the use of VV ECMO as a rescue strategy in selected adult trauma patients with severe ARDS, particularly when conventional ventilation fails. This aligns with broader ARDS management recommendations and echoes the conclusions of the prior single-arm meta-analysis, which emphasized the importance of modality selection and early referral to experienced centers. Future research should prioritize prospective trauma-specific ECMO registries, pragmatic trials, and long-term outcome studies to better define optimal patient selection, timing, and management strategies, including the nuanced role of ECMO in patients with concomitant brain injury.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eECMO reduced mortality in severe post-traumatic lung injury patients and presented a longer time in the ICU. However, no significant difference was observed in VAP incidence between groups. These promising findings need confirmation from further studies to guide the selection of the best clinical choice when it comes to patients with trauma-induced ARDS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e: All authors report no relationships that could be construed as a conflict of interest. All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e: No funding has been received for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor statements:\u003c/strong\u003e F.A. and J.M. contributed equally to this work and share first authorship. F.A. led the methodology, formal analysis, investigation, data curation, original draft writing, visualization, and project administration. J.M. contributed to methodology, formal analysis, investigation, validation, and critical revision of the manuscript. D.M. assisted with data curation, investigation, resource gathering, and manuscript review. M.B.M. provided supervision and contributed to manuscript review and editing. A.D.N. supported with resources and critical review. Conceptualisation and overall supervision were led by the senior author, R.E.N.N.O., who also contributed to validation, project administration, and manuscript revision. All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eExtracorporeal Life Support Organization (ELSO). ELSO International Summary Report [Internet]. Ann Arbor, MI: ELSO; 2025. Available from: https://www.elso.org/Registry/InternationalSummary.aspx\u003c/li\u003e\n\u003cli\u003eBellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA. 2016;315:788. \u003c/li\u003e\n\u003cli\u003eCombes A, Hajage D, Capellier G, Demoule A, Lavou\u0026eacute; S, Guervilly C, et al. Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome. N Engl J Med. 2018;378:1965\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eFan E, Brodie D, Slutsky AS. Acute Respiratory Distress Syndrome: Advances in Diagnosis and Treatment. JAMA. 2018;319:698. \u003c/li\u003e\n\u003cli\u003ePeek GJ, Mugford M, Tiruvoipati R, Wilson A, Allen E, Thalanany MM, et al. Efficacy and economic assessment of conventional ventilatory support versus extracorporeal membrane oxygenation for severe adult respiratory failure (CESAR): a multicentre randomised controlled trial. The Lancet. 2009;374:1351\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eVyas A, Bishop MA. Extracorporeal Membrane Oxygenation in Adults. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 [cited 2025 May 31]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK576426/\u003c/li\u003e\n\u003cli\u003eMiller A, Post M, Pellecchia C, Bini J. Analysis of ECMO usage in trauma patients at a major level 1 trauma center. Trauma Case Reports. 2025;56:101142. \u003c/li\u003e\n\u003cli\u003eMostafa Allam MGI. The Immunoadsorption Effect of Veno-arterial Extracorporeal Membrane Oxygenation in Refractory Septic Shock, Ventilator-associated Pneumonia, and Acute Respiratory Distress Syndrome Following Severe Pulmonary Contusions. TOATJ. 2023;17:e258964582303290. \u003c/li\u003e\n\u003cli\u003eHenry R, Ghafil C, Piccinini A, Liasidis PK, Matsushima K, Golden A, et al. Extracorporeal support for trauma: A trauma quality improvement project (TQIP) analysis in patients with acute respiratory distress syndrome. The American Journal of Emergency Medicine. 2021;48:170\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eGuirand DM, Okoye OT, Schmidt BS, Mansfield NJ, Aden JK, Martin RS, et al. 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Int J Artif Organs. 2022;45:81\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eTrivedi JR, Alotaibi A, Sweeney JC, Fox MP, Van Berkel V, Adkins K, et al. Use of Extracorporeal Membrane Oxygenation in Blunt Traumatic Injury Patients with Acute Respiratory Distress Syndrome. ASAIO Journal. 2022;68:e60\u0026ndash;1. \u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang L, Huang X, Ma N, Wang P, Li L, et al. ECMO in adult patients with severe trauma: a systematic review and meta-analysis. Eur J Med Res. 2023;28:412. \u003c/li\u003e\n\u003cli\u003eWang C, Zhang L, Qin T, Xi Z, Sun L, Wu H, et al. Extracorporeal membrane oxygenation in trauma patients: a systematic review. World J Emerg Surg. 2020;15:51.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ECMO, ARDS, Trauma, Mechanical ventilation","lastPublishedDoi":"10.21203/rs.3.rs-6951549/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6951549/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eExtracorporeal membrane oxygenation (ECMO) is increasingly used in severe acute respiratory distress syndrome (ARDS) when conventional mechanical ventilation (CMV) fails. While large trials such as CESAR and EOLIA have demonstrated ECMO’s benefit in general ARDS, trauma-induced ARDS remains underrepresented. This systematic review and meta-analysis aimed to assess ECMO’s efficacy and safety compared to CMV in adult trauma patients with ARDS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We systematically searched PubMed, Embase, and Cochrane Central up to March 2025 following PRISMA guidelines. Eligible studies included adult trauma patients with ARDS treated with ECMO (venovenous [VV] or venoarterial [VA]) versus CMV. The primary outcome was mortality; secondary outcomes included complications, ventilator-associated pneumonia (VAP), duration of mechanical ventilation, hospital length of stay (LOS), and intensive care unit (ICU) LOS. Risk of bias was assessed using the ROBINS-I tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eFour observational cohort studies (n = 1,526 patients) were included. ECMO was associated with significantly lower mortality (OR 0.29; 95% CI [0.14–0.62]; p = 0.001), with an even greater benefit in the VV ECMO subgroup (OR 0.19; 95% CI [0.07–0.53]; p = 0.002). ECMO recipients had significantly longer ICU stays (SMD 1.55; 95% CI [1.00–2.10]; p \u0026lt; 0.01) but no significant differences in total complications, VAP, or hospital LOS. Substantial heterogeneity was present across secondary outcomes, and sensitivity analyses identified specific studies contributing to variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eECMO significantly reduces mortality in adult trauma-induced ARDS but is associated with prolonged ICU stay and notable resource demands. Further prospective, trauma-focused studies are needed to refine patient selection, optimize management, and improve long-term outcomes in this complex population.\u003c/p\u003e","manuscriptTitle":"Use of Extracorporeal Membrane Oxygenation in Traumatic Injuries With Acute Respiratory Distress Syndrome: A Systematic Review And Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 07:11:32","doi":"10.21203/rs.3.rs-6951549/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50a3cd4e-5154-44b0-b1ff-87938679d71c","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-11T21:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 07:11:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6951549","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6951549","identity":"rs-6951549","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}