# Low-Tidal-Volume Ventilation and Mortality in Patients With Acute Brain Injury

## A Secondary Analysis of an International Observational Study

<figure><img src="image_1.png" /></figure>

Check for updates

Julian F. Daza, MD, PhD; Doulia M. Hamad, MD, PhD; Martin Umer, MD, PhD; Kuan Liu, PhD; Sarah Wahlster, MD; Chiara Robba, MD, PhD; Robert D. Stevens, MD, MBA; Victoria A. McCredie, MBChB, PhD; Raphael Cinotti, MD, PhD; and Shaurya Taran, MD; for the ENIO Study Group, on behalf of the PROtective VENTilation Network, the European Society of Intensive Care Medicine, the Colegio Mexicano de Medicina Critica, the AtlanRéa Group, and the Société Française d'Anesthésie-Réanimation-SFAR Research Network*

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

**BACKGROUND:** Low-tidal-volume ventilation (LTVV) improves outcomes in critically ill patients, but its impact in patients with acute brain injuries (ABIs) is less certain.

**RESEARCH QUESTION:** What is the association between LTVV and mortality in mechanically ventilated patients with ABI?

**STUDY DESIGN AND METHODS:** We did a secondary analysis of a prospective observational study (NCT03400904; https://clinicaltrials.gov/study/NCT03400904). We compared LTVV (≤ 8 mL/kg predicted body weight [PBW]) with tidal volumes > 8 mL/kg PBW over the first 7 days of mechanical ventilation. Alternate analyses used lower thresholds for LTVV. Marginal structural Cox models were used to evaluate the association between LTVV and ICU mortality up to 60 days. Stabilized inverse probability treatment and censoring weights were generated using multivariable logistic regression adjusted for baseline and time-dependent confounders.

**RESULTS:** A total of 1,510 patients from 73 ICUs across 18 countries were included. The mean age was 52 years, 513 patients (34.0%) were female, and the most common ABI etiology was traumatic brain injury (n = 726; 48.1%). ARDS developed in 137 patients (9.2%). In patients receiving LTVV, adjusted incidence of ICU mortality was 40.2% (95% CI, 19.2%-61.1%), vs 59.7% (95% CI, 44.0%-75.4%) in patients receiving tidal volumes > 8 mL/kg PBW (marginal hazard ratio, 0.54; 95% CI, 0.33-0.88). There was no heterogeneity of treatment effect in subgroup analyses, and sensitivity analyses for unmeasured confounding yielded similar results. However, associations were less clear at lower thresholds of LTVV.

**INTERPRETATION:** In this predominantly non-ARDS cohort of patients with ABI, LTVV over the first 7 days of mechanical ventilation was associated with lower ICU mortality up to 60 days, vs tidal volumes > 8 mL/kg PBW. Future research should investigate effects in patients with ABI and ARDS, use of lower LTVV thresholds, and impact on additional end points including functional outcomes and adverse events. CHEST 2025; 168(5):1141-1151

**KEY WORDS:** acute brain injury; low-tidal-volume ventilation; lung-protective ventilation; neurocritical care

FOR EDITORIAL COMMENT, SEE PAGE 1079
## Take-Home Points

**Study Question:** In patients with acute brain injury admitted to the ICU, what is the association between low-tidal-volume ventilation (≤ 8 mL/kg predicted body weight) during the first week of mechanical ventilation and ICU mortality?

**Results:** Patients ventilated with low tidal volumes had a lower adjusted risk of ICU mortality compared with those ventilated with > 8 mL/kg predicted body weight (marginal hazard ratio, 0.54; 95% CI, 0.33-0.88).

**Interpretation:** In this cohort of patients with acute brain injury, low-tidal-volume ventilation over the first 7 days of mechanical ventilation was associated with reduced ICU mortality, meriting further investigation in a clinical trial.

Low-tidal-volume ventilation (LTVV; defined as a tidal volume [Vt] of 4-8 mL/kg predicted body weight [PBW]) is a cornerstone of care in patients with ARDS.¹,² When combined with other lung-protective strategies, LTVV reduces morbidity and mortality by minimizing ventilator-induced lung injury (VILI).³⁻⁵ For patients without ARDS, LTVV increases the proportion of transplanted lungs available from brain-dead donors,⁶ reduces pulmonary and nonpulmonary complications after intraabdominal surgery,⁷ and limits the downstream occurrence of ARDS in patients receiving invasive ventilation for diverse conditions.⁸

Patients with acute brain injury (ABI) may also benefit from LTVV,⁹ but uptake in this population may be limited by concerns about intracranial hypertension and

inability to perform a neurologic examination, due to the need for additional sedation or neuromuscular blockade (often required to facilitate synchronous ventilation).¹⁰ Most landmark ARDS trials excluded patients with ABI,³,¹¹,¹² and existing guidance for V~T~ selection in the ABI population is based on indirect evidence.¹⁰ A systematic review of 8 observational studies including 5,639 patients with ABI found no significant association between LTVV (V~T~ < 8 mL/kg PBW) and short-term mortality. Although a 2024 multicenter randomized trial in patients with ABI found worse short- and long-term outcomes with LTVV (V~T~ = 6 mL/kg PBW) compared with V~T~ ≥ 8 mL/kg PBW,¹³ its clinical implications remain debated.¹⁴ Therefore, the optimal V~T~ in this population remains uncertain.

In the present analysis, we used observational data and causal inference methods that account for time-varying confounding to examine whether LTVV is associated with ICU mortality in patients with ABI. Standard regression methods present important shortcomings in this context, because they cannot sufficiently handle time-varying confounders that predict subsequent levels of the treatment or act as mediators for the outcome.¹⁵,¹⁶ Prior observational studies in ABI populations have evaluated LTVV as a static exposure,⁹,¹⁷ but this does not fully capture the dynamic nature of ventilator management in clinical practice.¹⁸ Therefore, we used marginal structure models to evaluate the association between LTVV (delivered as a dynamic treatment over the first 7 days of mechanical ventilation) and mortality in patients with ABI. This secondary analysis was conducted using data from a prospective international study in which most patients did not have ARDS at the onset of mechanical ventilation.¹⁹

**ABBREVIATIONS:** ABI = acute brain injury; ENIO = extubation in neurocritically ill patients and association with outcomes; GCS = Glasgow Coma Scale; HR = hazard ratio; ICP = intracranial pressure; IQR = interquartile range; LTVV = low-tidal-volume ventilation; PBW = predicted body weight; PEEP = positive end-expiratory pressure; Pplat = plateau pressure; VAP = ventilator-associated pneumonia; VILI = ventilator-induced lung injury; V~T~ = tidal volume

**AFFILIATIONS:** From the Division of General Surgery (J. F. D. and D. M. H.), Department of Surgery, University of Toronto; the Institute of Health Policy, Management and Evaluation (J. F. D., D. M. H., K. L., V. A. McC., and S. T.), University of Toronto; the Interdepartmental Division of Critical Care Medicine (M. U., V. A. McC., and S. T.), University of Toronto; the Department of Anesthesiology and Pain Medicine (M. U.), University of Toronto; the Division of Biostatistics (K. L.), Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; the Department of Neurology (S. W.), the Department of Neurosurgery (S. W.), and the Department of Anesthesiology and Pain Medicine (S. W.), University of Washington School of Medicine, Seattle, WA; the IRCCS Policlinico San Martino (C. R.), Genoa, Italy; the Department of Surgical Sciences and Integrated Diagnostics (C. R.), University of Genoa, Genoa, Italy; the

Department of Anesthesiology and Critical Care Medicine (R. D. S.), the Department of Neurosurgery (R. D. S.), and the Department of Radiology (R. D. S.), Johns Hopkins University School of Medicine, Baltimore, MD; the Department of Biomedical Engineering (R. D. S.), Whiting School of Engineering, Johns Hopkins University, Baltimore, MD; UMR 1246 (Methods in Patient-Centered Outcomes and Health Research; SPHERE) (R. C.), Nantes Université (Nantes) and Tours Université (Tours); and the Department of Anesthesia and Critical Care (R. C.), CHU Nantes, Nantes Université, Hôtel-Dieu, Nantes, France.

R. C. and S. T. contributed equally to this manuscript.

*Collaborators from the ENIO Study Group are listed in e-Appendix 3.

**CORRESPONDENCE TO:** Shaurya Taran, MD; email: shaurya.taran@mail.utoronto.ca

Copyright © 2025 The Author(s). Published by Elsevier Inc under license from the American College of Chest Physicians. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**DOI:** https://doi.org/10.1016/j.chest.2025.06.042
## Study Design and Methods

This was a secondary analysis of the Extubation in Neurocritically Ill Patients and Association with Outcomes (ENIO) prospective observational study.¹⁹ Ethics approval was granted by the research ethics boards of Nantes University (REB 23-153-12-180). Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology statement (e-Appendix 1 in the online article).²⁰

### Study Cohort

Details of the original study cohort are published in full elsewhere.¹⁹ Briefly, consecutive patients aged ≥ 18 years with ABI (traumatic brain injury, subarachnoid aneurysmal hemorrhage, intracranial hemorrhage, ischemic stroke, brain tumor, central nervous system infection) were recruited from 73 ICUs across 18 countries between June 2018 and November 2020. All patients received invasive ventilation for at least 24 hours. Data were collected on patient demographics, comorbidities, ABI diagnosis, treatments, and clinical outcomes. Mechanical ventilation and gas exchange variables were recorded on days 1, 3, and 7 of invasive ventilation. Available data included mode of ventilation (spontaneous or assist-controlled), $V_T$ (mL), plateau pressure ($P_{plat}$; cm H₂O), positive end-expiratory pressure (PEEP; cm H₂O), respiratory rate (breaths/min), $Pao_2$ (mm Hg), $Paco_2$ (mm Hg), and pH.

### Exposure

The exposure for the present analysis was LTVV, defined as a $V_T \le 8$ mL/kg PBW and calculated using the patient's sex and height as per the ARDS Network equation.³ We chose a threshold of 8 mL/kg PBW for the primary analysis, as this is the value commonly used to define lung-protective ventilation in past and present ARDS guidelines.¹,²,²¹ The comparator was $V_T > 8$ mL/kg PBW. The exposure was measured on days 1, 3, and 7 from the initiation of mechanical ventilation. To account for shifts over time in clinician comfort with using lower $V_T$ in patients with ABI,²²,²³ we repeated the analysis using longitudinal exposure profiles for $V_T$ with thresholds of 7.5, 7, 6.5, and 6 mL/kg PBW. We chose not to evaluate exposure thresholds below 6 mL/kg PBW due to potential concerns related to further reducing $V_T$ in patients with ABI (eg, resulting from effects of hypercarbia on intracranial pressure [ICP]).²⁴

### Primary End Point

The primary end point was ICU mortality, which included deaths from withdrawal of life-sustaining therapies. Our calculation of this end point assumed that all deaths occurred in close temporal proximity with the decision to withdraw.²⁵,²⁶ Patients were followed from initiation of mechanical ventilation (ie, time zero) until death or ICU day 60, whichever occurred earliest. Censoring occurred at ICU discharge. Patients who developed ARDS were also censored because they would be unlikely to continue receiving $V_T > 8$ mL/kg PBW. As the timing of ARDS onset was not recorded in the ENIO study, we assumed that ARDS occurred within the first 3 days of invasive mechanical ventilation; this approximation is consistent with prior data in the ABI population.⁹,²⁷,²⁸

### Adjustment Covariates

Confounders for the association between LTVV and mortality were selected on the basis of clinical relevance and subject matter knowledge. Time-fixed confounders included demographic characteristics, medical comorbidities, etiology of ABI, and severity of brain injury as estimated by the lowest Glasgow Coma Scale (GCS) score before endotracheal intubation. Time-varying confounders included ventilator mode, respiratory rate, $P_{plat}$, $Pao_2/Fio_2$ ratio, and $Paco_2$. Time-varying proxies of lung and brain injury severity, respectively, included use of a neuromuscular blocking agent (used to treat moderate/severe hypoxemic respiratory failure) and thiopental (Pentothal; used to treat refractory intracranial hypertension).

All time-varying confounders were measured on days 1, 3, and 7 of mechanical ventilation. Our assumptions regarding covariate relationships and the temporal sequence of confounder-exposure feedback loops are summarized in a directed acyclic graph (e-Fig 1). Further details on definitions and covariate handling procedures are provided in e-Table 1.

### Statistical Analyses

Descriptive characteristics were presented for the entire cohort and across strata defined by exposure status and number of days after initiation of mechanical ventilation. Continuous variables were summarized using means and SD or medians and interquartile range (IQR) as appropriate. Categorical variables were summarized using absolute values with percentages.
We evaluated the effect of LTVV on mortality using marginal structural models.²⁹ Logistic regression was used to generate stabilized inverse probability of treatment weights for each V<sub>T</sub> group on each day of mechanical ventilation.³⁰,³¹ For patients who progressed to spontaneous ventilation, where V<sub>T</sub> cannot be precisely regulated, we adjusted for ventilator mode in our weighted analyses. To mitigate selection bias introduced by censoring patients who developed ARDS or were discharged from the ICU, we generated stabilized inverse probability of censoring weights for these events. Weights were based on both time-fixed and time-varying factors. The combined cumulative weight (stabilized inverse probability of treatment weights and stabilized inverse probability of censoring weights) was truncated at the 99th percentile and incorporated into a weighted time-dependent Cox model.³² Point estimates therefore represent marginal hazard ratios (HRs) and describe the population-level average effect for those exclusively treated with LTVV (ie, V<sub>T</sub> ≤ 8 mL/kg at all 3 time points) vs those who never received LTVV (ie, V<sub>T</sub> > 8 mL/kg PBW at all 3 time points).³³ Importantly, individual patients did not contribute to the analysis as members of fixed exposure groups. Instead, their contribution to the overall pseudo-population was weighted according to their probability of remaining adherent to a given V<sub>T</sub> strategy over time, conditional on current and past covariate history. In addition, by censoring patients on the basis of ARDS development and ICU discharge, our effect estimates reflect those from a pseudo-population where neither of these events occur.

Robust SEs were used to calculate 95% CIs. Missing data were handled using multiple imputation by chained equations, with 30 imputed data sets, and pooled using Rubin's rules.³⁴ Cumulative incidence curves were

plotted using an average of the weighted cumulative incidences in the first 60 days across imputed data sets.

## Subgroup Analyses

We conducted prespecified subgroup analyses to explore potential effect modification of LTVV based on age (> 65 vs ≤ 65 years), brain injury severity (defined by admission GCS < 8 vs ≥ 8), baseline hypoxemic respiratory failure (PaO₂/FiO₂ ≤ 300 vs > 300), and ABI etiology (traumatic vs nontraumatic). We did not repeat subgroup analyses for different ABI subtypes because of the small sample sizes in several groups.

## Sensitivity Analyses

We performed several sensitivity analyses. First, to determine whether results were influenced by our definition of LTVV, we repeated the analysis using a series of incrementally lower V<sub>T</sub> thresholds for the main exposure (7.5, 7, 6.5, and 6 mL/kg PBW). Second, we excluded participants who had no V<sub>T</sub> data at any of the 3 time points and thus required imputation of their entire exposure history. Third, to detect potential model misspecification, we repeated the primary analysis using a flexible estimator (the “Super Learner”) to generate treatment weights.³⁵ Fourth, we applied a negative control outcome to explore the impact of unmeasured confounding.³⁶ For this analysis, we refit the models using ventilator-associated pneumonia (VAP) as our end point, anticipating a null association if the impact of unmeasured confounding was minimal.

Additional details of the statistical analyses are in e-Appendix 2. Sample size was determined in the original ENIO study, and no additional power calculations for this secondary analysis were done.³⁷ All analyses were performed with R version 4.0.2 (R Foundation for Statistical Computing).

## Results

From 1,512 patients in the parent study, 2 individuals were excluded for having completely missing V<sub>T</sub> and follow-up data, resulting in an analytic cohort of 1,510 patients. Mean age was 52 years (SD 18), 513 patients (34.0%) were female, and the most common etiologies of ABI were traumatic brain injury (n = 726; 48.1%) and intracranial hemorrhage (n = 521; 34.5%). Median GCS before endotracheal intubation was 7 (IQR, 5-9). Mean follow-up duration was 15 days (SD 13). Additional baseline characteristics are presented in Table 1.

The most common mode of ventilation at all 3 time points was volume assist/control (e-Table 2). On day 1 of invasive mechanical ventilation, 1,095 patients (76.8%) received LTVV, compared with 887 (72.6%) of those still intubated on day 3, and 565 (68.1%) of those still intubated on day 7. Mean V<sub>T</sub> in the LTVV and control groups, respectively, were 6.8 mL/kg (SD 0.7) and 9.0 mL/kg (SD 1.0) on day 1; 6.7 mL/kg (SD 0.7) and 9.3 mL/kg (SD 1.2) on day 3; and 6.8 mL/kg (SD 0.8) and 9.2 mL/kg (SD 1.2) on day 7 (e-Fig 2). Characteristics were similar between patients who had missing V<sub>T</sub> data at any time point and those with
<table><caption>TABLE 1] Baseline Characteristics</caption><thead><tr><th>Characteristic</th><th>All<br>(n = 1,510)</th><th>Day 1 V<sub>T</sub> ≤ 8<br>(n = 1,095)<sup>a</sup></th><th>Day 1 V<sub>T</sub> > 8<br>(n = 330)<sup>a</sup></th><th>Absolute<br>SMD</th></tr></thead><tbody><tr><td>Mean age, y</td><td>51.7 (18.2)</td><td>50.5 (18.4)</td><td>55.4 (16.7)</td><td>0.28</td></tr><tr><td>Data missing</td><td>12</td><td>6</td><td>5</td><td>NA</td></tr><tr><td>Sex (female)</td><td>513 (34%)</td><td>294 (27%)</td><td>192 (58%)</td><td>0.67</td></tr><tr><td>Median BMI, kg/m<sup>2</sup></td><td>25.58 (22.85-28.73)</td><td>25.26 (22.68-28.04)</td><td>26.67 (23.44-30.85)</td><td>0.38</td></tr><tr><td>Data missing</td><td>46</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Etiology of ABI</td><td></td><td></td><td></td><td></td></tr><tr><td>Traumatic brain injury</td><td>726 (48%)</td><td>564 (52%)</td><td>121 (37%)</td><td>0.30</td></tr><tr><td>Subarachnoid aneurysmal<br>hemorrhage</td><td>269 (18%)</td><td>190 (17%)</td><td>67 (20%)</td><td>0.08</td></tr><tr><td>Intracranial hemorrhage</td><td>521 (35%)</td><td>371 (34%)</td><td>120 (36%)</td><td>0.05</td></tr><tr><td>Ischemic stroke</td><td>141 (9.3%)</td><td>99 (9.0%)</td><td>30 (9.1%)</td><td>0.00</td></tr><tr><td>CNS infection</td><td>74 (4.9%)</td><td>54 (4.9%)</td><td>19 (5.8%)</td><td>0.04</td></tr><tr><td>Brain tumor</td><td>72 (4.8%)</td><td>49 (4.5%)</td><td>20 (6.1%)</td><td>0.07</td></tr><tr><td>Other</td><td>33 (2.2%)</td><td>21 (1.9%)</td><td>8 (2.4%)</td><td>0.04</td></tr><tr><td>Data missing</td><td>2</td><td>1</td><td>1</td><td>NA</td></tr><tr><td>Median GCS score before<br>intubation (IQR)</td><td>7.00 (5.00-9.00)</td><td>7.00 (5.00-8.50)</td><td>7.00 (5.00-9.00)</td><td>0.04</td></tr><tr><td>Health status before admission</td><td></td><td></td><td></td><td></td></tr><tr><td>Pulmonary disease</td><td>51 (3.4%)</td><td>33 (3.0%)</td><td>15 (4.6%)</td><td>0.08</td></tr><tr><td>Data missing</td><td>1</td><td>0</td><td>1</td><td>NA</td></tr><tr><td>Hypertension</td><td>450 (30%)</td><td>301 (27%)</td><td>117 (36%)</td><td>0.17</td></tr><tr><td>Data missing</td><td>1</td><td>0</td><td>1</td><td>NA</td></tr><tr><td>Heart failure</td><td>44 (2.9%)</td><td>31 (2.8%)</td><td>10 (3.0%)</td><td>0.01</td></tr><tr><td>Data missing</td><td>1</td><td>0</td><td>1</td><td>NA</td></tr><tr><td>Active smoking</td><td>330 (22%)</td><td>250 (23%)</td><td>68 (21%)</td><td>0.05</td></tr><tr><td>Data missing</td><td>9</td><td>3</td><td>4</td><td>NA</td></tr><tr><td>Diabetes mellitus</td><td>183 (12%)</td><td>119 (11%)</td><td>55 (17%)</td><td>0.17</td></tr><tr><td>Data missing</td><td>1</td><td>0</td><td>1</td><td>NA</td></tr><tr><td>History of malignancy</td><td>68 (4.5%)</td><td>51 (4.7%)</td><td>12 (3.6%)</td><td>0.05</td></tr><tr><td>Data missing</td><td>1</td><td>0</td><td>1</td><td>NA</td></tr><tr><td>Ventilatory parameters on admission</td><td></td><td></td><td></td><td></td></tr><tr><td>Mean PaO<sub>2</sub>/FiO<sub>2</sub> ratio</td><td>331.00 (150.00)</td><td>334.00 (157.00)</td><td>317.00 (130.00)</td><td>0.12</td></tr><tr><td>Data missing</td><td>36</td><td>13</td><td>3</td><td>NA</td></tr><tr><td>Mean respiratory rate</td><td>17.00 (4.00)</td><td>17.00 (4.00)</td><td>16.00 (3.00)</td><td>0.41</td></tr><tr><td>Data missing</td><td>24</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><td>Mean PaCO<sub>2</sub></td><td>38.00 (17.00)</td><td>39.00 (18.00)</td><td>37.00 (13.00)</td><td>0.11</td></tr><tr><td>Data missing</td><td>34</td><td>13</td><td>3</td><td>NA</td></tr><tr><td>Mean plateau pressure</td><td>16.50 (4.40)</td><td>16.30 (4.20)</td><td>16.80 (4.70)</td><td>0.11</td></tr><tr><td>Data missing</td><td>255</td><td>166</td><td>43</td><td>NA</td></tr><tr><td>Mean V<sub>T</sub></td><td>7.29 (1.22)</td><td>6.77 (0.71)</td><td>9.01 (1.00)</td><td>2.6</td></tr><tr><td>Data missing</td><td>85</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table>

Data are presented as No. (%) or mean (SD) unless otherwise indicated. ABI = acute brain injury; CNS = central nervous system; GCS = Glasgow Coma Scale; IQR = interquartile range; NA = not applicable; SMD = standardized mean difference; V<sub>T</sub> = tidal volume.

<footnote>ªExcludes 85 participants with missing V<sub>T</sub> data on day 1.</footnote>
complete data (e-Table 3). Mean $P_{plat}$ in the LTVV and control groups were, respectively, 16 cm H₂O (SD 4) and 17 cm H₂O (SD 5) on day 1; 17 cm H₂O (SD 4) and 16 cm H₂O (SD 5) on day 3; and 17 cm H₂O (SD 5) and 16 cm H₂O (SD 5) on day 7 (Fig 1, e-Fig 3). There was no difference between groups with respect to $P_{plat}$ PEEP, driving pressure, $PaCO_2$, and pH on day 1 (Fig 1). Stratified comparisons for these variables on days 3 and 7 are shown in e-Figure 3. Patients at risk of high ICP, as judged by the presence of an external ventricular drain or intraparenchymal probe, had similar $V_T$, $PaCO_2$, and pH compared with patients without a monitor (e-Fig 4).

By day 60, 122 patients (8.1%) died. A total of 1,185 (78.5%) were censored, of whom 137 (9.2%) developed ARDS during ICU admission and the remaining were discharged from the ICU before 60 days. In adjusted analyses, the cumulative incidence of mortality at 60 days in the pseudo-population of patients who received LTVV over the first 7 days of mechanical ventilation was 40.2% (95% CI, 19.2%-61.1%), compared with 59.7% (95% CI, 44.0%-75.4%) in patients who received $V_T$ > 8 mL/kg PBW (Fig 2). The marginal HR for mortality was 0.54 (95% CI, 0.33-0.88) (Fig 3). Results were consistent in subgroups of patients with GCS < 8, age > 65 years, and baseline $PaO_2/FiO_2$ > 300. For the remaining subgroups, point estimates were in a similar direction to the main analysis but with considerable imprecision as reflected by wide CIs (Fig 3).

Sensitivity analyses are presented in Table 2. Using the Super Learner and excluding patients with missing $V_T$ yielded similar results to the primary analysis. When LTVV was evaluated using lower thresholds for $V_T$, the association with 60-day mortality was indeterminate across all analyses, as reflected by wide CIs. In the analysis of a negative control outcome, LTVV was not associated with nosocomial VAP (HR, 1.14; 95% CI, 0.72-1.81).

## Discussion

In the present analysis, we found that LTVV with $V_T$ ≤ 8 mL/kg PBW over the first 7 days of mechanical ventilation was associated with lower ICU mortality in patients with ABI, compared with $V_T$ > 8 mL/kg PBW. There was no convincing heterogeneity of treatment effect in multiple subgroup analyses. When using $V_T$ thresholds below 8 mL/kg PBW to define LTVV, the association between ventilatory strategy and mortality was less clear. Taken together, these results cautiously

support a clinical strategy limiting exposure to high $V_T$ (> 8 mL/kg PBW) in patients with ABI, consistent with data from other critically ill populations.3,7,8

In our descriptive analysis, we found that exposure to $V_T$ > 8 mL/kg PBW remains common among patients with ABI. Of the patients in our cohort, 23% had $V_T$ above this threshold at ICU admission, which might reflect concerns about the effect of LTVV on ICP mediated by hypercarbia. Nevertheless, these concerns must be weighed against the risk of propagating VILI from high $V_T$ in a population that is already highly vulnerable to lung injury from sympathetic activation, proinflammatory cytokine release, and immune dysregulation caused by ABI.38,39 Current evidence in patients with ABI does not definitively answer the question of when, and in whom, LTVV should be used. As a result, ventilation strategies in this population remain highly variable.10,40 In a before-after study from 2 ICUs in France, implementation of a ventilatory bundle that incorporated use of $V_T$ at 6 to 8 mL/kg PBW was associated with a shorter duration of mechanical ventilation and more ICU-free days at day 90; however, findings could have been explained by other aspects of the bundle, such as early extubation.41 In 2 observational studies, high $V_T$ was associated with higher risk of developing ARDS, but results may have been susceptible to time-dependent confounding bias.9,42 In a consensus guideline, all 4 recommendations pertaining to lung-protective ventilation in patients with ABI were supported by no evidence, and good practice statements to inform recommendations relied on data from non-ABI populations.10

The Lung-Protective Mechanical Ventilation in Patients With Severe Acute Brain Injury (PROLABI) trial, published in 2024, found that a protective ventilation strategy (defined as $V_T$ = 6 mL/kg PBW and PEEP 4 cm H₂O) led to worse short-term outcomes (defined as a primary composite end point of death, ventilator dependence, or ARDS by day 28) and worse 6-month neurologic outcomes compared with conventional ventilation (defined as $V_T$ ≥ 8 mL/kg PBW and PEEP 4 cm H₂O).13 Caveats of this trial include underpowering, low interrater agreement for ARDS diagnosis, and lack of a clear physiologic rationale for the observed differences.14 In contrast, we found a significant association between LTVV and lower ICU mortality, which could reflect our use of a higher $V_T$ threshold (≤ 8 mL/kg PBW) to define LTVV. Findings from our study align with evidence from broader ICU populations3,8 and support best-practice recommendations for mechanical ventilation in ABI guidelines.10 Nevertheless, given
Figure 1 – Distribution of mechanical ventilation and gas exchange parameters on day 1 of mechanical ventilation. A-C, Histograms for tidal volume (in mL/kg PBW) in the overall cohort (A) and stratified by P/F ratio (B and C). The vertical blue line in each of the panels represents the median tidal volume. D-I, Boxplots for additional ventilatory parameters stratified by day 1 tidal volume (in mL/kg PBW). PBW = predicted body weight; PEEP = positive end-expiratory pressure; P/F = ratio of arterial partial pressure of oxygen to $FIO_2$; $V_T$ = tidal volume.

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /><figcaption>Figure 2 – Cumulative incidence of ICU mortality between patients treated with low-tidal-volume ventilation vs those who received tidal volume ventilation > 8 mL/kg predicted body weight. Survival probabilities were estimated for each imputed data set (n = 30) and averaged across data sets. Shaded areas represent 95% confidence intervals.</figcaption></figure>

conflicting results in the current body of evidence, additional high-quality trials are essential to determining the optimal $V_T$ strategy, while addressing potential risks related to hypercarbia and ICP and exploring effects in different ABI subgroups where clinical responses to LTVV may vary.

In sensitivity analyses, we found that associations between $V_T$ and ICU mortality were indeterminate at thresholds below 8 mL/kg PBW. Findings from these analyses cannot be used to support or refute a true protective effect of further reductions in $V_T$ on mortality. Conceptually, it is also possible that the

<figure><img src="image_14.png" /><figcaption>Figure 3 – Forest plot for the main analysis (top row) and subgroup analyses (additional rows). In each analysis, point estimates represent the hazard ratio for ICU mortality at 60 days in patients who received LTVV vs those who received tidal volume ventilation > 8 mL/kg predicted body weight. Horizontal lines indicate 95% CIs. GCS = Glasgow Coma Scale; LTVV = low-tidal-volume ventilation; PF = ratio of arterial partial pressure of oxygen to $FIO_2$.</figcaption></figure>
<table><caption>TABLE 2] Sensitivity Analyses</caption><thead><tr><th>Analysis</th><th>HR (95% CI)</th></tr></thead><tbody><tr><td>Super Learner (n = 1,510)</td><td>0.52 (0.27-1.00)</td></tr><tr><td>Excluding participants without tidal volumes at any time point (n = 1,448)</td><td>0.53 (0.33-0.87)</td></tr><tr><td>LTVV defined as ≤ 7.5 mL/kg PBW (n = 1,510)</td><td>0.73 (0.46-1.16)</td></tr><tr><td>LTVV defined as ≤ 7 mL/kg PBW (n = 1,510)</td><td>0.87 (0.54-1.41)</td></tr><tr><td>LTVV defined as ≤ 6.5 mL/kg PBW (n = 1,510)</td><td>0.73 (0.38-1.38)</td></tr><tr><td>LTVV defined as ≤ 6 mL/kg PBW (n = 1,510)</td><td>1.12 (0.39-3.19)</td></tr><tr><td>Nosocomial VAP (n = 1,510)</td><td>1.14 (0.72-1.81)</td></tr></tbody></table>

HR = hazard ratio; LTVV = low-tidal-volume ventilation; PBW = predicted body weight; VAP = ventilator-associated pneumonia.

increase in respiratory rate required to offset hypercarbia with $V_T$ reduction may increase mechanical power, which has been associated with increased mortality in critically ill patients, including those with ABI.¹⁰,⁴³ In this context, additional mediators of VILI beyond $V_T$ (including driving pressure and mechanical power) warrant further investigation as potential targets to improve clinical outcomes.

This study has important strengths. We demonstrate the application of a marginal structural model to evaluate LTVV in patients with ABI during a clinically relevant period of high acuity.²⁹,³⁰ Marginal structure models capture the complex interplay between dynamic treatments and confounders and may be especially useful in critical care research where treatments are frequently manipulated in response to changing illness characteristics.⁴⁴ Using traditional regression methods in this setting can lead to bias.²⁹,³⁰ We also included a novel Super Learner method to flexibly estimate the probability of receiving LTVV.³⁵ This data-adaptive approach to exposure assignment is robust to model misspecification. Finally, we used data from a large study of patients treated across 73 ICUs in 18 countries, enhancing the generalizability of our findings.

This study has limitations. First, our primary outcome reflects mortality in a pseudo-population in which all patients remained in the ICU up to 60 days and no patients developed ARDS. This restriction was necessary because the primary study did not record the day of ARDS onset or death after ICU discharge, and therefore did not support time-varying modeling of these events. While our approach limits generalizability to the

broader ABI population, it enabled valid estimation of the causal effect of sustained LTVV under well-defined and transparent assumptions. Second, we could not address confounding on a more granular timescale (eg, minutes or hours) because time-varying confounders were available only on days 1, 3, and 7 of mechanical ventilation. Third, unmeasured confounding is likely present because the parent study did not capture all variables clinicians would typically consider when manipulating $V_T$, such as ICP.¹⁹ In a descriptive analysis stratified by the presence or absence of an ICP monitor, we found that $V_T$ and other parameters of mechanical ventilation were similar between groups. We also adjusted for the use of Pentothal, which is typically used to treat high ICP. Nevertheless, time-varying ICP values were not available, and both the presence of an invasive monitor and Pentothal use are imperfect surrogates of ICP risk.⁴⁵ However, the association between LTVV and a negative control outcome (VAP) was not significant, suggesting that our findings are robust to residual confounding. Fourth, some subgroup and sensitivity analyses demonstrated wide CIs, likely resulting from small sample sizes or positivity violations as few patients remained adherent to their assigned strategy at all 3 time points. Fifth, we did not repeat subgroup analyses for each ABI condition because of small sample sizes in several groups. It is therefore possible that mortality within subgroups could differ from the overall cohort effect, potentially due to unique pathophysiologic responses that may influence the efficacy of LTVV by underlying diagnosis. Sixth, models were fitted for a single outcome (ICU mortality), and additional benefits of LTVV could exist for other end points, such as long-term neurologic outcomes.

## Interpretation

In this predominantly non-ARDS cohort of 1,510 patients with ABI, a strategy of LTVV during the first 7 days of invasive mechanical ventilation was associated with lower ICU mortality up to 60 days compared with ventilation with $V_T$ > 8 mL/kg PBW. Additional evidence from adequately powered randomized trials is necessary to establish whether sustained interventions to limit $V_T$ could reduce mortality in this population. Future studies should also explore effects on neurologic outcomes and safety end points including ICP.

## Funding/Support

J. F. D. is funded by research scholarships from the Canadian Institutes of Health Research (Vanier Canada
Graduate Scholarship) and the Department of Surgery at the University of Toronto. M. U. is supported by an IDCCM Scholar Award from the Interdepartmental Division of Critical Care Medicine, University of Toronto. S. W. receives support from the National Institutes of Health. S. T. is funded by the Eliot Phillipson Clinician-Scientist Training Program at the University of Toronto, the Canadian Institutes of Health Research (Doctoral Award), and an IDCCM Scholar Award from the Interdepartmental Division of Critical Care Medicine, University of Toronto.

## Financial/Nonfinancial Disclosures

The authors have reported to CHEST the following: C. R. received fees as speaker for Edwards and BT. R. D. S. received consulting fees from Ceribell Inc. and speaker honoraria from the University of Texas MD Anderson Cancer Center. R. C. received consulting fees from Viatris. S. T. received speaker fees from the Mechanical Ventilation Symposium, Interdepartmental Division of Critical Care Medicine, University of Toronto. None declared (J. F. D., D. M. H., M. U., K. L., S. W., and V. A. McC.).

## Acknowledgments

**Author contributions:** R. C. and S. T. are the study guarantors. J. F. D., D. M. H., and S. T. conceived the present substudy and developed the analysis plan. R. C. was the principal investigator of the original ENIO study. J. F. D. conducted the statistical analysis, and S. T. supervised the analysis. K. L. and M. U. provided methodologic input. J. F. D. and S. T. drafted the manuscript. J. F. D., D. M. H., M. U., K. L., S. W., C. R., R. D. S., V. A. McC., R. C., and S. T. critically revised the manuscript for intellectually important content.

**Availability of data:** The authors will consider reasonable access to statistical codes upon written request to the corresponding author. Enquiries will be reviewed on a case-by-case basis. Requests to access the primary data will require approval from the ENIO study committee.

**Role of sponsors:** The sponsors had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

**Other contributions:** The authors thank the ENIO study contributors for permission to use the data in the present analysis.

**Additional information:** The e-Appendixes, e-Figures, and e-Tables are available online under "Supplementary Data."

## References

1. Qadir N, Sahetya S, Munshi L, et al. An update on management of adult patients with acute respiratory distress syndrome: an official American Thoracic Society clinical practice guideline. *Am J Respir Crit Care Med.* 2024;209(1):24-36.

2. Grasselli G, Calfee CS, Camporota L, et al. ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies. *Intensive Care Med.* 2023;49(7): 727-759.

3. Brower RG, Matthay MA, Morris A, Schoenfeld D, Thompson BT, Wheeler A. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. *N Engl J Med.* 2000;342(18):1301-1308.

4. Briel M, Meade M, Mercat A, et al. Higher vs lower positive end-expiratory pressure in patients with acute lung injury and acute respiratory distress syndrome: systematic review and meta-analysis. *JAMA.* 2010;303(9):865-873.

5. Sud S, Friedrich JO, Adhikari NKJ, et al. Comparative effectiveness of protective ventilation strategies for moderate and severe acute respiratory distress syndrome: a network meta-analysis. *Am J Respir Crit Care Med.* 2021;203(11): 1366-1377.

6. Mascia L, Pasero D, Slutsky AS, et al. Effect of a lung protective strategy for organ donors on eligibility and availability of lungs for transplantation: a randomized controlled trial. *JAMA.* 2010;304(23): 2620-2627.

7. Futier E, Constantin JM, Paugam-Burtz C, et al. A trial of intraoperative low-tidal-volume ventilation in abdominal surgery. *N Engl J Med.* 2013;369(5):428-437.

8. Serpa Neto A, Cardoso SO, Manetta JA, et al. Association between use of lung-protective ventilation with lower tidal volumes and clinical outcomes among patients without acute respiratory distress syndrome: a meta-analysis. *JAMA.* 2012;308(16):1651-1659.

9. Mascia L, Zavala E, Bosma K, et al. High tidal volume is associated with the development of acute lung injury after severe brain injury: an international observational study. *Crit Care Med.* 2007;35(8):1815-1820.

10. Robba C, Poole D, McNett M, et al. Mechanical ventilation in patients with acute brain injury: recommendations of the European Society of Intensive Care Medicine consensus. *Intensive Care Med.* 2020;46(12):2397-2410.

11. Guérin C, Reignier J, Richard J-C, et al. Prone positioning in severe acute respiratory distress syndrome. *N Engl J Med.* 2013;368(23):2159-2168.

12. Cavalcanti AB, Suzumura ÉA, Laranjeira LN, et al. Effect of lung recruitment and titrated positive end-expiratory pressure (PEEP) vs low PEEP on mortality in patients with acute respiratory distress syndrome: a randomized clinical trial. *JAMA.* 2017;318(14):1335-1345.

13. Mascia L, Fanelli V, Mistretta A, et al. Lung-protective mechanical ventilation in patients with severe acute brain injury: a multicenter, randomized clinical trial (PROLABI). *Am J Respir Crit Care Med.* 2024;210(9):1123-1131.

14. Taran S, Stevens RD. Does lung protective ventilation work in acute brain injury? *Am J Respir Crit Care Med.* 2024;210(9): 1073-1075.

15. Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. *Math Model.* 1986;7(9-12): 1393-1512.

16. Naimi AI, Cole SR, Kennedy EH. An introduction to g methods. *Int J Epidemiol.* 2017;46(2):756-762.

17. Beitler JR, Ghafouri TB, Jinadasa SP, et al. Favorable neurocognitive outcome with low tidal volume ventilation after cardiac arrest. *Am J Respir Crit Care Med.* 2017;195(9):1198-1206.

18. Urner M, Jüni P, Hansen B, Wettstein MS, Ferguson ND, Fan E. Time-varying intensity of mechanical ventilation and mortality in patients with acute respiratory failure: a registry-based, prospective cohort study. *Lancet Respir Med.* 2020;8(9):905-913.

19. Cinotti R, Mijangos JC, Pelosi P, et al. Extubation in neurocritical care patients: the ENIO international prospective study. *Intensive Care Med.* 2022;48(11): 1539-1550.

20. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. *Lancet.* 2007;370(9596): 1453-1457.

21. Fan E, Del Sorbo L, Goligher EC, et al. An official American Thoracic Society/ European Society of Intensive Care Medicine/Society of Critical Care Medicine clinical practice guideline: mechanical ventilation in adult patients
with acute respiratory distress syndrome.
*Am J Respir Crit Care Med.* 2017;195(9):
1253-1263.

22. Picetti E, Pelosi P, Taccone FS, Citerio G, Mancebo J, Robba C. VENTILatOry strategies in patients with severe traumatic brain injury: the VENTILO Survey of the European Society of Intensive Care Medicine (ESICM). *Crit Care.* 2020;24(1): 158.

23. Peñuelas O, Muriel A, Abraira V, et al. Inter-country variability over time in the mortality of mechanically ventilated patients. *Intensive Care Med.* 2020;46(3): 444-453.

24. Beqiri E, Smielewski P, Guérin C, et al. Neurological and respiratory effects of lung protective ventilation in acute brain injury patients without lung injury: brain vent, a single centre randomized interventional study. *Crit Care.* 2023;27(1):115.

25. Turgeon AF, Lauzier F, Simard J-F, et al. Mortality associated with withdrawal of life-sustaining therapy for patients with severe traumatic brain injury: a Canadian multicentre cohort study. *Can Med Assoc J.* 2011;183(14):1581-1588.

26. Taran S, Liu K, McCredie VA, et al. Decisions to withdraw or withhold life-sustaining therapies in patients with and without acute brain injury: a secondary analysis of two prospective cohort studies. *Lancet Respir Med.* 2025;13(4):338-347.

27. Fan TH, Huang M, Gedansky A, et al. Prevalence and outcome of acute respiratory distress syndrome in traumatic brain injury: a systematic review and meta-analysis. *Lung.* 2021;199(6):603-610.

28. Fan TH, Huang M, Price C, et al. Prevalence and outcomes of acute respiratory distress syndrome in patients

with aneurysmal subarachnoid
hemorrhage: a systematic review and
meta-analysis. *J Neurocrit Care.*
2022;15(1):12-20.

29. Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. *Epidemiology.* 2000;11(5):561-570.

30. Robins JM, Hernán MÁ, Brumback B. Marginal structural models and causal inference in epidemiology. *Epidemiology.* 2000;11(5):550-560.

31. Howe CJ, Cole SR, Lau B, Napravnik S, Eron JJ Jr. Selection bias due to loss to follow up in cohort studies. *Epidemiology.* 2016;27(1):91-97.

32. Xiao Y, Abrahamowicz M, Moodie EE. Accuracy of conventional and marginal structural Cox model estimators: a simulation study. *Int J Biostat.* 2010;6(2). Article 13.

33. Hernán MA. How to estimate the effect of treatment duration on survival outcomes using observational data. *BMJ.* 2018;360: k182.

34. Zhang Z. Multiple imputation with multivariate imputation by chained equation (MICÈ) package. *Ann Transl Med.* 2016;4(2):30.

35. van der Laan MJ, Polley EC, Hubbard AE. Super learner. *Stat Appl Genet Mol Biol.* 2007;6:Article25.

36. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. *Epidemiology.* 2010;21(3):383-388.

37. Hernán MA. Causal analyses of existing databases: no power calculations required. *J Clin Epidemiol.* 2022;144: 203-205.

38. Pelosi P, Rocco PR. The lung and the brain: a dangerous cross-talk. *Crit Care.* 2011;15(3):168.

39. Mrozek S, Constantin JM, Geeraerts T. Brain-lung crosstalk: implications for neurocritical care patients. *World J Crit Care Med.* 2015;4(3):163-178.

40. Asehnoune K, Rooze P, Robba C, et al. Mechanical ventilation in patients with acute brain injury: a systematic review with meta-analysis. *Crit Care.* 2023;27(1): 221.

41. Asehnoune K, Mrozek S, Perrigault PF, et al. A multi-faceted strategy to reduce ventilation-associated mortality in brain-injured patients: the BI-VILI project: a nationwide quality improvement project. *Intensive Care Med.* 2017;43(7):957-970.

42. Elmer J, Hou P, Wilcox SR, et al. Acute respiratory distress syndrome after spontaneous intracerebral hemorrhage. *Crit Care Med.* 2013;41(8):1992-2001.

43. Wahlster S, Sharma M, Taran S, et al. Utilization of mechanical power and associations with clinical outcomes in brain injured patients: a secondary analysis of the extubation strategies in neuro-intensive care unit patients and associations with outcome (ENIO) trial. *Crit Care.* 2023;27(1):156.

44. Bekaert M, Timsit JF, Vansteelandt S, et al. Attributable mortality of ventilator-associated pneumonia: a reappraisal using causal analysis. *Am J Respir Crit Care Med.* 2011;184(10):1133-1139.

45. Robba C, Graziano F, Rebora P, et al. Intracranial pressure monitoring in patients with acute brain injury in the intensive care unit (SYNAPSE-ICU): an international, prospective observational cohort study. *Lancet Neurol.* 2021;20(7): 548-558.