Assessing the Effectiveness of Low Tidal Volume and Low Driving Pressure in Mechanically Ventilated Severe Hypoxemic Patients: a Multicenter Emulated Target Trial
Conditions: Hypoxemia, Ards
Sponsor: Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva

Study Protocol and Statistical Analysis Plan
avengARDS
Assessing the Effectiveness of Low Tidal Volume and Low Driving Pressure 
in Mechanically Ventilated Severe Hypoxemic Patients: a Multicenter 
Emulated Target Trial
NCT number: NCT06513299
Last edited on 23th December 2024

1. Background
Acute Respiratory Distress Syndrome (ARDS) is a life-threatening syndrome characterized by
severe hypoxemia, opacities on chest imaging, increased permeability, and histological damage due
to  lung  inflammation,  (Berlin  PMID:  22797452;  Matthay  PMID: 37487152).   Mechanical
ventilation is needed to provide oxygen, to maintain adequate ventilation, and to reduce the work of
breathing (Thompson PMID: 28792873).  Early ventilatory strategies focused on high tidal volumes
(VT) and airway pressures to “normalize” arterial blood gases (Pontoppidan  PMID: 14328102;
Kumar PMID: 4921310).  
Later, experimental (Trambley  PMID: 9062352), and clinical (Ranieri PMID: 10404912) studies
demonstrated  that  such  ventilatory  approaches  worsened  inflammation,  permeability,  and
histological  damage  (Ventilator-Induced  Lung  Injury:  VILI)  (Slutsky  PMID: 24283226).   A
multicenter randomized clinical trial (RCT) demonstrated that the use of a “lower” VT (6 mL/kg of
predicted body weight) compared to a traditional higher VT (12 ml/kg of predicted body weight)
significantly reduced mortality from 40 %, to 31% (ARDSnet PMID: 10793162). Although, the use
of 6 ml/kg was not shown to be superior to anything in between 11 and 6 ml/kg (Tobin PMID:
34186011), and the VT of the control group (12 ml/kg) has been suggested to be higher than routine
treatment and potentially harmful (Eichacker  PMID: 12406836), the VT of 6 mL/kg has been
proposed as the standard of care (Fan PMID: 28459336).  
However, a recent guideline concluded that the use of “lower” VT (i.e., 4–8 ml/kg) is not supported
by statistical significance (Grasselli PMID: 37326646).  Moreover, it has been suggested that
setting VT in terms of “milliliters per kilogram of predicted body weight” to normalize VT to lung
size may be misleading (Amato PMID: 25693014; Goligher PMID: 33439781) since it ignores that
in patients with ARDS, the proportion of lung available for ventilation is markedly decreased as
reflected by lower compliance of the respiratory system [(CRS =  VT / end-inspiratory plateau
pressure (PPLAT) - positive end-expiratory pressure (PEEP)] (Gattinoni: PMID: 15812622; Terragni:
PMID: 17038660).  
In order to scale VT to CRS, Amato and coworkers calculated driving pressure (ΔP = VT/CRS) and,
analyzing 3562 patients previously enrolled in RCTs, showed that the favorable effects of randomly
assigned reductions in VT depended only on their association with a decrease in ∆P (Amato PMID:
25693014).  Goligher and coworkers performed a secondary analysis of 1202 patients included in
previously performed RCTs and confirmed Amato’s observation suggesting that lung protective
ventilation strategies should primarily target ∆P rather than VT (Goligher PMID: 33439781).  
Randomized controlled trials (RCTs) are the gold standard for causal inference (Bhatt PMID:
21829774).  However, syndromes such as ARDS pose challenges for RCTs (Legrand  PMID:
30303091;  Laffey PMID: 30061048; Tonelli  PMID: 24667919).  Use of observational data to
simulate RCT is a recognized method for assessing the effectiveness of a treatment within a real-
world, uncontrolled context (Hernan PMID: 34596980; Hernan PMID: 36508210) that may be
especially valuable when conducting RCTs is challenging (Dickerman PMID: 31591592; Wang
PMID: 37097356).  

2. Objectives
The present study set up to answer two questions: (a) “how low VT must be to provide protective
ventilatory settings able to minimize the risk of death due to VILI?”; (b) “if protection from VILI is
better achieved by targeting ∆P instead of VT, what is the optimal target for ∆P?”.  Solving these
questions is pivotal for clinicians to personalized and precise mechanical ventilation practices to
reduce the risk of VILI and avoid unnecessary risks associated to protective ventilatory settings
(Spece PMID: 29073535; Mikkelsen PMID: 18364057; Pohlman PMID: 18824913).  Data will be
used to obtain the dose-response curve of lower VT and lower ∆P in mechanically ventilated patients
with acute severe hypoxemia.  
In a first emulated  target  trial,  we assess ICU  mortality  in patients  undergoing a protective
ventilatory approach, targeting various lower VT.  In a subsequent emulated target trial, we assessed
ICU mortality in patients subjected to a protective ventilatory strategy that targeted varying levels
of  lower  ∆P.  Design and analysis of the  emulated target trials were made in order to be as
structured,  transparent,  and  reproducible  as  possible.  Comparative  outcome  analyses  were
conducted after the final emulated target trials protocol have been fully specified and registered on
ClinicalTrials.gov. 

3. Study design
Multicentric observational retrospective study.
The study is made of two emulated target trials. Both "lower VT” and "lower ∆P” target trials were
designed following the study design of previously conducted randomized clinical trials in the field
of mechanical ventilation (ARDSnet PMID: 10793162; Brower: PMID: 15269312) and ARDS
(DOI:  10.1164/rccm.201012-2090OC;  DOI:  10.1056/NEJMoa1901686;  DOI:  10.1056/NEJ-
Moa1401520).
We used all consecutive adult patients diagnosed with hypoxemic respiratory failure included in the
multicenter database MargheritaTre (M3) of the Italian Group for the Evaluation of Interventions in
Intensive Care Medicine (GiViTI) (Finazzi PMID: 29972378).
3.1 Ethics
The MargheritaTre projecect was approved by the Ethics Review Board of the coordinating centre
(Ospedale Maggiore, Bologna) and of all the participating centres. Informed consent was collected
according to national and european regulation.
3.2 Participating centers
GiViTI is a network of about 200 Italian ICUs born in 1991, with tha aim of improving the quality
of care and optimize resource utilization through benchmarking activities and research projects. The
group is coordinated by the Laboratory of Clinical Data Science of the Istituto di Ricerche
Farmacologiche  Mario  Negri  IRCCS  (IRFMN)  and  collaborates  with  regional,  national  and
international universities and institutions.
In collaboration with intensivists, critical care nurses, computer scientists and data scientists,
GiViTI developed the MargheritaTre electronic health record. The EHR serves multiple purposes:
integrating medical practice, assessing the quality of care, conducting critical research projects and
physio-pathological studies.  MargheritaTre is currently in use in more than Italian 80 ICUs, of
which 50 participated in the associated research project, receiving Ethics approval and sending data
to the GiViTI coordinating centre.

4. Data collection
4.1 Software architecture
M3 has a client/server architecture, with one server per ICU, installed in the internal network of
each hospital. Clients are typically installed on bedside computers and at control desks. They have a
modular graphical interface that allows doctors and nurses to input and visualize data for diagnosis,
therapies, laboratory tests, etc. Automatic services import data from monitors, devices, and from the
hospital information system and save them in the database through the server. To ensure proper data
protection, clinical data patients’ direct identifiers, such as name, surname, birth date, social
security number are stored in separated databases.
From each server, only the database containing clinical data is sent in a pseudonymized form to a
dedicated server of IRFMN. The database with personal direct identifiers is not transferred. Thus,
this information is not accessible for the study by software design. In the IRFMN database, each
patient’s record has a sequential identification code that is unique to each admission and not derived
from direct identifiers.
In accordance with the European regulation 2016/679 for the protection of data (GDPR), the data
controller for the M3 research project is IRFMN, which is responsible for management and
analyses. 
4.2 Database description
The M3 database at IRFMN contains records of more than 140,000 ICU admissions from 50 ICUs.
Data are stored in a relational PostgresSQL database containing more than 150 tables. To ease data
analysis, information is organized in about 20 views, each corresponding to a single aspect of
patients’ clinical management (e.g., vital signs, laboratory tests, drug administration, nutrition,
etc...). 
The database contains about  108 clinical notes, 108 values of vital signs, 107 laboratory tests, 107
drug administrations. A complete description of the size of each view is reported in Table 1 of
Finazzi PMID: 29972378).
4.3 Data quality
The M3 software architecture and the development procedures are designed to ensure quality and
uniformity of data. 
M3 does not allow for ICU-specific customization of the database structure and of the collected
variables. Any modification to the software must be evaluated by the M3 study group and by the
coordinating center before implementation. Approved changes are then released to all ICUs.
Most information is stored by M3 in structured form (see Table 1 in Finazzi PMID: 29972378):
vital signs from ICU monitors, results of blood gas analyses and laboratory tests, pharmacological

therapies, procedures and treatments, nursing activities, infections, organ failures, injuries. To
facilitate data analysis, clinical notes, anamnesis, and epicrisis are a partially structured: notes can
be composes by selecting tags from a list of about 500 keywords and integrated with free-text.
To prevent input errors, most information is automatically imported from monitors, devices, and
laboratory informative systems. All data are validated by either doctors or nurses to minimize the
presence of artifacts and ensure that only clinically relevant information is stored in the database. 

5. Study population
Target Trial
Inclusion criteria:  (a) age ≥18 years; (b) invasive mechanical ventilation for ≤96 hours; (c)
presence of all the followings for ≤ 24 hours: (i) severe hypoxemia as identified by arterial oxygen
tension (PaO2) to inspiratory oxygen fraction (FiO2) ratio (P/F) ≤ 300 mmHg; (ii) hypoxemia
developed within one week of a known clinical insult and not fully explained by cardiac failure or
fluid  overload  (Berlin  PMID:  22797452;  Matthay  PMID: 37487152).   Exclusion  criteria:
pregnancy;  New York Heart Association  Class IV;  acute coronary syndrome; severe COPD;
chronic respiratory insufficiency with home ventilation or oxygen therapy; chronic liver disease;
patients were receiving ECMO therapy; acute brain injury; patients were moribund and/or clinician
decided to limit therapeutic interventions; poorly controlled neoplasm;  patients transferred from
other ICUs; patients transferred to the ICU from the Emergency Department after a duration
exceeding 24 hours in the Emergency Department.  The latter criteria ensure that the patient has not
been ventilated for more that 96 hours at randomization.
Emulated Target Trial
Inclusion  and  exclusion  criteria  are  the  same  as  in  the  target  trial  (see  Table  1).  Their
implementation using specific variable definitions of M3 database are detailed in S2.

6. Interventions
Target Trial
Patients  will  be  ventilated  with  assist/control  modes  of  mechanical  ventilation  (constant
flow/constant pressure) until weaning criteria are met:  P/F ratio > 250 mmHg; PEEP ≤ 8 cmH2O
and lower than the previous day; FiO2 < 0.5 and lower than the previous day; systolic arterial
pressure ≥ 85 mmHg (ARDSnet PMID: 10793162; Brower: PMID: 15269312).
In the lower VT target trial, patients would be randomly assigned to one of these two groups:  

VT1: 6.0 ml/kg PBW ≤ VT ≤ 8.0 ml/kg PBW with PPLAT ≤ 30 cmH2O; 

VT2: 8.0 ml/kg PBW < VT  ≤ 10.0 ml/kg PBW with PPLAT ≤ 30 cmH2O.
The value of VT inside the range fixed by the intervention arm and positive end-expiratory pressure
(PEEP) will be set according to clinical judgement.
In the lower ∆P target trial patients would be randomly assigned to one of these two ∆P groups: 

∆P1: 7.0 cmH2O ≤ ∆P ≤ 12.0 cmH2O with VT ≤ 10.0 ml/kg PBW; 

∆P2: 12.0 cmH2O < ∆P ≤ 18.0 cmH2O with VT ≤ 10.0 ml/kg PBW.
The value of ∆P inside the range fixed by the intervention arm and positive end-expiratory pressure
(PEEP) will be set according to clinical judgement.
The determination of breakpoints for defining the treatment arms was guided by analyzing the
distributions of VT and ∆P from the recorded observed data in MargheritaTre. For both treatment
strategies, the 10th, 50th, and 90th percentiles of these distributions were rounded to the nearest
integers. The breakpoints for VT treatments were defined as follows: the 10th percentile (5.94 ml/kg)
was rounded to 6 ml/kg, the 50th percentile (7.54 ml/kg) was rounded to 8 ml/kg, and the 90th
percentile (9.99 ml/kg) was rounded to 10 ml/kg. Similarly, for ΔP the 10th percentile (7.45
cmH2O) was rounded to 7 cmH2O, the 50th percentile (12.17 cmH2O) was rounded to 12 cmH2O,
and the 90th percentile (18.29 cmH2O) was rounded to 18 cmH2O.  The rounded percentiles were
hence used to define the ranges for the comparative effectiveness study.
Predicted body weight (PBW) will be calculated according to the following formulae:  PBW
(Males) = 50 + 0.91 [height (cm) -152.4 ].  Compliance of the respiratory system will be calculated
as (CRS = VT / PPLAT - PEEP) (Gattinoni: PMID: 15812622; Terragni: PMID: 17038660). Driving
pressure will be calculated as (ΔP = VT/CRS). 

Emulated Target Trial
To simulate patients’ trajectories in the emulated lower VT target trial, the value of VT will be set
according to the following algorithm:

VT1
◦if the natural value of VT  <  6.0 ml/kg PBW, then the intervention value will be set to 6.0
ml/kg PBW;
◦if 6.0 ml/kg PBW ≤ the natural value of VT ≤ 8.0 ml/kg PBW, then the intervention value
will be set equal to the natural value;
◦if the natural value of VT  > 6.0 ml/kg PBW, then the intervention value will be modified
set to 8.0 ml/kg PBW.

VT2
◦if the natural value of VT  ≤  8.0 ml/kg PBW, then the simulated value will be set to 8.0
ml/kg PBW;
◦if 8.0 ml/kg PBW < the natural value of VT  < 10.0 ml/kg PBW, then the simulated value
will be set equal to the natural value;
◦if the natural value of VT  ≥ 10.0 ml/kg PBW, then the simulated value will be set to 10.0
ml/kg PBW.
In each intervention arm of the target trial, the intervention is sustained until weaning criteria are
met. For instance, if in the lower VT  trial a patient is assigned to Arm VT1 (6.0 ≤ VT ≤ 8.0 ml/kg
PBW, with  PPLAT ≤ 30 cmH2O), the protocol prescribes that any value of  VT should be set
according to clinical judgement inside the corresponding range. When weaning criteria are met,
extubation may be attempted, it may either fail or not, or any other mechanical ventilation regime
may be chosen, according to clinical practice.
To simulate this complex dynamic scenario, in the simulated trial we implement a dynamic
intervention, where the categorical treatment variable VT is sustained to the level corresponding to
6.0 ≤ VT ≤ 8.0 ml/kg, as above described, until weaning criteria are met. After the latter condition is
satisfied, the treatment variable is allowed to follow its natural value,  any other type of mechanical
ventilation, or no mechanical ventilation. Accordingly, we introduce a binary variable tracing when
the patient is intubated or not.
To simulate patients’ trajectories in the emulated lower ∆P target trial, the value of ∆P will be set
according to the following algorithm:

∆P1
◦if the natural value of ∆P  < 7.0 cmH2O, then the intervention value will be set to 7.0
cmH2O;
◦if 7.0 cmH2O ≤ the natural value of ∆P ≤ 12.0 cmH2O, then the intervention value will
be set equal to the natural value;
◦if the natural value of ∆P  > 12.0 cmH2O, then the intervention value will be modified set
to 12.0 cmH2O.

∆P2
◦if the natural value of ∆P  < 12.0 cmH2O, then the intervention value will be set to 12.0
cmH2O;
◦if 12.0 cmH2O ≤ the natural value of ∆P ≤ 18.0 cmH2O, then the intervention value will
be set equal to the natural value;

◦if the natural value of ∆P  > 18.0 cmH2O, then the intervention value will be modified set
to 18.0 cmH2O.
In each intervention arm of the target trial, the intervention is sustained until weaning criteria are
met. For instance, if in the lower ∆P  trial a patient is assigned to Arm ∆P1 (7.0 ≤ ∆P1 ≤ 12.0
cmH2O, with  VT ≤ 10  ml/kg PBW), the protocol prescribes that any value of ∆P  should be set
according to clinical judgement inside the corresponding range. When weaning criteria are met,
extubation may be attempted, it may either fail or not, or any other mechanical ventilation regime
may be chosen, according to clinical practice.
To simulate this complex dynamic scenario, in the simulated trial we implement a dynamic
intervention, where the categorical treatment variable ∆P is sustained to the level corresponding to
7.0 ≤ ∆P ≤ 12.0 cmH2O, as above described, until weaning criteria are met. After the latter
condition is satisfied, the treatment variable is allowed to follow its natural value,  any other type of
mechanical ventilation, or no mechanical ventilation. Accordingly, we introduce a binary variable
tracing when the patient is intubated or not.

7. Outcomes
Target Trial
Primary endpoint: ICU all-cause mortality.  All patients will be classified as either alive if “alive at
ICU discharge” or dead if “dead at ICU discharge”.
Secondary endpoint: Number of ventilator-free days (VFDs) during the 14 days in ICU immediately
after  randomization. Number  of  days  of  unassisted  breathing  to  ICU  discharge  day  after
randomization, assuming a patient survives for at least two consecutive calendar days after initiating
unassisted breathing and remains free of assisted breathing.  
If a patient returns to assisted breathing and subsequently achieves unassisted breathing prior to
ICU discharge day, VFDs will be counted from the end of the last period of assisted breathing to
ICU discharge day unless a period of assisted breathing was less than 24 hours, and the purpose of
assisted breathing was a surgical procedure.  If a patient dies prior to end of follow-up or is still
receiving assisted breathing at the end of follow-up, his/her VFDs will be zero.  
Unassisted breathing is defined as any of the following: spontaneously breathing with face mask,
nasal prong oxygen, or room air; t-tube breathing;  tracheostomy  mask breathing;  continuous
positive airway pressure (CPAP) ≤ 5 cmH2O without pressure support (PS) or invasive mechanical
ventilation (IMV) assistance; use of CPAP or bilevel positive airway pressure (BIPAP) solely for
sleep apnea management.
Emulated Target Trial
The primary endpoint is defined as in the target trial. To compute the secondary end-point,
ventilator free days are computed from the dataset of the simulated trajectories. Follow-up time is
14 days after randomization. The follow-up time is limited to 14 days to guarantee that a non-
negligible fraction of patients is not dead or discharged from ICU before the end of follow-up.

8. Statistical methods
8.1 Statistical analyses
Target Trial
Primary analysis. Intention-to-treat analysis would be conducted comparing primary and secondary
outcome variables among the different VT and ∆P groups using the chi-squared and Mann-Whitney
test for mortality and VFD, respectively.  
In per-protocol analysis  patients  would be censored when they deviated  from their assigned
strategy. The per-protocol effect would be estimated after adjustment for baseline variables and for
time-varying variables associated with adherence to a ventilation strategy according the treatment
arms.  Mortality will be adjusted using a multivariate logistic regression model including variables
the following variables: age, risk of death according to the SAPS score at admission, arterial pH and
P/F ratio at study entry (Amato PMID: 25693014).  
Secondary analysis. Two secondary analysis will be conducted to (1) determine a dose-response
relationship, relating the magnitude of a dose of the intervention (the VT and ∆P level) to the clinical
response (the primary and secondary endpoints described above), and (2) compare which is the best
policy, between fixing VT versus fixing ∆P. Best policy is defined as the one with best treatment
effect among those used in intervention arms. 
Analysis of dose–response relationships  will be investigated using marginal structural models
(MSM) with inverse probability of treatment weighting (IPTW) (Lipkovich:  PMID:  18179713;
Lipkovich: PMID: 23060290) and with dynamic linear mixed-effects models (DLME) (Xu Steven
Xu: PMID: 22407972).  
Sensitivity analyses.  The robustness of the result will be assessed though sensitivity analyses as
listed in Table 1.
Emulated Target Trial
Primary analysis.  The effect size of interventions on ICU mortality will be evaluated by  g-
estimation methods (McGrath: PMID: 32656541), which allow to estimate the risk of the outcome
as if everybody in the population received the same intervention. Replicating the analysis for each
intervention  arm  and  for  the  natural  course,  one  can  obtain  the  counterfactual  outcome
corresponding to each intervention, evaluated on the same population.
G-estimation methods require the evaluation of complex multidimensional integrals, that can be
computed through Montecarlo techniques. For each intervention  arm, starting from the same
baseline conditions, we shall simulate patients histories and evaluate the risk of the outcome for
each history. Finally, the average risk for each arm is computed by averaging this results over all
the histories.
Patients histories are simulated by sampling covariates’ values, treatments, and outcomes at every
follow-up time from proper joint statistical distributions, conditioned to past values of covariates

and treatments. We shall estimate those conditional distributions through parametric regression
analysis (e.g., generalized linear models).
The first step of the simulation is the initializations of baseline conditions for the simulated
trajectories. This step is common to all interventions arms, in order to evaluate effect sizes of every
intervention on the same population. For each simulated trajectory we randomly extract one patient
from the observed dataset and baseline values of covariates are set to the baseline values of this
observed patient.
Second, for each intervention arm, trajectories are simulated starting from these initial conditions
with an iterative algorithm, starting from the first time after baseline, t1, and iterating over times up
to the last follow-up time. The values of patients’ covariates and treatments at each time t = ti are
sampled from joint statistical distributions conditioned to the values assumed by those variables at
times t < ti. 
At each time step of the simulation it is possible to modify the value the treatment would have
naturally assumed under clinical practice, replacing it with the value prescribed by the protocol of
the clinical trial for the considered intervention arm. Using the simulated values of the covariates at
t = ti and the possibly modified value of the treatment, one can compute the risk to the outcome at t
= ti+1 and simulate the outcome, by sampling from a Bernoulli distribution. If the simulated outcome
is dead the trajectory is ended, otherwise it will be prolonged, repeating the same procedure for  t =
ti+1.
We shall compute 95% confidence intervals by boostrap analysis with at least 500 samples.
The  effect  on  the  secondary  endpoint  will  be  computed from  the  datasets  of  the  simulated
trajectories for the four interventions.
Secondary analyses.  Dose response curves will be constructed using g-estimation methods for
continuous treatments. For a given VT or ∆P, we define a dynamic intervention by setting either the
treatment  VT or ∆P to the intervention value until weaning criteria are satisfied. After that the
natural course is followed.
The optimal strategy between fixing  VT or ∆P will be identified by comparing the average effect
sizes evaluated for the primary analysis in the two emulated target trials.
Sensitivity analyses. The same sensitivity analyses listed for the target trial will be performed in the
emulated trial. In addition, the same study design (inclusion and exclusion criteria) and data
analysis applied to the Toronto data set (Urner: PMID: 36971437).
8.2 Sample size
Target Trial
Lower VT target trial.
A previous randomized clinical trial showed an approximately 10% drop of mortality rate reducing
VT from 12 ml/kg PBW to 6 ml/kg PBW (from  40% to 30%, respectively) (ARDSnet PMID:
10793162).  Hager and coworkers showed a linear relationship between PPLAT and outcome (Hager:
PMID: 16081547). Assuming the relationship between VT and PPLAT is linear we estimated a 2%

change in mortality rate for every ml/kg PBW change of VT.  Assuming the mortality of the 6.0-8.0
ml/kg PBW group to be around 30% (ARDSnet PMID: 10793162), we expected an increase in
mortality to 34% with a VT > 8 ml/kg PBW.
Using chi-squared test on the contingency table, a sample size of about 4300 patients would be
required to detect at least one difference among the three groups with power of 80% and and type-I
error of 5% (Cohen, Statistical Power Analysis for the Behavioral Sciences). 
Lower ∆P target trial.
A pilot randomized clinical trial showed an approximately 16% reduction of mortality rate reducing
∆P from  15  cmH2O  to  10  cmH2O  (from  53%  to  37%,  respectively)  (Pereira  Romano:
PMID: 32069068).  Since Amato and coworkers showed that a change of slope in the relationship
between ∆P and risk of death occurs at a value of ∆P of approximately 15 cmH2O (Amato PMID:
25693014), we estimated a 3% change in mortality rate for every cmH2O change of ∆P.  Assuming
the mortality of the > 12 cmH2O group to be around 53%, we expected a reduction in mortality rate
to 44%, with a 7 cmH2O < ∆P ≤ 12 cmH2O.  
Using chi-squared test on the contingency table, a sample size of about 1000 patients would be
required to detect at least one difference among the three groups with power of 80% and error I type
of 5%. 
Emulated Target Trial
We expect that to properly estimate effect size in either lower VT or lower ∆P emulated trials, the
sample size should be the same order of magnitude as the corresponding randomized  trials.
According to these considerations, 4300 patients, i.e., 2150 patients per arm, would be sufficient to
detect possible differences in the primary outcome.
While in a real randomized controlled trial each patient undergoes a single intervention, in the
emulated trial the counterfactual outcomes corresponding to all interventions are simulated for
every patient.
However, this is possible only if the observed dataset  contains enough information to explore the
full space of treatments considered in the intervention arms, as required by the summation over
histories in the g-computation, that is if there is enough variability in the treatments in the observed
datasets.
For  these  considerations,  it  is  not  possible  to  precisely  estimate  the  required  sample  size.
Nevertheless we may argue that, if in the observed dataset, the numbers of patients receiving
treatment in the range of VT1 and VT2 (∆P1 and ∆P2, respectively) are comparable the required
order of magnitude is about 4300 patients.
8.3 Feasibility analysis of the emulated target trial

Feasibility analyses of the lower VT and of the lower ∆P emulated trials were performed (a) drawing
CONSORT diagram table for cohort inclusion and exclusion criteria; (b) tabulating follow-up times
for each treatment group (c) tabulating patient characteristics by treatment group, including balance
metrics; (d) tabulating the number of overall outcome events (with no exposure-specific event
counts or rates).  The initial power analysis was conducted after preliminary 1:1 high dimensional
propensity score matching (Schneeweiss: PMID: 19487948) based on age, risk of death according
to the SAPS score at admission, arterial pH and P/F ratio at study entry (Amato PMID: 25693014).
8.4 Statistical tools
All the analyses will be performed with R software. g-estimation method will be implemented using
the package gfoRmula.

Table 1. Summary of protocols of target trial and emulated target trial.
TARGET TRIAL
TARGET TRIAL EMULATION
Inclusion Criteria
ALL THE FOLLOWINGS:
(a) age ≥ 18 years.
(b) all the following conditions present continuously for
24  hours commencing  within  36  hours  of  ICU
admission, while the patient is undergoing invasive
mechanical ventilation in either flow or pressure-
regulated assist/controlled modes:
a.
arterial  oxygen  tension  (PaO2)  to
inspiratory oxygen fraction (FiO2) ratio
P/F ≤ 300 mmHg
b.
hypoxemia developed within one week
of a known clinical insult
c.
hypoxemia not fully explained by cardiac
failure or fluid overload*
(a) same as TT
(b) same as TT*
Exclusion Criteria
ANY OF THE FOLLOWINGS:
(a) pregnancy
(b) expected duration of mechanical ventilation < 48h
(c) severe or moderate COPD
(d) chronic liver disease
(e) acute brain injury
(f) patient admitted for palliative sedation
(g) tumor with metastases
(h) prior cardiac arrest
(i)
New York Heart Association Class IV
(j)
acute coronary syndrome
(k) patients transferred from other ICUs
(l)
patients transferred to the ICU from the Emergency
Department after a duration exceeding 24 hours in
the Emergency Department
(m)patients  on  Pressure  Support  Ventilation  and/or
patients in whom end-inspiratory plateau pressure
was not measured
(a) same as TT*
(b) same as TT*
(c) same as TT*
(d) same as TT*
(e) same as TT*
(f) same as TT*
(g) same as TT*
(h) same as TT*
(i)
same as TT*
Treatment strategies
Mode  of  mechanical  ventilation:  constant  flow/constant
pressure, assist/control; RR up to 35 bpm.  PEEP will be set
according to clinical judgement.
Predicted Body Weight (PBW) will be calculated according to
the following formulae:
PBW (Males) = 50+0.91 [height (cm)-152.4];
PBW (Females) = 45.5 + 0.91 height (cm)-152.4].
Oxygenation Goal: PaO2 55-80 mmHg or SpO2 88-95%;
Arterial pH Goal: 7.30-7.45.  
TIDAL VOLUME
GUIDED PROTECTIVE VENTILATION
Tidal volume (VT) will be set to:
Arm VT 1.  VT  6.0-8.0 ml/kg PBW with PPLAT ≤ 30 cmH2O;
Arm VT 2.  VT  8.0-10.0 ml/kg PBW with  PPLAT ≤ 30 cmH2O.
DRIVING PRESSURE
GUIDED PROTECTIVE VENTILATION
Driving pressure (∆P) will be set to:
Arm ∆P1. 7.0-12.0 cmH2O with VT  ≤ 10.0 ml/kg PBW;
Arm ∆P2. 12.0- 18.0 cmH2O with VT  ≤ 10.0 ml/kg PBW.
The value of  VT and ∆P in the corresponding arm interval is
chosen according to clinical judgement. Intervention will be
maintained  until  the  patient  will  be  considered  eligible  for
respiratory weaning when all the following criteria will be met:

(P/F)  > 250 mmHg

PEEP ≤ 8 cmH2O and lower than the previous day

FiO2 < 0.5 and lower than the previous day
Simulated interventions
For each treatment arm, the value of the continuous 
variable VT  (∆P,  respectively) is set equal to its 
natural value if the natural value is inside the range of
the corresponding arm. Otherwise, it is set equal to 
the value at of the edge of the range which is closer 
to the natural value (e,g. For VT1, if the natural value
of VT is lower than 6 ml/kg PBW, the intervention 
value is set to 6  ml/kg PBW; if the natural value is 
greater than 8 ml/kg PBW, the intervention value is 
set to 8 ml/kg PBW),
Treatment corresponding to the TT intervention is 
maintained until patient is eligible for respiratory 
weaning, then the natural value of treatment is 
applied (either no mechanical ventilation or 
ventilation with natural value of VT  or ∆P.


systolic arterial pressure ≥ 85 mmHg 
Treatment
assignment
Randomization
Simulation of dynamic interventions on the same
population
Primary End-Point
ICU all-cause mortality:
All patients will be classified as either alive if  alive at ICU
discharge or dead if dead during ICU stay
Same as TT
Secondary End-Point
Number of ventilator free-days during follow-up 
Same as TT
Secondary Analysis

Dose response curve for VT and ∆P

Comparison of best policy with fixed VT versus best
policy with fixed ∆P. Best policy is defined as the
one with best treatment effect among those used in
intervention arms.

Treatment effect computed simulating a 
modified treatment policy with constant 
VT and ∆P, respectively until conditions for
weaning are satisfied, then natural value 
of treatment

Same as TT
Follow up
14 days after randomization
Same as TT
Causal Contrasts
Intention-to-treat effect
Effect on study outcomes will be analyzed in those patients that 
were assigned to the treatment arm.  
Per-protocol effect
Effect on study outcomes will be analyzed in those patients that 
effectively received the treatment (either tidal volume or 
driving pressure). 
Observation analogue of per-protocol effect
Effect on study outcomes will be analyzed using 
simulated treatment strategies.
Statistical Analysis
In the per-protocol analysis, patients will be censored when 
they deviated from their assigned strategy. The per-protocol 
effect will be estimated after adjustment for baseline variables 
and for time-varying variables associated with adherence to a 
ventilation strategy according the treatment arms.
Subgroup analysis by previously selected baseline clinical (age,
severity of ARDS, SAPS and SOFA scores) and physiological 
(quartiles of compliance, driving pressure, Plateau Pressure, 
ventilatory index) variables.
The per-protocol effect under full adherence will be
estimated using the g-estimation methods. The value
of each density function for all possible covariate
histories was estimated under parametric modeling
assumptions. 
Confounders
baseline: demographics, comorbidities (Charlson 
Index),  respiratory and cardiovascular components 
of SOFA scores at ICU admission.
time varying: P/F, PEEP, FiO2, SaO2, respiratory 
frequency, and systolic pressure.
Competing events
ICU discharge prior to end of follow-up
Subgroup analysis same as the target trial
Sensitivity Analysis

Same inclusion criteria but no exclusion criteria

patients satisfying radiological criteria for ARDS

patients ventilated in flow regulated assist/controlled
modes vs. patients ventilated with pressure-regulated
assist/controlled modes

P/F 300-200 vs. P/F 200-100 vs. P/F < 100

quartiles of static compliance

quartiles of dynamic compliance

quartiles of peak pressure

quartiles of static pressure

quartiles of mechanical power

presence of inclusion criteria (c) starting from within
12 hours from ICU admission

Same as TT
* see Appendix.

Appendix
Implementation of inclusion and exclusion criteria in the emulated target trial in
the database MargheritaTre
Inclusion criteria
Inclusion criterion (c) was modified as follows to ensure that the patient is hypoxemic for 24 h
starting within 36 h from ICU admission.
We defined  tH as the first time at which the P/F value  ≤ 300 mmHg within 36 h from ICU
admission while patient is intubated. Inclusion criteria are considered satisfied if there is at least one
value of P/F ≤ 300 mmHg between tH + 6h and tH + 24h. See Fig. S2.1. If there is such a time where
hypoxia is confirmed, the patients is eligible for the study and can be enrolled. The time of hypoxia
confirmation it defined as t0.
Exclusion criteria
Expected duration of mechanical ventilation < 48h was implemented by excluding patients admitted
for monitoring/weaning from ventilation after surgery.
Patients with one of the following comorbidities or diagnoses at ICU admission were excluded
Comorbidities
ALS 
Asthma
Bronchodysplasia
Chronic obstructive pulmonary disease (COPD) moderate
Chronic obstructive pulmonary disease (COPD) severe
Congenital heart disease
Heart failure NYHA class 4
Infarction
Moderate or severe liver disease
Moderate chronic pneumopathy
Myocardial infarction
Myocardiopathy
Myocarditis
Neuromuscular/neurodegenerative disease
Non-congenital valvulopathy
Figure A.1: Graphical representation of inclusion criterion (c) for hypoxemia

Pulmonary hypertension
Restrictive lung disease
Severe chronic pneumopathy
Tumor with metastases
Valvulopathy
Diagnoses at ICU admission
Abnormalities of pulmonary venous return 
Abnormalities of thoracic veins and arteries 
Abnormalities of systemic veins
Acute cardiac ischemia 
Acute congenital valvulopathy 
Acute myocardial infarction 
Anterior AMI
Anterolateral AMI 
Inferior AMI 
Inferolateral AMI 
Inferoposterior AMI 
Lateral AMI 
Posterior AMI 
Aortic valve failure 
ASD ostium primum 
Cardiogenic pulmonary edema 
Cardiomegaly 
Chronic cardiac ischemia 
Chronic obstructive asthma with exacerbation 
Chronic obstructive bronchitis with exacerbation 
Coma from other causes
Congestive heart failure 
COPD 
Coronaropathy 
Dilated cardiomyopathy 
Dysfunction of prosthetic valve 
Extrinsic asthma 
Extrinsic asthma with asthmatic status 
Extrinsic asthma with exacerbation 
Hypertensive cardiomyopathy 
Hypertensive heart disease with congestive failure 
Intrinsic asthma 
Intrinsic asthma with asthmatic status 
Intrinsic asthma with flare-up 
Ischemic heart disease 
Left heart failure 
Left/right heart abnormalities 
Malignant tumors, lung 
Mitral valve insufficiency
NSTEMI 
Patent Foramen Ovale  / ASD ostium secundum 
Pericardial effusion (nontraumatic) 
Pregnancy, other specified complications 

Primary dilated cardiomyopathy 
Primary pulmonary hypertension 
Pulmonary emphysema
Pulmonary fibrosis 
Pulmonary hypertension secondary to valvulopathy 
Pulmonary metastases 
Pulmonary valve disorders 
Secondary cardiomyopathy 
Septal defects 
Single ventricle 
Toxic coma 
Transposition of the Great Arteries (TGA)
Tricuspid valve disorders 
VSD (ventricular septal defect)