Associations of Intraoperative Hypocapnia With Patient Demographics, Ventilation Characteristics and Outcomes--Statistical Analysis Plan for an Individual Patient Data Analysis of PROVHILO and PROBESE
Conditions: Mechanical Ventilation Complication, Surgery, Pulmonary Complication, Intraoperative Complications, Postoperative Complications
Sponsor: NMC Specialty Hospital

 
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Statistical Analysis Plan 
 
 
 
 
 
 
Associations of Intraoperative Hypocapnia 
with Patient Demographics, Ventilation 
Characteristics and Outcomes––statistical 
analysis plan for an individual patient data 
analysis of PROVHILO and PROBESE 
 
 
Prashant Nasa, David MP van Meenen, Frederique Paulus, Carlos Ferrando, 
Thomas Bluth, Marcelo Gama de Abreu, Lorenzo Ball, Paolo Pelosi, Marcus J 
Schultz, Ary Serpa Neto, and Sabrine NT Hemmes, for the ‘Intraoperative 
Hypocapnia in PROVHILO and PROBESE’ (iHypoPRO) investigators 
 
 
On behalf of the PROVHILO and PROBESE–investigators and the PROVE Network 
Investigators 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Correspondence 
Prashant Nasa 
Department of Intensive Care 
Amsterdam University Medical Centers, location ‘AMC’ Amsterdam, The Netherlands 
E-mail: dr.prashantnasa@hotmail.com 
 
 

 
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Rationale 
Lung–protective intraoperative ventilation (LPV) has the potential to improve the 
outcome of surgery patients through a reduction in postoperative pulmonary 
complications [1, 2]. Use of intraoperative ventilation strategies that use a low tidal 
volume could result in intraoperative hypercapnia. However, hypocapnia remains 
surprisingly common during intraoperative ventilation [3, 4], possibly meaning that 
anesthesiologists continue to use high, if not too high respiratory rates or tidal volumes. 
Previous studies suggested associations between intraoperative derangement 
of end–tidal carbon dioxide (etCO2) and postoperative outcomes [5, 6]. Indeed, two 
studies in highly selected patient groups showed associations of intraoperative 
hypocapnia with prolonged length of hospital stay, in patients undergoing 
pancreaticoduodonectomy [7], and in patients undergoing hysterectomy [8]. 
To gain a better understanding of the epidemiology of intraoperative 
hypocapnia, in particular the associations of intraoperative hypocapnia with patient 
demographics, ventilator characteristics, and perioperative complications we will 
perform an Individual patient–level meta–analysis of two recent randomized clinical 
trials of intraoperative ventilation [3, 9]. 
 
Hypothesis 
Intraoperative hypocapnia has an independent association with patient demographics, 
ventilation parameters, and perioperative complications. 
 
Objectives 
The current analysis will assess associations of intraoperative hypocapnia with patient 
demographics, ventilation characteristics, occurrence of intraoperative complications, 
postoperative pulmonary complications, and length of hospital stay in patients 
undergoing planned major surgery [3, 9]. 
 
Study design 
For this analysis we will use the database of the two randomized clinical trials, named 
the ‘High versus Low Positive End–expiratory Pressure During General Anesthesia for 
Open Abdominal Surgery (PROVHILO) [3], and the ‘PRotective Ventilation with Higher 
versus Lower PEEP during General Anesthesia for Surgery in OBESE Patients’ 

 
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(PROBESE) [9]. PROVHILO and PROBESE investigated the effects of an open lung 
approach during low tidal volume ventilation in patients undergoing major surgery. 
 
Study details 
The full list of inclusion and exclusion criteria of PROVHILO and PROBESE have been 
reported with the original publications [3, 9]. In short, patients were eligible for 
participation in the original studies if: (1) planned for major (abdominal) surgery; and 
(2) at risk for postoperative pulmonary complications. Major exclusion criteria in the 
original studies were planned thoracic surgery or neurosurgery. 
For the purpose of this current analysis, we will additionally exclude patients 
that underwent unscheduled, i.e., urgent or emergent surgeries, because we consider 
it likely that these patients may have had metabolic abnormalities at the moment of 
surgery, i.e., metabolic acidosis, for which the anesthesiologist may have adjusted the 
intraoperative ventilator settings. This may have led to a ‘compensatory’ low etCO2. 
We will also exclude patients for whom etCO2 recordings are missing in the study 
databases. 
 
Patient categories 
We will use the intraoperatively collected etCO2 levels to classify patients as either 
‘with hypocapnia’ or ‘without hypercapnia’, using the cutoff of 35 mmHg. A patient is 
considered ‘hypocapnic’ if the etCO2 was < 35 mm Hg at any point during surgery, 
from start of the study till end of the study, and classified as ‘without hypocapnia’ 
otherwise. In case of a missing value immediately before extubation, we will use the 
values as reported in the last hour of surgery [10]. 
 
Study outcomes 
The primary outcome of this analysis is a composite of predefined and collected 
postoperative pulmonary complications within the first seven days after the surgery, as 
used in the parent studies. Postoperative pulmonary complications included mild, 
moderate, and severe respiratory failure; acute respiratory distress syndrome; 
bronchospasm; new pulmonary infiltrate; pulmonary infection; aspiration pneumonitis; 
pleural effusions; atelectasis; cardiopulmonary edema; and pneumothorax. 
Secondary outcomes include intraoperative complications, a set of 
intraoperative ventilation parameters, intraoperative vital signs (heart rate, mean 

 
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arterial pressure, and SpO2) and any new arrhythmias needing intervention. Other 
endpoints are unexpected need for intensive care unit admission, ICU readmission, 
and length of stay in hospital up till day 90. 
 
Calculations 
Intraoperative ventilation variables collected in the parent studies included tidal volume 
(VT), positive end–expiratory pressure (PEEP), plateau or maximum airway pressure 
(Pplat or Pmax) and respiratory rate (RR).  
Ventilation parameters are calculated as follows: 
For VT, expressed in ml/kg predicted body weight: 
50 kg + 2.3 kg (height in inches -60) (in males) 
45.5 kg + 2.3 Kg (height in inches -60) (in females) 
For driving pressure (ΔP): 
ΔP = Pplat – PEEP (with volume–controlled ventilation [VCV])  
ΔP = Pmax – PEEP (with pressure–controlled ventilation [PCV]) [11] 
For mechanical power of ventilation (MP): 
MP = 0.098 * VT * RR* (Ppeak – 0.5 * ΔP) (with VCV) [12] 
MP = 0.098 * VT * RR * (Pmax – 0.5 * ΔP) (with PCV) [13] 
For respiratory system compliance (CRS): 
CRS = VT/ΔP 
 
Sample size 
The sample size will be based on the number of available patients. 
 
Statistical analysis plan 
Demographic, clinical and outcome variables are presented as medians with 
interquartile ranges (IQR), or number with percentage, where appropriate. Differences 
in baseline characteristics between patients with and patients without hypocapnia will 
be analyzed using the Pearson Chi–squared or Fisher exact tests for categorical 
variables and with a one–way ANOVA or Kruskal–Wallis test for continuous variables. 
Data points for intraoperative variables (vital signs, ventilator settings and 
ventilation parameters) will include those collected in the first, second and third hour 
after start of the study, and before extubation. 

 
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Occurrence of postoperative pulmonary complications will be compared using 
a Fisher’s exact test, time to event analyses will be performed using a Cox proportional 
hazard model or a competing risk model where appropriate. Next, we will perform a 
propensity matched cohort. Patients will be matched 1:1 with a maximum caliper of 
0.02. The following covariates will be used for propensity matching: age, gender, BMI, 
type of surgery, compliance, history of COPD. We will calculate interaction term for the 
ARISCAT score as sensitivity analysis. We will perform time-weighted analysis for 
etCO2 levels in the primary end-points. Kaplan-Meier curve will be used to plot the 
relation between hypocapnia and the primary outcome.   
A LOESS regression will be used to visualize the relationship of etCO2 as a 
continuous variable with the primary outcome. The association between etCO2 and 
the primary outcome will be determined using a generalized mixed model in which age, 
gender, BMI, type of surgery, compliance and history of COPD will be used as 
covariates. Sankey plot will be used to demonstrate flow association between etCO2 
and primary or secondary outcomes. 
As a sensitivity analysis the main analyses will be repeated in obese patients 
and non–obese patients, using a cutoff of 30 kg/m2 for BMI. Another sensitivity analysis 
will be performed to determine the effect of PEEP settings on outcome. For this 
analysis the interaction term for PEEP on the generalized mixed model will be 
calculated.  
A P–value of < 0.05 will be considered statistically significant. All analyses will 
be performed with R statistics version 4.0.4. 
 
References 
1. Deng QW, Tan WC, Zhao BC, Wen SH, Shen JT, Xu M. Intraoperative ventilation 
strategies to prevent postoperative pulmonary complications: a network meta-
analysis of randomised controlled trials. Br J Anaesth. 2020;124(3):324-335. doi: 
10.1016/j.bja.2019.10.024.  
2. Serpa Neto A, Hemmes SN, Barbas CS, Beiderlinden M, Biehl M, Binnekade JM, 
et al. Protective versus Conventional Ventilation for Surgery: A Systematic Review 
and Individual Patient Data Meta-analysis. Anesthesiology. 2015;123(1):66-78. doi: 
10.1097/ALN.0000000000000706. 
3. PROVE Network Investigators for the Clinical Trial Network of the European 
Society of Anaesthesiology, Hemmes SN, Gama de Abreu M, Pelosi P, Schultz MJ. 

 
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High versus low positive end-expiratory pressure during general anaesthesia for 
open abdominal surgery (PROVHILO trial): a multicentre randomised controlled 
trial. Lancet. 2014;384(9942):495-503. doi: 10.1016/S0140-6736(14)60416-5. 
4. Akkermans A, van Waes JAR, Thompson A, Shanks A, Peelen LM, Aziz MF, et al. 
An observational study of end-tidal carbon dioxide trends in general anesthesia. 
Can J Anaesth. 2019;66(2):149-160. doi: 10.1007/s12630-018-1249-1. 
5. Dony P, Dramaix M, Boogaerts JG. Hypocapnia measured by end-tidal carbon 
dioxide tension during anesthesia is associated with increased 30-day mortality 
rate. J Clin Anesth. 2017;36:123-126. doi: 10.1016/j.jclinane.2016.10.028. 
6. Dong L, Takeda C, Yamazaki H, Kamitani T, Kimachi M, Hamada M, et al. 
Intraoperative end-tidal carbon dioxide and postoperative mortality in major 
abdominal surgery: a historical cohort study. Can J Anaesth. 2021;68(11):1601-
1610. English. doi: 10.1007/s12630-021-02086-z. 
7. Park JH, Lee HM, Kang CM, Kim KS, Jang CH, Hwang HK, et al. Correlation of 
Intraoperative End-Tidal Carbon Dioxide Concentration on Postoperative Hospital 
Stay in Patients Undergoing Pylorus-Preserving Pancreaticoduodenectomy. World 
J Surg. 2021;45(6):1860-1867. doi: 10.1007/s00268-021-05984-x. 
8. Wax DB, Lin HM, Hossain S, Porter SB. Intraoperative carbon dioxide management 
and 
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Anaesthesiol. 
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doi: 
10.1097/EJA.0b013e32833cca07. 
9. Writing Committee for the PROBESE Collaborative Group of the PROtective 
VEntilation Network (PROVEnet) for the Clinical Trial Network of the European 
Society of Anaesthesiology, Bluth T, Serpa Neto A, Schultz MJ, et al. Effect of 
Intraoperative High Positive End-Expiratory Pressure (PEEP) With Recruitment 
Maneuvers vs Low PEEP on Postoperative Pulmonary Complications in Obese 
Patients: A Randomized Clinical Trial. JAMA. 2019;321(23):2292-2305. doi: 
10.1001/jama.2019.7505. 
10. Neto AS, Hemmes SN, Barbas CS, Beiderlinden M, Fernandez-Bustamante A, 
Futier E, et al. Association between driving pressure and development of 
postoperative pulmonary complications in patients undergoing mechanical 
ventilation for general anaesthesia: a meta-analysis of individual patient data. 
Lancet Respir Med. 2016;4(4):272-80. doi: 10.1016/S2213-2600(16)00057-6. 

 
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11. Amato MB, Meade MO, Slutsky AS, Brochard L, Costa EL, Schoenfeld DA, et al. 
Driving pressure and survival in the acute respiratory distress syndrome. N Engl J 
Med. 2015;372(8):747-55. doi: 10.1056/NEJMsa1410639. 
12. Gattinoni L, Tonetti T, Cressoni M, Cadringher P, Herrmann P, Moerer O, et al. 
Ventilator-related causes of lung injury: the mechanical power. Intensive Care Med. 
2016;42(10):1567-1575. doi: 10.1007/s00134-016-4505-2. 
13. van Meenen DMP, Serpa Neto A, Paulus F, Merkies C, Schouten LR, Bos LD, et 
al. The predictive validity for mortality of the driving pressure and the mechanical 
power of ventilation. Intensive Care Med Exp. 2020;8(Suppl 1):60. doi: 
10.1186/s40635-020-00346-8. 
 
Planned inserts 
Figure 1: CONSORT 
Figure 2: Sankey plots 
Figure 3: distribution plots of ventilation characteristics 
Figure 4: LOESS regressions 
Figure 5: Kaplan-Meier curve  
Table 1: Patient characteristics 
Table 2: Intraoperative characteristics, including ventilation and hemodynamic 
characteristics 
Table 3: Comparison of outcomes between the patients with or without hypocapnia 
eTable 1: Patient characteristics for studies 
eTable 2: Ventilation characteristics 
eTable 3: Sensitivity analyses