Introduction

The incidence of postoperative pulmonary complications (PPCs) is high and depends on the used definitions and the studied population [1]. Their occurrence is associated with increased morbidity and mortality [2, 3]. PPCs can be prevented by reducing lung strain by using a low tidal volume (VT) [4], ,and by using sufficient positive end–expiratory pressure (PEEP) [5]. Since the driving pressure (ΔP), defined as the difference between plateau pressure and PEEP, is associated with the development of PPCs [5, 6], titrating VT and PEEP to obtain the lowest ΔP could be an effective preventive strategy against PPCs.

The overall behaviour of the respiratory system depends on the properties of its components, i.e., the artificial and native airways, and the lung tissue, but also the chest wall consisting of the rib cage and diaphragm. Most of the force applied during invasive ventilation is needed to expand the chest wall, and only a lesser amount to inflate lung tissue [7]. When the chest wall elastance increases, e.g., during pneumoperitoneum, the ΔP increases, even when VT is left unchanged [8]. This rise in ΔP is often interpreted as ‘innocent’, and therefore accepted during intraoperative pneumoperitoneum. However, the cephalad shift of the diaphragm could induce, or worsen atelectases during intraoperative ventilation, and the resulting increase in ΔP is related with a rise in lung applied force [9]. In other words, it should be questioned if a rise in ΔP during pneumoperitoneum with closed abdominal surgery can be accepted.

To determine and compare the independent associations of ΔP with PPCs in patients undergoing open abdominal surgery versus patients undergoing closed abdominal surgery, we reassessed the database of the ‘Local ASsessment of Ventilatory management during General Anaesthesia for Surgery’ (LAS VEGAS) study [10]. The LAS VEGAS study was a large observational study that included a large proportion of patients at an increased risk for PPCs. The primary hypothesis tested here was that the association of ΔP with PPCs is weaker in closed versus open abdominal surgery patients. The primary objective was to test the association of a time–weighted average driving pressure (ΔPTW) with PPCs. The secondary objective was to test the association of ΔPTW with intraoperative adverse events.

Methods

Study design and setting

This is a posthoc analysis of the LAS VEGAS study [10], carried out following current guidelines and the recommendations of the statement for strengthening the reporting of observational studies in epidemiology (STROBE) (www.strobe-statemenent.org). The statistical analysis plan was predefined, updated, and finalised before data extraction, and is presented as Additional file 1. The LAS VEGAS study is a worldwide international multicentre prospective seven–day observational study describing intraoperative ventilation practice, complications during anaesthesia, PPCs in the first five postoperative days, hospital length of stay, and hospital mortality.

The ethical committee of the Academic Medical Center, Amsterdam, the Netherlands, approved the LAS VEGAS study protocol (W12_190#12.17.0227). Each participating centre obtained approval from their institutional review board if needed, and patients were included after obtaining written informed consent when dictated by national or regional legislation. The LAS VEGAS study was partially funded and endorsed by the European Society of Anaesthesiology and registered at clinicaltrials.gov (study identifier NCT01601223, first posted date: 17/05/2012).

Inclusion and exclusion criteria

The LAS VEGAS study recruited consecutive patients undergoing general anaesthesia with mechanical ventilation during anaesthesia for surgery during a seven–days timeframe between 14 January and 4 March 2013. Exclusion criteria of the LAS VEGAS study were: (1) age < 18 years, (2) having received mechanical ventilation in the preceding month, (3) obstetric or ambulatory surgical interventions, and (4) cardiothoracic surgery cardiopulmonary bypass.

For the current analysis, inclusion was restricted to patients undergoing abdominal surgery. The following additional exclusion criteria were used: (1) insufficient data to calculate ΔP, i.e., on at least two timepoints sufficient data had to be available to calculate the driving pressure for a patient to be included; (2) to increase the homogeneity of the compared patient cohorts and avoid using erroneous data, patients who received intraoperative ventilation through an airway device other than an endotracheal tube as well as patients under an assisted or spontaneous ventilation mode were excluded; (3) patients in whom laparoscopy only assisted the surgery, i.e., surgeries that could not be classified as mere open or mere closed abdominal surgery, were also excluded from the current analysis.

Data recording and calculations

Full details on data collection can be found in the original publication of the LAS VEAGS study [10], and in Additional file 2. In the LAS VEGAS study database, ventilatory parameters at every hour of surgery, from induction up to the last hour of surgery, were recorded. Data in the LAS VEGAS database was validated through two rounds of extensive data cleaning to check for invalid or incomplete data. Local investigators were queried on incorrect or missing data and had to correct those in the cleaning rounds.

The following calculations were performed. ΔP was calculated by subtracting PEEP from plateau pressure or inspiratory pressure at every hour in volume–controlled and pressure–controlled ventilated patients, respectively. ΔPTW, i.e., the pressure that is proportional to the amount of time spent at each driving pressure in relation to the total time, was calculated by summing the mean values between consecutive time points multiplied by the time between those points and then dividing by the entire time [11]. Similarly, time–weighted average peak pressure and PEEP were determined. Details on calculations are provided in the Additional file 2 Figure S1.

Definitions

PPCs were defined as a collapsed composite of the following events: unexpected postoperative invasive or non–invasive ventilation, acute respiratory failure, acute respiratory distress syndrome, pneumonia, and pneumothorax. The occurrence of each type of complication was monitored until hospital discharge but restricted to the first five postoperative days.

Intraoperative adverse events were defined as an ordinal composite of the following events: any oxygen desaturation or lung recruitment manoeuvres performed to rescue from hypoxemia, any need for adjusting ventilator settings for reducing airway pressures or correction of expiratory flow limitation, any hypotension or need for vasoactive drugs, and any new cardiac arrhythmia.

A detailed list of definitions of the composites of PPCs and intraoperative adverse events is provided in Additional file 2 Table S1 and Table S2.

Endpoints

The primary endpoint was the composite of PPCs. The secondary endpoint was the composite of intraoperative adverse events.

Analysis plan

The analysis plan was prespecified before data access, and we used data of all available patients in the LAS VEGAS database without formal sample size calculation. Also, as the purpose of the analysis was exploring a physiological hypothesis, we did not specify any a priori effect size.

Continuous variables were reported as median and interquartile ranges; categorical variables expressed as n (%). Normality of distributions was assessed by inspecting quantile–quantile plots. If variables were normally distributed, the two–sample t–test was used; if not, the Wilcoxon rank sum test was used. We used the Chi–square test or Fisher’s exact test for categorical variables, or when appropriate, as relative risks. Statistical uncertainty was expressed by showing the 95%–confidence intervals (CI). Since the simultaneous occurrence of various intraoperative adverse events is frequent, we analysed them as an ordinal variable with a range spanning from zero to seven adverse events.

To control for confounding effects, we estimated the association of ΔPTW with PPC with a weighted mixed–effect logistic regression, and the association of ΔPTW with intraoperative adverse events with a weighted mixed ordinal regression. To fit the models, we introduced centres as a random intercept, and an inverse probability weighting factor computed from the covariate–balancing propensity score (CBPS) method to simultaneously optimise treatment assignment prediction, i.e., ΔPTW as a continuous variable, and confounders influence [12]. The CBPS procedure sets mean independence between treatment, i.e., ΔPTW, and covariates to ensure covariate balancing and estimates the propensity score with the generalised method of moments method. For both outcomes, we fitted the model for each of the compared patient cohorts respectively, i.e., patients who underwent open surgery intervention and those who underwent closed surgical intervention. We used a Wald z-test to test the difference between odds ratios from models fitted on closed and open surgery cohort. Models’ goodness of fit was assessed by residual diagnosis based on scaled quantile residuals (R DHARMa package v. 0.2.4) and simulated residuals (R sure package v 0.2.0) for logistic and ordinal models, respectively.

To build the CBPS to relate exposure variable, i.e., ΔPTW, with potential confounders, we included by clinical judgment the Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) risk class [13, 14], and the average intraoperative VT. Then we performed feature selection with an augmented backward elimination selection method introducing 37 preoperative and intraoperative variables (Additional file 2:Statistics for a detailed list). The selection was based on a sequential process where initially all variables entered the model and finally those preoperative and intraoperative factors that yielded a change in the effect estimate > 0.1 and a significance criterion (alpha) <  0.1 were included. The algorithm stopped when all variables left in the model complied with both criteria [15]. We carried out a selection process of potential variables to avoid bias in the effect estimates using a comprehensive strategy to prevent the drawbacks of simple stepwise methods [16]. The model’s internal validation was assessed by bootstrap using 5 hundred generated samples and estimating the Area Under Curve (AUC) of the full and stepwise–selected variables models.

To further unravel the effect of the surgical approach on PPCs, we performed a sensitivity analysis fitting a mixed logistic regression with a random intercept for centre on a propensity score matched cohort. The propensity score was used to match patients with a similar covariable structure using the R matchit package carrying out the matching with the nearest neighbour method with a caliper of 0.1 with a ratio of patients in the open surgery arm of 2 to 1. Full details on the covariables introduced in the propensity score matching procedure can be found in the Additional file 2: Statistics. To assess the type of surgery as an effect modifier, we carried out another sensibility analysis fitting a weighted mixed logistic regression model on all data, i.e., both surgery cohorts, introducing the type of surgery as an independent variable and an interaction term between ΔPTW and type of surgery.

Statistical significance was considered for two–tailed P <  0.05. No imputation routine of missing values and no correction for multiple comparisons was prespecified; thus, all the findings should be viewed as exploratory. All analyses were performed with R 3.5.2 (The R Foundation for Statistical Computing, www.r-project.org). Additional explanation on the used methods can be found in the Additional file 2: Statistics.

Results

Patients

Of a total of 3265 patients undergoing abdominal surgery in the LAS VEGAS study, 1231 had insufficient data for calculating the ΔP (37.7%).

Out of the remaining 2034 patients, 1218 (60%) patients underwent an open abdominal intervention, and 906 (40%) patients, a closed abdominal surgical procedure (Fig. 1). ΔP could be calculated on two different timepoints in 34.4 and 53.7% of patients in the open and closed surgery group, respectively (Fig. 2 and Table S3). In 87% of patients, ΔP could be calculated on up to four timepoints.

Fig. 1
figure 1

Patients’ inclusion flowchart

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Fig. 2
figure 2

Mechanical ventilation settings over time. Green: open surgery, Orange: closed surgery. Hour 0 h represents the induction of general anaesthesia. Solid lines are means, and bandwidths is 95% bootstrapped confidence intervals. Gray boxes: More than 95% of data points represented

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Baseline demographic data, surgery–related and intraoperative ventilation characteristics are presented in Tables 1 and 2, and Fig. 2. Open abdominal surgery patients had higher ASA class and ARISCAT risk score, lower functional status, and fewer elective procedures, longer surgery times, less neuromuscular reversals, and received more intraoperative transfusions and fluids. Lower abdomen surgeries were the most frequently performed in the open abdominal surgery patients, while upper abdomen interventions were performed more often in closed abdominal surgery patients. ΔPTW was not different between the open and closed surgery groups (Table 2).

Table 1 Patients demographics and surgery–related characteristics
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Table 2 Intraoperative ventilatory setting by group
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Primary and secondary outcome rates

In 102 (5%) patients, one or more PPC occurred, with a higher prevalence in open surgery patients than in patients who underwent a closed surgical procedure (7 versus 3%; P <  0.001). Hypotension, or need for vasopressors was more frequently observed during open surgery, while the need for airway pressure reduction was more often needed during closed surgery (Table 3).

Table 3 Intraoperative and postoperative outcomes
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Propensity score estimation variables

The variables that finally entered the propensity score and covariate balance assessment are detailed in the Additional file 2: Statistics and Fig. S2 and S3.

Association of ΔPTW with PPCs

ΔPTW was significantly associated with PPCs in both surgical groups. The association was stronger in closed abdominal surgery patients (odds ratio (OR), 1.17 [95%CI 1.16 to 1.19]; P <  0.001; risk ratio (RR), 1.11 [95%CI 1.10 to 1.20], P <  0.001) than in patients who underwent an open abdominal surgical intervention (OR, 1.07 [95%CI 1.06 to 1.08]; P <  0.001; RR 1.05 [95% CI 1.05 to 1.05]), with a significant difference (difference between ORs: 0.09 [95%CI 0.07 to 0.10]; P <  0.001; risk difference 0.05: [95%CI 0.04 to 0,06]), P <  0.001. Residuals plots are reported in Additional file 2: Figure S4.

Association of ΔPTW with the occurrence of adverse events

ΔPTW was significantly associated with intraoperative adverse events in both open and closed surgery patients. Also, here the association was stronger in closed surgery patients (1.13 [95%CI 1.12 to 1.14]) than in patients who underwent an open abdominal intervention (1.07 [95%CI 1.05 to 1.10]), difference between ORs 0.05 [95%CI 0.03 to 0.07]; P <  0.001.

Sensitivity analyses

ΔPTW was significantly associated with PPCs (OR, 1.08 [95%CI 1.06 to 1.09], P <  0.001) with closed surgery patients having a lower probability of occurrence (OR, 0.14 [95%CI 0.12 to 0.16, P <  0.001) with a significant interaction between ΔPTW and closed surgery (OR, 1.09 [95%CI 1.08 to 1.11], P <  0.001). The marginal effect of ΔPTW by type of surgery on PPCs probability is showed in Fig. 3. A rise in ΔPTW was associated with an increased probability of PPCs in both surgery types, with a steeper increase in closed surgery patients for ΔPTW above 20 cmH2O ∙ hour− 1.

Fig. 3
figure 3

Marginal effect plot of time–weighted average driving pressure on the probability of postoperative pulmonary complications by type of surgery. Green: open surgery, Orange: closed surgery; solid lines are estimated marginal mean effect, and bandwidths are 95% confidence intervals

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After matching, the resulting cohort consisted of 344 open surgery patients and 254 closed surgery patients. Baseline characteristics between groups were well balanced (Additional file 2: Table S4 and S5). Type of surgery at matched levels of driving pressure was not associated with either outcome. (Additional file 2: Table S5 and S6).

Discussion

The main findings of this posthoc analysis of the LAS VEGAS study can be summarised as follows: (i.) the intraoperative ΔPTW was not different between open and closed surgery groups; (ii.) ΔPTW was associated with PPCs in both closed and open surgery patients; (iii.) ΔPTW was associated with intraoperative adverse events in both closed and open surgery patients; and (iv.) the type of surgery had a modifying effect on the association between ΔPTW and PPCs, with an increasing probability of PPCs at high ΔPTW in closed surgery. The last finding, though, was not confirmed in the matched cohort analysis.

This analysis uses the database of a worldwide international multicentre prospective observational study as a convenience sample [10], strictly followed a plan, and was characterised by a robust method accounting for the multilevel data structure and allowing precise estimation and confounder control, even with seven or fewer events per confounder [17, 18]. Also, the outcome of interest, i.e., PPCs, was predefined, well–described, and largely followed the European Perioperative Clinical Outcome (EPCO) group definitions [19]. Furthermore, the study population was defined to minimise information and selection bias and to have a sufficient number of patients while keeping an acceptable number of timepoints at which ΔPTW could be calculated per patient.

A recent metanalysis of individual trials on protective ventilation during general anaesthesia for cardiac or thoracic surgery found a significant association between ΔPTW and PPCs (OR 1.16, 95% CI 1.13 to 1.19; p <  0·0001) [5]. We found an almost identical association in patients undergoing closed abdominal surgery. Thus, our results confirm that ΔPTW is a promising target for interventions to prevent PPCs after closed abdominal surgery. The sensitivity analysis showed that the association between ΔPTW and PPCs was lower in patients who underwent a closed surgical procedure. However, this was not confirmed in the propensity score matched analysis, probably because of smaller sample size due to the matching procedure.

ΔP is an indicator of the amount of strain delivered to the respiratory system during mechanical ventilation [7]. Several studies investigated the effect of pneumoperitoneum on respiratory mechanics. Pneumoperitoneum was consistently found to decrease chest wall compliance, whereas lung compliance seems mostly spared by it [20,21,22,23,24,25,26,27]. Thus, inferring the amount of lung strain from plateau pressure and PEEP during pneumoperitoneum is challenging, since the part of the rise in plateau pressure caused by chest wall stiffening should not be regarded as a rise in lung strain [28]. Consequently, a higher ΔP during closed abdominal surgery is often seen as innocent. The current analysis results reject this assumption, as the association of ΔP with PPCs was stronger in patients undergoing closed abdominal surgery than in patients undergoing open abdominal surgery.

Pneumoperitoneum can affect lung mechanics in several ways [20,21,22,23,24,25,26,27]. A cranial shift of the diaphragm during laparoscopic surgery increases alveolar collapse, especially in lung parts close to the diaphragm. This is particularly true in upper abdominal surgery, which was the most common surgical procedure in patients undergoing closed surgery in the here studied cohort [29, 30]. PEEP may partially prevent this, and usually only when using high PEEP [31]. In the patients studied here, mostly low PEEP was used, regardless of the group. Additional studies are needed to test how high PEEP affects the association between ΔP with PPCs during pneumoperitoneum. Also, we found that ΔP was higher in patients undergoing closed surgery than in patients undergoing open abdominal surgery. However, open abdominal surgery lasted longer, resulting in a comparable ΔPTW in the two groups. The higher absolute ΔP was compensated for by a shorter duration of intraoperative ventilation, and vice versa. Using the ΔPTW allowed us to estimate an exposure limit threshold to an injurious factor as in occupational health. The steeper increase in probability of PPCs above a 20 cm H2O∙hour− 1 found in the sensitivity analysis can be related to an increase in collapsed lung tissue.

As expected, PPCs occurred more frequently in open abdominal surgery patients. An increased baseline risk could explain this due to typical differences in patient characteristics and the duration and the type of surgery. However, this finding strengthens the current analysis since we observed the association even in a cohort of patients, i.e., closed abdominal surgery, at low risk for PPCs and even after controlling for confounding effects with propensity score analysis.

Several intraoperative ventilation approaches, like the use of recruitment manoeuvres and higher PEEP, may result in a lower ΔP [32, 33]. Findings of a metanalysis including clinical trials on intraoperative ventilation suggest that PEEP titrations that resulted in a ΔP rise increased the risk of PPCs [5]. One randomised clinical trial showed an intraoperative PEEP strategy targeting the best compliance to reduce PPCs, though this was only a secondary endpoint in that study [34]. Thus, the best approach to minimise PPCs remains a matter of debate.

ΔPTW was associated with intraoperative adverse events in both closed and open surgery patients. Among all adverse events, airway pressure reduction was more frequently needed in closed surgery group underlining the need for ventilation strategies to lower peak and plateau pressures in this group of patients reflecting unacceptable high airway pressure during surgery.

Several limitations must be acknowledged. We used the parent LAS VEGAS definition of PPCs. This definition differs from what was somewhat recently proposed [1], but they remain reasonably comparable. The protocol of the LAS VEGAS study did not include the collection of oesophageal pressure recordings. Information regarding surgical positioning was not collected, and intra–abdominal pressure levels were also not recorded in the database of the LAS VEGAS study. Both could influence ΔPTW, though [35,36,37]. Due to the additional strict exclusion criteria, we excluded a considerable number of patients. Thus, the findings of this analysis need confirmation in other studies. Also, some patients had only a few timepoints at which ΔP could be calculated. Furthermore, we only included patients with an endotracheal tube and patients who received controlled ventilation, limiting our focus on a specific type of intraoperative airway device and ventilation mode. Of note, 25% of patients had a Body Mass Index (BMI) > 30 kg∙m− 2. Extrapolating this analysis’s findings to obese or morbidly obese patients should be done with some caution. Also, the original LAS VEGAS study was performed 7 years ago. Since then, there could have been changes in clinical practice, e.g., in the use of ‘Enhanced Recovery After Surgery’ (ERAS) pathways and muscle relaxant monitoring during and reversal at the end of surgery. Although the time gap between research findings and practice changes usually lasts longer than a decade [38,39,40], still could be that more immediate changes may affect the associations. Finally, we did not set any a priori effect threshold nor multiple comparisons correction; hence the results’ statistical significance and the exploratory nature of secondary outcome analysis must be confirmed in future trials.

Conclusions

ΔPTW is associated with the occurrence of PPCs and intraoperative adverse events in abdominal surgery. These associations are present regardless of the type of surgical approach and depend on the duration and actual ΔP. Both in patients undergoing open or closed abdominal surgery, the ΔP is a promising target for future strategies to reduce PPCs.