Saturation of respiratory strain during robotic hysterectomy in obese women with endometrial cancer

Abstract

To evaluate intraoperative ventilatory mechanics during robotic-assisted hysterectomy in obese women with endometrial cancer and introduce the concept of a physiologic “ceiling effect” in respiratory strain. We conducted a retrospective cohort study of 89 women with biopsy-confirmed endometrial cancer who underwent robotic-assisted total hysterectomy between 2011 and 2015. Intraoperative ventilatory parameters, including plateau airway pressure and static lung compliance, were recorded at five-minute intervals. Each patient’s peak plateau pressure was identified to calculate static compliance and estimate maximum ventilatory strain. Patients were stratified by body mass index (BMI), and ventilatory parameters were compared across BMI categories at baseline (post-induction, supine) and during steep Trendelenburg positioning with carbon dioxide pneumoperitoneum. At baseline, increasing BMI was significantly associated with higher plateau airway pressure and lower static compliance. For example, plateau pressure increased from 18.6 ± 3.4 cm of water (cm H₂O) in patients with BMI less than 30 kg per square meter to 25.9 ± 3.3 cm H₂O in those with BMI greater than or equal to 50 (p < 0.001). However, following Trendelenburg positioning with pneumoperitoneum, peak plateau pressures converged across BMI categories, averaging 35.0 ± 3.3 cm H₂O (p = 0.167). Static compliance also converged across BMI strata, averaging 17.2 ± 4.2 ml per cm H₂O (p = 0.129). Pulmonary complications occurred in 4.5% of patients, with no cases of barotrauma or prolonged mechanical ventilation. Intraoperative ventilatory strain appears driven primarily by surgical positioning and pneumoperitoneum, rather than obesity alone. These findings support the feasibility and safety of robotic-assisted hysterectomy across a wide range of body mass index values and introduce the novel concept of a physiologic ceiling effect in ventilatory stress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11701-025-02739-x.

Introduction

Minimally invasive surgery (MIS) is the standard of care for endometrial cancer (EC) [1]. Compared to laparotomy, both conventional laparoscopy and robotic-assisted laparoscopic surgery (RALS) offer substantial perioperative advantages, including reduced blood loss, shorter hospitalization, less postoperative pain, faster recovery, and comparable oncologic outcomes [2–5].

Among MIS techniques, RALS has become the preferred approach for obese patients with EC, who are at increased risk of surgical complications. Robotic systems provide enhanced dexterity, visualization, and ergonomics, facilitating complex pelvic procedures such as hysterectomy and lymphadenectomy in patients with high body mass index (BMI) or challenging anatomy.

Multiple studies have demonstrated the benefits of RALS over traditional laparoscopy in obese EC patients. A meta-analysis of over 10,000 cases found significantly lower conversion rates to laparotomy, particularly in those with BMI ≥ 40 kg/m² [6]. Additional studies have reported reduced blood loss, shorter hospital stays, fewer complications, and similar cancer outcomes, in obese patients undergoing robotic hysterectomy, despite longer operative times [7–9].

Despite these advantages, RALS introduces anesthetic challenges due to the need for Trendelenburg positioning and pneumoperitoneum (T/p), both of which significantly impair respiratory mechanics. These factors elevate plateau airway pressures (Pplat), reduce static compliance (Cstat), and increase the risk of pulmonary complications such as atelectasis, transient hypoxemia, hypercapnia, and, rarely, barotrauma or ventilator-induced lung injury (VILI) [10–13]. Central obesity further compromises lung mechanics by reducing thoracic compliance, limiting functional residual capacity, and impairing oxygenation [13–16]. In this context, careful intraoperative monitoring of ventilatory parameters is essential to mitigate respiratory risk and optimize safety in obese patients undergoing robotic-assisted hysterectomy.

This study evaluates the combined impact of obesity and T/p on intraoperative respiratory mechanics during robotic-assisted hysterectomy for endometrial cancer. Using five-minute interval sampling, we identified each patient’s peak Pplat to capture th of maximal ventilatory strain. During analysis, we observed a physiologic convergence, peak Pplat and Cstat values became statistically indistinguishable across BMI categories once T/p was established. This unanticipated finding suggests a saturation of respiratory strain, wherein the imposed mechanical load of surgical positioning and pneumoperitoneum exceeds the incremental impact of additional adiposity. This concept, described here as a "ceiling effect", emerges as a novel physiologic insight into ventilatory tolerance during robotic pelvic surgery.

Materials and methods

Patient selection

This retrospective cohort study was approved by the Institutional Review Boards (IRBs) of the University of Texas Health San Antonio and the University Health System (UHS). We identified 89 consecutive women with histologically confirmed endometrial carcinoma who underwent robotic-assisted total hysterectomy with surgical staging between 2011 and 2015. Four patients had incomplete intraoperative ventilator data were excluded from the respiratory mechanics analysis.

Surgical care

All procedures were performed using the da Vinci Si Surgical System (Intuitive Surgical, Sunnyvale, CA) by board-certified gynecologic oncologists with assistance from obstetrics and gynecology residents. Surgical staging consisted of total hysterectomy, bilateral salpingo-oophorectomy, and pelvic and/or para-aortic lymph node assessment, guided by clinical and pathologic factors [17]. Ovarian preservation was considered in premenopausal women with early-stage disease at the discretion of the surgeon [18].

Anesthesia care

General anesthesia was induced with midazolam, a short-acting opioid (typically fentanyl), and either propofol or etomidate. Neuromuscular blockade was achieved with rocuronium or succinylcholine, followed by endotracheal intubation. Maintenance anesthesia was provided with sevoflurane or desflurane, supplemented with intravenous opioids as needed. Anesthetic care was delivered by board-certified anesthesiologists, with participation of anesthesia residents and certified registered nurse anesthetists (CRNAs).

Physiologic monitoring and patient positioning

Standard intraoperative monitoring included electrocardiography, pulse oximetry, capnography, core temperature (via esophageal or bladder probes), and non-invasive blood pressure. In higher-risk patients, radial arterial catheters were inserted to enable continuous blood pressure monitoring and arterial blood gas sampling. Patients were positioned in dorsal lithotomy, secured per institutional protocol, and placed in steep Trendelenburg positioning (~ 30°). Carbon dioxide pneumoperitoneum was maintained at 15 mm Hg throughout robotic access.

Intraoperative ventilation management

Anesthetic ventilation practices reflected institutional protocols in use between 2011 and 2015 [19]. Ventilation was delivered in volume-controlled mode with tidal volumes set at 8–10 mL/kg based on actual body weight (ABW). PEEP was set at a default of 5 cm H₂O and was not systematically optimized or individualized for obese patients during this period. Fraction of inspired oxygen (FiO₂) was titrated to maintain oxygen saturation > 94%, and respiratory rate was adjusted to achieve normocarbia. Compliance-adjusted PEEP titration, recruitment maneuvers, and advanced ventilatory monitoring techniques such as esophageal pressure-guided ventilation or electrical impedance tomography were not routinely used at the time. Ventilator settings were modified intraoperatively according to dynamic airway pressures, oxygenation, and individual patient physiology.

Intraoperative respiratory data acquisition

For study purposes, ventilatory parameters were obtained exclusively from archived data recorded by the Servo-i ventilator system (Servo Screen 390 V2.0, Siemens-Elma AB). Measurements were automatically stored at fixed 5-min intervals, including peak inspiratory pressure (PIP), Pplat, tidal volume (TV), respiratory rate, PEEP, and FiO₂. Baseline values were collected after induction in the supine position, following hemodynamic stabilization, and subsequent measurements were archived throughout the case, including during pneumoperitoneum with steep Trendelenburg positioning. Peak Pplat measurements were not used during trocar placement and robotic docking.

For each patient, all intervals were systematically reviewed, and the highest recorded Pplat (peak Pplat) was identified as the physiologic anchor. Static compliance (Cstat) was calculated at the same interval using the formula:

Cstat = Tidal Volume (TV) / (peak Pplat–PEEP)

On average, each patient had ~ 52 plateau pressure values available for review (mean operative time 259 min). The 5-min sampling interval provided a practical balance between feasibility and physiologic resolution, minimizing the risk of missing transient but clinically meaningful increases in airway pressure.

To clarify the distinction between routine clinical care and analytic design, Supplementary Figure S1 illustrates the study methodology. During the study period, anesthesiologists occasionally measured Pplat intraoperatively as a confirmatory check for elevated PIP or unexplained desaturation, typically resulting in one or two clinical values. These ad hoc clinical measurements did not form the basis of this study, which relied exclusively on the systematically archived ventilator data recorded at 5-min intervals throughout each case.

Comorbidities and confounding variables

A detailed EMR review was performed to capture comorbidities and validated risk factors for perioperative pulmonary complications. Variables included obstructive sleep apnea (OSA), prior respiratory infection, chronic obstructive pulmonary disease (COPD) or asthma, tobacco use, heart disease, hypertension, end-stage renal disease (ESRD), diabetes mellitus (DM), age ≥ 70 years, BMI ≥ 30 kg/m², ASA classification ≥ 3, and operative time ≥ 120 min [20]. Central obesity could not be assessed because waist circumference and other anthropometric measures were not available; BMI was therefore used as the sole measure of adiposity.

Quality check / confirmation of uniform anesthetic practice

Ventilation practices were further confirmed by analysis of archived ventilator data (Supplementary Figure S2), which demonstrated consistent use of volume-control ventilation across BMI categories, with delivered tidal volumes capped at ~ 500 mL and PEEP maintained at 5 cm H₂O.

Statistical analysis

Continuous variables were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR), depending on distribution assessed via histograms, Q–Q plots, and the Shapiro–Wilk test. Categorical variables were reported as frequencies and percentages.

One-way ANOVA was used to compare continuous variables across BMI categories. Post hoc pairwise comparisons were conducted as appropriate. The Wilcoxon signed-rank test was used for paired comparisons between baseline (post-intubation, supine) and intraoperative measures in steep Trendelenburg with pneumoperitoneum. Chi-square (χ²) or Fisher’s exact tests were applied for categorical data.

Linear regression was used to examine the relationship between BMI and intraoperative respiratory mechanics (e.g., peak plateau pressure [Pplat], static compliance [Cstat]) at baseline and in Trendelenburg with pneumoperitoneum. Regression coefficients and 95% confidence intervals (CI) were reported.

For supplemental analyses, univariate logistic regression was used to evaluate predictors of pulmonary complications, intraoperative ventilatory mechanics, and of arterial line (A-line) placement. Variables with p < 0.10 in univariate analysis were considered for multivariate modeling; however, multivariate models were limited by the small number of events within subgroups, which precluded stable estimates. These limitations are noted in the results and figure/table legends.

Missing data were minimal (< 8%) and managed using complete case analysis. No adjustments for multiple comparisons were applied to preserve statistical power. All p-values were two-tailed, with significance defined as p < 0.05. All analyses were performed using SPSS version 29.0 (IBM Corp., Armonk, NY).

Results

Patient demographics, tumor characteristics, and perioperative metrics

A total of 89 women underwent robotic-assisted hysterectomy for endometrial cancer and were included in this retrospective analysis. The mean BMI for the cohort was 37.5 ± 9.9 kg/m². Class III obesity (BMI ≥ 40 kg/m²) was observed in 40.4% of patients, with 10.1% classified as BMI ≥ 50 kg/m². Table 1 summarizes demographic, tumor, and perioperative characteristics stratified by BMI.

Table 1.

Demographic, Pathologic, and Perioperative Characteristics Stratified by Body Mass Index (BMI) Category

Variable (unit)	< 30 (n = 22)	30–34.9 (n = 15)	35–39.9 (n = 18)	40–44.9 (n = 16)	45–49.9 (n = 9)	≥ 50 (n = 9)	Total (n = 89)	P-value
BMI (kg/m2)	25.6 ± 2.9	32.4 ± 1.5	37.2 ± 1.6	42.4 ± 1.7	47.4 ± 1.9	56.9 ± 4.9	37.5 ± 9.9
Age (years)	60.2 ± 11.7	60.7 ± 10.7	51.6 ± 14.3	54.4 ± 9.7	43.7 ± 14.0	47.1 ± 9.8	54.5 ± 12.9	0.002
Tumor Grade 1	7 (31.8%)	5 (33.3%)	13 (72.2%)	10 (62.5%)	7 (77.8%)	7 (77.8%)	49 (55.1%)	0.013
Tumor Grade 2	7 (31.8%)	6 (40.0%)	4 (22.2%)	6 (37.5%)	2 (22.2%)	2 (22.2%)	27 (30.3%)
Tumor Grade 3	8 (36.4%)	4 (26.7%)	1 (5.6%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	13 (14.6%)
Tumor Stage – I	14 (63.6%)	9 (60.0%)	17 (94.4%)	12 (75.0%)	9 (100.0%)	9 (100.0%)	70 (78.7%)	0.039
Tumor Stage – II	2 (9.1%)	4 (26.7%)	1 (5.6%)	1 (6.2%)	0 (0.0%)	0 (0.0%)	8 (9.0%)
Tumor Stage – III	6 (27.3%)	2 (13.3%)	0 (0.0%)	3 (18.8%)	0 (0.0%)	0 (0.0%)	11 (12.4%)
Histology – Low Risk	17 (77.3%)	13 (86.7%)	18 (100.0%)	16 (100.0%)	9 (100.0%)	9 (100.0%)	82 (97.8%)
Histology – High Risk	5 (22.7%)	2 (13.3%)	0 (0.0%)	0 (0.0%)	0	0 (0.0%)	7 (2.2%)	0.033
LN Procedure – None	9 (40.9%)	5 (33.3%)	11 (61.1%)	7 (43.8%)	8 (88.9%)	7 (77.8%)	47 (52.8%)
LN Procedure – Pelvic Only	6 (27.3%)	4 (26.7%)	4 (22.2%)	5 (31.2%)	1 (11.1%)	0 (0.0%)	20 (22.5%)
LN Procedure – Pelvic + Para-aortic	7 (31.8%)	6 (40.0%)	3 (16.7%)	4 (25.0%)	0 (0.0%)	2 (22.2%)	22 (24.7%)	0.200
Pelvic LN Count	21.2 ± 9.0	22.6 ± 13.1	23.3 ± 5.1	26.9 ± 8.0	42.0	48.5 ± 36.1	25.0 ± 12.3	0.039
Para-aortic LN Count	8.3 ± 6.8	8.7 ± 3.9	12.3 ± 6.7	10.3 ± 3.6	N = 0	12.0 ± 4.2	9.6 ± 5.1	0.769
Uterine Weight (g)	153.9 ± 89.4	144.4 ± 107.8	160.4 ± 90.7	168.8 ± 94.1	128.7 ± 59.9	180.2 ± 75.4	156.4 ± 88.9	0.835
Estimated Blood Loss (mL)	143.0 ± 115.8	109.7 ± 65.8	182.5 ± 207.0	218.4 ± 232.4	165.0 ± 147.4	155.6 ± 72.6	162.4 ± 158.3	0.515
Length of Stay (hrs)	45.4 ± 17.6	51.9 ± 28.0	59.0 ± 69.5	47.9 ± 62.3	51.6 ± 28.4	52.1 ± 23.5	51.0 ± 43.6	0.620
Any Complication	3 (13.6%)	3 (20.0%)	7 (38.9%)	4 (25.0%)	1 (11.1%)	2 (22.2%)	20 (22.5%)	0.649
IOC	1 (4.5%)	0 (0.0%)	1 (5.6%)	1 (6.2%)	0 (0.0%)	0 (0.0%)	3 (3.4%)	0.861
POC	0 (0.0%)	2 (13.3%)	4 (22.2%)	2 (12.5%)	1 (11.1%)	2 (22.2%)	11 (12.4%)	0.352
Transfusion	2 (9.1%)	1 (6.7%)	2 (11.1%)	1 (6.2%)	0 (0.0%)	0 (0.0%)	6 (6.7%)	0.843
Urgent Care or ER Visit ≥ 1	6 (27.3%)	2 (13.3%)	5 (27.8%)	4 (25.0%)	3 (33.3%)	2 (22.2%)	22 (24.7%)	0.53

Data are presented as mean ± standard deviation (SD) for continuous variables and as number (percentage) for categorical variables

BMI Body Mass Index (kg/m²), LN Lymph Node, IOC Intraoperative Complication, POC Postoperative Complication

P-values were calculated using one-way ANOVA for continuous variables and chi-square or Fisher’s exact test for categorical variables

Bolded P-values indicate statistical significance (P < 0.05)

Patient age varied significantly by BMI category, with younger patients more commonly represented in the higher BMI groups (p = 0.002). Tumor biology also differed across BMI strata. Grade 1 (low-grade) tumors were more prevalent in higher BMI categories (p = 0.013), while Grade 3 tumors were restricted to patients with BMI < 40 kg/m². Similarly, Stage I disease predominated in higher BMI groups (p = 0.039), whereas Stage III disease was seen exclusively in patients with BMI < 45. High-risk histologic subtypes were rare (7 of 89 patients, 2.2%), occurring only in the lowest and mid-range BMI groups (p = 0.033).

Lymphadenectomy was performed in 47.2% of patients, without significant variation in lymph node procedure type (pelvic and para-aortic) across BMI categories (p = 0.200). However, pelvic lymph node yield increased significantly with increasing BMI (p = 0.039), while para-aortic node counts did not (p = 0.769). Uterine weight, estimated blood loss, and length of hospital stay were not significantly associated with BMI (p = 0.835, p = 0.515, and p = 0.620, respectively).

The cohort demonstrated a high prevalence of comorbidities, as summarized in Supplementary Table S1. Hypertension (68.3%) and diabetes mellitus (37.8%) were most common, followed by tobacco use (17.1%), COPD/asthma (13.4%), and heart disease (12.2%). Less frequent comorbidities included prior respiratory infection (4.9%), obstructive sleep apnea (4.9%), and end-stage renal disease (1.2%). Nine patients (10.8%) were aged ≥ 70 years. Data on comorbidities were missing from seven patients.

A total of 20 patients (22.5%) experienced complications, including intraoperative (3.4%), postoperative (12.4%), and blood transfusion-related events (6.7%). Complication rates did not differ significantly by BMI group (Table 1), and detailed complication data are provided in Table 2. Pulmonary complications occurred in 4 patients (4.5%): two required delayed extubation or reintubation, one developed postoperative pneumonia, and one experienced pulmonary embolism. No cases of barotrauma or prolonged mechanical ventilation (> 24 h) were observed. Conversion to laparotomy was required in one patient (1.1%) due to a vascular injury unrelated to respiratory mechanics. For transparency, all complications were included in the total, including minor events such as four trocar site infections and three cases of postoperative ileus.

Table 2.

Perioperative Complications Among Patients Undergoing Robotic-Assisted Hysterectomy

Complication
Pulmonary complications	4 (4.5%)
Delayed extubation or reintubation	2
Pneumonia	1
Pulmonary embolism	1
All-cause complications	10 (11.2%)
Conversion to laparotomy	1
Vascular injury	1
Ureteral injury	1
Wound infection	4
Postoperative ileus	3
Transfusion	6 (6.7%)
Total patients with any complication	20 (22.5%)

Complications are presented as number (percentage) of the total cohort (N = 89). Pulmonary complications include delayed extubation, pneumonia, pulmonary embolism, and reintubation. One conversion to laparotomy occurred for vascular repair. No cases of barotrauma or ventilator-induced lung injury (VILI) were observed

Analysis of comorbidities and perioperative factors in relation to pulmonary complications is presented in Supplementary Table S2. None of the tested predictors reached statistical significance, reflecting the very small number of pulmonary events (n = 4). Nevertheless, the largest point estimates were observed for prior respiratory infection (OR 8.33, 95% CI 0.66–105.71) and ESRD (OR 5.74, 95% CI 0.20–161.80), suggesting possible physiologic vulnerability in these groups, though wide confidence intervals precluded meaningful inference. Other factors, including OSA, COPD/asthma, tobacco use, and elevated BMI, were not associated with excess risk. Because of the limited number of events, multivariate modeling was not feasible.

Univariate associations between patient comorbidities and intraoperative ventilatory mechanics are summarized in Supplementary Table S3. Prior respiratory infection was the only comorbidity significantly associated with increased peak Pplat (38.0 vs 34.7 cm H₂O, p = 0.040) and showed a borderline association with reduced Cstat (30.7 vs 34.2 mL/cm H₂O, p = 0.053). Hypertension and elevated BMI demonstrated nonsignificant trends toward higher peak Pplat (p = 0.094 and p = 0.065, respectively) and lower Cstat (p = 0.055 for HTN). No other comorbidities, including OSA, COPD/asthma, or advanced age, were associated with changes in plateau pressure or compliance. These findings highlight that prior respiratory infection may represent an underrecognized risk factor for both pulmonary complications and intraoperative ventilatory strain.

Intraoperative anesthesia parameters and respiratory mechanics

Table 3 details intraoperative ventilatory and anesthetic characteristics by BMI. ASA classification did not differ significantly with BMI (p = 0.496), with 77.5% of patients classified as ASA Class 3.

Table 3.

Anesthesia and Intraoperative Respiratory Mechanics Stratified by Body Mass Index (BMI) Category

Variable	< 30 (n = 22)	30–34.9 (n = 15)	35–39.9 (n = 18)	40–44.9 (n = 16)	45–49.9 (n = 9)	≥ 50 (n = 9)	Total (n = 89)	P-value
ASA 2	4 (18.0%)	4 (27.0%)	3 (17.0%)	0 (0.0%)	1 (11.0%)	0 (0.0%)	12 (13.5%)	0.496
ASA 3	17 (77.0%)	10 (67.0%)	14 (78.0%)	14 (88.0%)	6 (67.0%)	8 (89.0%)	69 (77.5%)
ASA 4	1 (5.0%)	1 (7.0%)	1 (6.0%)	2 (12.0%)	2 (22.0%)	1 (11.0%)	8 (9.0%)
Arterial Line	13 (59.1%)	9 (60.0%)	15 (83.3%)	14 (87.5%)	9 (100.0%)	8 (88.9%)	69 (77.5%)	0.020*
PIP (Baseline)	20.2 ± 4.3	23.1 ± 3.3	24.5 ± 3.8	25.6 ± 3.1	27.1 ± 2.8	29.6 ± 4.2	24.3 ± 4.6	< 0.001*
PIP, peak (T/p)	38.1 ± 3.1	40.2 ± 2.3	39.9 ± 2.0	40.8 ± 6.8	40.9 ± 3.9	41.7 ± 3.2	40.0 ± 4.0	0.198
Pplat (Baseline)	18.6 ± 3.4	20.6 ± 2.4	21.5 ± 3.5	22.9 ± 3.1	23.9 ± 4.3	25.9 ± 3.3	21.7 ± 3.9	< 0.001*
Pplat, peak (T/p)	33.6 ± 3.1	35.9 ± 2.6	35.9 ± 2.1	34.0 ± 3.9	36.0 ± 4.2	34.8 ± 3.7	35.0 ± 3.3	0.167
Cstat (Baseline)	39.4 ± 8.6	33.0 ± 4.9	36.4 ± 6.0	33.2 ± 5.8	31.4 ± 7.4	29.4 ± 4.4	34.6 ± 7.1	0.002*
Cstat, peak (T/p)	17.4 ± 3.1	15.4 ± 2.9	18.3 ± 4.2	18.9 ± 5.5	15.0 ± 4.0	16.7 ± 5.1	17.2 ± 4.2	0.129
P/F Ratio (T/p)	355.1 ± 108.8	327.9 ± 95.9	276.6 ± 110.0	288.2 ± 97.8	246.8 ± 99.7	262.2 ± 125.2	294.8 ± 109.1	0.214
Operative Time (min)	259.7 ± 71.1	246.7 ± 73.8	235.5 ± 85.7	260.1 ± 72.1	329.6 ± 338.1	251.3 ± 57.3	258.9 ± 125.4	0.610
Anesthesia Time (min)	344.4 ± 85.0	330.7 ± 72.1	341.8 ± 114.8	359.8 ± 71.2	330.4 ± 68.4	362.3 ± 62.8	344.9 ± 83.0	0.905
In room to cut, TTSS (min)	59.7 ± 15.1	60.8 ± 13.6	62.5 ± 12.3	66.7 ± 18.3	72.8 ± 11.9	73.6 ± 14.4	64.6 ± 15.0	0.085

Values are presented as mean ± standard deviation (SD) for continuous variables or number (percentage) for categorical variables

Comparisons across BMI categories were performed using one-way ANOVA for continuous variables and chi-square or Fisher’s exact test for categorical variables

PIP Peak Inspiratory Pressure, Pplat Plateau Pressure, Cstat Static Lung Compliance, P/F Ratio Arterial oxygen partial pressure to fractional inspired oxygen ratio (PaO₂/FiO₂), T/p Trendelenburg positioning with pneumoperitoneum, TTSS Time from operating room entry to surgical start, ASA American Society of Anesthesiologists Physical Status Classification

P-values < 0.05 were considered statistically significant

Arterial lines were placed in 62 of 82 patients (75.6%). Frequency increased significantly with BMI (p = 0.020), from 59.1% in patients with BMI < 30 to 100% in those with BMI 45–49.9 (Table 3). On univariate analysis, higher BMI (p = 0.010), higher ASA class (p = 0.004), and hypertension (OR 4.41, 95% CI 1.39–13.97, p = 0.014) were significantly associated with A-line placement, while tobacco use demonstrated a borderline association (p = 0.061). In multivariate analysis, BMI (adjusted OR 1.10, 95% CI 1.01–1.19, p = 0.022) and hypertension (adjusted OR 3.40, 95% CI 1.04–11.14, p = 0.043) remained independent predictors (Supplementary Table S4).

At baseline (intubated, supine, pre-insufflation), respiratory mechanics were significantly affected by BMI. Peak inspiratory pressure (PIP) increased from 20.2 ± 4.3 cmH₂O in the lowest BMI group to 29.6 ± 4.2 cmH₂O in those with BMI ≥ 50 (p < 0.001). Baseline Pplat similarly rose with increasing BMI (p < 0.001), increasing from 18.6 ± 3.4 to 25.9 ± 3.3 cmH₂O. Static compliance (Cstat) decreased significantly from 39.4 ± 8.6 to 29.4 ± 4.4 mL/cmH₂O across the BMI range (p = 0.002).

However, during T/p these BMI differences diminished. Peak PIP during T/p averaged 40.0 ± 4.0 cmH₂O and did not differ significantly across BMI categories (p = 0.198). Similarly, peak Pplat reached a cohort mean of 35.0 cmH₂O and was statistically indistinguishable between BMI groups (p = 0.167). Cstat values during T/p ranged from 17.4 ± 3.1 to 16.7 ± 5.1 mL/cmH₂O and did not differ significantly across BMI categories (p = 0.129).

The arterial oxygen partial pressure (PaO₂) to fractional inspired oxygen (FiO₂), (P/F ratio), during T/p showed a non-significant downward trend with increasing BMI (p = 0.214), with the lowest average observed in the BMI 45–49.9 group (246.8 ± 99.7). Operative time, anesthesia duration, and in-room-to-incision time (TTSS) were similar across BMI groups (p = 0.610, p = 0.905, and p = 0.085, respectively), although TTSS tended to be longer in patients with higher BMI.

Changes in respiratory mechanics with T/p

Figure 1 illustrates the changes in peak Pplat and Cstat moving from baseline (intubated, supine position) to surgery in T/p. Median peak Pplat increased from 22.0 cmH₂O to 33.0 cmH₂O (p < 0.001), and median Cstat decreased from 33.3 to 16.7 mL/cmH₂O (p < 0.001), highlighting the pronounced effect of T/p on respiratory mechanics across all BMI categories.

Fig. 1.

Intraoperative Respiratory Mechanics at Baseline and During Trendelenburg with Pneumoperitoneum. Box plots display A peak plateau airway pressure (cm H₂O) and B static respiratory compliance (mL/cm H₂O), measured at two timepoints: baseline (supine position post-intubation, pre-insufflation) and during steep Trendelenburg positioning with pneumoperitoneum (30° head-down tilt, insufflation pressure 15 mmHg). Each box represents the interquartile range (IQR), with the median indicated by a horizontal line. Individual patient values are overlaid as scatter plots, and medians are shown numerically. Whiskers denote 1.5 × IQR. Peak plateau pressure increased significantly from a median of 22.0 cm H₂O at baseline to 35.0 cm H₂O during Trendelenburg with pneumoperitoneum (p < 0.001). Static compliance decreased significantly from 33.3 to 16.7 mL/cm H₂O (p < 0.001). Comparisons were performed using the Wilcoxon signed-rank test (two-tailed, significance threshold p < 0.05)

Figure 2 presents the association between BMI and respiratory mechanics. At baseline, BMI was significantly associated with higher peak Pplat (β = 0.29 cmH₂O per kg/m²; 95% CI, 0.14–0.45; p < 0.001) and lower Cstat (β = – 0.91 mL/cmH₂O per kg/m²; 95% CI, – 1.26 to – 0.57; p < 0.001). In contrast, no significant associations were seen between BMI and peak Pplat (p = 0.32) or Cstat (p = 0.663) during T/p, suggesting a physiologic convergence or saturation point in respiratory mechanics.

Fig. 2.

Relationship Between Body Mass Index and Intraoperative Respiratory Mechanics. Linear regression plots depicting the relationship between body mass index (BMI, kg/m2) and ventilatory parameters at baseline (red X, red line) and during steep Trendelenburg with pneumoperitoneum (blue triangle, blue line). A Peak plateau airway pressure (cm H₂O): significantly associated with higher BMI at baseline (p < 0.001), but not during Trendelenburg with pneumoperitoneum (p = 0.32). B Static compliance (mL/cm H₂O): inversely associated with BMI at baseline (p < 0.001), with no significant relationship during Trendelenburg and pneumoperitoneum (p = 0.663). Each data point represents an individual subject. P-values reflect two-tailed significance testing

Discussion

Key findings

In this retrospective study of 89 women undergoing robotic-assisted hysterectomy for endometrial cancer, we observed that surgery in Trendelenburg with pneumoperitoneum exerted the strongest influence on intraoperative respiratory mechanics, surpassing the baseline effects of obesity. While higher BMI was associated with modest elevations in airway pressure and reductions in compliance at baseline, these differences were small compared with the pronounced and uniform changes seen after insufflation and positioning. Once insufflation and positioning were established, peak Pplat and Cstat values appeared to converge across BMI groups, suggesting that surgical conditions, rather than adiposity alone, were the dominant determinants of ventilatory strain.

Comparison to existing literature

Our rate of A-line placement (75.6%) was higher than reported in other robotic pelvic series, such as robotic-assisted prostatectomy (46.6%) [21]. This likely reflects institutional practice during 2011–2015 and the specific anesthetic challenges of obese gynecologic oncology patients. In our cohort, A-line use was selectively applied to patients with higher BMI, ASA class, or hypertension (Supplementary Table S4), suggesting that anesthesiologists prioritized invasive monitoring in those at greater perceived risk of hemodynamic variability, need for continuous arterial pressure monitoring, or blood gas sampling. Although our study was not powered to evaluate whether A-lines improved outcomes, these findings highlight variability in practice and underscore the need for evidence-based guidelines to define their role in robotic hysterectomy.

Our findings are consistent with prior reports demonstrating low rates of perioperative pulmonary complications in obese women undergoing robotic hysterectomy [6–9]. In our cohort, the pulmonary complication rate was 4.5%, similar to studies reporting rates below 5% in comparable populations. For example, Wysham et al. found a 3.0% complication rate in a multicenter analysis, with age, but not BMI, predicting pulmonary morbidity [22]. Likewise, Burks et al. found no association between BMI and postoperative respiratory outcomes despite higher intraoperative airway pressures in obese patients [23]. Eddib et al. similarly documented a low complication rate in obese women, reinforcing the safety of robotic hysterectomy across BMI strata [24]. Together, these data, including ours, support the feasibility of robotic surgery in patients with obesity [25].

We noted that increasing BMI was associated with impaired baseline respiratory mechanics before insufflation and positioning. Patients with BMI ≥ 50 kg/m² had higher baseline Pplat and lower Cstat compared to patients with BMI < 30 kg/m², consistent with the restrictive ventilatory physiology of obesity. These results align with prior studies describing reduced thoracic compliance, increased intra-abdominal pressure, and diminished diaphragmatic excursion in obesity [13–16, 26–28].

Our findings also echo prior reports that Trendelenburg positioning and pneumoperitoneum increase Pplat and decrease Cstat [10, 13, 14, 26–30]. However, unlike earlier studies, we did not observe a continued linear increase in ventilatory strain with rising BMI once Trendelenburg positioning and insufflation were established. This discrepancy may reflect methodological differences. Previous investigations sampled ventilatory parameters at fixed time points (e.g., post-induction, mid-procedure), which likely missed transient but physiologically important spikes in pulmonary strain [26–28]. In contrast, our use of systematic five-minute interval sampling allowed identification of individualized peak plateau pressures (~ 35–36 cm H₂O), capturing maximal periods of pulmonary strain.

Possible convergence in plateau pressure

Our analysis suggests a possible convergence of peak Pplat across BMI categories once Trendelenburg and pneumoperitoneum were established. Beyond this point, additional BMI did not correspond to higher plateau pressures. This observation, which we describe as a potential “ceiling effect,” should be considered hypothesis-generating rather than definitive. It is plausible that the combination of positioning and insufflation imposes a maximal diaphragmatic load, after which further increases in adiposity add little incremental strain. While intriguing, this interpretation requires validation in larger, prospective cohorts before generalization.

Clinical and scientific implications

These findings suggest that BMI alone may not reliably predict intraoperative ventilatory burden once Trendelenburg and pneumoperitoneum are applied. Instead, intraoperative strain appears to be driven largely by surgical conditions and transient events. Future work should explore additional physiologic markers such as central adiposity, abdominal compliance, and thoracic geometry. Parameters like driving pressure (ΔP = Pplat – PEEP) and mechanical power may provide a more direct measure of parenchymal stress and could be incorporated into real-time monitoring [31].

We also propose the conceptual framework of “surgical lung stress load” (SLSL) to describe the cumulative burden imposed by pneumoperitoneum, Trendelenburg positioning, operative manipulation, and case duration. Our data suggest that SLSL, not BMI, may be the dominant driver of ventilatory strain. This construct, though preliminary, may guide personalized ventilation strategies and warrants study in prospective trials.

Strengths and limitations

Strengths

This study has several strengths. First, it examined a homogeneous surgical population of women undergoing robotic-assisted hysterectomy for endometrial cancer, thereby minimizing heterogeneity from procedure type or surgical approach. Second, it included patients across the full spectrum of obesity, allowing for robust evaluation of ventilatory mechanics across BMI strata, including those with class III and super-obesity who are often underrepresented in surgical series.

Methodologically, the use of systematic interval sampling—with plateau pressure (Pplat) values archived every five minutes throughout surgery—represents a major strength. This approach ensured that true physiologic peaks in airway strain were captured, rather than relying on fixed or arbitrarily chosen intraoperative time points. As a result, the analysis reflects the maximum ventilatory load experienced by each patient. The parallel calculation of static compliance (Cstat) at the same physiologic anchor point further strengthens the internal consistency of the analysis.

Surgical and anesthetic care were also highly standardized, with all procedures performed in ~ 30° Trendelenburg position under a 15 mm Hg pneumoperitoneum, and anesthesia delivered with full paralysis, volume-controlled ventilation, tidal volumes indexed to actual body weight (capped at ~ 500 mL), and PEEP maintained at 5 cm H₂O. These practices minimized variability from confounders such as ventilator mode, inconsistent PEEP use, or surgical positioning.

Finally, the decision to focus on plateau pressure as a measure of pulmonary strain offers unique advantages. Pplat more directly reflects alveolar pressure and static lung mechanics than peak inspiratory pressure (PIP), making it a more physiologically relevant surrogate for ventilatory stress during pneumoperitoneum and Trendelenburg. By capturing Pplat repeatedly and systematically, the study provides novel insights into the concept of a physiologic “ceiling effect” in respiratory mechanics during robotic surgery.

Limitations

This study has important limitations. First, it was retrospective in design and not prospectively protocolized, which introduces the possibility of unmeasured confounders. While anesthetic and surgical practices were highly uniform across the study period, we acknowledge that our findings may not generalize to contemporary protocols that incorporate individualized PEEP titration, lung-protective ventilation, or advanced monitoring techniques [32].

Second, our analysis was constrained by the number of adverse pulmonary events. Only four complications occurred, which precluded robust multivariable modeling and limited our ability to adjust for potential confounders. To address this, we performed exploratory univariate analyses across comorbidities and intraoperative ventilatory mechanics (Supplementary Table S3). These analyses confirmed that, aside from prior respiratory infection, comorbidities were not significantly associated with plateau pressure or static compliance.

Third, although plateau pressure was selected as our physiologic anchor, it reflects only a surrogate of alveolar strain. Measurements such as transpulmonary pressure or esophageal manometry, which were not available in this cohort, would provide additional precision. Nonetheless, systematic five-minute interval sampling allowed us to capture ~ 52 values per patient and ensured that true physiologic peaks were recorded, reducing the risk of underestimating pulmonary stress.

Finally, this was a single-institution series performed between 2011 and 2015. Practice patterns, patient selection, and ventilatory strategies may have evolved since that time. The findings should therefore be viewed as hypothesis-generating and require confirmation in prospective studies incorporating modern lung-protective ventilation strategies.

Conclusion

This study provides a physiologic perspective on intraoperative respiratory mechanics in obese women undergoing robotic-assisted hysterectomy. Using systematic five-minute interval sampling, we observed that once pneumoperitoneum and Trendelenburg were established, peak plateau airway pressures appeared to converge at approximately 35–36 cm H₂O across BMI categories. While this possible “ceiling effect” is hypothesis-generating rather than definitive, it suggests that surgical conditions, rather than obesity alone, are the dominant determinants of peak ventilatory strain. Recognition of this pattern may inform future investigations and guide refinement of lung-protective ventilation strategies during robotic pelvic surgery.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

E. K.: Conceptualization, study design, supervision, data interpretation, manuscript drafting, and critical revision. M. G.: Statistical analysis, data interpretation, and manuscript editing. J.G.: Statistical oversight, methodological guidance, and manuscript review. D. B.: Clinical interpretation of anesthetic data, manuscript review, and technical guidance on ventilatory parameters. G. M., P.R.: Study coordination, clinical validation, manuscript drafting, and critical manuscript revision. E.K., M.G., P.R., D.B., J.G., G.M.: made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work; drafted the work or revised it critically for important intellectual content; approved the version to be published; and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding

No external funding was received for this work.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Conflict of interest

The authors declare no competing interests.