Five Year Trends in the Utilization of Robotic Bariatric Surgery Procedures, United States 2015-2019

doi:10.21203/rs.3.rs-987424/v1

Background: To evaluate the national 5-year utilization trends and outcomes in robotic metabolic and bariatric surgery (MBS).

Study design: Retrospective analysis of 2015-2019 MBSAQIP data. Kruskal-Wallis test/Wilcoxon rank-sum test and generalized linear models were used to compare continuous/categorical variables. Chi square tests were used to compared robotic versus laparoscopic approaches.

Results: The use of robotic MBS increased from 6.2% in 2015 to 13.5% in 2019 (N= 775,258). Robotic MBS patients had marginally higher age, body mass index, and likelihood of diabetes, hypertension, hyperlipidemia, renal insufficiency, gastroesophageal reflux, chronic obstructive pulmonary disease, sleep apnea, vena cava filter, coronary angioplasty, anticoagulation therapy, mobility devices, and dependency, but lower organ transplantations compared to laparoscopic patients. Robotic MBS patients showed higher 30-day reinterventions and readmissions alongside a longer surgery time (26-38 min).

Conclusion: Robotic MBS shows higher reintervention and readmission rates which could be driven by the higher baseline comorbidity rates of this group.

General Surgery

Robotic bariatric surgery

laparoscopic bariatric surgery

MBS

Robotic MBS increased from 6.2–13.5% in 2015 to 2019, most notoriously in 2019.
Baseline age, BMI, and 12 diseases/complications were higher in the robotic group.
Robotic SG decreased 11 min while robotic RYGB increased 2 min from 2015 to 2019.
Robotic MBS has 29 and 13% higher 30-day reintervention and readmission rates.

Metabolic and bariatric surgery (MBS) procedures have evolved from open to laparoscopic allowing shorter intervention times, length of hospital stay, and postoperative recovery, as well as lower postoperative pain [1]. Robotic surgeries are a more recent approach to MBS that have gained popularity over the past several years. According to the Society of American Gastrointestinal and Endoscopic Surgeons, a robotic surgery is ‘‘a surgical procedure or technology that adds a computer technology-enhanced device to the interaction between the surgeon and the patient during a surgical operation’’ [2]. One of the main proposed benefits of using robotic approaches involves the improved dexterity and precision in tissue manipulation [1].

Comparison of robotic versus laparoscopic MBS has shown conflicting results in previous studies. MBSAQIP 2015-2018 (N= 571,417 cases) data shows that robotic surgeries have resulted in in lower bleeding, complication rates, and length of stay compared to laparoscopic surgeries [3]. However, these improvements were made at the expense of higher leakage rates and longer surgery duration [4]. A systematic review and meta-analysis involving 34 studies (N= 27,997 cases) found no significant differences between robotic and laparoscopic procedures regarding postoperative complications, length of stay, reoperation, conversion, and mortality [5]. However, robotic procedures had longer operative times and hospital costs compared to laparoscopic surgeries [5]. Converse to what was observed in the MBSAQIP 2015-2018 data, according to this meta-analysis, the incidence of anastomotic leak was lower in robotic versus laparoscopic procedures [5]. A more recent systematic review and meta-analysis involving 30 clinical trials (N= 210,420 cases) showed robotic surgeries resulted in lower mortality (2.4 OR) but longer operation times (~27 minutes) compared to laparoscopic procedures [6]. Length of stay, reoperation within 30 days, overall complications, pulmonary embolism, blood loss, and stricture were not different between robotic and laparoscopic procedures [6]. Similarly, a recent study utilizing a matched approach using the 2015-2018 MBSAQIP database (n= 93,802) showed robotic surgeries increased the time invested by 30% for SG and 25% for RYGB, respectively. Length of stay and readmission rates were also higher in robotic procedures versus their laparoscopic counterparts [7]. Thus, based on the current literature, particularly considering the results from the national database, the beneficial effects of performing robotic versus laparoscopic MBS is still in debate, with the most consistent finding being a longer intervention time by the surgeon [8].

The main goal of the present study was to identify the trends in utilization of robotic MBS between 2015 to 2019 using the MBSAQIP database. The secondary goal was to compare robotic versus laparoscopic outcomes in MBS. It was hypothesized that the use of robotic procedures had increased throughout the 5-year time frame and that robotic surgery outcomes had improved over time.

Study Design. A retrospective cross-sectional analysis was conducted using the 2015-2019 MBSAQIP data. The UT Health Committee for the Protection of Human Subjects considers a retrospective analysis of public, anonymized data, such as the MBSAQIP dataset, exempt from review.

Data Source. The American College of Surgeons and the American Society for Metabolic and Bariatric Surgery merged their programs in 2012 to form the MBSAQIP which evaluates the national accreditation standards for bariatric surgery centers in the United States and Canada [9]. The MBSAQIP is the largest, bariatric specific, clinical dataset in the country. The merged 2015-2019 MBSAQIP participant use data file (PUF) was used for this analysis. All MBS patients who received clinical care at an accredited center were included in the PUF. Data input is standardized across centers ensuring reliable data [10].

Variables. Surgical approaches were classified into three groups with CPT code 43775 as laparoscopic SG; CPT codes 43845 and 43633 as biliopancreatic diversion with duodenal switch (BPD-DS); and CPT codes 43846, 43847, 43644, and 43645 as RYGB. Independent and dependent outcome variables were dichotomized using yes/no categories. The covariates used for analysis were age, ethnicity/race, BMI, sex, and staple line reinforcement. Age was classified into four groups: patients less than 18, 18 to 30 years, 31 to 60 years, and greater than 60. Ethnic groups were classified as Hispanic, non-Hispanic White (NHW), non-Hispanic Black (NHB), and other. For supplementary table 1, preoperative BMI was classified into four groups using the following cutoff points: BMI<35, BMI 35 to 39, BMI 40 to 49, and BMI \(\ge\)50kg/m².

Participants. The PUF contains 173 HIPAA-compliant variables on 206,570 cases submitted from 868 centers in 2019; 204,837 cases submitted from 854 centers in 2018; 200,374 from 832 centers in 2017; 186,772 from 791 centers in 2016; and 168,903 from 742 centers in 2015. Inclusion criteria were limited to patients aged > 19 and < 70 years. Cases with missing age (n= 648) were excluded from the analysis. The final analytical sample utilized for the present study included 775,258 adult patients after excluding patients with prior MBS.

Statistical Analysis. For continuous variables, means (± SD) were compared using the Kruskal-Wallis test or the Wilcoxon rank-sum test. For categorical variables, frequencies (%) were compared using generalized linear models. To compare patient’s characteristics between the approaches (Laparoscopic vs Robotic), chi-square was utilized. Additionally, logistic regression analysis was used to calculate outcome variables between Laparoscopic and Robotic surgeries. Both crude odds ratios and adjusted odds ratios (controlling for age, sex, ethnicity/race, BMI, and staple line reinforcement) were generated. P-value was considered significant if <0.05. All statistical analyses were performed using SAS, version 9.4 (SAS Institute).

A total of 775,258 MBS procedures were captured in the database within our inclusion criteria between 2015 and 2019. During this period, the utilization of laparoscopic MBS decreased from 93.2–85.7%, whereas the percentage of robotic MBS significantly increased from 6.2–13.5% (p <0.01) (Table 1).

Table 1

Frequency of metabolic and bariatric surgery procedures, MBSAQIP, 2015-2019.
Approach	2015	2016	2017	2018	2019	p^a
Approach	N= 141,447 N (%)	N= 156,901 N (%)	N= 168,324 N (%)	N= 171,527 N (%)	N= 172,490 N (%)	0.019
Laparoscopic	131,846 (93.21)	144,296 (91.97)	153,011 (90.90)	152,923 (89.15)	147,808 (85.69)	0.123
NOTES	191 (0.14)	1,406 (0.90)	1,763 (1.05)	1,473 (0.86)	1,104 (0.64)	0.349
Open	582 (0.41)	474 (0.30)	408 (0.24)	274 (0.16)	219 (0.13)	0.017
Robotic	8,828 (6.24)	10,725 (6.84)	13,142 (7.81)	16,857 (9.83)	23,359 (13.54)	0.008
^a Generated from GLM procedure NOTES: Natural orifice transluminal endoscopy

SG was the most common utilized procedure (71.9%), followed by RYGB (27.0%), and BPD-DS (1.1%). A robotic approach for SG (6.0–13.7% [p <0.01]), RYGB (7.2–13.5% [p <0.05]), and BPD-DS (21.2–25% [p <0.05]) showed a significant incremental trend from 2015 to 2019 (Table 2). BMI did not significantly impact the rate of utilization of robotic or laparoscopic SG and RYGB (Supplementary table 1).

Table 2

Frequency of metabolic and bariatric surgery procedures by type and year, MBSAQIP 2015 to 2019.
Approach	Total (2015-2019)	2015	2016	2017	2018	2019	Trend p^a
	N= 775,258	N= 133,552	N= 149,026	N= 160,897	N= 165,234	N= 166,549
SG	557,236 (71.9)
Laparoscopic	506,010 (90.81)	85,871 (93.94)	99,347 (93.00)	107,965 (91.95)	108,830 (89.95)	103,997 (86.23)	0.382
NOTES	285 (0.05)	4 (0)	99 (0.09)	88 (0.07)	59 (0.05)	35 (0.03)	0.886
Open	93 (0.02)	20 (0.02)	20 (0.02)	14 (0.01)	20 (0.02)	19 (0.02)	0.846
Robotic	50,848 (9.13)	5,515 (6.03)	7,354 (6.88)	9,349 (7.96)	12,082 (9.99)	16,548 (13.72)	0.003
RYGB	209,379 (27.0)
Laparoscopic	189,126 (90.33)	37,711 (91.89)	37,638 (92.08)	38,281 (91.64)	3,7817 (89.98)	37,679 (86.30)	0.912
NOTES	120 (0.06)	4 (0.01)	46 (0.11)	48 (0.11)	16 (0.04)	6 (0.01)	0.758
Open	931 (0.44)	362 (0.88)	244 (0.60)	173 (0.41)	87 (0.21)	65 (0.15)	0.017
Robotic	19,202 (9.17)	2,961 (7.22)	2,948 (7.21)	3273 (7.83)	4,108 (9.77)	5,912 (13.54)	0.041
BPD-DS	8,643 (1.1)
Laparoscopic	6,503 (75.24)	818 (74.09)	1,018 (76.54)	1,315 (77.08)	1,676 (75.67)	1,676 (73.25)	0.005
NOTES	0 (0.00)	0 (0)	0 (0)	0 (0)	0 (0.00)	0 (0)	-
Open	317 (3.67)	52 (4.71)	78 (5.86)	93 (5.45)	54 (2.44)	40 (1.75)	0.561
Robotic	1,823 (21.09)	234 (21.20)	234 (17.59)	298 (17.47)	485 (21.90)	572 (25.00)	0.015
^a GLM procedure BPD-DS: Biliopancreatic Diversion with Duodenal Switch, NOTES: Natural orifice transluminal endoscopic surgery, RYGB: Roux-en-Y Gastrectomy, and SG: Sleeve gastrectomy.

Patients undergoing MBS through a robotic approach had significantly higher age and BMI, and a higher prevalence of diabetes mellitus, hypertension, hyperlipidemia, renal insufficiency, gastroesophageal reflux disease (GERD), chronic obstructive pulmonary disease (COPD), sleep apnea, inferior vena cava filter, percutaneous transluminal coronary angioplasty (PTCA), anticoagulation therapy, and mobility devices, while also having lower functional health status. Patients undergoing robotic MBS were significantly less likely to be female and to have a prior organ transplant in comparison to patients undergoing laparoscopic MBS (Table 3).

Table 3

Patient characteristics by metabolic and bariatric surgery procedure.
Variable	Laparoscopic	Robotic-assisted	p value^a
	(N= 729,884)	(N= 72,911)
Age	44.51 ± 12.01	44.70 ± 12.04	<0.001
BMI	45.22 ± 7.93	45.49 ± 8.09	<0.001
	N (%)	N (%)
Race/Ethnicity			<0.001
Hispanic	72,411 (9.92)	6,593 (9.04)	<0.001
Non-Hispanic White	425,802 (58.34)	41,998 (57.60)
Non-Hispanic Black	118,955 (16.30)	13,966 (19.15)
Other	112,716 (15.44)	10,354 (14.20)
Male	148,803 (20.39)	14,479 (19.87)	<0.001
Smoker	61,296 (8.40)	5,988 (8.21)	0.085
GERD	221,657 (30.37)	23,433 (32.14)	<0.001
Mobility device	10,872 (1.49)	1,439 (1.97)	<0.001
Oxygen dependent	4,992 (0.68)	493 (0.68)	0.808
MI history	9,075 (1.24)	854 (1.17)	0.093
Prior cardiac surgery	7,688 (1.05)	742 (1.02)	0.368
PTC	13,657 (1.87)	1,440 (1.98)	0.049
Hypertension	345,479 (47.33)	35,578 (48.80)	<0.001
Hyperlipidemia	170,882 (23.41)	17,529 (24.04)	<0.001
Prior DVT	11,860 (1.62)	1,142 (1.57)	0.232
Dialysis	2,238 (0.31)	248 (0.34)	0.120
Renal insufficiency	4,512 (0.62)	535 (0.73)	<0.001
Therapeutic anticoagulation	19,855 (2.72)	2,185 (3.00)	<0.001
Pre-Op Steroid/Immunosuppressant	1,.679 (1.74)	1,326 (1.82)	0.109
DM			<0.001
Insulin	60,264 (8.26)	6,558 (8.99)
Non-insulin	128,226 (17.57)	13,254 (18.18)
Functional health status			<0.001
Independent	723,035 (99.06)	71,967 (98.71)
Partially Dependent	4,483 (0.61)	518 (0.71)
Totally Dependent	2,118 (0.29)	401 (0.55)
COPD	11,474 (1.57)	1,344 (1.84)	<0.001
History of PE	8,566 (1.17)	885 (1.21)	0.337
Sleep apnea	274,800 (37.65)	28,086 (38.52)	<0.001
IVC filter	4,035 (0.55)	474 (0.65)	0.001
Previous organ transplant	1,030 (0.23)	84 (0.16)	0.001
^a Wilcoxon rank-sum test for continuous variables and Fisher's exact test for categorical variables for laparoscopic vs. robotic-assisted approaches ASA: American Society of Anesthesiologists, BMI: body mass index, COPD: chronic obstructive pulmonary disease, DM: type 2 diabetes mellitus, DVT: deep vein thrombosis, GERD: gastroesophageal reflux disease, IVC: inferior vena cava, MI: myocardial infarction PE: pulmonary embolism, and PTC: percutaneous transluminal coronary

Robotic SG and RYGB are on average ~26 and ~37.7 minutes longer than laparoscopic approaches, representing a 27.4% and 24.2% longer time investment, respectively. Analysis showed a decrease in both laparoscopic SG time (from 74.1 ± 36.2 to 66.0 ± 33.6 minutes [~8.0 minutes]) and robotic SG time (from 102.3 ± 42.9 to 91.0 ± 39.8 [~11.3 minutes]) from 2015 to 2019. Conversely, the time invested in both laparoscopic (117.1 ± 52.5 to 118.4 ± 52.8 [~1.3 minutes]) and robotic RYGB (152.6 ± 63.7 to 154.0 ± 60.6 [~2.0 minutes]) slightly but significantly increased from 2015 to 2019 (Table 4).

Table 4

Operative time in minutes by metabolic and bariatric surgery approach and procedure
Operation type	Total average (Time ± SD)	2015 (Time ± SD)	2016 (Time ± SD)	2017 (Time ± SD)	2018 (Time ± SD)	2019 (Time ± SD)	p value^a
SG
Laparoscopic	69.60 ± 34.73 (n= 505,524)	74.06 ± 36.20 (n= 85,803)	71.11 ± 34.19 (n= 99,245)	68.82 ± 34.20 (n= 107,863)	68.03 ± 33.51 (n= 108,735)	66.03 ± 33.56 (n= 103,878)	<0.001
Robotic	95.85 ± 42.42 (n= 50,848)	102.31 ± 42.89 (n= 5,514)	99.36 ± 43.79 (n= 7,349)	96.38 ± 44.77 (n= 9,345)	96.94 ± 42.31 (n= 12,075)	91.02 ± 39.76 (n= 16,538)	<0.001
RYGB
Laparoscopic	117.80 ± 52.67 (n= 189,005)	117.14 ± 52.47 (n= 37,696)	118.00 ± 53.29 (n= 37,610)	117.14 ± 52.58 (n= 38,270)	118.37 ± 52.16 (n= 37,784)	118.39 ± 52.82 (n= 37,645)	<0.001
Robotic	155.50 ± 62.37 (n= 19,186)	152.00 ± 63.66 (n= 2,961)	155.55 ± 64.86 (n= 2,948)	159.73 ± 64.86 (n= 3,273)	156.30 ± 61.99 (n= 4,103)	154.01 ± 60.58 (n= 5,905)	<0.001
^a Kruskal-Wallist test SG: sleeve gastrectomy, RYGB: Roux-en-Y gastrectomy

Crude logistic regression models showed robotic surgeries to have a 16% higher risk of 30-day reoperation (OR= 1.16, 95% CI= 1.09-1.24), a 20% higher risk of 30-day reintervention (OR= 1.20, 95% CI= 1.12-1.28), and a 19% higher risk of 30-day readmission (OR= 1.19, 95% CI= 1.15-1.24) compared to laparoscopic surgeries. Logistic regression models adjusted for age, sex, ethnicity, BMI, and staple line reinforcement showed a 29% higher 30-day reintervention (OR= 1.29, 95% CI= 1.08, 1.55) and a 13% higher 30-day readmission (OR= 1.13, 95% CI= 1.03, 1.25), in the robotic versus the laparoscopic group (Table 5).

Table 5

Odds of Mortality and Other Primary Outcomes, Robotic versus Laparoscopic Surgery Approach, MBSAQIP, 2015 to 2019
Outcome
		Crude OR	95% CI	p value		Adj OR^a		95% CI	p value
30-day mortality	1.0 (ref)	0.877	(0.675, 1.140)		0.327		0.607	(0.320, 1.153)		0.127
60-day mortality	1.0 (ref)	1.756	(0.414, 7.456)		0.445		0.246	(0.012, 4.877)		0.358
30-day reoperation	1.0 (ref)	1.160	(1.088, 1.238)		<0.001		1.040	(0.845, 1.281)		0.708
30-day reintervention	1.0 (ref)	1.197	(1.122, 1.277)		<0.001		1.291	(1.080, 1.545)		<0.005
30-day readmission	1.0 (ref)	1.194	(1.150, 1.239)		<0.001		1.133	(1.025, 1.252)		0.015
Stroke	1.0 (ref)	1.306	(0.716, 2.384)		0.384		2.639	(0.871, 7.994)		0.086
Intraoperative/ post MI	1.0 (ref)	0.572	(0.320, 1.023)		0.060		0.441	(0.059, 3.284)		0.424
Pulmonary embolism	1.0 (ref)	1.142	(0.918, 1.421)		0.233		1.210	(0.749, 1.956)		0.437
Postoperative DVT	1.0 (ref)	0.917	(0.761, 1.105)		0.361		0.982	(0.638, 1.511)		0.933
Septic Shock	1.0 (ref)	0.589	(0.078, 4.425)		0.607		- ^b	-		0.969
Sepsis	1.0 (ref)	0.589	(0.141, 2.451)		0.467		- ^b	-		0.953
^a ORs and 95% CI derived from logistic regression. Outcomes adjusted for potential confounders (age, sex, ethnicity/race, BMI and Staple line reinforcement). ^b value not reported because of disproportionality and the low sample size within the procedure. MI: myocardial infraction, DVT: Deep vein thrombosis

Results here show that the use of robotic MBS has been steadily increasing from 2015 to 2019 with the most pronounced increment observed in 2018 to 2019. Specifically, there was a 217% increase in number of primary robotic MBS completed from 2015 to 2019. After stratifying by MBS type, the rate of utilization of both robotic SG and RYGB procedures doubled from 2015 to 2019. Analysis showed a higher 30-day reintervention and readmission rates as well as a longer surgery time (26-38 min) in comparison to laparoscopic approaches.

Performing MBS in patients with high degrees of obesity comes with surgical challenges such as space constraints caused by increased liver size, presence of intra-abdominal fat, and a thick abdominal wall which complicate the handling of manual instruments [1]. Robotic MBS has been proposed to overcome these issues, potentially improving surgery outcomes but these claims have not been supported by the presented results.

Previous research using the MBSAQIP database shows a significant improvement in operative time from 2015 to 2018 in the robotic cohort of MBS [3]. However, our analysis using 2015-2019 data, shows that after stratifying by surgery type, robotic surgeries were significantly shorter by ~11 min only in the SG but not the RYGB group, where time marginally but significantly increased by ~2 min from 2015 to 2019. According to a recent world-wide systematic review and meta-analysis, robotic surgeries have ~27.6 min longer operative times in comparison to laparoscopic procedures [6]. Our analysis shows a similar trend in surgery duration between robotic versus laparoscopic procedures for both SG (28.3 min longer in the robotic SG) and RYGB (~34.9 min longer in the robotic RYGB) with a more pronounced difference in the later one. When comparing robotic SG versus robotic RYGB we observed ~ 59.7 min longer surgery time in the RYGB group, whereas comparison of laparoscopic SG versus laparoscopic RYGB led to ~48.2 min longer duration in the RYGB group. These results show longer surgery duration in robotic procedures, particularly when utilized to perform an already longer MBS surgery, RYGB.

When using the MBSAQIP 2015-2018 data, similar rates were observed in 30-day readmission and 30-day reintervention between robotic and laparoscopic approaches [3]. However, with the addition of 2019 data, a significantly higher rate of 30-day reintervention and readmission were observed in the robotic surgery group. It is possible that the higher reintervention and readmission rates could be driven by the higher comorbidity rate present in this group. Having a higher number of risk factors pre-MBS has shown to increase post-MBS complication rates [11–13]. Likewise, reintervention and readmission reasons are unknown and therefore it is difficult to speculate if causes, other than the surgical procedure, are involved.

Several studies have been published using the MBSAQIP database, however, this is the only study that has compared the trends and outcomes of robotic versus laparoscopic procedures using the most recent dataset 2015-2019 containing a total of 775,258 cases. We present both crude and adjusted covariates to control for any potential confounders. Despite these strengths, this review has several limitations that should be mentioned. First, bariatric surgeons generally perform only one type of MBS procedure (either robotic or laparoscopic), therefore, comparison between procedures is indirectly also comparing surgeons’ skills. Second, this is a retrospective analysis on a prospectively collected database and thus is vulnerable to the biases associated with retrospective analyses, such as 1) coding errors and missing data, 2) models adjustment limited to variables that have already been collected, and 3) lack of relevant variables such as: surgeon’s experience, surgery cost, stapler or robotic platform utilized (newer platform involves a shorter learning curve), as well as information on weight loss, comorbidity resolution, and long-term complications >30-days post-surgery. Third, since statistical significance is largely dependent on sample size and since the present analysis was performed on a large database, it is possible that we observe significant statistical differences that are not clinically relevant. Lastly, there is no standardization on the quantity of robotic system utilization for procedures listed as “robotic” and therefore, it is not possible to determine the percentage of robotic system use in a “robotic-assisted” surgery. Future data collection should separate hybrid surgeries from surgeries involving a single approach.

Analysis of MBSAQIP 2015-2019 data showed robotic surgeries to have a higher 30-days reintervention and readmission rates, alongside a longer surgery time (26-38 min) in comparison to laparoscopic approaches. However, it is possible that the higher baseline comorbidity rates observed in the robotic group are driving the higher risk of reintervention and readmission. Future research comparing the effectiveness and safety of robotic versus laparoscopic approaches would benefit from the creation of an experimental study in which both procedures are 1) performed by the same surgeons, who should ideally have similar skills in robotic (new platform, Vinci Xi) and laparoscopic approaches, and 2) have participants with similar comorbidity rates.

Author Contributions:

__ conceived the research idea, wrote the first draft of the manuscript, and worked on authors’ and reviewers’ feedback. __ and __ analyzed the data and created the tables. __ obtained the database, reviewed and edited the manuscript, and oversaw the analysis. __, __, __, and __ reviewed and edited the manuscript. All authors approved of the final version of the manuscript.

Conflict of Interest Disclosure:

This work was supported by the National Institute on Minority Health and Health Disparities [grant ______________]. _______ (surgeon) has received funds from Intuitive Surgical Inc. for education purposes since 2019. Dr. _______ relationship with Intuitive Surgical Inc. has been reviewed by the University of _______, _______ in accordance with its conflict-of-interest policies. Dr. _______ (surgeon) is a consultant and key opinion leader for Intuitive Surgical Inc. since 2019. All other authors report no conflicts of interest.

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Supplementary table 1. Frequency of MBS procedures by BMI category.

Approach	SG		RYGB		BPD/DS
	N	%	N	%	N	%
N= 709,208	510,507	72	190,620	27	8,081	1
Laparoscopic
Total	464,076	90.91	172,082	90.27	6,055	74.93
BMI < 35	27,133	5.85	7,081	4.11	166	2.74
35 ≤ BMI ≤ 39	87,154	18.78	27,386	15.91	373	6.16
40 ≤ BMI ≤ 49	243,133	52.39	89,395	51.95	2,140	35.34
BMI ≥ 50	106,656	22.98	48,220	28.02	3,376	55.76
p value	0.442		0.320		0.035
NOTES
Total	264	0.04	110	0.06	0	0.00
BMI ≤ 35	28	10.61	6	5.45	0	0
35 ≤ BMI ≤ 39	40	15.15	9	8.18	0	0
40 ≤ BMI ≤ 49	106	40.15	44	40.0	0	0
BMI ≥ 50	90	34.09	51	46.36	0	0
p value	0.140		0.058		-
Open
Total	87	0.02	881	0.46	301	3.72
BMI ≤ 35	6	6.90	33	3.75	11	3.65
35 ≤ BMI ≤ 39	13	14.94	84	9.53	24	7.97
40 ≤ BMI ≤ 49	48	55.17	322	36.55	92	30.56
BMI ≥ 50	21	24.14	442	50.17	174	57.81
p value	0.524		0.026		0.039
Robotic
Total	46,080	9.03	17,547	9.21	1,725	21.65
BMI ≤ 35	2,298	4.99	874	4.98	49	2.84
35 ≤ BMI ≤ 39	8,464	18.37	3,049	17.38	87	5.04
40 ≤ BMI ≤ 49	24,300	52.73	8,818	50.25	549	31.83
BMI ≥ 50	1,1018	23.91	4,806	27.39	1,040	60.29
p value	0.415		0.326		0.047

BPD/DS: Biliopancreatic Diversion with Duodenal Switch, NOTES: Natural orifice transluminal endoscopic surgery, RYGB: Roux-en-Y Gastrectomy, and SG: Sleeve gastrectomy

Five Year Trends in the Utilization of Robotic Bariatric Surgery Procedures, United States 2015-2019

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Abstract

Key Points

Introduction

Methods

Results

Discussion

Conclusion

Declarations

References

Supplementary Table

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