Predictors and Outcomes Associated with Bariatric Robotic Delivery: An MBSAQIP Analysis of 318,151 Patients

Author:

Nasser Khadija1,Jatana Sukhdeep1ORCID,Switzer Noah J.12,Karmali Shahzeer12,Birch Daniel W.12,Mocanu Valentin1ORCID

Affiliation:

1. Department of Surgery, University of Alberta, Dvorkin Lounge Mailroom 2G2 Walter C. Mackenzie Health Sciences Centre, 8440-112 ST NW, Edmonton, AB T6G 2B7, Canada

2. Centre for Advancement of Surgical Education and Simulation (CASES), Royal Alexandra Hospital, Edmonton, AB T5H 3V9, Canada

Abstract

Background: The adoption of robotic bariatric surgery has increased dramatically over the last decade. While outcomes comparing bariatric and laparoscopic approaches are debated, little is known about patient factors responsible for the growing delivery of robotic surgery. A better understanding of these factors will help guide the planning of bariatric delivery and resource allocation. Methods: Data were extracted from the MBSAQIP registry from 2020 to 2021. The patient population was organized into primary robot-assisted sleeve gastrectomy or Roux-en-Y gastric bypass (RYGB) versus those who underwent laparoscopic procedures. Bivariate analysis and multivariable logistic regression modeling were conducted to characterize cohort differences and identify independent patient predictors of robotic selection. Results: Of 318,151, 65,951 (20.7%) underwent robot-assisted surgery. Patients undergoing robotic procedures were older (43.4 ± 11.8 vs. 43.1 ± 11.8; p < 0.001) and had higher body mass index (BMI; 45.4 ± 7.9 vs. 45.0 ± 7.6; p < 0.001). Robotic cases had higher rates of medical comorbidities, including sleep apnea, hyperlipidemia, gastroesophageal reflux disease (GERD), and diabetes mellitus. Robotic cases were more likely to undergo RYGB (27.4% vs. 26.4%; p < 0.001). Robotic patients had higher rates of numerous complications, including bleed, reoperation, and reintervention, resulting in higher serious complication rates on multivariate analysis. Independent predictors of robotic selection included increased BMI (aOR 1.02), female sex (aOR 1.04), GERD (aOR 1.12), metabolic dysfunction, RYGB (aOR 1.08), black racial status (aOR 1.11), and lower albumin (aOR 0.84). Conclusions: After adjusting for comorbidities, patients with greater metabolic comorbidities, black racial status, and those undergoing RYGB were more likely to receive robotic surgery. A more comprehensive understanding of patient factors fueling the adoption of robotic delivery, as well as those expected to benefit most, is needed to better guide healthcare resources as the landscape of bariatric surgery continues to evolve.

Publisher

MDPI AG

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