The Impact of Procedure Type on 30-day Readmissions Following Metabolic and Bariatric Surgery: Postoperative Complications of Bariatric Surgery

Overview

Journal Surg Endosc

Publisher Springer

Specialties Gastroenterology
General Surgery
Radiology

Date 2022 Nov 1

PMID 36316585

Authors

Benjamin M Vierra

Colston A Edgerton

Scott A Shikora

Affiliations

Soon will be listed here.

Abstract

Background: Hospital readmission (HR) rates following metabolic/bariatric surgery (MBS) are used as a surrogate for quality outcomes and are increasingly tied to reimbursement rates. There are limited data concerning predictors of HR rates with regard to type of bariatric procedure.

Methods: This study is a retrospective review of prospectively collected data from patients who underwent MBS from January 2014 to December 2019 at Brigham and Women's Hospital in Boston, Massachusetts. The causes of all HRs and reoperations within 30 days of the original discharge were analyzed. Statistical significance was determined using Chi Squared test and T test.

Results: 2815 patients underwent MBS. 2373 patients (84.3%) had primary procedures, while 442 patients (15.7%) had secondary or revisional procedures. The overall 30-day readmission rate was 5.7%, with no significant difference for patients who underwent primary vs. secondary MBS. Among primary procedures, the readmission rate was higher for Roux-en-Y Gastric Bypass (RYGB) than laparoscopic sleeve gastrectomy (SG) (10.32% vs. 4.77%). Readmissions were most often due to nontechnical causes. The overall reoperation rate was 1.14% and was higher for patients undergoing secondary vs. primary procedures (2.94% vs 0.80%).

Conclusions: Readmission rate was similar to that in existing literature. Revisional/secondary surgery did not lead to increased readmissions, although was associated with a higher reoperation rate. Most HRs were due to nontechnical causes. Optimization of postoperative care, such as fluid status, may reduce the incidence of postoperative complications.

Citing Articles

Machine learning analysis of lab tests to predict bariatric readmissions.

Zhang M, Chen R, Yang Y, Sun X, Shan X Sci Rep. 2024; 14(1):16845.

PMID: 39039130 PMC: 11263698. DOI: 10.1038/s41598-024-67710-6.

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