Sociodemographic factors leading to preventable emergency department visits after bariatric surgery: a single-institution analysis

Background: An important quality benchmark after bariatric surgery is 30-day emergency department (ED) visits. Objectives: We aimed to identify risk factors for ED visits not requiring readmission and thus deemed preventable. Setting: University Hospital. Methods: Patients who underwent a minimally invasive sleeve gastrectomy between 2017 and 2022 at a single institution were identified. Among these patients, those who presented to the ED within 30 days after surgery were matched 3:1 to controls. Sociodemographic and clinical variables were collected from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program database and the electronic medical record. Univariate conditional logistic regression analysis was performed to determine predictive factors of ED visits. Results: Overall, 648 patients underwent sleeve gastrectomy, of which 53 (8.2%) presented to the ED within 30 days postoperatively without requiring readmission. Patients who presented to the ED were more likely to be unemployed (42% versus 24%, P = .04) and have government insurance (68% versus 41%, P = .001). Significant risk factors included lower versus upper socioeconomic bracket (odds ratio [OR] 3.6, P = .042), primary care physician (PCP) outside the health system versus within (OR 2.15, P = .032), greater number of PCP visits within the past year (OR 1.27, P < .001), and greater number of postoperative clinic phone calls (OR 2.04, P < .001). The number of ED visits within 1 year before surgery was a significant risk factor, with an OR of 1.44 for each visit (P < .001). Conclusions: Modifiable and unmodifiable risk factors contribute to ED visits after bariatric surgery. Identifying these risk factors can aid in the development of quality improvement initiatives. (c) 2024 American Society for Metabolic and Bariatric Surgery. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.