Asian race is not associated with increased 30-day serious complications or mortality: a MBSAQIP analysis of 594,837 patients.

INTRODUCTION: With expansion of bariatric surgery indications to include Asian patients with diabetes and body mass index (BMI) ≥ 27.5, or BMI ≥ 32.5, it is important to characterize Asian patient population undergoing bariatric surgery and assess their postoperative outcomes. METHODS: This retrospective study analyzed the 2015-2019 Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database. All patients undergoing Roux-en-Y gastric bypass (RYGB) and laparoscopic sleeve gastrectomy (LSG) who self-reported as Asian or White race were included. The primary outcomes were to characterize the Asian race population in North American and to identify if Asian race was associated with serious complications or mortality at 30 days. RESULTS: Overall, 594,837 patients met inclusion, with 4229 self-reporting Asian racial status. Patients of Asian race were younger (41.8 vs 45.5 years, p < 0.001) and had a lower BMI (42.8 vs 44.7 kg/m2 p < 0.001) than White patients. They were also more likely to have insulin dependent diabetes (10.9% vs 8.2%, p < 0.001), have received prior cardiac surgery (10.0% vs 1.2% p < 0.001), and suffer from renal insufficiency (1.0% vs 0.5%, p < 0.001). There were no significant differences between rates of RYGB (28.3% vs 28.9%, p = 0.4) and mean operative duration (87.7 vs 87.5 min, p = 0.7). Additionally, there were no differences in 30 day outcomes including leak (0.5% vs 0.5%, p = 0.625), bleeding (1.2% vs 1.0%, p = 0.1), serious complications (3.4% vs 3.5%, p = 0.6), or mortality (0.1% vs 0.1%, p = 0.7) and after confounder control, Asian race was not independently associated with serious complications (OR 1.0, CI 0.9-1.2, p = 0.7), or mortality (OR 1.1, CI 0.3-3.3, p = 0.1). CONCLUSIONS: Despite the increased metabolic burden of Asian patients, no differences in 30-day outcomes compared to White patients occurs. This data supports evidence suggesting these patients may safely undergo bariatric surgery independent of their increased metabolic burden.

Harnessing artificial intelligence in bariatric surgery: comparative analysis of ChatGPT-4, Bing, and Bard in generating clinician-level bariatric surgery recommendations.

BACKGROUND: The formulation of clinical recommendations pertaining to bariatric surgery is essential in guiding healthcare professionals. However, the extensive and continuously evolving body of literature in bariatric surgery presents considerable challenge for staying abreast of latest developments and efficient information acquisition. Artificial intelligence (AI) has the potential to streamline access to the salient points of clinical recommendations in bariatric surgery. OBJECTIVES: The study aims to appraise the quality and readability of AI-chat-generated answers to frequently asked clinical inquiries in the field of bariatric and metabolic surgery. SETTING: Remote. METHODS: Question prompts inputted into AI large language models (LLMs) and were created based on pre-existing clinical practice guidelines regarding bariatric and metabolic surgery. The prompts were queried into 3 LLMs: OpenAI ChatGPT-4, Microsoft Bing, and Google Bard. The responses from each LLM were entered into a spreadsheet for randomized and blinded duplicate review. Accredited bariatric surgeons in North America independently assessed appropriateness of each recommendation using a 5-point Likert scale. Scores of 4 and 5 were deemed appropriate, while scores of 1-3 indicated lack of appropriateness. A Flesch Reading Ease (FRE) score was calculated to assess the readability of responses generated by each LLMs. RESULTS: There was a significant difference between the 3 LLMs in their 5-point Likert scores, with mean values of 4.46 (SD .82), 3.89 (.80), and 3.11 (.72) for ChatGPT-4, Bard, and Bing (P < .001). There was a significant difference between the 3 LLMs in the proportion of appropriate answers, with ChatGPT-4 at 85.7%, Bard at 74.3%, and Bing at 25.7% (P < .001). The mean FRE scores for ChatGPT-4, Bard, and Bing, were 21.68 (SD 2.78), 42.89 (4.03), and 14.64 (5.09), respectively, with higher scores representing easier readability. CONCLUSIONS: LLM-based AI chat models can effectively generate appropriate responses to clinical questions related to bariatric surgery, though the performance of different models can vary greatly. Therefore, caution should be taken when interpreting clinical information provided by LLMs, and clinician oversight is necessary to ensure accuracy. Future investigation is warranted to explore how LLMs might enhance healthcare provision and clinical decision-making in bariatric surgery.