Faculty Bibliography

BACKGROUND:Semaglutide and tirzepatide have transformed obesity treatment, but recent changes to bariatric surgery utilization are not well understood. METHODS:Epic's nationwide Cosmos database was queried for patients who underwent primary sleeve gastrectomy or gastric bypass between 2018 and 2025. Patient characteristics including preoperative semaglutide or tirzepatide dispense history were compared using chi-squared and Wilcoxon rank sum tests. Modified Poisson regression was used to identify factors independently associated with pre-surgery GLP-1RA use. Multilevel models were used to examine hospital- and state-level variation in pre-surgery GLP-1RA use. RESULTS:Bariatric surgery utilization increased after Q3 2018, peaked in Q4 2022, and subsequently decreased 39% through Q4 2025. Between Q4 2018 and Q4 2025, the proportion of Hispanic bariatric surgery patients increased (8.1% vs. 16.8%, p < 0.001), and the proportion of patients who received pre-surgery GLP-1RA increased (0.2% vs. 35.3%, p < 0.001). Factors associated with receiving pre-surgery GLP-1RA were year, private insurance, White race, type 2 diabetes (RR 2.94 [2.88-3.00]), older age, sleep apnea, and metabolic dysfunction-associated steatotic liver disease. Factors associated with receiving surgery upfront were Hispanic ethnicity, Black race, and public or no insurance. After adjusting for patient characteristics and year, there was a 15-fold difference in pre-surgery GLP-1RA use between the highest and lowest hospitals (RR 0.19-2.88). CONCLUSIONS:In the Epic Cosmos database, bariatric surgery utilization decreased from 2022 to 2025, and those who underwent bariatric surgery increasingly received GLP-1RA before surgery. Patients who received pre-surgery GLP-1RA were older, White, privately insured, with diabetes and other weight-related comorbidities, while patients who received surgery upfront were Hispanic, Black, and publicly insured. Pre-surgery GLP-1RA use was also driven by center-specific non-clinical factors.

KEY POINTS/CONCLUSIONS:In this cohort study of 720,348 adults referred for kidney transplantation from 2014 to 2025, only 48% were evaluated and 19% were waitlisted. Progression from referral to evaluation, waitlisting and kidney transplantation was limited by individual, center-level, and geographic factors. Some centers evaluated and waitlisted patients at rates far below the national average, and low-volume centers had lower rates of transplantation. BACKGROUND:Kidney transplantation is a cost-effective, lifesaving treatment of kidney failure, compared with dialysis. Unfortunately, most patients with kidney failure never undergo transplantation. METHODS:Using Epic Cosmos electronic health record data on all patients referred for kidney transplantation from 2014 to 2025, we assessed the stage-specific progression and attrition in the process of evaluation, waitlisting, and kidney transplantation. Center-level and individual (socioeconomic, geographic, and insurance status) factors associated with access to evaluation, waitlisting, and kidney transplantation were characterized using modified Poisson regression. RESULTS:Among 720,348 referred candidates, the median age was 55 years (interquartile range [IQR], 42-64); 47% of patients were White, 52% were male, and 87% were English speaking. Eighty-five percent of patients lived in urban areas. Of the referred candidates, 48% initiated evaluation, 19% were waitlisted, and 10% ultimately underwent transplantation. Among the referred patients who initiated evaluation, the median (IQR) time to evaluation initiation was two (1-4) months after referral; among the patients who were waitlisted, the median (IQR) time to waitlisting was four (2-9) months after evaluation initiation. Patients who were never married (0.94; 95% confidence interval [CI], 0.93 to 0.94), had severe obesity (0.70; 95% CI, 0.69 to 0.72), or were from rural zip codes (relative risk, 0.98; 95% CI, 0.97 to 1.00) were less likely to initiate evaluation. Low-volume centers had lower relative rates of transplantation (0.92; 95% CI, 0.88 to 0.96). In centers with documentation for nonprogression to evaluation, reasons for removal included not meeting criteria/not a candidate (18%), patient decision (13%), unable to contact (12%), death (4%), and financial/insurance complications (7%). CONCLUSIONS:Our study shows substantial attrition before kidney transplant waitlisting.

BACKGROUND:Robotic pancreaticoduodenectomy (RPD) is increasingly performed in the United States. Understanding factors associated with safe adoption of RPD is critical to reducing perioperative mortality during the learning curve. METHODS:Using the Epic Cosmos database, the study identified adult patients (age ≥18 years) who underwent pancreaticoduodenectomy (PD) between 2019 and 2025. Modified Poisson regression was used to assess factors associated with 30-day mortality using adjustment for age, sex, race, ethnicity, insurance, marital status, rural/urban residence, socioeconomic status, and diagnosis. Among surgeons performing two or more RPDs, mortality trends were analyzed across case-number thresholds. Mortality risk was assessed by cumulative RPD and open PD (OPD) experience, with adjustment for age and diagnosis. RESULTS:Among 23,995 patients with a median age of 69 years (interquartile range [IQR], 62-75 years), 1578 (6.6 %) underwent RPD. Use of RPD increased from 4% of PD in 2019 to 10% in 2025. The 30-day mortality was higher for RPD than for OPD (2.7 % vs 2.0 %; adjusted relative risks [aRR], 1.43 (IQR, 1.02-1.95; p = 0.029). In RPD, mortality decreased with increasing surgeon prior experience: 3.9 % (Q1: 0-1 cases), 3.9 % (Q2: 2-4 cases), 2.22 % (Q3: 5-8 cases), 2.67 % (Q4: 9-18 cases), 0.92 % (Q5: 19-71 cases). Increased RPD experience was associated with decreased mortality (per doubling RPD experience: aRR, 0.78 (95 % confidence interval [CI], 0.63-0.96; p = 0.02). The patients who underwent PD between 2023 and 2025 showed no adjusted increase in mortality with robotic technique (aRR, 1.04; 95 % CI, 0.61-1.65; p = 0.85). CONCLUSIONS:Nationwide, adoption of RPD is associated with increased 30-day mortality, which decreases substantially with increasing surgeon RPD experience. These findings suggest that structured, competency-based training pathways are needed to ensure safe dissemination of novel technology, including RPD.

BACKGROUND:Before KAS250 (circles-based allocation), donor service area (DSA) of listing was the largest contributor to deceased donor kidney transplantation (DDKT) rate disparities. Both before and after KAS250, it is unclear to what extent DSA-level disparities are attributable to center-level practice variation. We aimed to disentangle contributions to DDKT rate variation from: (1) center practices, (2) kidney distribution within sharp policy boundaries (DSAs, OPTN Regions), and (3) other geographic variation in kidney scarcity. METHODS:With national transplant registry data, we studied transplant rate variation in the pre-KAS250 era, which prioritized patients based on DSAs and Regions, and under KAS250, which prioritizes patients within 250 nautical mile circles. We modeled candidate DDKT rates with multilevel Poisson regression, adjusting for candidate factors, and calculated median incidence rate ratios (MIRR) to summarize variation attributable to DSAs, OPTN regions, states, census divisions, and to centers within those units. RESULTS:). Adjusted center-level DDKT rates under KAS250 were highly associated with offer acceptance rates (ρ = 0.60, p < 0.001). CONCLUSIONS:Though geographic disparities are driven primarily by center-level practice differences including offer acceptance, KAS250 did reduce DSA-level disparities. Further allocation policy changes are unlikely to substantially reduce geographic variation in DDKT rates.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Traditional medical board examinations present clinical information in static vignettes with multiple-choices (MC), fundamentally different from how physicians gather and integrate data in practice. Recent advances in large language models (LLMs) offer promising approaches to creating more realistic clinical interactive conversations. However, these approaches are limited in neurosurgery, where patient communication capacity varies significantly and diagnosis heavily relies on objective data such as imaging and neurological examinations. We aimed to develop and evaluate a multi-artificial intelligence (AI) agent conversation framework for neurosurgical case assessment that enables realistic clinical interactions through simulated patients and structured access to objective clinical data. METHODS:We developed a framework to convert 608 Self-Assessment in Neurological Surgery first-order diagnosis questions into conversation sessions using 3 specialized AI agents: patient AI for subjective information, system AI for objective data, and clinical AI for diagnostic reasoning. We evaluated generative pretrained transformer 4o's (GPT-4o's) diagnostic accuracy across traditional vignettes, patient-only conversations, and patient + system AI interactions, with human benchmark testing from 10 neurosurgery residents. RESULTS:= .0030) using fewer interactions and reported high educational value of the interactive format. CONCLUSION/CONCLUSIONS:This multi-AI agent framework provides both a more challenging evaluation method for LLMs and an engaging educational tool for neurosurgical training. The significant performance drops in conversational formats suggest that traditional MC testing may overestimate LLMs' clinical reasoning capabilities, while the framework's interactive nature offers promising applications for enhancing medical education.

Since their early development in the 1980s, Simulated Allocation Models (SAMs) have helped policymakers forecast the impact of proposed allocation policy changes on patient outcomes before implementation. In the United States, models like the Kidney-Pancreas Simulated Allocation Model, Liver Simulated Allocation Model, and Thoracic Simulated Allocation Model have been instrumental in shaping organ allocation policies. Analogous models have emerged globally, including the ETKidney and Eurotransplant Liver Allocation System simulators for the Eurotransplant region, to address country and region-specific allocation challenges. This review categorizes and compares SAMs based on their core assumptions, data, and modeling approaches. We highlight challenges in model validation, the use of synthetic data, and model transparency. While simplifying assumptions are often necessary because of limited data, their influence on results should be clearly communicated to ensure policymakers can interpret model predictions accurately. Furthermore, model validation using both retrospective and prospective data is essential to assess performance under evolving policies. Greater transparency through open-source models, detailed reporting of assumptions, and validation efforts can enhance collaboration, reproducibility, and confidence in transplant research. By providing a global perspective on SAMs, this review aims to inform future research and policy development, promoting evidence-based policy development in organ transplantation.

Liver transplant (LT) recipients experience a wide range of comorbidities, leading to frequent healthcare encounters. Until now, national registries, which have limited exposures and outcomes, and laborious small cohort studies have been the main data sources for LT research. Cosmos database offers electronic health record (EHR)-based insights into LT recipients at the national level with granular data. We evaluated if Cosmos data is representative of the entire US LT recipient population. Using Cosmos (N=20,235) and the national Scientific Registry of Transplant Recipients (SRTR) (N=51,281), we identified adult, first-time LT recipients between 7/2016-12/2022. We compared demographics, clinical data, and mortality across datasets, calculating Kaplan-Meier survival estimates and multi-variable Cox regressions. Recipient characteristics were highly comparable (e.g., female: Cosmos=36.5% vs. SRTR=36.4%, Black: 6.8% vs. 7.2%; BMI: 28.5 kg/m2 [24.8-32.9] vs. 28.2 [24.6-32.4]). Lab values were similar across cohorts, including MELD (24 [17-30] vs. 23 [16-30]). Transplant indications, donor characteristics, and 5-year survival (Cosmos 83.1% [82.3-83.8) vs. SRTR 80.9% [80.4-81.3]) were similar. The associations of clinical factors with survival were similar across both groups. Cosmos database demonstrated acceptable generalizability to the general US LT recipient population, which may advance LT research through a better understanding about LT recipients' experiences and outcomes.

Artificial intelligence (AI) is rapidly transforming healthcare, and the field of kidney transplantation (KT) is no exception. While much of the AI-related work has focused on deceased donor KT, there is a growing body of research applying AI tools to living kidney donation (LKD). This review explores AI's current and potential roles in LKD, focusing on predictive and social applications of AI in LKD. Additionally, we discuss the challenges and limitations of implementing AI in clinical settings and highlight emerging research trends. This review consolidates existing research and provides a foundation for both transplant professionals and data scientists seeking to integrate AI responsibly into living donor programs.