Faculty Bibliography

INTRODUCTION/BACKGROUND:ChatGPT has shown the ability to answer clinical questions in general medicine but may be constrained by the specialized nature of kidney transplantation. Thus, it is important to explore how ChatGPT can be used in kidney transplantation and how its knowledge compares to human respondents. METHODS:We prompted ChatGPT versions 3.5, 4, and 4 Visual (4 V) with 12 multiple-choice questions related to six kidney transplant cases from 2013 to 2015 American Society of Nephrology (ASN) fellowship program quizzes. We compared the performance of ChatGPT with US nephrology fellowship program directors, nephrology fellows, and the audience of the ASN's annual Kidney Week meeting. RESULTS:Overall, ChatGPT 4 V correctly answered 10 out of 12 questions, showing a performance level comparable to nephrology fellows (group majority correctly answered 9 of 12 questions) and training program directors (11 of 12). This surpassed ChatGPT 4 (7 of 12 correct) and 3.5 (5 of 12). All three ChatGPT versions failed to correctly answer questions where the consensus among human respondents was low. CONCLUSION/CONCLUSIONS:Each iterative version of ChatGPT performed better than the prior version, with version 4 V achieving performance on par with nephrology fellows and training program directors. While it shows promise in understanding and answering kidney transplantation questions, ChatGPT should be seen as a complementary tool to human expertise rather than a replacement.

BACKGROUND:The Organ Procurement and Transplantation Network (OPTN) is eliminating geographic boundaries in liver allocation, in favor of continuous distribution. Continuous distribution allocates organs via a composite allocation score (CAS): a weighted sum of attributes like medical urgency, candidate biology, and placement efficiency. The opportunity this change represents, to include new variables and features for prioritizing candidates, will require lengthy and contentious discussions to establish community consensus. Continuous distribution could instead be implemented rapidly by computationally translating the allocation priorities for pediatric, status 1, and O/B blood type liver candidates that are presently implemented via geographic boundaries into points and weights in a CAS. METHODS:Using simulation with optimization, we designed a CAS that is minimally disruptive to existing prioritizations, and that eliminates geographic boundaries and minimizes waitlist deaths without harming vulnerable populations. RESULTS:Compared with Acuity Circles (AC) in a 3-year simulation, our optimized CAS decreased deaths from 7771.2 to 7678.8 while decreasing average (272.66 NM vs. 264.30 NM) and median (201.14 NM vs. 186.49 NM) travel distances. Our CAS increased travel only for high MELD and status 1 candidates (423.24 NM vs. 298.74 NM), and reduced travel for other candidates (198.98 NM vs. 250.09 NM); overall travel burden decreased. CONCLUSION/CONCLUSIONS:Our CAS reduced waitlist deaths by sending livers for high-MELD and status 1 candidates farther, while keeping livers for lower MELD candidates nearby. This advanced computational method can be applied again after wider discussions of adding new priorities conclude; our method designs score weightings to achieve any specified feasible allocation outcomes.

BACKGROUND:Since the occurrence of coronavirus disease in 2019 (COVID-19), the global community has witnessed its exponential spread with devastating outcomes within the general population and specifically within hemodialysis patients. OBJECTIVES/OBJECTIVE:Compare the state of immunity to SARS-CoV-2 among hemodialysis patients and staff. DESIGN/METHODS:Cross-sectional study with a prospective follow-up period. SETTING/METHODS:Hemodialysis centers in Madinah region. PATIENTS AND METHODS/METHODS:We prospectively tested for SARS-CoV-2 antibodies in dialysis patients using dialysis centers staff as controls. The participants were tested on four occasions when feasible for the presence of anti-SARS-CoV-2 antibodies. We also analyzed factors that might be associated with seropositivity. MAIN OUTCOME MEASURES/METHODS:SARS-CoV-2 positivity using immunoglobulin G (IgG) levels SAMPLE SIZE: 830 participants, 677 patients and 153 dialysis centers staff as controls. RESULTS:<.0001). Surprisingly, positivity was also center-dependent. In a multivariable logistic regression, a history of infection and related symptoms contributed significantly to developing immunity. CONCLUSION/CONCLUSIONS:The high prevalence of SARS-CoV-2 antibody among hemodialysis patients and previously asymptomatic staff suggested past asymptomatic infection. Some centers showed more immunity effects than others. LIMITATIONS/CONCLUSIONS:Unable to collect four samples for each participant; limited to one urban center. CONFLICT OF INTEREST/BACKGROUND:None.