Evaluating Global and Temporal Trends in Pancreas and Islet Cell Transplantation: Public Awareness and Engagement.

BACKGROUND: Pancreas transplantation is a crucial surgical intervention for managing diabetes, but it faces challenges such as its invasive nature, stringent patient selection criteria, organ scarcity, and centralized expertise. Despite the steadily increasing number of pancreas transplants in the United States, there is a need to understand global trends in interest to increase awareness of and participation in pancreas and islet cell transplantation. METHODS: We analyzed Google Search trends for "Pancreas Transplantation" and "Islet Cell Transplantation" from 2004 to 14 November 2023, assessing variations in search interest over time and across geographical locations. The Augmented Dickey-Fuller (ADF) test was used to determine the stationarity of the trends (p < 0.05). RESULTS: Search interest for "Pancreas Transplantation" varied from its 2004 baseline, with a general decline in peak interest over time. The lowest interest was in December 2010, with a slight increase by November 2023. Ecuador, Kuwait, and Saudi Arabia showed the highest search interest. "Islet Cell Transplantation" had its lowest interest in December 2016 and a more pronounced decline over time, with Poland, China, and South Korea having the highest search volumes. In the U.S., "Pancreas Transplantation" ranked 4th in interest, while "Islet Cell Transplantation" ranked 11th. The ADF test confirmed the stationarity of the search trends for both procedures. CONCLUSIONS: "Pancreas Transplantation" and "Islet Cell Transplantation" showed initial peaks in search interest followed by a general downtrend. The stationary search trends suggest a lack of significant fluctuations or cyclical variations. These findings highlight the need for enhanced educational initiatives to increase the understanding and awareness of these critical transplant procedures among the public and professionals.

AI-driven translations for kidney transplant equity in Hispanic populations.

Health equity and accessing Spanish kidney transplant information continues being a substantial challenge facing the Hispanic community. This study evaluated ChatGPT's capabilities in translating 54 English kidney transplant frequently asked questions (FAQs) into Spanish using two versions of the AI model, GPT-3.5 and GPT-4.0. The FAQs included 19 from Organ Procurement and Transplantation Network (OPTN), 15 from National Health Service (NHS), and 20 from National Kidney Foundation (NKF). Two native Spanish-speaking nephrologists, both of whom are of Mexican heritage, scored the translations for linguistic accuracy and cultural sensitivity tailored to Hispanics using a 1-5 rubric. The inter-rater reliability of the evaluators, measured by Cohen's Kappa, was 0.85. Overall linguistic accuracy was 4.89 ± 0.31 for GPT-3.5 versus 4.94 ± 0.23 for GPT-4.0 (non-significant p = 0.23). Both versions scored 4.96 ± 0.19 in cultural sensitivity (p = 1.00). By source, GPT-3.5 linguistic accuracy was 4.84 ± 0.37 (OPTN), 4.93 ± 0.26 (NHS), 4.90 ± 0.31 (NKF). GPT-4.0 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 4.95 ± 0.22 (NKF). For cultural sensitivity, GPT-3.5 scored 4.95 ± 0.23 (OPTN), 4.93 ± 0.26 (NHS), 5.00 ± 0.00 (NKF), while GPT-4.0 scored 5.00 ± 0.00 (OPTN), 5.00 ± 0.00 (NHS), 4.90 ± 0.31 (NKF). These high linguistic and cultural sensitivity scores demonstrate Chat GPT effectively translated the English FAQs into Spanish across systems. The findings suggest Chat GPT's potential to promote health equity by improving Spanish access to essential kidney transplant information. Additional research should evaluate its medical translation capabilities across diverse contexts/languages. These English-to-Spanish translations may increase access to vital transplant information for underserved Spanish-speaking Hispanic patients.

Empowering inclusivity: improving readability of living kidney donation information with ChatGPT.

Background: Addressing disparities in living kidney donation requires making information accessible across literacy levels, especially important given that the average American adult reads at an 8th-grade level. This study evaluated the effectiveness of ChatGPT, an advanced AI language model, in simplifying living kidney donation information to an 8th-grade reading level or below. Methods: We used ChatGPT versions 3.5 and 4.0 to modify 27 questions and answers from Donate Life America, a key resource on living kidney donation. We measured the readability of both original and modified texts using the Flesch-Kincaid formula. A paired t-test was conducted to assess changes in readability levels, and a statistical comparison between the two ChatGPT versions was performed. Results: Originally, the FAQs had an average reading level of 9.6 ± 1.9. Post-modification, ChatGPT 3.5 achieved an average readability level of 7.72 ± 1.85, while ChatGPT 4.0 reached 4.30 ± 1.71, both with a p-value <0.001 indicating significant reduction. ChatGPT 3.5 made 59.26% of answers readable below 8th-grade level, whereas ChatGPT 4.0 did so for 96.30% of the texts. The grade level range for modified answers was 3.4-11.3 for ChatGPT 3.5 and 1-8.1 for ChatGPT 4.0. Conclusion: Both ChatGPT 3.5 and 4.0 effectively lowered the readability grade levels of complex medical information, with ChatGPT 4.0 being more effective. This suggests ChatGPT's potential role in promoting diversity and equity in living kidney donation, indicating scope for further refinement in making medical information more accessible.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

BACKGROUND: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS: We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS: Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality ( P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS: The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.