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OBJECTIVE: Semaglutide, a glucagon-like peptide-1 receptor agonist is approved for weight loss and diabetes treatment, but limited literature exists regarding semaglutide use in patients with advanced chronic kidney disease (CKD). Therefore, this project assessed the safety and efficacy of semaglutide among patients with estimated glomerular filtration rate (eGFR) 15-29 mL/min/1.73 m2 (CKD stage 4), eGFR<15 mL/min/1.73 m2 (CKD stage 5) or on dialysis. METHODS: This is a retrospective electronic medical record based analysis of consecutive patients with advanced CKD (defined as CKD 4 or greater) who were started on semaglutide (injectable or oral). Data was collected between January 2018 and January 2023. Investigators verified CKD diagnosis and manually extracted data. Data were analyzed using Fisher's exact test, paired t test, linear mixed effects models and Wilcoxon signed rank test. RESULTS: Seventy-six patients with CKD 4 or greater who initiated semaglutide were included. Most patients had a history of type 2 diabetes mellitus (96.0%), and most were males (53.9%). The mean age was 66.8 y (SD 11.5) with the mean body mass index was 36.2 (SD 7.5). The initial doses were 3 mg orally and 0.25 mg by injection. Maximum prescribed dose was 1 mg (injectable) in 28 (45.2%) patients and 14 mg (orally) in 2 (14.2%) patients. Patients received semaglutide for a median duration of 17.4 (IQR 0.43, 48.8) months. Forty-eight (63.1%) patients reported no adverse effects associated with the therapy. Mean weight decreased from 106.2 (SD 24.2) to 101.3 (SD 27.3) kg (P < .001). Eight patients (16%) with type 2 diabetes mellitus T2DM discontinued insulin after starting semaglutide. Mean hemoglobin A1c (HbA1c) decreased from 8.0% (SD 1.7) to 7.1% (SD 1.3) (P < .001). Adverse effects were the primary reason for semaglutide discontinuation (37.0%), with nausea, vomiting, and abdominal pain being the most common complaints. CONCLUSIONS: Based on this retrospective study semaglutide appears to be tolerated by most individuals with CKD 4 or greater despite associated gastrointestinal side effects similar to those observed in patients with better kidney function and leads to an improvement of glycemic control and insulin discontinuation in patients with T2DM. Modest weight loss (approximately 4.6% of the total body weight) was observed on the prescribed doses. Larger prospective randomized studies are needed to comprehensively assess the risks and benefits of semaglutide in patients with CKD 4 or greater and obesity.
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In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.
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COVID-19 , Humanos , COVID-19/epidemiologia , COVID-19/prevenção & controle , Vacinas contra COVID-19 , Transplantados , Inteligência Artificial , Pandemias/prevenção & controle , VacinaçãoRESUMO
The integration of large language models (LLMs) into healthcare, particularly in nephrology, represents a significant advancement in applying advanced technology to patient care, medical research, and education. These advanced models have progressed from simple text processors to tools capable of deep language understanding, offering innovative ways to handle health-related data, thus improving medical practice efficiency and effectiveness. A significant challenge in medical applications of LLMs is their imperfect accuracy and/or tendency to produce hallucinations-outputs that are factually incorrect or irrelevant. This issue is particularly critical in healthcare, where precision is essential, as inaccuracies can undermine the reliability of these models in crucial decision-making processes. To overcome these challenges, various strategies have been developed. One such strategy is prompt engineering, like the chain-of-thought approach, which directs LLMs towards more accurate responses by breaking down the problem into intermediate steps or reasoning sequences. Another one is the retrieval-augmented generation (RAG) strategy, which helps address hallucinations by integrating external data, enhancing output accuracy and relevance. Hence, RAG is favored for tasks requiring up-to-date, comprehensive information, such as in clinical decision making or educational applications. In this article, we showcase the creation of a specialized ChatGPT model integrated with a RAG system, tailored to align with the KDIGO 2023 guidelines for chronic kidney disease. This example demonstrates its potential in providing specialized, accurate medical advice, marking a step towards more reliable and efficient nephrology practices.
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Nefrologia , Humanos , Reprodutibilidade dos Testes , Escolaridade , Alucinações , IdiomaRESUMO
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.
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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.
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Transplante de Rim , Humanos , Alanina Transaminase , Inteligência Artificial , Colina O-Acetiltransferase , Promoção da Saúde , Hispânico ou Latino , Reprodutibilidade dos Testes , Medicina Estatal , Americanos MexicanosRESUMO
Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.
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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.
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The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT's performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT's potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care.
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The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.
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Objetivos: Describir los resultados de las manometrías anorrectales (MAR) en pacientes pediátricos con estreñimiento crónico y patología anorrectal adquirida. Materiales y métodos: Se revisaron los expedientes de pacientes pediátricos referidos entre 2004 y 2016 al Laboratorio de Motilidad Gastrointestinal del Hospital San José Tec de Monterrey para evaluación por manometría anorrectal y que presentaron patología anorrectal adquirida. Resultados: Se revisaron 170 expedientes. Edad 7,18 ± 4,51 años. La prevalencia de patología anorrectal (PA) fue de 73%. Síntomas con mayor incidencia: dificultad para evacuar (78%), dolor al evacuar (67%), heces duras (50%) e incontinencia fecal asociado (49%). El 44% de los pacientes con esfínter anal externo (EAE) hipotónico presentaron incontinencia y 74% estos últimos, presentaron menor volumen máximo tolerable (VMT). Los valores manométricos con mayor significancia: presión en reposo del EAE (promedio ± DE) 14,16 ± 10,19 en PA y de 26,08 ± 13,65 en SPA; presión en contracción del EAE 48,4 ± 34,1 en PA y 68,3 ± 37,7 en SPA; VMT 120,8 ± 60,4 en PA y de 173,2 ± 78,0 en SPA. El 97,97% de los pacientes en los que se evaluó la coordinación abdomino-pélvica tuvieron disinergia del piso pélvico. Conclusiones: A diferencia de la población adulta, los valores manométricos de niños con patología anorrectal se encontraron dentro de rangos normales excepto por el EAE y el VMT los cuales estuvieron disminuidos. Esto puede sugerir un mecanismo diferente en la población pediátrica. La disinergia del piso pélvico podría explicar el estreñimiento crónico en estos pacientes.
Objective: To describe the anorectal manometry results in the pediatric population with chronic constipation and acquired anorectal disease. Materials and methods: We reviewed the records of children who were referred to the Motility and Pelvic Floor Laboratory of the Hospital San Jose Tecnologico de Monterrey between 2004-2016 for further evaluation with anorectal manometry and who presented acquired anorectal disease. Results: We reviewed 170 records. The mean age was 7.18 ± 4.51 years old. The prevalence of anorectal disease was 73%. The symptoms more frequently presented were difficult evacuation (78%), painful defecation (67%), large and hard stool (50%) and fecal soiling (49%). 44% of patients with hypotonic external anal sphincter (EAS) presented with soiling and 74% of those had diminished critical volume. Significant manometric values (p<0.05) were EAS resting pressure, maximal squeeze pressure, and critical volume. 97.7% of those who underwent abdomino pelvic coordination evaluation had pelvic floor dyssynergia (anismus). Conclusions: Contrary to adult population, the manometric values in children with acquire anorectal pathology were within normal values except for the EAS resting pressure and critical volume that were diminished. This could suggest a different mechanism in the pediatric population. Pelvic floor dyssynergia could explain chronic constipation in these patients.