Arriola-Montenegro, Jose

Background Tacrolimus dosing after kidney transplantation is complex, highly individualized, and prone to variability, which can impact graft outcomes. While machine learning (ML) approaches have been used primarily to predict tacrolimus concentrations, large language models (LLMs) may enable protocol-constrained clinical decision support by generating dosing recommendations aligned with established treatment guidelines. Methods We developed TacroDose AI, a protocol-constrained LLM based on GPT-4, to generate tacrolimus dosing recommendations aligned with institutional guidelines. Using 300 structured simulated clinical scenarios, we evaluated protocol adherence, error patterns, and reproducibility of model-generated dosing recommendations. Following review of initial outputs, a refined prompt ( TacroAI 2.0 ) was implemented to strengthen protocol constraints and structured output verification, and performance was reassessed. Results TacroDose AI achieved 72.9% protocol adherence, with 27.1% protocol deviations, including 13.9% sentinel errors with potential clinical significance. Prompt refinement substantially improved performance: TacroAI 2.0 achieved 91.7% adherence, reduced sentinel errors to 1.0%, and improved reproducibility from 77.3 to 86.7% (all p < 0.01). Although rounding discrepancies increased, these represented minor numerical differences without clinical relevance. Performance improvements were observed across subtherapeutic, therapeutic, and supratherapeutic tacrolimus scenarios. Conclusion Protocol-constrained LLMs can produce guideline-concordant tacrolimus dosing recommendations with high adherence and improved reliability in simulated clinical settings. These findings highlight a potential framework for integrating generative AI with rule-based clinical protocols to support safe medication management. With further validation using real-world data, such systems could be integrated into electronic health records to support clinician-supervised, protocol-based immunosuppressive dosing in transplant care.

Background Kidney transplantation offers substantial clinical and economic benefits for patients with end-stage kidney disease (ESKD), yet organ shortages persist. Enhancing public awareness and health literacy regarding kidney donation is essential for effective donor recruitment. While online patient education materials are primary drivers of public perception, their readability, digital engagement, and accessibility remain underexplored barriers. Methods We analyzed the most prominent kidney donation websites identified through U.S.-based Google searches, including 11 primary organizations and 16 affiliated subdomains, for a total of 27 websites. Readability was benchmarked using Flesch-Kincaid, SMOG, and Gunning Fog indices. To assess qualitative metrics, we deployed a generative AI framework utilizing a Large Language Model (Claude) to conduct automated sentiment analysis and tone evaluation, validated by human review. We systematically mapped digital engagement features, including multimedia, interactive tools, and multilingual support, to determine content comprehensiveness. Results Websites consistently provided accurate information with a generally positive or neutral tone. Average readability exceeded recommended levels, with a combined mean grade of 12.3; 34% of websites were written at a college-level reading standard. Consensus across content was high. Multimedia elements were widely used, but engagement features were limited; only 30% of sites included extensive testimonials, and interactive tools were absent. AI-based analysis enabled standardized and reproducible evaluation, highlighting opportunities to improve accessibility, tone, and inclusivity. Conclusion Current U.S. kidney donation digital resources present a barrier to health equity due to excessive reading complexity and static engagement models. AI provides a scalable, reproducible framework to audit and optimize patient education materials. Future initiatives must leverage AI-guided content optimization to bridge the literacy gap, potentially increasing donor registration and access to transplantation.

Abstract Background Women with advanced chronic kidney disease (CKD) encounter reproductive health challenges, including reduced fertility, menstrual irregularity, and sexual dysfunction. This study examined changes in sex hormones in patients with CKD, their correlation with sexual function, and changes after transplant. Methods This study recruited women aged 18–51 years with CKD stages 3b-5 from outpatient clinics and dialysis units. Exclusion criteria included active pregnancy, menopause, prior hysterectomy/bilateral oophorectomy, or use of hormonal contraceptives/therapy. Data were collected at enrollment and at 6 months post-transplant. Results Thirty-eight subjects met eligibility criteria and enrolled in the trial (8 in CKD stage 3b, 10 in CKD stage 4, and 20 in CKD stage 5/5D). Sexual dysfunction (25/38, 65.8%), defined as total female sexual function index score < 26.55, and menstrual irregularities (17/38, 44.7%) were common, with abnormal elevation of luteinizing hormone present in the follicular phase in the majority of patients. There was a significant negative association between total female sexual function index score and prolactin (β = -0.26, P = 0.03) and dehydroepiandrosterone sulfate (β = -0.07, P = 0.01) after adjusting for age. After transplant, there was also a significant improvement in both prolactin and dehydroepiandrosterone sulfate levels, although sexual dysfunction persisted. Conclusions This study highlights the complex interplay between advanced CKD, reproductive endocrinology, and sexual health. Significant hormonal dysregulation and associated reproductive challenges were observed. The prominent role of dehydroepiandrosterone sulfate in sexual dysfunction among women with CKD requires further study.

Abstract Background: Chronic kidney disease (CKD) has been associated with higher risk of gastrointestinal disorders, particularly Gastroesophageal reflux disease (GERD). However, the magnitude of this association and the underlying mechanisms remains unclear. Methods: A systematic search was conducted across major databases from inception to November 2024. We included cross-sectional and case-control studies evaluating the relationship between CKD and GERD. Data were extracted and analyzed using a random-effects model to calculate pooled odds ratios (ORs) and prevalence rates. Study quality was assessed using the Newcastle-Ottawa Scale, and heterogeneity was evaluated using the Cochran’s Q test and I² statistic. Results: Nine studies involving 4,650,709 participants were included. The pooled prevalence of GERD among CKD patients was 18% (95% CI: 0.10–0.26, I² =93.64%). The pooled crude OR for the association between CKD and GERD was 2.53 (95% CI: 1.30–4.92) and adjusted OR was 1.48 (95% CI: 1.05–2.08). Conclusion: This meta-analysis reveals a marginally significant association between CKD and GERD, highlighting higher prevalence of GERD among individuals with CKD. Furthers studies are needed to elucidate the underlying pathophysiological mechanisms and potential clinical implications.