Valencia, Oscar A. Garcia

Delayed graft function (DGF) is a frequent complication after deceased donor kidney transplantation, yet its long-term impact remains understudied. Heterogeneous populations, short follow-up, and inadequate control of donor-related factors have limited prior studies. Using the Scientific Registry of Transplant Recipients (2005–2024), we evaluated the association between DGF and long-term graft outcomes, including a mate-kidney (same donor, discordant DGF status) analysis to isolate its independent effect. We identified 103,678 dialysis-dependent deceased donor kidney recipients maintained on tacrolimus and mycophenolate. Mixed-effects Cox models, adjusted for donor, recipient, immunologic, and procurement factors. DGF occurred in 30.1% of recipients and was associated with increased death-censored graft failure (HR 1.40; 95% CI 1.33–1.47) and overall graft failure (HR 1.27; 95% CI 1.23–1.31). DGF recipients had higher rejection rates at 6 (6.0% vs. 4.7%) and 12 months (8.9% vs. 7.3%), longer hospital stay, and higher 1-year rehospitalization. In the mate-kidney analysis (6,818 donors), DGF remained strongly associated with death-censored graft failure (HR 1.58; 95% CI 1.39–1.80) and overall graft failure (HR 1.35; 95% CI 1.25–1.46), confirming the independent effect of DGF. DGF is a strong predictor of adverse long-term outcomes irrespective of rejection and warrants targeted prevention strategies and intensified follow-up.

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.

Circulating extracellular vesicles, proinflammatory/pro-oxidant metabolites, and history of preeclampsia may be used as biomarkers for diagnosis of early CAC in postmenopausal women.

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.

Background Effective patient education is critical for informed decision-making and adherence in kidney transplantation. Generative artificial intelligence (AI), particularly large language models (LLMs), has the potential to enhance patient education in kidney transplantation; however, its factual accuracy and readability remain incompletely characterized. Methods We evaluated the performance of the GPT-5.1 (2025) model in generating plain-language explanations for 100 clinically relevant kidney transplantation terms. Explanations were generated using a standardized prompt (first round) and a revised prompt explicitly requesting an eighth-grade reading level or lower (second round). Accuracy was assessed by expert reviewers using a 5-point Likert scale, while readability was evaluated using the Flesch Reading Ease (higher score indicated easier readability of the text) and Flesch–Kincaid Grade Level (higher score indicated higher education level required to understand the text) score. The study was conducted in November 2025. Results All AI-generated explanations demonstrated high accuracy, with no clinically significant errors. In the first round, the mean Flesch Reading Ease score was 23.6 ± 23.4, indicating very difficult readability, and 46% of explanations required a college-level reading ability (mean Flesch–Kincaid Grade Level 13.4 ± 4.8). Following prompt revision, readability improved substantially. The mean Flesch Reading Ease score increased to 62.4 ± 7.5, corresponding to standard readability, and all explanations were written at a middle school level or below (mean Flesch–Kincaid Grade Level 6.3 ± 1.1). Conclusion GPT-5.1 generated highly accurate explanations of kidney transplantation terms across prompting strategies. Explicit readability-focused prompting substantially improved readability without compromising accuracy, underscoring the importance of prompt design when deploying LLMs for patient-centered education in transplantation.

Background. Living kidney donation (KD) evaluation particularly focuses on glomerular filtration rate (GFR). Assessing GFR in living kidney donors can be done by estimation (eGFR) or measurement (mGFR). This study aims to evaluate the impact of eGFR equations, focusing on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 equation for donor eligibility screening and the reclassification of donors excluded by eGFR criteria to mGFR assessments. Methods. Retrospective, single-center observational study of 2512 kidney donors evaluated between 2011 and 2023, at the Mayo Clinic. We performed analyses and comparisons of 4 eGFR formulae and 2 mGFR methods in terms of performance, eligibility criteria according to Kidney Disease: Improving Global Outcomes guidelines/age-adjusted criteria, eligibility reclassification, and subgroup analyses. Results. The CKD-EPI 2021 resulted in a higher percentage of patients being eligible for KD (81.4%) and achieved the highest P10 (32%–34%) and P30 (87%–88%) across all mGFR methods. When patients were deemed eligible for donation by any eGFR formula, 95% retained this status on reassessment with mGFR. Conversely, when an eGFR formula classified a patient as ineligible, this was revised to be eligible in about 90% of cases on reassessment with mGFR, with a higher reclassification rate observed when the IoCl was used instead of CrCl. Conclusions. The CKD-EPI 2021 performs best for screening potential KD. If a KD is deemed ineligible by CKD-EPI 2021, there is a high likelihood that they will be reclassified as eligible when assessed with mGFR. This study strongly supports using mGFR in donor evaluations, especially when the eGFR does not meet the criteria to ensure equity of access to being a living donor.

Background We aimed to assess the longitudinal changes of glycocalyx, a proteoglycan/glycoprotein layer that lines and protects endothelial cells in normotensive pregnancies (NPs); to correlate glycocalyx parameters with kidney injury markers (podocin‐positive to nephrin‐positive [P/N] ratio); and to explore changes in P/N ratio in preeclampsia. Methods Plasma and urine samples and glycocalyx measurements using side‐stream darkfield imaging were collected at each trimester from pregnant women (n=31). Plasma glycocalyx components were measured by ELISA, and urinary P/N‐positive extracellular vesicles by flow cytometry. Results In NPs (n=26), glycocalyx degradation increased from the first to the second trimester ( P =0.008) and returned to first‐trimester values in the third trimester ( P =0.008). Microvascular perfusion decreased from the first to the second trimester ( P =0.006) and increased back to first‐trimester values by the third trimester ( P =0.039). Plasma syndecan 1 level showed a steady increase from the first to the second trimester ( P <0.001) and further in the third trimester ( P <0.001); hyaluronic acid increased from the first to the second trimester ( P =0.016) without further changes. Glycocalyx degradation was associated with an increase in P/N ratio from the first to the second trimester of NPs and remained stable through the third trimester. P/N ratio strongly correlated with elevated syndecan 1 and hyaluronic acid levels in the third trimester. Comparison of P/N ratios between NP and preeclampsia (n=5) showed higher values in preeclampsia across all trimesters ( P =0.028). Conclusions Glycocalyx changes during NPs are associated with elevated urinary podocyte markers. Increase in P/N ratio across NPs, and further in preeclampsia, suggests that podocyte injury in preeclampsia is extension of physiological changes in pregnancy, which may predate its clinical presentation.