Citations

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.

Key Points Both sodium zirconium cyclosilicate and patiromer effectively lower serum potassium and provide clinical benefits. Sodium zirconium cyclosilicate is associated with higher risk of edema. Patiromer is associated with the increased risk of constipation and hypokalemia. Background Sodium zirconium cyclosilicate (SZC) and patiromer have recently been approved for the treatment of hyperkalemia. However, their efficacy in lowering serum potassium according to the phase of intervention, safety profile, and impact clinical outcomes remains to be fully defined. This study aims to assess their efficacy and tolerability in the management of hyperkalemia. Methods A comprehensive search of PubMed, Scopus, and Cochrane Central Register of Controlled Trials was conducted up to June 6, 2025. Eligible studies included randomized controlled trials reporting the effects of SZC and patiromer on laboratory parameters, clinical outcomes, and safety profiles. Meta-analyses were synthesized using a random-effects model, with results presented as weighted mean differences (WMD) or relative risks (RR), along with 95% confidence intervals (CI). Results Nineteen randomized controlled trials involving 3627 patients (1911 on SZC; 1716 on patiromer) were analyzed. During the initial 48–72 hours, only SZC significantly reduced serum potassium levels (WMD, −0.45 mEq/L; 95% CI, –0.61 to –0.29). At the end of treatment, both agents effectively lowered serum potassium (SZC: WMD, –0.50 mEq/L; 95% CI, –0.63 to –0.38; patiromer: WMD, –0.36 mEq/L; 95% CI, –0.51 to –0.21). SZC significantly normalized serum potassium and reduced hyperkalemia incidence, while patiromer enabled renin-angiotensin-aldosterone system inhibitor optimization by supporting target dosing and reducing discontinuation due to hyperkalemia. However, SZC was associated with a higher incidence of edema (RR, 2.92; 95% CI, 1.08 to 7.90) and patiromer with increased rates of constipation (RR, 2.96; 95% CI, 1.21 to 7.22) and hypokalemia (RR, 1.46; 95% CI, 1.05 to 2.03). Conclusions Both SZC and patiromer effectively lower serum potassium and provide clinical benefits, with distinct safety considerations. Therapy selection should be guided by treatment goals and the patient's individual risk profile.

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 Prior authorization (PA) is a major source of administrative burden, treatment delay, and clinician burnout. Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used to assist with clinical documentation, yet its reliability for payer-facing administrative tasks remains uncertain. Objective To evaluate the quality of PA letters drafted by ChatGPT-5 for commonly used medications requiring PA in nephrology. Quality was evaluated based on correctness and strength of clinical reasoning. Methods We created a single standardized prompt and applied it across 29 nephrology scenarios to generate PA letters. Each PA letter was reviewed against four criteria: 1) absence of false statements or hallucinations, 2) correctness of ICD-10 coding, 3) presence and validity of citations, and 4) clinical reasoning, rated on a 4-point Likert scale (illogical, weak, adequate and strong). FDA drug labels, KDIGO guidelines and related randomized controlled trials were used as reference standards. Results Out of 29 letters, one letter (3.5%) contained false statements mentioning an irrelevant clinical trial. The ICD-10 diagnosis code was correct in 23 letters (79.3%), most errors were related to chronic kidney disease (CKD) staging or internal diagnostic inconsistencies. 27 letters (93.1%) cited valid references, with one letter citing an incorrect trial and another one citing a correct KDIGO guideline with inaccessible link. Twenty-six letters (89.7%) demonstrated strong clinical reasoning, supported by guideline-oriented or FDA label–aligned justification. The remaining 3 letters were rated as adequate reasoning. The main areas for improvement involved citing relevant references and emphasizing special considerations, for example Risk Evaluation and Mitigation Strategy (REMS) compliance for eculizumab. Conclusions ChatGPT-5 can generate clinically coherent PA drafts for nephrology medications, but limitations in coding precision and citation reliability persist. With appropriate oversight, AI-assisted documentation may reduce administrative burden while maintaining safety and accuracy.

Key Points Baseline kidney function and chronic structural injury strongly predicted both renal recovery and long-term clinical outcomes. Tubulointerstitial nephritis was the predominant lesion in Sjögren syndrome with renal involvement. Early detection and management of renal involvement in Sjögren syndrome are critical to preventing irreversible kidney damage. Background Renal involvement in primary Sjögren syndrome (SS) is uncommon but clinically consequential. Previous studies have been limited by small samples and incomplete biopsy data. We evaluated a large biopsy-confirmed cohort to characterize clinicopathologic features of SS with renal involvement and identify predictors of renal recovery and long-term outcomes. Methods We retrospectively identified adults with SS and kidney biopsy at Mayo Clinic (2012–2025). Clinical, laboratory, and histologic data were extracted. Predictors of complete renal recovery within 6 months, defined as serum creatinine (sCr) returning to within 25% of baseline or <1.4 mg/dl if baseline was unknown, were evaluated using logistic regression. Cox regression assessed long-term risk of a composite end point: newly developed or progressive CKD, ESKD, or all-cause mortality. Results Fifty-six patients were included (median age 57 years; 91% female). Extraglandular manifestations occurred in 76% and baseline CKD in 65%. The median sCr at baseline and biopsy was 1.3 and 1.6 mg/dl, respectively. Anti-Sjogren's syndrome antigen A, often linked with Ro ribonucleoprotein was positive in 80%. Low C3 occurred in 14% and low C4 in 25%. Tubulointerstitial nephritis was the predominant lesion (71%). Moderate/severe interstitial fibrosis/tubular atrophy and arteriosclerosis were present in 36% and 41%, respectively. Immunosuppressive therapy was applied in 84%. Complete recovery occurred in 67%. Over a median 3.9 years (interquartile range, 1.4–7.2), 43% reached the composite end point. Baseline sCr ≥2 mg/dl, 24-hour proteinuria ≥1 g, and presence of segmental glomerulosclerosis were associated with lower odds of recovery (odds ratio=0.16, 0.13 and 0.11, P = 0.02, 0.001 and 0.008, respectively) and higher long-term risk (hazard ratio=4.51, 2.92 and 3.52; P = 0.002, 0.01 and 0.02, respectively). Moderate/severe arteriosclerosis also increased long-term risk (hazard ratio=2.56; P = 0.03). Conclusions In biopsy-confirmed SS-related renal involvement, the severity of kidney dysfunction and the extent of chronic glomerular and vascular injury strongly predict renal prognosis. Early detection and targeted management of high-risk features may improve long-term renal outcomes.

Hemodiafiltration (HDF) is increasingly used because of its enhanced theoretical clearance of diverse uremic toxins, particularly middle molecules and inflammatory cytokines, relative to conventional hemodialysis (HD), yet evidence on its biochemical benefits remains conflicting. Therefore, this meta-analysis was performed to evaluate the effects of HDF versus HD on uremic toxins, inflammation, anemia, and nutritional parameters. A systematic literature search was conducted using PubMed, Scopus, and the Cochrane Central Register of Controlled Trials to identify relevant studies. Only randomized controlled trials (RCTs) were included. Random-effects meta-analyses were performed to evaluate changes in the prespecified outcomes. Twenty-four RCTs involving 6072 dialysis patients were included. Compared with conventional HD, HDF was associated with significant reductions in serum phosphorus (weighted mean difference [WMD] −0.28 mg/dL; 95% CI −0.44 to −0.12) and β2-microglobulin (WMD −4.84 mg/dL; 95% CI −6.13 to −3.54). HDF also significantly reduced serum urea and C-reactive protein (CRP) levels, along with weekly erythropoietin requirements. Serum albumin levels were slightly but significantly lower in the HDF group than in the conventional HD group (WMD –0.06 g/dL; 95% CI −0.10 to −0.01); however, the clinical significance of such a difference remains uncertain. Higher convective volumes were identified as a key determinant of greater reductions in β2-microglobulin and CRP. Compared with conventional HD, HDF demonstrated superior reductions in several surrogate endpoints, including serum phosphorus, urea, β2-microglobulin, CRP, and weekly erythropoietin requirements. Reduced need for phosphate binders and anemia management may lower treatment-related costs.

Background/Objectives: The study aimed to investigate whether malnutrition influences the obesity paradox and to explore the relationship between obesity with/without malnutrition and in-hospital outcomes among hospitalized ESKD patients. Methods: The study used the National Inpatient Sample database from 2016 to 2021. Hospitalized ESKD patients were included and categorized into three groups: non-obese, obese without malnutrition, and obese with malnutrition. The association between obesity with/without malnutrition and in-hospital outcomes, compared to non-obese patients, were analyzed. Results: Of 674,367 hospitalized ESKD patients included, 125,978 (18.7%) had obesity. Obese ESKD patients without malnutrition were associated with a decreased risk of mortality (odd ratio [OR] 0.87, 95% CI 0.84–0.91), whereas obese patients with malnutrition were associated with an increased risk of mortality (OR 2.08, 95% CI 1.90–2.27), compared to non-obese patients. Furthermore, obesity, with or without malnutrition, was linked to higher infection-related complications and resource utilization, especially when malnutrition was present. Conclusions: Our findings show that obesity is significantly associated with lower in-hospital mortality among ESKD patients without malnutrition. However, when malnutrition coexists, this survival advantage is reversed, underscoring the importance of detecting malnutrition in obese ESKD patients.