Thongprayoon, Charat

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.

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.

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: ( 1 ) rigorous multicenter validation focused on clinical outcomes, ( 2 ) integration of uncertainty quantification and causal modeling into AI tools, ( 3 ) development of clinician-centered interfaces that minimize cognitive load, and ( 4 ) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.

We appreciate Dr Akcay's insightful comments. His observations provide valuable context and highlight important considerations for future research. We hope our work will serve as a foundation for further investigation into sex-associated risks in peritoneal dialysis and contribute to optimizing high-quality patient care for all individuals. Looking ahead, future studies should integrate dialysis-specific measures, explore mechanistic pathways, and incorporate patient-centered outcomes to better characterize sex-associated differences in peritoneal dialysis.

Background Sex-based differences may influence the clinical management, complication risks, and healthcare resource utilization of hospitalized end-stage kidney disease (ESKD) patients undergoing peritoneal dialysis (PD). Understanding these disparities is essential for optimizing patient care and informing healthcare policy. Methods This study was conducted using the National Inpatient Sample to identify hospitalized adult ESKD patients receiving PD from the year 2003 to 2018. The outcomes included 1) PD-related outcomes, defined as a composite of peritonitis, mechanical complications, catheter removal or revision, and adequacy issues, 2) non-PD-related outcomes, defined as a composite of sepsis, cardiac arrest, and need for mechanical ventilation 3) transfer to hemodialysis, and 4) in-hospital mortality. The associations between sex and in-hospital outcomes were analyzed using multivariable logistic regression and were adjusted for demographic factors, comorbidities, primary diagnoses, admission types, and hospital characteristics. Discharge weights were applied to generate nationally representative estimates. Results Of 97,036 hospitalized ESKD patients receiving PD analyzed, 48,906 (50.4%) were females. In adjusted analyses, there were no overall sex differences in PD-related outcomes, non-PD-related outcomes, or in-hospital mortality. However, age- and comorbidity-based variations were observed in PD-related outcomes. Female sex was associated with lower odds of transfer to hemodialysis, particularly among younger patients and those without heart failure or peripheral vascular disease. Notably, sex differences in in-hospital mortality were observed only among patients with elective admissions. Conclusion There were sex-based disparities in the outcomes and healthcare utilization of hospitalized ESKD patients receiving PD. These findings underscore the need for sex-specific, individualized strategies to improve PD care and inform clinical and policy decisions.

Key Points Our study found a high prevalence of monoclonal gammopathy (MG) in patients diagnosed with ANCA-negative pauci-immune crescentic GN. Patients with MG showed a higher degree of fibrosis on kidney biopsy than those without MG with higher mortality rate. Our data suggest an association between MG and ANCA-negative pauci-immune crescentic GN, emphasizing this population's need for MG testing and monitoring. Background In a small subset of patients with pauci-immune crescentic GN (PICGN), ANCA are not identified, and patients are referred to as having ANCA-negative vasculitis. The cause of the disease in this subset of patients has remained elusive. We explored the role of monoclonal gammopathy (MG) in those with ANCA-negative PICGN. Methods We conducted a retrospective study and identified patients with PICGN who had undergone ANCA and monoclonal testing. Those positive for ANCA were excluded. Results We identified 14 patients with ANCA-negative vasculitis, of whom eight (57%) tested positive for MG with a mean age of 60.3±12.2 years, 75% were female, and with a mean serum creatinine of 4.3±1.92 mg/dl. The most common monoclonal proteins were IgG ( n =4, 50%) and kappa ( n =4, 50%). One patient had diagnosis of multiple myeloma. Most common extra-renal manifestations were skin rash (leukocytoclastic vasculitis) and neuropathy. Kidney biopsy showed a higher degree of interstitial fibrosis and tubular atrophy compared with those without MG. The median follow-up time was 3.2 years, with 50% mortality in those with MG. Conclusions More than 50% of the patients with ANCA-negative PICGN have MG, and patients present with more evidence of fibrosis on kidney biopsy, with higher mortality rate observed in our cohort. Monoclonal proteins may trigger the development of PICGN; their specific role in the pathogenesis of this disease needs further investigation.

Key Points Preexisting CKD, elevated serum creatinine, and severe interstitial fibrosis and tubular atrophy were linked to poor renal prognosis in renal sarcoidosis. Steroid therapy was strongly associated with renal recovery and improved long-term outcomes in renal sarcoidosis. Early detection and management of renal involvement in sarcoidosis are critical to preventing irreversible kidney damage. Background Renal sarcoidosis (RS) is a rare but potentially severe manifestation of sarcoidosis, primarily affecting the kidneys through granulomatous interstitial nephritis and calcium metabolic disturbances. This study evaluates the clinicopathologic features and renal outcomes of biopsy-proven RS, focusing on identifying predictors of renal recovery and disease progression. Methods This retrospective study included 43 biopsy-proven RS at Mayo Clinic (2012–2024). Demographic, clinical, laboratory, and histopathologic data were analyzed. Renal recovery within 6 months was classified as complete, partial, or no recovery based on serum creatinine (sCr) changes. Long-term adverse composite outcomes included the CKD onset, CKD progression, ESKD, dialysis initiation, or death. Logistic regression and Cox proportional hazards regression were used to assess predictors of renal outcomes. Results Among the cohort, 84% exhibited interstitial nephritis, with granulomatous interstitial nephritis in 33%. Calcium phosphate deposits were found in 58% of cases. Hypercalciuria (90%) was more common than hypercalcemia (74%). Within 6 months, 86% achieved recovery (74% complete, 12% partial), while 40% experienced long-term adverse composite outcomes. Higher sCr at biopsy (≥2.4 mg/dl) and moderate-to-severe interstitial fibrosis and tubular atrophy were associated with reduced complete recovery and worse long-term outcomes, while preexisting CKD significantly increased the risk of adverse long-term outcomes. Steroid therapy significantly increased the likelihood of complete recovery and reduced the risk of adverse long-term outcomes. Conclusions Preexisting CKD, elevated sCr at biopsy, interstitial fibrosis and tubular atrophy, and steroid therapy are key predictors of renal outcomes in RS. Early diagnosis and intervention are crucial to preventing irreversible kidney damage.

Background The digital transformation in medical education is reshaping how clinical skills, such as point-of-care ultrasound (POCUS), are taught. In nephrology fellowship programs, POCUS is essential for enhancing diagnostic accuracy, guiding procedures, and optimizing patient management. To address these evolving demands, we developed an artificial intelligence (AI)-driven POCUS curriculum using a multiagent approach that integrates human expertise with advanced AI models, thereby elevating educational standards and better preparing fellows for contemporary clinical practice. Methods In April 2024, the Mayo Clinic Minnesota Nephrology Fellowship Program initiated a novel AI-assisted process to design a comprehensive POCUS curriculum. This process integrated multiple advanced AI models—including GPT-4.0, Claude 3.0 Opus, Gemini Advanced, and Meta AI with Llama 3—to generate initial drafts and iteratively refine content. A panel of blinded nephrology POCUS experts subsequently reviewed and modified the AI-generated material to ensure both clinical relevance and educational rigor. Results The curriculum underwent 12 iterative revisions, incorporating feedback from 29 communications across AI models. Key features of the final curriculum included expanded core topics, diversified teaching methods, enhanced assessment tools, and integration into inpatient and outpatient nephrology rotations. The curriculum emphasized quality assurance, POCUS limitations, and essential clinical applications, such as fistula/graft evaluation and software integration. Alignment with certification standards further strengthened its utility. AI models contributed significantly to the curriculum's foundational structure, while human experts provided critical clinical insights. Conclusion This curriculum, enhanced through a multiagent approach that combines AI and human collaboration, exemplifies the transformative potential of digital tools in nephrology education. The innovative framework seamlessly integrates advanced AI models with expert clinical insights, providing a scalable model for medical curriculum development that is responsive to evolving educational demands. The synergy between technological innovation and human expertise holds promising implications for advancing fellowship training. Future studies should evaluate its impact on clinical competencies and patient outcomes across diverse practice environments.