American Journal of Nephrology最新文献

Introduction: Guidelines recommend that patients with a self-reported history of kidney stones or stones on imaging during living kidney donor (LKD) evaluation undergo 24-h urine stone risk testing. We examined eligibility decisions for LKD candidates at two high-volume academic transplant centers based on 24-h urine testing and imaging findings.

Methods: We identified potential LKDs with a self-reported history of kidney stones or stones identified on imaging, who underwent 24-h urine collection. Patients who could not donate due to other medical conditions were excluded. Differences in characteristics of patients approved versus rejected for donation were determined using t tests and chi-square tests, or nonparametric tests when appropriate.

Results: In total, 105 candidates met study criteria, of whom 22 (21%) were rejected for donation. Candidates rejected for donation had higher urinary calcium excretion (p < 0.001), supersaturation of calcium oxalate (p < 0.001), and supersaturation of calcium phosphate (p = 0.02). Thirty-four candidates repeated 24-h urine analyses following dietary or medical interventions for stone prevention. Candidates approved for donation had an increase in urinary volume (p = 0.045), reduction in urinary calcium excretion (p = 0.02), reduction in urinary oxalate excretion (p = 0.04), and reduction in supersaturations of calcium oxalate (p < 0.001), calcium phosphate (p = 0.004), and uric acid (p = 0.004). Those rejected for donation had no statistically significant changes in urinary parameters. While those rejected for donation had more stones on imaging compared to those approved, this did not reach statistical significance (p = 0.06).

Conclusion: Overall, urinary risk factors for nephrolithiasis and improvement in them following dietary or medical management were associated with approval for donation.

Introduction: Patients with severe acute kidney injury (AKI) with associated acute hypoxemic respiratory failure (AHRF) experience poorer outcomes, including higher rates of in-hospital mortality, relative to patients with less severe AKI, or those without associated AHRF. Zegocractin is a calcium release-activated calcium (CRAC) channel inhibitor with potent anti-inflammatory and pulmonary endothelial protective properties. Preclinical and early phase clinical studies suggest that zegocractin may be an effective agent for the treatment of AKI. Methods: KOURAGE (NCT06374797) is a multicenter, phase 2, randomized, double blind, placebo-controlled trial that aims to enroll approximately 150 patients with severe AKI and AHRF. Eligible patients will be randomized 1:1 to receive a total of five daily doses of zegocractin intravenous emulsion (Auxora™) or matching placebo. The objective was to evaluate the safety and efficacy of Auxora in patients with severe AKI, with the primary efficacy endpoint defined as the number of days alive, ventilator-free and kidney replacement therapy-free from the start of the first infusion of the study drug through day 30. A key secondary efficacy endpoint is the proportion of patients with major adverse kidney events at day 90. Conclusion: The KOURAGE trial will investigate the safety and efficacy of Auxora in patients with severe AKI and AHRF.

Introduction: While the triglyceride-glucose (TyG) index, a validated surrogate for insulin resistance, has demonstrated prognostic value in IgA nephropathy (IgAN) progression, its specific relationship with tubular atrophy/interstitial fibrosis (TA/IF) remains undetermined. Given the established association between insulin resistance and hypertension in IgAN, we aimed to investigate the association of the TyG index and systolic blood pressure (SBP) with TA/IF and to develop a predictive model for early TA/IF detection.

Methods: This cross-sectional study included 691 patients with primary IgAN. Exposures examined included the TyG index and SBP at the time of kidney biopsy, the former being the logarithmized product of fasting triglyceride and glucose concentrations. We tested association between TyG index and TA/IF, defined as Oxford T1-2 scores, using logistic regression models. Mediation analysis was performed to assess the potential mediating role of SBP in this relationship. A novel model was established based on the identified variables to predict risk of TA/IF. The performance of this model was evaluated for discrimination (receiver operating characteristic curves), calibration (calibration curve), and clinical utility (decision-curve analysis).

Results: Patients in the highest tertile of TyG index had 3.09-fold higher risk for TA/IF compared with those in the lowest tertile. The TyG index was independently and positively associated with the risk of TA/IF (odds ratio [OR]:3.830, 95% confidence interval [CI] 2.578-5.691; p < 0.001). SBP was found to mediate the association between TyG index and TA/IF, with a proportion mediated of 20.7% observed in the highest TyG index tertile (OR [indirect association]: 1.319, 95% CI: 1.118-1.558). The developed predictive nomogram model incorporated SBP, estimated glomerular filtration rate, TyG index, high-density lipoprotein cholesterol, and proteinuria; it demonstrated good predictive performance with strong discrimination (area under the curve: 0.864; bootstrap corrected: 0.859) and calibration (calibration curves). Decision-curve analysis confirmed the model's clinical utility showing a positive net benefit over a wide range of threshold probabilities.

Conclusion: In patients with IgAN, the TyG index was independently associated with the risk of TA/IF and SBP partially mediating this relationship. The developed nomogram, consisting of TyG index, SBP, and other conventional risk factors, provides a practical tool for risk stratification of TA/IF and guidance on IgAN management.

Background: Acute kidney injury (AKI) is one of the leading causes of in-hospital mortality in critically ill patients, with few treatment options other than supportive care. While preclinical studies suggest carvedilol may offer renal protection, its effect on outcomes in this population remains unclear.

Methods: This retrospective study included 26,230 adult patients with AKI from the MIMIC-IV database to evaluate outcomes associated with carvedilol use. The primary endpoint was 30-day all-cause mortality. Secondary endpoints included ICU and in-hospital mortality, renal replacement therapy (RRT) requirement, renal function recovery, and ICU/hospital length of stay. Additional outcomes assessed were the incidence of hyperkalemia, the need for vasopressors, and mortality at 90, 180, and 360 days. Multivariable Cox proportional hazards models and logistic regression were employed, along with propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) to ensure robustness. A separate cohort of 36793 critically ill patients with AKI from the eICU Collaborative Research Database was analyzed for the external validation.

Results: Carvedilol intervention was associated with significantly lower 30-day mortality (adjusted HR 0.53, 95% CI 0.44-0.65; p<0.001), ICU mortality (adjusted HR 0.37, 95% CI 0.26-0.50; p<0.001) and in-hospital mortality (adjusted HR 0.42, 95% CI 0.32-0.54; p<0.001), with sustained benefits up to 360 days (adjusted HR 0.71, 95% CI 0.63-0.880; p<0.001). These findings were supported by external validation in the eICU cohort, where carvedilol was independently associated with reduced ICU and in-hospital mortality (adjusted HRs 0.52 and 0.64, respectively; both p<0.001). Carvedilol-treated patients had higher rates of renal function recovery (adjusted OR 1.29, 95% CI 1.09-1.54; p<0.001), shorter ICU stays (median 4.1 vs 4.2 days, p=0.012), and no increased risk of hyperkalemia (p>0.05).

Conclusion: In critically ill patients with AKI, carvedilol use was associated with improved short- and long-term survival, without an increased risk of hyperkalemia.

Introduction: Blood clot formation and capillary fiber blockage in dialyzers remain critical challenges for patients with end-stage kidney disease undergoing hemodialysis. This study aimed to develop a machine learning model that effectively quantifies residual blood clots in dialyzer images captured using bedside smartphone cameras.

Methods: Dialyzer images were collected using mobile phones, and preprocessing techniques - such as background noise removal and image segmentation - were applied to focus on relevant regions. Data augmentation was used to increase model robustness. Composite images were created by combining views from both ends of the dialyzer, enhancing the model's ability to detect residual clots. We developed a binary classification model to distinguish between <10% and ∼30% blood clot levels using a pre-trained ConvNeXt architecture. Explainable AI (LIME) was incorporated to ensure the model focused on clinically relevant areas in its predictions.

Results: The dataset was split into training (60%), validation (20%), and testing (20%) sets, with 10 random trials for robustness. The ConvNeXt model achieved an accuracy of 0.6971 without pre-training or data augmentation, which increased to 0.7572 with pre-trained weights. Our combined framework yielded the highest accuracy (0.7672) and reduced standard deviation, indicating greater robustness. For comparison, two nephrology nurses achieved accuracies of 0.6271 and 0.6005 when manually classifying clot levels based solely on end images.

Conclusions: Our approach effectively detects residual blood clots in dialyzers using ConvNeXt by leveraging image data from both ends. The use of explainable AI tools confirmed the model's ability to accurately identify blood clots by focusing on relevant regions. Our study emphasizes the need to balance model complexity with computational efficiency. The ConvNeXt-Base model successfully avoided overfitting while maintaining practical performance, which could lead to improved clinical decision-making by minimizing circuit downtime and optimizing anemia management.

Introduction: Fibroblast growth factor 23 (FGF23) levels are markedly elevated in patients with kidney failure, but the mechanisms are incompletely understood. Recent evidence suggests that kidney-derived glycerol-3-phosphate (G-3-P), a glycolytic byproduct, mediates FGF23 production in response to dietary phosphate loading. However, the role of G-3-P is unknown in patients with kidney failure.

Methods: Serum G-3-P levels were quantified by LC/MS in 35 healthy individuals and 650 patients undergoing hemodialysis. Association between serum phosphorus and G-3-P was examined using unadjusted and multivariable linear regression models. We next analyzed the associations of G-3-P and known regulators of FGF23 production with serum FGF23.

Results: Median serum G-3-P level in patients undergoing hemodialysis was 220 ng/mL (IQR, 118-325), 2.2-fold higher than that of healthy individuals (98 ng/mL; IQR, 80-129). In patients undergoing hemodialysis, higher serum phosphorus was strongly associated with increased G-3-P in the unadjusted model; this association persisted after multivariate adjustment and when restricted to patients undergoing hemodialysis for over 10 years. In univariate analyses, higher serum phosphorus, calcium, intact PTH, G-3-P, and active vitamin D use were each significantly associated with higher FGF23. Multivariate analysis identified G-3-P as an independent predictor of FGF23. Further adjustment for transferrin saturation, ferritin, and C-reactive protein did not change these findings.

Conclusion: Even in patients with kidney failure, G-3-P may rise in response to phosphate retention and act as a regulator of FGF23 production. Further studies are needed to test these hypotheses and determine whether the apparently nonfunctioning kidney retains the capacity to produce G-3-P.

Introduction: Previous studies on the effects of urate-lowering therapy in patients with chronic kidney disease (CKD) and asymptomatic hyperuricemia have yielded conflicting results regarding renal outcomes. This study aimed to investigate the impact of initiating febuxostat on kidney prognosis in patients with stage 3-4 CKD and asymptomatic hyperuricemia using real-world data.

Methods: Using data from Zhejiang Provincial People's Hospital, we conducted a target trial emulation study involving 3,232 patients newly diagnosed with stage 3-4 CKD and asymptomatic hyperuricemia between January 1, 2018, and December 31, 2024. Using a clone-censor-weight approach, we compared the strategy of initiating febuxostat within 1 year of the first detected serum uric acid (UA) level >420 μmol/L versus to no initiation. Patients were followed for up to 5 years after hyperuricemia was diagnosed. The primary outcome was a composite kidney outcome consisting of a ≥40% decline in estimated glomerular filtration rate (eGFR), end-stage kidney disease (eGFR <15 mL/min/1.73 m2), or initiation of kidney replacement therapy. Secondary outcomes included all-cause mortality and major adverse cardiovascular events (MACE).

Results: Among patients newly diagnosed with CKD and hyperuricemia (mean age 71.8 years, 64% male, mean eGFR 42.8 mL/min/1.73 m2, mean serum UA level 499.4 μmol/L), 631 individuals (20%) initiated febuxostat therapy. Compared with non-initiation, febuxostat initiation was not significantly associated with the primary composite kidney outcome (HR: 1.07; 95% CI: 0.91-1.20), all-cause mortality (HR: 1.00; 95% CI: 0.66-1.35), or MACE (HR: 1.03; 95% CI: 0.95-1.11).

Conclusion: In patients with stage 3-4 CKD and asymptomatic hyperuricemia, initiation of febuxostat was not associated with kidney outcomes, all-cause mortality, or MACE. Further studies are warranted to validate these findings.

Introduction: The global incidence of chronic kidney disease (CKD) continues to rise, but delayed epidemiological data pose challenges to public health policy. Traditional surveillance methods often suffer from reporting delays. Recent advances in artificial intelligence (AI) offer novel opportunities for enhancing disease burden predictions.

Methods: We collected CKD incidence data from 21 Global Burden of Disease (GBD) regions spanning from 1990 to 2021. Using five advanced AI models (GPT-4o, Claude-3.7, DeepSeek-R1, Grok-3, and Gemini 2.5) and two traditional forecasting methods (autoregressive integrated moving average and Bayesian age-period-cohort), we predicted CKD incidence for 2023. The performance of the models was evaluated by comparing the predicted values to the actual observed data. All models were trained using the same data and instructions.

Results: The AI models and traditional models performed similarly, with near-perfect accuracy in predicting incidence rates in regions such as the Americas, Central Europe, East Asia, high-income Asia Pacific, Southeast Asia, and tropical Latin America. Among the models, GPT-4o demonstrated the highest mean accuracy of 0.722, with all models achieving average accuracies above 0.65. No statistically significant difference in accuracy was observed between AI-based and traditional models (ANOVA p = 0.27).

Conclusion: State-of-the-art AI models, when systematically prompted and standardized, can predict global CKD incidence with accuracy comparable to traditional statistical models. AI-driven epidemiological forecasting holds promise for enhancing real-time public health planning and resource allocation, particularly in regions with stable historical data.