Clinical Kidney Journal最新文献

[This corrects the article DOI: 10.1093/ckj/sfaf289.].

Background: Culture-negative peritoneal dialysis-associated peritonitis (CNPDP) carries a high risk of treatment failure but lacks validated prediction tools. This study aimed to develop and validate a clinical nomogram for individualized risk assessment of treatment failure in CNPDP patients.

Methods: In this multicenter retrospective study, 288 CNPDP patients treated at Jining Medical University Affiliated Hospital (2013-23) were randomly allocated to training (n = 173) and internal validation (n = 115) cohorts. An independent external cohort (n = 103) from Zaozhuang Municipal Hospital and Heze Municipal Hospital assessed generalizability. First, we used Random Forest to estimate missing data for variables with <30% missing values. Then, we used LASSO regression to analyze 32 candidate predictors. These predictors covered areas like patient demographics, clinical scores and lab test results. The final multivariate logistic regression model was visualized as a clinical nomogram. Performance was rigorously evaluated through area under receiver operating characteristic curve (AUC), calibration plots and decision curve analysis. The primary endpoint was composite treatment failure (catheter removal or peritonitis-related mortality ≤30 days).

Results: LASSO identified five independent predictors: effluent white blood cell count on Day 3 (Eff_WBC_D3), serum albumin (ALB), total cholesterol (TC), magnesium (Mg) and phosphorus (P). The nomogram achieved excellent discrimination: training cohort AUC = 0.897 (95% confidence interval 0.817-0.978), internal validation AUC = 0.861 (0.770-0.952) and external validation AUC = 0.849 (0.750-0.948) with minimal optimism (ΔAUC = 0.036). Eff_WBC_D3 demonstrated the strongest univariate predictive power (AUC = 0.830). Calibration curves showed optimal fit (Hosmer-Lemeshow P = .32), while decision curve analysis confirmed clinical utility across probability thresholds of 5%-50%. For bedside implementation, an interactive web tool was developed (https://liuliangmianhua.shinyapps.io/dynnomapp/).

Conclusion: This externally validated five-variable nomogram, deployed as a freely accessible online tool, offers a robust, practical tool for predicting treatment failure in CNPDP. Its integration of dynamic dialysate markers with routine laboratory data enables personalized early intervention and supports timely clinical decision-making.

Background: Rheumatoid arthritis (RA) has been linked to an increased risk of chronic kidney disease (CKD), but the predominant renal phenotype and its independence from established risk factors remain unclear. We examined the RA-CKD association in a large, population-based cohort.

Methods: RA and CKD were defined using American College of Rheumatology/European Alliance of Associations for Rheumatology and KDIGO criteria. Logistic regression models were employed with stepwise adjustment: first for cardiovascular risk [Systematic COronary Risk Evaluation 2 (SCORE2)], followed by a model including age, sex, metabolic syndrome, smoking status, non-steroidal anti-inflammatory drug (NSAID) use and high-sensitivity C-reactive protein. Interaction terms were tested to evaluate effect modification.

Results: Among 9665 participants from the Paracelsus 10,000 cohort, 296 (3.1%) had RA. CKD prevalence was higher in the RA group compared with controls (11.8% vs 6.7%, P < .001). Albuminuria at preserved estimated glomerular filtration rate was the dominant renal manifestation in RA (6.8% vs 4.2%, P = .027). In unadjusted analyses, RA was associated with higher odds of CKD [odds ratio (OR) 1.86, 95% confidence interval (CI) 1.30-2.68], an association that persisted after cardiovascular risk adjustment. However, the association was attenuated and no longer statistically significant in the fully adjusted model (OR 1.43, 95% CI 0.96-2.13). A significant interaction was observed with NSAID use (P = .042), whereby the association was largely confined to RA patients not using NSAIDs.

Conclusions: RA is associated with a higher prevalence of CKD, primarily driven by albuminuria at preserved kidney function. This distinct renal phenotype appears largely mediated by metabolic comorbidities rather than inflammation alone. Our findings highlight the need for systematic albuminuria screening in RA patients to enable earlier detection and intervention.

Background: Chronic pain significantly impacts health-related quality of life (HRQOL) in patients with non-dialysis chronic kidney disease (ND-CKD), yet the management of pain in this population is challenging. We hypothesized that analgesic prescription practices vary internationally, influencing the pain experience and HRQOL of patients with stage 3-5 ND-CKD.

Methods: This descriptive, observational, multinational cohort study utilized data from the Chronic Kidney Disease Outcomes and Practice Patterns Study (CKDopps), enrolling adult patients from nephrology practices in Brazil, France and the USA between 2013 and 2020. Analgesic prescriptions within 6 months before HRQOL assessment were categorized as non-steroidal anti-inflammatory drugs (NSAIDs), opioids or other analgesics. HRQOL was measured using the Kidney Disease Quality of Life Short Form, assessing multiple subdomains.

Results: Among 3945 patients, analgesics were most frequently prescribed in the USA across all CKD stages, with opioids prescribed nearly twice as often compared with Brazil and France. NSAIDs are frequently prescribed in Brazil, including in advanced CKD stages, contrasting sharply with practices in France and the USA. Higher reported pain intensity consistently correlated with poorer outcomes across all HRQOL subdomains.

Conclusions: This study identifies considerable international variability in pain reporting and analgesic prescription patterns in patients with stage 3-5 ND-CKD. Randomized controlled trials evaluating the efficacy and safety of analgesics are warranted to improve key patient-reported outcomes such as pain in patients with ND-CKD.

Long-term peritoneal dialysis (PD) treatment can lead to the destruction of peritoneal structure and function, which can lead to PD failure or even a poor prognosis. However, validated early biomarkers for patients undergoing PD are lacking. PD effluent (PDE) is rich in various biological components, such as nucleic acids, proteins, and metabolites, and is now an important source of noninvasive biomarkers for the dynamic monitoring of disease progression. In recent studies, a variety of histological techniques have provided unprecedented depth and breadth to PD biomarker research, and are becoming key tools in the early diagnosis, prognosis, and therapeutic monitoring of PD patients. Correspondingly, artificial intelligence (AI) approaches, which can flexibly handle data and excel at mining nonlinear and high-dimensional relationships in multimodal data, have moved from theory to practice. AI-based multi-omics analysis has not only greatly improved the understanding of the pathophysiological mechanisms of PD-associated fibrosis (PF) but has also contributed to the development of new biomarkers and novel targets. This review provides a comprehensive summary of recent advances in the development of PDE biomarkers using AI-based multi-omics approaches. We highlight the application of AI-based multi-omics techniques for early diagnosis, evaluation of peritoneal injury, assessment of peritoneal function, and prediction of prognosis. Finally, we discuss the challenges and limitations of PDE biomarkers from the perspectives of multi-omics and AI. In conclusion, AI-based multi-omics analysis holds great promise for the development of PDE biomarkers, which are expected to significantly improve the prognosis of PD patients and ultimately facilitate precision medicine.

Nephrology has benefited from a growing body of high-quality clinical evidence, including clinical trials of pharmacological therapies and health service research on alternative care approaches. Consequently, there is an increasing need to perform economic evaluations in kidney disease to inform reimbursement decisions and optimise healthcare spending, thereby improving patient care within budget constraints. Cost-effectiveness assesses if the additional health gains are worth any additional costs by estimating differences in the quality and quantity of life, and the costs, from the point of intervention over observed but also longer (even lifetime) timelines, capturing the entire patient pathway through healthcare, e.g. from early-stage chronic kidney disease (CKD) through to dialysis or transplantation. Working with stakeholders to define the decision problem, merging evidence from a range of sources, including clinical trials complicated by limited follow-up and non-generalisable participants, surrogacy studies to estimate the intervention's impact on longer-term kidney failure risk, quality of life data collected ideally using instruments sensitive to kidney disease progression and other real-world data are required to make extrapolations sufficiently far into the patient's lifetime to capture kidney failure. Consideration of disadvantaged populations and how interventions may operate differently in certain groups may be indicated. Failure to capture competing risks of cardiovascular disease and death will bias estimates of kidney failure. Application of our tips, combined with an understanding of how decision-makers use cost-effectiveness results and information about factors like rarity and disease severity maximises the likelihood of new kidney treatments and care approaches being adopted.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a severe multisystem autoimmune disease in which renal involvement is common and often progresses, without timely intervention, to end-stage kidney disease. Standard remission induction therapy combines high-dose glucocorticoids (GCs) with cyclophosphamide or rituximab. While effective, cumulative GC exposure drives substantial treatment-related morbidity, including infection, diabetes, osteoporosis and cardiovascular complications, highlighting the urgent need for GC-sparing strategies. Avacopan, an oral selective C5a receptor antagonist, represents a novel therapeutic approach targeting the alternative complement pathway, a key mediator of neutrophil activation and vascular injury in AAV. The pivotal phase 3 ADVOCATE trial demonstrated that avacopan achieved non-inferior remission at 26 weeks and superior sustained remission at 52 weeks compared with a standard GC taper, while reducing GC-related toxicity and improving renal recovery, particularly in patients with advanced kidney impairment. Since approval in 2021, real-world studies and case series have given further confidence in avacopan's efficacy across diverse patient subgroups, including those with severe renal disease, diffuse alveolar haemorrhage and refractory manifestations. However, real-world data also highlight variability in GC tapering practices and safety signals, particularly hepatotoxicity in Japanese cohorts. Several unanswered questions remain, including the long-term safety, clinical benefit of treatment beyond 1 year and optimal GC concomitant use or even the feasibility of complete GC avoidance. Ongoing large-scale studies and international real-world evidence will be essential to define avacopan's optimal role in clinical practice, ensuring equitable access for patients with AAV.

Background: Peritoneal dialysis (PD)-associated peritonitis remains a major complication affecting patient outcomes and modality survival. This study aims to evaluate temporal trends in pathogen distribution and antibiotic susceptibility over four decades as well as clinical outcomes in PD-associated peritonitis.

Methods: We retrospectively analyzed 832 peritonitis cultures of PD patients across four decades from 1979 to 2024 treated at Robert Bosch Hospital, Stuttgart (Germany). For longitudinal comparison of pathogen distribution and antibiotic susceptibility, the study period was divided into four time periods: P1 (1979-1992), P2 (1993-2003), P3 (2004-2014), and P4 (2015-2024). Clinical response and outcomes were assessed in P4.

Results: Gram-positive bacteria was the most frequent causative organisms (56%), followed by Gram-negative bacteria (30%) and culture-negative peritonitis (CNP, 13%). Gram-negative peritonitis increased significantly in P4 compared to P1-P3, while coagulase-negative staphylococci (CNS) declined from 31% in P1 to 14% in P4 (P = .0446). Vancomycin susceptibility among Gram-positive organisms remained high, whereas cefazolin susceptibility changed over time. In P4, the overall cure rate was 63%, with the highest in gram-positive (72%) and lowest in polymicrobial peritonitis (43%).Regarding clinical outcomes, transition to permanent hemodialysis (HD) was significantly more frequent in Gram-negative than Gram-positive peritonitis (27% vs. 12%; P = .03). Both catheter removal and transition to permanent HD occurred significantly more often in polymicrobial peritonitis (54% and 40%) compared with Gram-positive (24% and 12%; P = .001 and P = .0008) and CNP (30% and 17%; P = .01 and P = .04). Regarding individual pathogens, Staphylococcus aureus (MSSA) was associated with a significantly higher catheter removal rate compared to other Gram-positive organisms.

Conclusion: Our findings show temporal changes of microbiological spectrum of PD-associated peritonitis over four decades. Polymicrobial and Gram-negative peritonitis were associated with poorer outcomes, emphasizing the need for ongoing microbiological surveillance and antibiotic stewardship to optimize PD care.