Chronic kidney disease (CKD) is a major global health problem and an important driver of cardiovascular morbidity and mortality. Sleep disorders are highly prevalent in people with, or at risk of, CKD, but their specific contribution to CKD onset and progression has not been clearly defined. Unlike previous reviews that have focused mainly on symptom burden, quality of life, or general management of sleep problems in CKD, this narrative review is explicitly centred on renal outcomes. We examine whether common sleep disorders-insomnia, abnormal sleep duration, restless legs syndrome, periodic limb movement disorder, and obstructive sleep apnoea (OSA) and central sleep apnoea-are associated with an increased risk of incident CKD and with faster progression of established CKD [estimated glomerular filtration rate (eGFR) decline, albuminuria, end-stage kidney disease]. We synthesize evidence from prospective cohorts, administrative databases, and Mendelian randomization studies, with particular attention to residual confounding, incomplete sleep phenotyping, and overlap between sleep disorders, especially unrecognized OSA. Observational data suggest that poor sleep quality and abnormal sleep duration are modestly associated with incident CKD and CKD progression, although independence from OSA remains uncertain. In contrast, evidence linking OSA to reduced eGFR, albuminuria, and accelerated CKD progression is more consistent, and bidirectional relations between CKD and OSA are increasingly recognized. We also review pathophysiological pathways that plausibly connect sleep disorders to renal injury and critically appraise preliminary interventional data on OSA treatment and kidney outcomes. Finally, we outline the clinical implications of integrating outcome-oriented sleep assessment into nephrology and hypertension care and propose a research agenda to determine whether systematic detection and treatment of sleep disorders, particularly OSA, should be adopted as a strategy to prevent CKD onset and slow CKD progression.
This perspective examines how artificial intelligence (AI) may reshape nephrology over the next two decades while keeping the nephrologist's role central. Prediction models for acute kidney injury and chronic kidney disease progression, and multimodal tools such as KidneyIntelX, will deliver continuous, patient-level risk estimates from electronic health records, biomarkers, imaging, and wearable devices. Large language models (LLMs) are "copilots" for documentation, triage, education, and patient counseling, with potential to reduce administrative burden but also risks of hallucinations, bias, and uneven accuracy. Nephrologists will need new competencies in model calibration, fairness, communication, and ethics to decide when to follow or override algorithmic advice, especially for older, frail, and multimorbid patients. Overall, AI can support proactive, person-centred kidney care only if clinicians help design, govern, evaluate, and critically supervise these emerging technologies within robust, learning healthcare systems.
Online hemodiafiltration (OL-HDF) and medium cut-off (MCO) dialyzers augment diffusion-based hemodialysis (HD) with convective clearance to enhance removal of middle molecules. In large-scale randomized trials, OL-HDF appears to reduce all-cause, cardiovascular, and infection-related mortality compared with high-flux HD, particularly when convection volumes exceed 23 L per session. Data suggest a graded effect; higher achieved convection volumes are associated with greater benefit, and advantages have been observed across the analyzed subgroups. Evidence also indicates better preservation of patient-reported quality of life compared with high-flux HD. Large-scale observational registry data, while subject to inherent limitations, support beneficial outcomes and generalizability to routine clinical practice. MCO membranes enhance middle-molecule clearance on conventional hemodialysis machines via enlarged pore size and internal-filtration back-filtration. However, the long-term clinical data remain limited, and the convective component is not externally measured or prescribed. This perspective distils mechanistic and clinical insights on both OL-HDF and MCO-HD and evaluates the published evidence, including solute clearance studies, mortality outcomes, and patient-reported quality-of-life data. We outline actionable prescription strategies and opportunities for individualized treatment optimization. Our goal is to provide clinicians with a concise roadmap to personalize and integrate convection-enhancing therapies in everyday practice.
Randomized studies have demonstrated that high-volume hemodiafiltration results in reduced mortality compared to conventional hemodialysis treatment. However, eligibility criteria in these trials may limit generalizability to routine clinical practice. Some of these trials reported a limited number of events, underscoring the need to further evaluate the effect of hemodiafiltration on mortality. We will conduct a target trial emulation study using data from routine clinical practice. The primary aim of this study is to evaluate whether high-volume hemodiafiltration reduces all-cause mortality. The secondary aim is to assess cause-specific mortality. Other aims include assessing all-cause and cause-specific hospitalizations, as well as cumulative length of hospital stay and the dose–response relationship between convection volume in hemodiafiltration and the outcomes. Data will be obtained from the second version of ApolloDialDb (Apollo), an anonymized dialysis dataset capturing over 1000 variables from patients from all over the world. For this study, we will include adult patients from European countries with kidney failure who initiated with at least one treatment of high-flux hemodialysis or hemodiafiltration between 01 January 2018 and 30 June 2024, and who were prescribed a thrice-weekly dialysis schedule at the start. Patients starting with home dialysis will be excluded. We will use a target trial emulation approach with a clone-censor-weight design and marginal structural models, controlling for selection bias, survivor bias, and competing risk bias. Sub-analyses will be performed to investigate the effect of high-volume hemodiafiltration (≥ 23 L of convection volume). Inverse probability weighting will be applied to adjust for predefined confounders including sociodemographic, clinical, and anthropometric factors, as well as comorbidities to achieve balance between treatment groups. In addition to randomized studies, prior large observational studies have indicated a survival benefit for hemodiafiltration, as well as a possible reduction of hospitalizations. The target trial emulation study outlined in this protocol will expand this knowledge and provide generalizable insights on the effects of hemodiafiltration on outcomes by using real-world data representative of routine clinical practice while appropriately addressing sources of bias. This protocol outlines a study in which we will examine the effects of hemodiafiltration (HDF) compared with high flux hemodialysis (HD) using data from standard day-to-day dialysis care, collected from across Europe. Clinical trials have previously shown that HDF provides benefits for survival and quality of life. However, it remains uncertain whether these benefits apply to all patients or only in healthier patients, who meet the eligibility criteria to participate in a clinical trial. We will use advanced statistical methods, specifically target trial emulation, to closely mimic a randomized clinical trial using real-world data and thereby reduce bias. The study will evaluate overall 5-year survival, causes of death, hospitalizations, and the impact of higher HDF convection volumes to help guide future dialysis care decisions.
