Our Team

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 to high-flux HD, particularly when convection volumes exceed 23 L per session. Data suggests a graded effect; higher achieved convection volumes are associated with greater benefit, and advantages are observed across analysed subgroups. Evidence also indicates better preservation of patient-reported quality of life compared to 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, 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 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.

Large language models (LLMs) such as ChatGPT are increasingly positioned to be integrated into various aspects of daily life, with promising applications in healthcare, including personalized nutritional guidance for patients with chronic kidney disease (CKD). However, for LLM-powered nutrition support tools to reach their full potential, active collaboration of healthcare professionals, patients, caregivers and LLM experts is crucial. We conducted a comprehensive review of the literature on the use of LLMs as tools to enhance nutrition recommendations for patients with CKD, curated by our expertise in the field. Additionally, we considered relevant findings from adjacent fields, including diabetes and obesity management. Currently, the application of LLMs for CKD-specific nutrition support remains limited and has room for improvement. Although LLMs can generate recipe ideas, their nutritional analyses often underestimate critical food components such as electrolytes and calories. Anticipated advancements in LLMs and other generative artificial intelligence (AI) technologies are expected to enhance these capabilities, potentially enabling accurate nutritional analysis, the generation of visual aids for cooking and identification of kidney-healthy options in restaurants. While LLM-based nutritional support for patients with CKD is still in its early stages, rapid advancements are expected in the near future. Engagement from the CKD community, including healthcare professionals, patients and caregivers, will be essential to harness AI-driven improvements in nutritional care with a balanced perspective that is both critical and optimistic.