Radhakrishnan, Yeshwanter

ABSTRACT Background Up to 20% of patients with antineutrophil cytoplasmic antibody–associated vasculitis (AAV) and kidney involvement progress to end-stage kidney disease (ESKD) despite therapy. The role of monoclonal gammopathy (MG) in disease progression in patients with AAV is not well understood. Methods We conducted a retrospective study to identify patients diagnosed with AAV and renal involvement who had undergone monoclonal protein testing between November 1999 and December 2023. A total of 167 patients were included in the renal survival analysis and were categorized into monoclonal positive (n = 34) and monoclonal negative (n = 133) groups. For the mortality analysis, we expanded the cohort to include an additional 26 patients who were dialysis-dependent at baseline and remained on dialysis throughout follow-up, resulting in a total of 193 patients. We performed multivariate Cox analysis to identify predictors of adverse renal events [>40% decline in estimated glomerular filtration rate (eGFR) or ESKD (eGFR <15 mL/min/1.73 m², need for dialysis or transplantation)] and mortality. Results Median age of the cohort was 68 (58.5–74.5) years, with 50.8% males, 65.8% MPO positive, and 64.6% receiving rituximab as induction therapy. After adjusting for baseline proteinuria, sclerotic Berden class and induction therapy, MG was associated with adverse renal events [hazard ratio (HR) 3.29 (95% confidence interval 1.12–9.68), P = .03]. MG at baseline was not associated with mortality [HR 1.65 (0.87–3.12), P = .12], however patients with persistent MG at 6 months had higher mortality compared with those without MG or those whose MG was not sustained [HR 3.95 (1.10–14.21), P = .035]. Conclusions Patients with AAV and renal involvement who have MG are at significantly increased risk of adverse renal events even after adjusting for factors such as eGFR and degree of chronicity (known predictors of poor renal outcome). The exact mechanism by which MG portends poor outcomes is poorly understood, and additional research is required.

Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI’s traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology’s unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.