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Artificial intelligence-based personalised rituximab treatment protocol in membranous nephropathy (iRITUX): protocol for a multicentre randomised control trial

Por: Teisseyre · M. · Destere · A. · Cremoni · M. · Zorzi · K. · Brglez · V. · Benito · S. · Bailly · L. · Fernandez · C. · Seitz-Polski · B.
Introduction

Membranous nephropathy is an autoimmune kidney disease and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults. Rituximab is now recommended as first-line therapy for membranous nephropathy. However, Kidney Disease Improving Global Outcomes guidelines do not recommend any specific protocol. Rituximab bioavailability is reduced in patients with membranous nephropathy due to urinary drug loss. Underdosing of rituximab is associated with treatment failure. We have previously developed a machine learning algorithm to predict the risk of underdosing. We have retrospectively shown that patients with a high risk of underdosing required higher doses of rituximab to achieve remission. The aim of this prospective study is to evaluate the efficacy of algorithm-driven rituximab treatment in patients with membranous nephropathy compared to standard treatment.

Methods

A multicentre, randomised, controlled, open-label, prospective superiority clinical trial will be conducted in 13 French hospitals. 130 consecutive patients with primary membranous nephropathy and active nephrotic syndrome will be randomised to either the standard protocol control group (two 1 g rituximab infusions on days 0 and 15) or the algorithm-driven rituximab treatment group. In the latter, the rituximab dose will depend on the algorithm-estimated risk of underdosing. Patients with an algorithm-estimated risk of underdosing ≤50% will receive 1 g of rituximab on days 0 and 15. Patients with an algorithm-estimated risk of underdosing between 51% and 75% will receive 1 g of rituximab on days 0, 15 and 30. Finally, patients with an estimated risk of underdosing >75% will receive 1 g of rituximab on days 0, 15, 30 and 45. The primary study outcome is the rate of clinical remission (complete or partial) at month 6 after treatment initiation. The secondary outcomes include clinical remission at month 12, immunological remission, proteinuria, albuminuria, serum creatinine, estimated glomerular filtration rate, phospholipase A2 receptor type 1 antibody titre, anti-rituximab antibody occurrence, lymphocyte count, serum rituximab level and related adverse events.

Ethics and dissemination

The trial received ethics approval from the local ethics boards. The results of this study will confirm whether algorithm-driven rituximab treatment is more effective in inducing remission than the standard regimen and thus may contribute to improving management of patients with membranous nephropathy. The results of our study will be submitted to a peer-review journal.

Trial registration number

NCT06341205 trial number. Registered on 2 April 2024.

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals

by Michele Salvagno, Alessandro De Cassai, Stefano Zorzi, Mario Zaccarelli, Marco Pasetto, Elda Diletta Sterchele, Dmytro Chumachenko, Alberto Giovanni Gerli, Razvan Azamfirei, Fabio Silvio Taccone

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community’s understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.
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