Pioneering Leadership in AI, Computational Medicine, and Data Analytics

RRI brings together physicians, data scientists, mathematicians, engineers, theoretical physicists, and analysts, all united by one purpose: to transform ground-breaking research into transformative clinical care. Viewing complex problems through multiple lenses broadens our perspective and opens new avenues of inquiry. The simple words “I have a thought” can spark a discovery or change the trajectory of a research project.

Our Focus Areas

We use AI, computational medicine, and advanced analytics to develop new clinical strategies, treatments, protocols, and diagnostic tools that support clinicians and improve outcomes for patients. We operate at the intersection of clinical data, machine data, and real-world practice, with access to a large patient population and one of the world's largest and richest renal datasets. Because we have access to data that spans over 20 countries, we also have a unique opportunity to answer global questions and evaluate the effectiveness of specific healthcare interventions.

Research Innovations

Our interdisciplinary approach, the quality of our data, and our agile development process all contribute to the breadth and depth of our innovations. We've pioneered models designed to optimize anemia management and help reduce hospitalizations, and identify dialysis patients most likely to benefit from home therapy. We’ve published over 600 peer-reviewed studies since 2000, contributing not only to innovation but to a growing body of evidence-based science that shapes practice and policy.

Latest Research & News

Latest Research

  • Sanne Roos, John Larkin, Linda H Ficociello, Len A Usvyat, Yue Jiao, Luca Neri, Vivi Zhou, Menno Brandjes, Peter J Blankestijn, Michiel L Bots, Saskia Haitjema, Marianne C Verhaar, Roemer J Janse, Robin W M Vernooij

    DISCUSSIONIn 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.BACKGROUNDRandomized 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.METHODSData 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.

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Latest News

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The MONDO Initiative

RRI is a founding member of the MONitoring Dialysis Outcomes (MONDO) initiative, an international consortium of kidney care providers, academic institutions, and subject matter experts dedicated to advancing data-driven research on kidney disease. MONDO integrates clinical data, domain expertise, and advanced analytics to:

 Deepen understanding of patient characteristics, practice patterns, and physiology
 Evaluate the real-world generalizability of therapies and technologies
 Develop advanced models and AI-driven solutions to improve outcomes