The growing complexity of patients and the pressure on healthcare systems make it necessary to move towards value-based care models that allow for more personalized care and, at the same time, a more efficient use of resources. In this context, Renal-Trust was born, an innovation project that uses clinical data and artificial intelligence to identify patients with chronic kidney disease who are at higher risk of rapid disease progression.
The project is headed by Dr Ignacio Revuelta, from the Nephrology and Renal Transplant Department at the Hospital Clínic Barcelona, and Dr Santiago Frid, from the Clinical Informatics Department. Renal-Trust builds on the development of Nefro Data Space, a data architecture that allows clinical information to be integrated and analysed securely, ensuring the protection of particularly sensitive data and facilitating its use to generate knowledge and develop new tools to support clinical practice.
Nefro Data Space was developed as part of the RETECH 2025 and was implemented in hospitals within the C-17 Network. The DataMesh project at the Hospital Clínic Barcelona also contributed to the development of this architecture. Unlike traditional data storage systems, the data space model allows information from different sources to be organized and linked, facilitating its use for different use cases, both within an institution and in collaboration with other healthcare organizations.
From static diagnosis to predicting progression
Renal-Trust was developed on top of this architecture, in order to move from a static classification of chronic kidney disease towards a model capable of anticipating how the condition may progress in each patient.
To do this, the project automatically analysed information from more than 4,000 patients, identifying those who present a pattern of rapid progression towards advanced stages of kidney disease. The artificial intelligence models developed also allow for the incorporation of explainability tools to identify the factors that contribute to a given prediction.
“With Renal-Trust, we go from knowing what stage a patient is at to being able to anticipate how their disease may progress,” explains Dr Ignacio Revuelta. The approach can help identify patients at higher risk at an earlier stage and adapt care pathways to their needs, going beyond the traditional staging classification.
Chronic kidney disease represents a significant healthcare and economic burden for healthcare systems. In this regard, having tools that allow its progression to be anticipated can contribute to both improving patient care and planning resources and developing more efficient care pathways.
A collaborative project
Renal-Trust is the result of collaboration between different departments and organizations. At the Hospital Clínic Barcelona the Nephrology and Renal Transplantation Department and the Clinical Informatics Department are participating, together with EVIDENZE HEALTH ESPAÑA - ANTARES, which has co-led the project.
Various companies and organizations specializing in areas such as data infrastructure and management, including EURECAT and i2CAT; the development of explainable models, such as OMNIOS COGNITIVE SOLUTIONS; and data governance and regulation through BLOWWW 4 ANALYTICS have also participated.
The project received recognition in the sixth edition of the ENNOVA HEALTH 2026 awards, organized by Diario Médico and Correo Farmacéutico. Renal-Trust was selected as the winner in the AI and data management category, which recognizes projects related to the application of artificial intelligence, predictive models and decision support tools in the healthcare sector.
The award was presented on 1 October in Madrid. Dr Ignacio Revuelta, representing the Hospital Clínic Barcelona, and Pedro Lisbona, on behalf of EVIDENZE HEALTH ESPAÑA, accepted the award.
Beyond the award, the project continues its development with the aim of transferring these tools to new healthcare processes. The goal is to move towards increasingly patient-centred medicine, focusing on the information generated by the patient and using data to anticipate needs, improve health outcomes and contribute to the sustainability of organizations and the healthcare system.
