Virtual Twins as Tools for Personalized Clinical Care in Renal Denervation - Pilot Study

Summary

High blood pressure (hypertension) is a common condition. Despite the availability of several medications, many patients continue to have poorly controlled blood pressure. Renal denervation (RDN), a minimally invasive procedure that reduces overactive kidney nerves, has emerged as a potential treatment for certain patients with difficult-to-control hypertension. However, not all patients benefit from this procedure, and there is currently no reliable way to predict who will respond.

The VITAL-RDN study (Virtual twins as tools for personalized clinical care in renal denervation - Pilot study) aims to test whether a "digital twin" can help predict which patients are most likely to respond to renal denervation. A digital twin is a personalized computer model built from an individual patient's clinical, biological, and physiological data. It is designed to simulate how that patient's cardiovascular system functions and how it may respond to treatment.

The main objective of this pilot study is to evaluate whether the digital twin can accurately distinguish between patients who will respond to renal denervation and those who will not. A responder is defined as a patient whose daytime systolic blood pressure decreases by at least 5 mmHg three months after the RDN.

Three groups of patients will be enrolled:

30 patients with resistant hypertension or other clinical indications for renal denervation.

20 patients with untreated primary hypertension who are starting blood pressure-lowering medication.

10 patients with primary aldosteronism who are scheduled for surgical adrenalectomy.

In addition to evaluating prediction of response to renal denervation, the study will also assess the performance of the digital twin in predicting response to antihypertensive medication or adrenalectomy in the respective subgroups.

If successful, this approach could support more personalized treatment decisions, reduce unnecessary procedures, and improve blood pressure control by matching the right therapy to the right patient.

Conditions

• Hypertension

Interventions

OTHER

Data collection in order to train the digital twin

In this research, results from routine examinations performed to monitor the patient's health will be used to train the digital twin. These include resting blood pressure and heart rate measurements, vascular assessments (pulse wave velocity, carotid stiffness), electrocardiogram (ECG), echocardiography, extracellular water measurement by bioimpedance, cardiopulmonary exercise testing, and routine blood tests for renal and neurohumoral markers. In addition, the patient's autonomic nervous system will be evaluated using functional tests. The patient will place his or her hand in cold water and squeeze a handgrip device to assess blood pressure and heart rate reactivity (about 20 minutes). The patient will wear a portable device (Calamari) recording heart rhythm, chest vibrations, and blood volume. The patient will also complete questionnaires.

Sponsors & Collaborators

• University Ghent

  collaborator OTHER

• University of Auckland, New Zealand

  collaborator OTHER

• University of Pisa

  collaborator OTHER

• Ecole Polytechnique Fédérale de Lausanne

  collaborator OTHER

• Delft University of Technology

  collaborator OTHER

• Maastricht University

  collaborator OTHER

• AIT Austrian Institute of Technology GmbH

  collaborator OTHER

• Stichting IMEC-NL

  collaborator OTHER

• ELEM Biotech, Spain

  collaborator UNKNOWN

• Westerhof Cardiovascular Research, Netherlands

  collaborator UNKNOWN

• Assistance Publique - Hôpitaux de Paris

  lead OTHER

Principal Investigators

• Rosa Maria BRUNO · Assistance Publique - Hôpitaux de Paris

Eligibility

Min Age

18 Years

Max Age

90 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-04-30

Primary Completion

2028-10-31

Completion

2028-10-31

Countries

• France

Study Locations