Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2026-03-26 @ 3:19 PM
Ignite Modification Date:
2026-03-26 @ 3:19 PM
NCT ID:
NCT07345533
Brief Summary:
Background:
Chronic heart failure represents a major public health challenge, affecting approximately 64 million people worldwide and generating high costs in terms of mortality, frequent hospitalizations, and medical expenses. In France, this disease is expected to cause nearly 70,000 deaths and 181,000 hospital admissions in 2022. Current management, based on periodic consultations, fails to effectively prevent acute exacerbations, highlighting the importance of technological solutions such as remote monitoring.
Objective:
This study aims to demonstrate the value of regular monitoring of electrophysiological and mechanical cardiac signals and parameters in patients with chronic heart failure. Its goal is to build a database of signals from an external measuring device to identify parameters that evolve in relation to biological and/or hemodynamic changes and/or the patient's clinical status. The results of this study will enable the further development of an automated monitoring solution for heart failure patients to enable early detection and management of decompensation.
Materials and Methods:
A total of 70 patients diagnosed with chronic heart failure will be included, including 30 patients hospitalized for heart failure decompensation and 40 patients hospitalized for hemodynamic assessment. Electrophysiological and cardiac mechanical data will be collected using a skin-based measuring device. These data will then be correlated with biological and/or hemodynamic changes and/or the patient's clinical status. They will contribute to the training of an algorithm to detect the risk of decompensation.
Hypothesis Tested:
The study will test the hypothesis that regular, automated remote monitoring of the data collected during the study can identify the risk of decompensation. Ultimately, this approach could improve the management of heart failure by maintaining a state of balance, while reducing the mental burden on patients.
Study:
NCT07345533
Study Brief:
Protocol Section:
NCT07345533