Ignite Creation Date:
2025-12-25 @ 1:22 AM
Ignite Modification Date:
2025-12-25 @ 11:32 PM
Study NCT ID:
NCT06014593
Status:
TERMINATED
Last Update Posted:
2025-02-04
First Post:
2023-08-11
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Heart Failure Diagnostic Performance of an Expirogram Analysis Algorithm Evaluating 4 Biomarkers
Sponsor:
University Hospital, Montpellier
Organization:
Study Overview
Official Title:
Evaluation of the Diagnostic Performance of an Algorithm for Analyzing Expirograms of 4 Biomarkers in Exhaled Air in Patients With Heart Failure
Status:
TERMINATED
Status Verified Date:
2025-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
The early termination of this research is due to administrative and financial reasons.
As the funding ends on 31/12/2024, we had to close the SenseIR-IC study in order to start the analyses before the end of the funding.
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
SenseIR-IC
Brief Summary:
Telemonitoring is a key clinical issue in heart failure (HF). Bedside measurement systems using handheld devices provide "digital biomarkers" useful for remote monitoring. A recent systematic review and meta-analysis showed that teleconsultations and telemonitoring at home improved the prognosis of HF patients compared with usual care. Biomarkers contained in exhaled air could constitute "digital biomarkers" in HF, as measurement is non-invasive, and 4 different species have shown their potential interest: NO, CO, acetone and isoprene. The assessment of these species in the exhaled air to remains an issue in the perspective of non-invasive biomarkers in HF. Indeed, it requires selective sensors with low limit of detection. In addition, these sensors should be miniaturizable. Quartz-enhanced photoacoustic spectroscopy (QEPAS) are sensors that are suitable in this context. Last, the measured concentration should be informative and directly related to the HF. However, the concentration each of these biomarkers is not homogeneous during the expiration as it reflects the different lung compartments. While the end-expiratory concentration constitutes a sample of the alveolar concentration (AC) that reflects the blood concentration (BC) of one specie, the relationship between alveolar and blood concentrations is complex as exchanges that take place within these different compartments. Thus, measuring the concentration of a specie in exhaled air during a complete exhalation (or "expirogram") depends not only on the BC of the specie, but also on changes in lung function. Because both BC and changes in lung function depend on the severity of the HF, obtaining a full expirogram each specie should provide valid diagnosis information in HF. The mathematical modelization of real-time QEPAS sensors based expirograms together with lung function parameters (volume, flow) and lung compartment identification (capnography i.e. exhaled CO2 concentration) could provide valid algorithms with a an acceptable diagnosis performance in HF.
Detailed Description:
In the context of chronic diseases and heart failure (HF) in particular, telemedicine and telemonitoring have emerged as major clinical challenges today. The development of point-of-care measurement systems using hand-held devices provides "digital biomarkers" that are a key element in remote monitoring.
Biomarkers contained in exhaled air could constitute "digital biomarkers", as the measurement of molecules in exhaled air is non-invasive. Currently, 6 exhaled biomarkers are validated by the US FDA and used in a clinical context. In heart failure, 4 different species have been shown to be of potential diagnostic or prognostic interest: NO, CO, acetone and isoprene. However, while the concentration of these species in alveolar air (CA) reflects their blood concentration (Cs), the relationship is more complex, having to take into account the different compartments of the bronchial tree and the exchanges that take place within these different compartments. Thus, measuring the concentration of a species in exhaled air during a complete exhalation (or "expirogram") using a real-time measurement, turns out to be dependent not only on the systemic concentration of the species, but also on changes in lung function.Thus, obtaining an expirogram not only makes it possible to specify the measurement of the endogenous source of the species, but also provides information on changes in pulmonary function, directly induced by heart failure, and which have a well-recognized prognostic value.
→ The combination of different candidate exhaled biomarkers in IC, during a real-time measurement of forced expiration, using selective, sensitive and miniaturizable sensors would provide diagnostic, prognostic and patient outcome information in heart failure.
Quartz-enhanced photoacoustic spectroscopy (QEPAS) is a suitable method for remote monitoring of heart failure patients. It enables the creation of sensors characterized by good selectivity and low detection thresholds. What's more, real-time analysis is possible, and the sensors are potentially miniaturizable. These sensors are therefore capable of providing expirograms for different species (rather than simply measuring CA at the end of expiration). Complex signals of this type can be analyzed using mathematical modeling and artificial intelligence techniques such as "deep neural networks". These mathematical modelling methods have been used to model pulmonary, neurological or cardiac function parameters.
As part of a translational research project in collaboration with Dr. A. Vicet (MCF- UM, Institut d'Electronique et des Systèmes) and Prof. N. Molinari (CHRU Montpellier, Institut Desbrest d'Épidémiologie et de Santé Publique), the research team is currently developing sensors for various exhaled biomarkers using the QEPAS method, which have been coupled with synchronous quantification of volumes, flows and lung compartments. These sensors are currently undergoing analytical validation (in the laboratory). The first expirograms have been obtained and modelled using a spline regression dimension reduction method.
Biomarkers contained in exhaled air could constitute "digital biomarkers", as the measurement of molecules in exhaled air is non-invasive. Currently, 6 exhaled biomarkers are validated by the US FDA and used in a clinical context. In heart failure, 4 different species have been shown to be of potential diagnostic or prognostic interest: NO, CO, acetone and isoprene. However, while the concentration of these species in alveolar air (CA) reflects their blood concentration (Cs), the relationship is more complex, having to take into account the different compartments of the bronchial tree and the exchanges that take place within these different compartments. Thus, measuring the concentration of a species in exhaled air during a complete exhalation (or "expirogram") using a real-time measurement, turns out to be dependent not only on the systemic concentration of the species, but also on changes in lung function.Thus, obtaining an expirogram not only makes it possible to specify the measurement of the endogenous source of the species, but also provides information on changes in pulmonary function, directly induced by heart failure, and which have a well-recognized prognostic value.
→ The combination of different candidate exhaled biomarkers in IC, during a real-time measurement of forced expiration, using selective, sensitive and miniaturizable sensors would provide diagnostic, prognostic and patient outcome information in heart failure.
Quartz-enhanced photoacoustic spectroscopy (QEPAS) is a suitable method for remote monitoring of heart failure patients. It enables the creation of sensors characterized by good selectivity and low detection thresholds. What's more, real-time analysis is possible, and the sensors are potentially miniaturizable. These sensors are therefore capable of providing expirograms for different species (rather than simply measuring CA at the end of expiration). Complex signals of this type can be analyzed using mathematical modeling and artificial intelligence techniques such as "deep neural networks". These mathematical modelling methods have been used to model pulmonary, neurological or cardiac function parameters.
As part of a translational research project in collaboration with Dr. A. Vicet (MCF- UM, Institut d'Electronique et des Systèmes) and Prof. N. Molinari (CHRU Montpellier, Institut Desbrest d'Épidémiologie et de Santé Publique), the research team is currently developing sensors for various exhaled biomarkers using the QEPAS method, which have been coupled with synchronous quantification of volumes, flows and lung compartments. These sensors are currently undergoing analytical validation (in the laboratory). The first expirograms have been obtained and modelled using a spline regression dimension reduction method.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: