Clinical trial NCT05942729: Etiological DiagnOsis of caRdiac Diseases Based on echoCardiograpHIc Images and Clinical Data.
Official title: Etiological DiagnOsis of caRdiac Diseases Based on echoCardiograpHIc Images and Clinical Data.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hypertension Arterial
Hypokinetic
Cardiomyopathies
Left Ventricular Hypertrophy
Keywords: Hypertension
HTN
Arterial Hypertension
Artificial Intelligence
AI
Cardiac remodelling
Left ventricular hypertrophy
Lead sponsor: Hospices Civils de Lyon
Sponsor class: OTHER
First posted: 2023-07-12
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Two distinct populations are targeted in this project. The first concerns patients with hypokinetic cardiomyopathy for whom echocardiographic data do not easily distinguish between a cause related to coronary artery disease or related to primary myocardial dysfunction, imposing the performance of an invasive intervention, i.e., a coronary angiography. The second concerns patients with left ventricular hypertrophy, whose etiology can be diverse and whose assessment is particularly exhaustive and costly. In this project, we will study the two most frequent causes: arterial hypertension and infiltrative myocardial disease. The cohort will be composed in a balanced way with respect to the four pathologies mentioned above as well as through data from patients without significant heart disease. This database will be constructed from patients who have been hospitalized at the Cardiology Department during the period January 2018 to December 2022.

Brief summary: Research hypothesis - Recent studies have shown that high-dimensional descriptors of the cardiac function can be efficiently exploited to characterize targeted pathologies. In this project, the investigators hypothesize that echocardiograms possess a wealth of information that is currently under-exploited and that, combined with relevant patient data, will allow the development of robust and accurate digital tools for etiological diagnosis.

Objectives - Based on key advances recently obtained in image analysis, notably by members of the consortium, the objective of this project is to develop rigorous and explainable cardiac disease prediction models from echocardiography based on the transformer paradigm (AI). The strength of this study lies in the development of a strong AI framework to model the complex interactions between high-quality image-based measurements extracted from echocardiograms and relevant patient data to automatically predict etiological diagnosis of cardiac diseases


Eligibility criteria: Inclusion Criteria:

* Patients with transthoracic echocardiography with satisfactory image quality (sufficient echogenicity)

Exclusion Criteria:

* Minor patients
* Patients under curatorship or guardianship

Primary outcomes:
measure: the comparison of the performance of the etiological diagnosis obtained by the artificial intelligence with the etiological diagnosis already established and validated by a physician from the complementary examinations performed on the targeted patients.
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Pierre-Bénite
country: France
facility: Hopital Lyon Sud
Study officials:
References: