An observational study in Hypertension Arterial, Hypokinetic and Cardiomyopathies, sponsored by Hospices Civils de Lyon. Recruiting at 1 site in France. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2023-07-13.
Sponsored by Hospices Civils de Lyon · Observational
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
6,114 studies on the registry are indexed under Hypertension; 912 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 350 across 1,163 observational studies indexed under Hypertension.
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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.
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Exclusion Criteria:
The first arm includes patients with hypokinetic cardiomyopathy for whom echocardiographic data do not readily distinguish between a cause related to coronary artery disease or related to primary myocardial dysfunction, requiring invasive intervention, i.e., coronary angiography.
Other: Determine the etiology of hypokinetic and hypertrophic heart disease on transthoracic echocardiography data alone
The second arm includes patients with left ventricular hypertrophy, whose aetiology may be various and whose workup is particularly extensive and expensive.
Other: Determine the etiology of hypokinetic and hypertrophic heart disease on transthoracic echocardiography data alone
The origin of the pathology will have been previously diagnosed for each patient thanks to complementary examinations performed as part of routine care (e.g. cardiac CT, cardiac MRI, coronary angiography, thorough biology, nuclear medicine). This information will be used (i) to guide the learning of the AI method developed during the project from a sub-population (80% of the collected database will be used to train the algorithms); (ii) to serve as an evaluation criterion from a test sub-population (remaining 20% of the collected database)
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.
The origin of the pathology being previously diagnosed for each patient thanks to complementary examinations carried out in routine (e.g.: cardiac scanner, cardiac MRI, coronary angiography, thorough biology, nuclear medicine). This information will be used (i) to guide the learning of the AI method developed during the project from a sub-population (80% of the collected database will be used to train the algorithms); (ii) to serve as an evaluation criterion from a test sub-population (remaining 20% of the collected database). In addition, visualization tools will be developed to allow clinicians to analyze and interpret the results, particularly with respect to the decision mechanism performed by the algorithm to predict the origin of the pathology. In particular, attention maps will be displayed that will simply allow clinicians to see which data or part of the data was assembled in order to make the decision.
Time frame: Baseline
Started Jan 2023 (actual). Primary completion is estimated for Jan 2025, 24 months after the start.
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