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| ID | Type | Description | Link |
|---|---|---|---|
| 20211029191554 | Other Identifier | registre général des traitements de l'APHP |
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| Name | Class |
|---|---|
| Bichat Hospital | OTHER |
| Bioquantis | UNKNOWN |
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The goal of this study is to develop an algorithm using artificial intelligence (AI) to assist identification of potential ATTR-CM cases using routine transthoracic echocardiography.
The main questions it aims to answer are:
This is a non interventional study. Participant' echocardiographies will be, after deidentification, used to train, valid and test the algorithm.
Transthyretin (TTR) amyloidosis is a serious systemic disease affecting multiple target organs including the peripheral nervous system, heart, and kidney. In the absence of treatment, the median survival for symptomatic forms with cardiac involvement is 3 to 4 years.
In recent years, new treatments have proven their effectiveness in transthyretin amyloidosis, making it possible to slow the progression of neuropathy and cardiac damage. These treatments seem particularly effective when they are initiated at an early stage of the disease.
It is therefore necessary to establish the diagnosis as early as possible in order to benefit the most from the treatment. However, during the clinical examination, the electrocardiogram or the routine echocardiography, the signs evoking cardiac amyloidosis are not specific. The initial diagnosis is therefore often difficult, missed or delayed and the median time between the first symptoms and the initiation of treatment is approximately 3 years.
It is therefore the initial phase of diagnosis that must be improved in a sufficiently sensitive and specific manner to detect potential cases early while avoiding unnecessary examinations in the event of a low probability.
The objective of the study is to develop and validate a tool to assist the screening of cardiac transthyretin amyloidosis, from standard echocardiography, without the need for active participation of the cardiologist in the diagnostic process. This diagnostic contribution will allow the cardiologist to evoke the diagnosis of cardiac amyloidosis and to consider additional explorations.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Transthyretin cardiac amyloidosis (ATTR-CM) | Patients with an ATTR-CM and undergoing a transthoracic echocardiography |
| |
| Controls | Patients without cardiac amyloidosis undergoing transthoracic echocardiography as part of cardiological follow-up |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| non interventional study | Other | non interventional study |
|
| Measure | Description | Time Frame |
|---|---|---|
| Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTR-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis. A confusion matrix will be built and the following diagnostic performance metrics be computed:
| year 1 |
| Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTR-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis ATTR. A confusion matrix will be built and the following diagnostic performance metrics be computed: Accuracy, Sensitivity or Recall, Specificity, False positive rate, False Negative Rate, Precision (all are expressed as ratio) | year 1 |
| Measure | Description | Time Frame |
|---|---|---|
| Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTRwt-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis. A confusion matrix will be built and the following diagnostic performance metrics be computed: Accuracy, Sensitivity or Recall, Specificity, False positive rate, False Negative Rate, Precision (all are expressed as ratio) |
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ATTR-CM patients:
Inclusion Criteria:
Cardiac transthyretin amyloidosis diagnosed on the classic criteria:
2-Presence of a cardiac biopsy showing transthyretin (Congo red positive) cardiac amyloidosis (demonstrated either by immunostaining or by mass spectrometry) OR 3-Presence of a peripheral biopsy showing transthyretin amyloidosis (see above) associated with cardiac infiltration (parietal thickness >12mm without other cause of cardiac hypertrophy)
No opposition to research
Non-inclusion criteria:
Control patients:
Inclusion criteria:
Non-inclusion criteria:
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The ATTR-CM cohort will be recruited in referral tertiary centers for cardiac amyloidosis The Control cohort will be recruited in the same centers from patients presenting an indication for transthoracic echocardiography as part of cardiological follow-up
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Vincent Algalarrondo, MD, PhD | Contact | +33140257785 | vincent.algalarrondo@aphp.fr | |
| Gregory Ducrocq, MD, PhD | Contact | +33140256600 | gregory.ducrocq@aphp.fr |
| Name | Affiliation | Role |
|---|---|---|
| Gabriel Steg, MD, PhD | Bichat Hospital | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Bichat | Recruiting | Paris | 75018 | France |
Investigators will provide access to individual de-identified participant data and related study documents (e.g. protocol, Statistical Analysis Plan (SAP), Clinical Study Report (CSR)) upon reasonable request from qualified researchers, and subject to certain criteria, conditions, and exceptions.
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| ID | Term |
|---|---|
| D028227 | Amyloid Neuropathies, Familial |
| ID | Term |
|---|---|
| D020271 | Heredodegenerative Disorders, Nervous System |
| D019636 | Neurodegenerative Diseases |
| D009422 | Nervous System Diseases |
| D017772 | Amyloid Neuropathies |
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| year 1 |
| Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTRv-V122I-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis. A confusion matrix will be built and the following diagnostic performance metrics be computed: Accuracy, Sensitivity or Recall, Specificity, False positive rate, False Negative Rate, Precision (all are expressed as ratio) | year 1 |
| Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTRv-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis. A confusion matrix will be built and the following diagnostic performance metrics be computed: Accuracy, Sensitivity or Recall, Specificity, False positive rate, False Negative Rate, Precision (all are expressed as ratio) | year 1 |
| Building and validating the diagnostic performance metrics of the AI algorithm to differentiate ATTR-CM from LV hypertrophy (LVH) : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis from LVH. A confusion matrix will be built and the following diagnostic performance metrics be computed: Accuracy, Sensitivity or Recall, Specificity, False positive rate, False Negative Rate, Precision (all are expressed as ratio) | year 1 |
| Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTRwt-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis (ATTRwt subgroup). A confusion matrix will be built and the following diagnostic performance metrics be computed:
| year 1 |
| Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTRv-V122I-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis (ATTRv-V122I subgroup). A confusion matrix will be built and the following diagnostic performance metrics be computed:
| year 1 |
| Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTRv-CM : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis (ATTRv-subgroup). A confusion matrix will be built and the following diagnostic performance metrics be computed:
| year 1 |
| Building and validating the diagnostic performance metrics curves of the AI algorithm to differentiate ATTR-CM from LV hypertrophy (LVH) : | To develop and validate a tool using artificial intelligence an algorithm that will improve the automatic detection on routinely acquired echocardiography images of aspects suggestive of transthyretin amyloidosis (ATTRv-subgroup) in a subset of patients with LVH. A confusion matrix will be built and the following diagnostic performance metrics be computed:
| year 1 |
| D010523 | Peripheral Nervous System Diseases |
| D009468 | Neuromuscular Diseases |
| D030342 | Genetic Diseases, Inborn |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D028226 | Amyloidosis, Familial |
| D008661 | Metabolism, Inborn Errors |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D000686 | Amyloidosis |
| D057165 | Proteostasis Deficiencies |