Amyloid CardiomyopathyAI-ATTR-ECHONCT06328075
Artificial Intelligence to Assist the Echocardiographic Identification of Transthyretin Cardiac Amyloidosis
An observational study of Amyloid Cardiomyopathy and Transthyretin Amyloid Cardiomyopathy, run by Algalarrondo Vincent at 1 site in France.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Amyloid Cardiomyopathy, Transthyretin Amyloid Cardiomyopathy, Transthyretin Cardiac Amyloidosis
- What is being tested?
- non interventional study
- How many people
- 15,000 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jan 2022to 1 Jan 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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
Who may be able to join
- Cardiac transthyretin amyloidosis diagnosed on the classic criteria:
- Absence of monoclonal immunoglobulin AND
- Presence of a bisphosphonate scintigraphy with enhancement in the cardiac area OR
- 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:
Show all 18Show fewer
- Another cause of cardiac amyloidosis: AL AA amyloidosis…
- Mixed heart disease with associated presence of non-amyloid heart disease (ischemic heart disease, dilated, etc.)
- Control patients:
- Indication for transthoracic echocardiography as part of cardiological follow-up
- Patient affiliated with social security
- Patient's agreement to participate in the research and signature of the consent form.
- Technical conditions of the examination and echogenicity allowing acquisition of good quality echocardiographic images, allowing post processing
- Non-inclusion criteria:
- Presence of cardiac amyloidosis as defined above
- Presence of transthyretin amyloidosis even without demonstrated cardiac involvement
- Patient monitored for asymptomatic transthyretin mutation
- Minor patient or patient unable to give their consent (unconscious patient, under guardianship)
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
France1 site, 1 recruiting
- Recruiting
Bichat
Paris, 75018
Vincent Algalarrondo, MD, PhD, +33140257785, vincent.algalarrondo@aphp.fr
Gregory Ducrocq, PD, PhD, gregory.ducrocq@aphp.fr
About this study
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:
- is the algorithm able to diagnose ATTR-CM
- is the algorithm able to diagnose different types of ATTR-CM (ATTRv, ATTRwt)
This is a non interventional study. Participant' echocardiographies will be, after deidentification, used to train, valid and test the algorithm.
Read the detailed description
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.
- Conditions
- Amyloid Cardiomyopathy, Transthyretin Amyloid Cardiomyopathy, Transthyretin Cardiac Amyloidosis
- Being tested
- non interventional study
- Keywords
- echocardiography, artificial intelligence, Transthyretin Amyloid Cardiomyopathy
- Medical subject headings
- Amyloid Neuropathies, Familial
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
non interventional studyOther
non interventional study
| Study group | Role | What they receive |
|---|---|---|
| Transthyretin cardiac amyloidosis (ATTR-CM) | Patients with an ATTR-CM and undergoing a transthoracic echocardiography Receives: Other: non interventional study | |
| Controls | Patients without cardiac amyloidosis undergoing transthoracic echocardiography as part of cardiological follow-up Receives: Other: non interventional study |
What is being measured
The primary outcome is the main question the study is designed to answer.
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:
- receiver operating characteristic curve (ROC) and area under curve (AUC) of the ROC : AUROC
- Precision recall curve (PR) and area under curve (AUC) of the PR curve : AUC-PR
Measured: 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)
Measured: year 1
Secondary outcomes (8)
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)
Measured: 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)
Measured: 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)
Measured: 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)
Measured: 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:
- receiver operating characteristic curve (ROC) and area under curve (AUC) of the ROC : AUROC
- Precision recall curve (PR) and area under curve (AUC) of the PR curve : AUC-PR
Measured: 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:
- receiver operating characteristic curve (ROC) and area under curve (AUC) of the ROC : AUROC
- Precision recall curve (PR) and area under curve (AUC) of the PR curve : AUC-PR
Measured: 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:
- receiver operating characteristic curve (ROC) and area under curve (AUC) of the ROC : AUROC
- Precision recall curve (PR) and area under curve (AUC) of the PR curve : AUC-PR
Measured: 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:
- receiver operating characteristic curve (ROC) and area under curve (AUC) of the ROC : AUROC
- Precision recall curve (PR) and area under curve (AUC) of the PR curve : AUC-PR
Measured: year 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Mar 2024
- Study start1 Jan 2022
- Primary completion1 Jan 2025 (estimated)
- Study completion1 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Amyloid CardiomyopathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 15,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Algalarrondo Vincent
- Sponsor type
- University, hospital or other
- Collaborators
- Bichat Hospital, Bioquantis
- Responsible party
- Sponsor-investigator, Algalarrondo Vincent, Bichat Hospital
- Investigators
- Gabriel Steg, MD, PhD, Study chair, Bichat Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06328075
- Sponsor's protocol ID
- AI-ATTR-ECHO
- Other IDs
- 20211029191554
- Acronym
- AI-ATTR-ECHO
- Status last verified
- Mar 2024
- First submitted
- 12 Mar 2024
- Last updated
- 25 Mar 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence to Assist the Echocardiographic Identification of Transthyretin Cardiac Amyloidosis
- ClinicalTrials.gov ID
- NCT06328075
- Acronym / sponsor ID
- AI-ATTR-ECHO / AI-ATTR-ECHO
- Conditions
- Amyloid Cardiomyopathy, Transthyretin Amyloid Cardiomyopathy, Transthyretin Cardiac Amyloidosis
- Keywords
- echocardiography, artificial intelligence, Transthyretin Amyloid Cardiomyopathy
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 15,000 participants (estimated)
- Lead sponsor
- Algalarrondo VincentUniversity, hospital or other
- Collaborators
- Bichat Hospital, Bioquantis
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 1 Jan 2025 (estimated)
- Study completion
- 1 Jan 2026 (estimated)
- First posted
- 25 Mar 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- is the algorithm able to diagnose ATTR-CM
- is the algorithm able to diagnose different types of ATTR-CM (ATTRv, ATTRwt)
This is a non interventional study. Participant' echocardiographies will be, after deidentification, used to train, valid and test the algorithm.
Detailed description
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.
Study design
- Observational model
- Case-control
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Transthyretin cardiac amyloidosis (ATTR-CM) | – | Other: non interventional studyPatients with an ATTR-CM and undergoing a transthoracic echocardiography |
| Controls | – | Other: non interventional studyPatients without cardiac amyloidosis undergoing transthoracic echocardiography as part of cardiological follow-up |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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
Key inclusion criteria (18 listed)
- 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
- Another cause of cardiac amyloidosis: AL AA amyloidosis…
- Mixed heart disease with associated presence of non-amyloid heart disease (ischemic heart disease, dilated, etc.)
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| non interventional studynon interventional study | Other | Controls, Transthyretin cardiac amyloidosis (ATTR-CM) |
Primary outcome
Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTR-CM :
Time frame: year 1
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: * receiver operating characteristic curve (ROC) and area under curve (AUC) of the ROC : AUROC * Precision recall curve (PR) and area under curve (AUC) of the PR curve : AUC-PR
Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTR-CM :
Time frame: year 1
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)
8 secondary outcomes
Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTRwt-CM :
Time frame: year 1
Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTRv-V122I-CM :
Time frame: year 1
Building and validating the diagnostic performance metrics of the AI algorithm to diagnose ATTRv-CM :
Time frame: year 1
Building and validating the diagnostic performance metrics of the AI algorithm to differentiate ATTR-CM from LV hypertrophy (LVH) :
Time frame: year 1
Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTRwt-CM :
Time frame: year 1
Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTRv-V122I-CM :
Time frame: year 1
Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTRv-CM :
Time frame: year 1
Building and validating the diagnostic performance metrics curves of the AI algorithm to differentiate ATTR-CM from LV hypertrophy (LVH) :
Time frame: year 1
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Mar 2024.
- Individual participant dataSharing planned: No. 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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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Gabriel Steg, MD, PhD study chair
Bichat Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Amyloid Cardiomyopathy and Transthyretin Amyloid Cardiomyopathy, run by Algalarrondo Vincent at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 18 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 25 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Vincent Algalarrondo, MD, PhD (+33140257785, vincent.algalarrondo@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Building and validating the diagnostic performance metrics curves of the AI algorithm to diagnose ATTR-CM : (time frame: year 1). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Mar 2024.