Sports CardiologyVALETUDONCT06285084
Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
An observational study of Sports Cardiology and Preventive Cardiology, run by I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio at 1 site in Italy.
Trial summary
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease. Read more about this study
- Who is it for?
- Ages 18 to 60 yearsHealthy volunteers can take part
- Conditions the study is for
- Sports Cardiology, Preventive Cardiology, Electrocardiogram
- How many people
- 531 participants
- Where
- 1 siteItaly
- Study dates
- 2 Feb 2024to 2 Feb 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 to 60 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: Adults requiring medical evaluation for competitive sports eligibility
Who may be able to join
- Athletes in need of cardiac or sports medical evaluation for the issuance of competitive eligibility.
- Enlisted athletes involved in sports like soccer or those with mixed or aerobic cardiovascular demands according to the COCIS 2017 classification.
- Aged 18 years or older but not exceeding 60 years.
- No history of cardiovascular disease.
- Signed Informed Consent.
Who cannot join
- Athletes engaging in skill-based sports as per the COCIS 2017 classification.
- High clinical probability of cardiovascular disease, such as typical angina or heart failure.
- Pregnancy and/or breastfeeding (confirmed through self-declaration).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site, 1 recruiting
- Recruiting
Ospedale Galeazzi-Sant'Ambrogio
Milan, Lombardy, 20157
davide marchetti, MD, +390283506734, davide.marchetti@grupposandonato.it
About this study
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease.
Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model.
Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests
Read the detailed description
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease.
The DL model requires training to be calibrated. The project plans to conduct accuracy evaluations on the validation population (76 athletes) and training trials on a different dataset (455 athletes).
There will be an initial phase of system training. Athletes will be assessed according to current guidelines and the italian cardiological guidelines for competitive sports participation - COCIS, with the required diagnostic tests on a case-by-case basis. At the end of the cardiac evaluation, athletes can be classified as "fit" or "unfit" for competitive activity.
Participants will submit the ECGs of "fit" and "unfit" athletes, categorized into these two groups, to a deep learning algorithm to train the artificial intelligence system.
A population of consecutive athletes will then be recruited to form the validation set for the test. These athletes have indications for evaluation for the granting of competitive fitness, as indicated by the referring sports physicians. In this case as well, athletes in the validation set will be assessed according to guidelines and COCIS with appropriate tests on a case-by-case basis to evaluate fitness for competition.
Participants will subject the ECGs of the validation set athletes to the artificial intelligence model to assess accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC in discriminating athletes judged "fit" from those judged "unfit" for competitive activity after cardiac investigations.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Training Cohort | 455 Athletes already evaluated for sports participation clearance, whom ECG and clinical evaluation (cleared - not cleared for competitive sports participation) will be fed into the DL model | |
| Validation Cohort | 76 Athletes evaluated using standard sports eligibility clearance tests and our DL model |
What is being measured
The primary outcome is the main question the study is designed to answer.
DL model accuracy
The accuracy of the DL model in recognizing the ECGs of athletes deemed fit or unfit will be evaluated by comparing the results with those obtained from the assessment performed by the sports physician (gold standard).
Participants will categorize the athletes into true positives, false positives, true negatives, and false negatives.
To define the ability of the DL model to discriminate between ECGs of athletes deemed fit or unfit, the receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) will be calculated.
Measured: From first medical evaluation with ECG until the final medical decision on competitive sports eligibility, up to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Feb 2024
- Study start2 Feb 2024
- Primary completion2 Nov 2025 (estimated)
- Study completion2 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Sports CardiologyStudies of the same condition, open and under way.
- More trials run by I.R.C.C.S Ospedale Galeazzi-Sant'AmbrogioThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 531 participants (estimated)
Sponsor and investigators
- Lead sponsor
- I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06285084
- Sponsor's protocol ID
- VALETUDO Trial (L4195)
- Acronym
- VALETUDO
- Status last verified
- Feb 2024
- First submitted
- 5 Feb 2024
- Last updated
- 29 Feb 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 29 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
- ClinicalTrials.gov ID
- NCT06285084
- Acronym / sponsor ID
- VALETUDO / VALETUDO Trial (L4195)
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 531 participants (estimated)
- Lead sponsor
- I.R.C.C.S Ospedale Galeazzi-Sant'AmbrogioUniversity, hospital or other
- Study start
- 2 Feb 2024 (actual)
- Primary completion
- 2 Nov 2025 (estimated)
- Study completion
- 2 Feb 2027 (estimated)
- First posted
- 29 Feb 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 observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease.
Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model.
Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests
Detailed description
The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease.
The DL model requires training to be calibrated. The project plans to conduct accuracy evaluations on the validation population (76 athletes) and training trials on a different dataset (455 athletes).
There will be an initial phase of system training. Athletes will be assessed according to current guidelines and the italian cardiological guidelines for competitive sports participation - COCIS, with the required diagnostic tests on a case-by-case basis. At the end of the cardiac evaluation, athletes can be classified as "fit" or "unfit" for competitive activity.
Participants will submit the ECGs of "fit" and "unfit" athletes, categorized into these two groups, to a deep learning algorithm to train the artificial intelligence system.
A population of consecutive athletes will then be recruited to form the validation set for the test. These athletes have indications for evaluation for the granting of competitive fitness, as indicated by the referring sports physicians. In this case as well, athletes in the validation set will be assessed according to guidelines and COCIS with appropriate tests on a case-by-case basis to evaluate fitness for competition.
Participants will subject the ECGs of the validation set athletes to the artificial intelligence model to assess accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC in discriminating athletes judged "fit" from those judged "unfit" for competitive activity after cardiac investigations.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training Cohort | – | –455 Athletes already evaluated for sports participation clearance, whom ECG and clinical evaluation (cleared - not cleared for competitive sports participation) will be fed into the DL model |
| Validation Cohort | – | –76 Athletes evaluated using standard sports eligibility clearance tests and our DL model |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Adults requiring medical evaluation for competitive sports eligibility
Key inclusion criteria (5 listed)
- Athletes in need of cardiac or sports medical evaluation for the issuance of competitive eligibility.
- Enlisted athletes involved in sports like soccer or those with mixed or aerobic cardiovascular demands according to the COCIS 2017 classification.
- Aged 18 years or older but not exceeding 60 years.
- No history of cardiovascular disease.
- Signed Informed Consent.
Key exclusion criteria (3 listed)
- Athletes engaging in skill-based sports as per the COCIS 2017 classification.
- High clinical probability of cardiovascular disease, such as typical angina or heart failure.
- Pregnancy and/or breastfeeding (confirmed through self-declaration).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
DL model accuracy
Time frame: From first medical evaluation with ECG until the final medical decision on competitive sports eligibility, up to 12 months
The accuracy of the DL model in recognizing the ECGs of athletes deemed fit or unfit will be evaluated by comparing the results with those obtained from the assessment performed by the sports physician (gold standard). Participants will categorize the athletes into true positives, false positives, true negatives, and false negatives. To define the ability of the DL model to discriminate between ECGs of athletes deemed fit or unfit, the receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) will be calculated.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Feb 2024.
- Individual participant dataSharing planned: Undecided
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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.
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 Sports Cardiology and Preventive Cardiology, run by I.R.C.C.S Ospedale Galeazzi-Sant'Ambrogio at 1 site in Italy.
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 5 inclusion and 3 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: looking for participants now, as of the sponsor's last update on 29 Feb 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 Italy. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Davide Marchetti, MD (+390283506734, davide.marchetti@grupposandonato.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
DL model accuracy (time frame: From first medical evaluation with ECG until the final medical decision on competitive sports eligibility, up to 12 months). 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 29 Feb 2024.