Ignite Creation Date:
2024-05-06 @ 8:11 PM
Last Modification Date:
2024-10-26 @ 3:22 PM
Study NCT ID:
NCT06285084
Status:
RECRUITING
Last Update Posted:
2024-02-29
First Post:
2024-02-05
Brief Title:
Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Sponsor:
IRCCS Ospedale Galeazzi-SantAmbrogio
Organization:
IRCCS Ospedale Galeazzi-SantAmbrogio
Study Overview
Official Title:
Deep Learning ECG Evaluation and Clinical Assessment for Competitive Sport Eligibility
Status:
RECRUITING
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
VALETUDO
Brief 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
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
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None