Ignite Creation Date:
2024-10-26 @ 3:40 PM
Last Modification Date:
2024-10-26 @ 3:40 PM
Study NCT ID:
NCT06591923
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-09-08
Brief Title:
a Foundational Model for Cardiovascular Disease Diagnosis and Prediction
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Development and Clinical Application of a Foundational Model for Cardiovascular Disease Diagnosis and Prediction Based on Multimodal Medical Big Data
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this observational study is to develop and evaluate the efficacy of a foundational model that integrates multimodal medical data to improve the diagnosis and prediction of cardiovascular diseases in patients aged 18 and older including those with various heart conditions such as coronary artery disease heart failure and arrhythmias The main questions it aims to answer are
Can a multimodal data-based diagnostic model match or exceed the accuracy of traditional gold-standard methods like coronary angiography MRI and echocardiography Does integrating different types of data ECG imaging biochemical tests improve diagnostic accuracy and prediction of cardiovascular disease outcomes Researchers will compare the foundational model with traditional diagnostic methods to see if the model offers better sensitivity specificity and prediction accuracy across different heart disease types
Participants will
Provide data from past medical records including ECG echocardiography cardiac MRI and biochemical tests
Undergo further data collection if necessary in line with standard clinical procedures for cardiovascular disease management
Can a multimodal data-based diagnostic model match or exceed the accuracy of traditional gold-standard methods like coronary angiography MRI and echocardiography Does integrating different types of data ECG imaging biochemical tests improve diagnostic accuracy and prediction of cardiovascular disease outcomes Researchers will compare the foundational model with traditional diagnostic methods to see if the model offers better sensitivity specificity and prediction accuracy across different heart disease types
Participants will
Provide data from past medical records including ECG echocardiography cardiac MRI and biochemical tests
Undergo further data collection if necessary in line with standard clinical procedures for cardiovascular disease management
Detailed Description:
This study aims to develop and validate a foundational model that uses multimodal medical data for the diagnosis and prediction of cardiovascular diseases By integrating data from ECG echocardiography cardiac MRI CTA nuclear imaging SPECTPET and biochemical tests the model seeks to improve diagnostic accuracy and predict disease outcomes
Study Design Study Type Retrospective multicenter observational study Study Population Adults aged 18 and older including patients with coronary artery disease CAD heart failure arrhythmias and valvular heart disease VHD
Objectives Primary Objective To create a model that improves the diagnosis and prediction of cardiovascular diseases using multimodal data
Secondary Objective To compare the performance of the model against traditional diagnostic methods like coronary angiography echocardiography and MRI
Methodology Data from 2009 to 2023 will be collected from multiple hospitals The model will use deep learning techniques to integrate the data for more accurate diagnosis and prediction
The performance of the model will be compared with current gold-standard methods
Expected Outcomes Improved diagnostic accuracy and early disease detection Enhanced prediction of long-term outcomes allowing for better treatment planning
Study Design Study Type Retrospective multicenter observational study Study Population Adults aged 18 and older including patients with coronary artery disease CAD heart failure arrhythmias and valvular heart disease VHD
Objectives Primary Objective To create a model that improves the diagnosis and prediction of cardiovascular diseases using multimodal data
Secondary Objective To compare the performance of the model against traditional diagnostic methods like coronary angiography echocardiography and MRI
Methodology Data from 2009 to 2023 will be collected from multiple hospitals The model will use deep learning techniques to integrate the data for more accurate diagnosis and prediction
The performance of the model will be compared with current gold-standard methods
Expected Outcomes Improved diagnostic accuracy and early disease detection Enhanced prediction of long-term outcomes allowing for better treatment planning
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None