Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Using Multimodal Feature Structure Technology

Sponsor

Xuanwu Hospital, Beijing (Other)

Overall Status

Recruiting

CT.gov ID

NCT06062316

Collaborator

Beijing Institute of Technology (Other)

3,000

Enrollment

Location

37.7

Anticipated Duration (Months)

79.5

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Acute myocardial infarction (AMI) is one of the most important diseases threatening human life. The existing MI prognosis prediction scales mostly predict the incidence of death, recurrent MI and heart failure through 6-8 clinical text indicators, and the data are collected relatively simply. Myocardial remodeling, as an adverse pathological change that can start and continue to progress in the early stage after myocardial infarction, is the main pathological mechanism of heart failure and death. However, there is no quantitative early-warning model of myocardial remodeling, and the clinical guidance of early intervention is lacking.

Our previous study found that cardiac magnetic resonance imaging can accurately quantify the necrotic area and recoverable myocardium in the edematous myocardium after myocardial infarction. In this study, machine learning algorithm, variable convolution network (DCN) and capsule network (capsnet) are used to build a new neural network architecture. Structural feature extraction of multi-modal clinical image data such as MRI and ultrasound is realized. Combined with the established database of 3000 patients with myocardial infarction, the multimodal feature matrix will be constructed, and a variety of classifiers such as support vector machine (SVM) and random forest (RF) will be used for quantitative prediction of myocardial remodeling, and the effects of different classifiers were evaluated. It is expected that this project will establish a quantitative early warning model of myocardial remodeling after acute myocardial infarction in line with the characteristics of Chinese people. The same type of data outside the database will be used for verification to establish an efficient and stable early warning model.

Condition or Disease	Intervention/Treatment	Phase
Myocardial Infarction

Study Design

Study Type:

Observational

Anticipated Enrollment :

3000 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology

Actual Study Start Date :

Oct 10, 2022

Anticipated Primary Completion Date :

Oct 19, 2023

Anticipated Study Completion Date :

Dec 1, 2025

Outcome Measures

Primary Outcome Measures

Novel convolutional neural network algorithm and cardiac magnetic resonance imaging to evaluate the occurrence of myocardial remodeling.remodeling after myocardial infarction. [1year]
(Quantitative characterization of myocardial remodeling, cardiac magnetic resonance imaging quantifying necrotic areas and recoverable myocardium within the edematous myocardium after myocardial infarction).

Secondary Outcome Measures

The multi-dimensional indexes of existing database were compared with the location and course of myocardial remodeling by artificial intelligence method Degree of correlation analysis. [1year]
Through the new Deformable Convolutional Capsule network that has been developed The Networks (DCCN) study focused on the existing clinical and imaging comprehensive database of patients with acute myocardial infarction Machine learning was performed on the data to complete the extraction of relevant features and logical relationship analysis of myocardial remodeling after myocardial infarction. Strong correlation features were screened.

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 80 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

1.Patients with acute myocardial infarction, aged 18-80 years; 2.The time from onset to treatment is less than 72h 3.Myocardial enzyme Tni/Tnt(+).

Exclusion Criteria:

1.Patients with malignant tumors; 2.Patients who could not receive conventional treatment 3.Patients who did not receive coronary angiography, lacking anatomical and imaging data; 4.Patients who have undergone cardiac surgery (except coronary artery bypass surgery)