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

Myocardial Infarction Clinical Trial

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06062316
• Other study ID #: xuanwuliuzhi
• Status: Active, not recruiting
• Start date: October 10, 2022
• Est. completion date: June 1, 2024

Study information

• Verified date: October 2023
• Source: Xuanwu Hospital, Beijing
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 4000
• Est. completion date: June 1, 2024
• Est. primary completion date: June 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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)

Related Conditions & MeSH terms

• Infarction
• Myocardial Infarction

Locations

Country	Name	City	State
China	Xuanwu Hospital, Capital Medical University	Beijing	Xicheng

Sponsors (2)

Lead Sponsor	Collaborator
Xuanwu Hospital, Beijing	Beijing Institute of Technology

Country where clinical trial is conducted

China, 

References & Publications (24)

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* Note: There are 24 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Novel convolutional neural network algorithm and cardiac magnetic resonance imaging to evaluate the occurrence of myocardial remodeling.remodeling after myocardial infarction.	(Quantitative characterization of myocardial remodeling, cardiac magnetic resonance imaging quantifying necrotic areas and recoverable myocardium within the edematous myocardium after myocardial infarction).	1year
Secondary	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.	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.	1year