Clinical trial NCT06062316: Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Using Multimodal Feature Structure Technology
Official title: Early Warning Model of Myocardial Remodeling After Acute Myocardial Infarction Based on Multimodal Feature Structure Technology
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Myocardial Infarction
Keywords: Computer aided diagnosis
Myocardial Remodeling
Early warning model
Myocardial infarction
MRI
Lead sponsor: Xuanwu Hospital, Beijing
Sponsor class: OTHER
Collaborators: name: Beijing Institute of Technology
First posted: 2023-10-02
Enrollment: 4000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Criteria for the diagnosis of acute myocardial infarction:

increased or decreased cardiac biomarkers (preferably cTn), at least once exceeding the 99th percentile of the upper reference value, cut-off variability ≤10%, and at least one evidence of myocardial ischemia (including symptoms, electrocardiographic ischemic changes, pathological Q-waves, or imaging evidence).

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.


Eligibility criteria: 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)

Primary outcomes:
measure: Novel convolutional neural network algorithm and cardiac magnetic resonance imaging to evaluate the occurrence of myocardial remodeling.remodeling after myocardial infarction.
timeFrame: 1year
Secondary outcomes:
measure: 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.
timeFrame: 1year
Other outcomes:
Locations:
city: Beijing
country: China
facility: Xuanwu Hospital, Capital Medical University
state: Xicheng
Study officials:
affiliation: Xuanwu Hospital, Beijing
name: Zhi Liu
role: PRINCIPAL_INVESTIGATOR
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