An observational study in Myocardial Infarction, sponsored by Xuanwu Hospital, Beijing. Active, not recruiting at 1 site in China. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2024-04-30.
Sponsored by Xuanwu Hospital, Beijing · Observational
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.
2,744 studies on the registry are indexed under Myocardial Infarction; 418 are open to participants now.
This study's enrollment of 4,000 is above the median of 500 across 983 observational studies indexed under Myocardial Infarction.
Browse Myocardial Infarction studies →Xuanwu Hospital, Beijing is the lead sponsor of 346 studies on the registry; 217 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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).
Exclusion Criteria:
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).
Time frame: 1year
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.
Time frame: 1year
Plan to share: Undecided
This study is active, not recruiting, as verified in Oct 2023. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Xuanwu Hospital, Beijing