Construction and Analysis of a Risk Prediction Model for Acute Myocardial Infarction Based on Spatiotemporal Heterogeneous Data

Acute myocardial infarction (AMI), as the leading cause of death among cardiovascular diseases, has its diagnosis and treatment efficiency directly affecting survival. Although the current diagnosis and treatment system has significantly improved in-hospital outcomes, delays in seeking medical care due to patients' insufficient awareness and out-of-hospital deaths are common, representing the biggest bottleneck in improving diagnostic and treatment capabilities. This study takes intelligent-assisted diagnosis of AMI as the entry point and proposes a technical approach that combines a deep learning algorithm based on 12-lead electrocardiograms with wearable monitoring devices. By utilizing morphological feature extraction and deep learning models, it aims to achieve early identification and warning of AMI. The study plans to build a multi-center AMI long-term follow-up cohort covering the Beijing area based on spatiotemporal heterogeneous data. By integrating and forming a precise high-risk cohort of 3,000 acute myocardial infarction cases, it seeks to construct an AMI risk prediction model that combines deep learning with a retrieval-augmented generative expert system, breaking through bottlenecks in ECG recognition and temporal prediction, enhancing model generalization and transferability. Ultimately, it will support the application of wearable devices, shorten pre-hospital delays, achieve early warning and precise diagnosis of AMI, reduce reinfarction and cardiac-related mortality, and carry significant clinical and public health importance.

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Key information

Conditions

Acute Myocardial Infarction (AMI)

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

High-risk population for acute myocardial infarction, previously confirmed by cardiac imaging to have coronary artery disease and at least one of the following risk factors:

Multivessel coronary artery disease (defined as ≥50% stenosis in at least 2 coronary artery regions, including the left main coronary artery)
Previous myocardial infarction
Patients with diabetes currently undergoing glucose-lowering treatment
Chronic kidney disease (CKD) defined as an estimated glomerular filtration rate <60 mL/min/1.73 m² with a known history of chronic kidney disease or biomarkers indicating chronic kidney damage
Peripheral artery disease, defined as any of the following: ankle-brachial index <0.85; >50% stenosis in lower limb arteries confirmed by angiography (invasive or non-invasive) or duplex ultrasound; limb amputation, peripheral artery bypass, or vascular surgery (such as angioplasty or endarterectomy) secondary to ischemia
Age ≥18 years
Ability to understand and comply with the study protocol and sign the informed consent form

Exclusion criteria

Researchers consider diseases or conditions that are not suitable for participation in this study (such as mental illness, significant cognitive impairment, neurodegenerative diseases, advanced malignant tumors) or situations (such as inability to communicate well with researchers in the local language, unwillingness to comply with study procedures/instructions, inability to understand study-specific training, physically fragile and vulnerable subjects).
Participation in a clinical trial involving drugs or devices within 3 months prior to enrollment.
Women who are pregnant, planning to become pregnant, or breastfeeding.
Known life-threatening diseases with an expected survival of less than 12 months.

Treatment and study plan

Acute Myocardial Infarction Risk Prediction Model

Diagnostic Test

For high-risk patients with myocardial infarction, screen and integrate relevant previous prospective, multicenter AMI cohorts, and establish a multicenter, multi-treatment precise high-risk acute myocardial infarction cohort in the Beijing region, with long-term follow-up and supplementation of multidimensional data. Subsequently, based on semantic knowledge-guided cross-modal and cross-timepoint data alignment, use domain adaptation methods to perform fusion modeling of spatiotemporal and modal heterogeneous AMI cohorts. Through multimodal interpretable artificial intelligence models, mine the fused models to complete the construction and validation of an AMI risk prediction model based on spatiotemporally heterogeneous data.

Primary outcomes

The time of the first occurrence of acute myocardial infarction (AMI) within 1 year after enrollment.

Time frame: 1 year

Study contacts

Contact information is provided by the study sponsor or research team.

Yan Yan

CONTACT

[email protected]

+861064456782

Sponsors and collaborators

Lead sponsor

Beijing Anzhen Hospital

Other

Registry information

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2027
First posted: Mar 27, 2026
Registry last updated: Mar 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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