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

NCT07496931 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 3000

Last updated 2026-03-27

No results posted yet for this study

Summary

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.

Conditions

  • Acute Myocardial Infarction (AMI)

Interventions

DIAGNOSTIC_TEST

Acute Myocardial Infarction Risk Prediction Model

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.

Sponsors & Collaborators

  • Beijing Anzhen Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-03-25
Primary Completion
2026-05-31
Completion
2027-05-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07496931 on ClinicalTrials.gov