AI early warning system for complications after ST-elevation heart attack
Official title: Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction
Run by Beijing Friendship Hospital · NCT07139860 · Data updated 2026-11-12
This project tests an AI-based early warning system to see if it can predict serious complications in patients hospitalized with ST-elevation myocardial infarction.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 1400 (estimated) |
| Sponsor | Beijing Friendship Hospital Academic / other |
| Locations | 1 site (Beijing) |
| Status | Recruiting |
| Started | November 26, 2022 |
| Main results expected | December 31, 2026 |
| Study ends | December 31, 2026 |
| Registry record updated | August 24, 2025 |
| Trial ID | NCT07139860 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Hospitalized adults who meet diagnostic criteria for acute ST-elevation myocardial infarction, can provide informed consent, and can complete follow-up are ideal candidates.
✕Probably not a fit
Patients with terminal malignancy and expected survival under three months, those who cannot communicate, or those unable to comply with follow-up are unlikely to benefit from the system.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study will collect routine clinical records, diagnostic tests, imaging, and treatment-pathway data from patients hospitalized with ST-elevation myocardial infarction at a tertiary hospital. Researchers will integrate these multimodal data using cross-modal representation methods and build predictive models to generate early warnings of adverse events. Patients will be followed during their hospital stay and in subsequent clinical follow-up to compare AI predictions with actual outcomes. The study aims to determine the feasibility, accuracy, and clinical value of the AI system in real-world AMI care.
Why it matters
Potential benefit: If successful, the system could provide earlier alerts for impending complications, enabling faster treatment and potentially reducing morbidity and mortality after AMI.
How similar studies have gone: Related AI models have shown promising accuracy for predicting in-hospital deterioration and outcomes in cardiac care, but large prospective validations across diverse centers remain limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 1\. Hospitalized patients who meet the diagnostic criteria for acute myocardial infarction. 2. Patients who agree to participate and sign the informed consent form. Exclusion Criteria: * 1\. Patients with terminal malignant tumors and an expected survival time of less than 3 months. 2. Patients with complete disability and inability to communicate. 3. Patients unable to comply with follow-up.
Where it's running
-
Beijing Friendship Hospital
Beijing, China Recruiting
Study contacts
- Study coordinator: Hui Chen
- Email: 13910710028@163.com
- Phone: +86 13910710028
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
Image-guided platform helps find sentinel lymph nodes in early gastric cancer
This study tests whether an image-guided platform with a deep learning model can find and retrieve sentinel lymph nodes better than standard palpation in people with early gastric cancer undergoing LECS-SNNS.
-
Medical AI fine-tuning platform for major diseases
This study tests a smart AI system that uses hospital data to help diagnose and guide care for patients with chronic gastritis, gastric cancer, reflux, coronary artery disease, or stroke.
-
GERD patient registry study using a large prospective clinical database
A large group of adults with gastroesophageal reflux disease are being looked at in real-world care to see who they are, what treatments they get, and how symptoms and well-being change after treatment.
-
Early Gastric Cancer in China: Real-World Outcomes by Treatment Type
This project will look at real-world outcomes for about 5,000 Chinese patients with early gastric cancer treated with endoscopic resection, laparoscopic surgery, open surgery, or a combined approach to see how treatments relate to survival and recurrence.
-
Nine-grid Zoning Method to guide vertebroplasty for osteoporotic vertebral fractures
This trial tests whether the Nine-grid Zoning Method can make percutaneous vertebroplasty safer and faster for adults with single-segment lumbar osteoporotic vertebral compression fractures.
-
Predicting Colorectal Adenoma Recurrence and Cancer Risk Using Integrated TCM and Western Data
This study tests a risk-prediction model that combines Traditional Chinese Medicine and Western medical features to see if it can forecast recurrence and malignant change in colorectal adenomas.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.