Ignite Creation Date:
2025-12-25 @ 12:38 AM
Ignite Modification Date:
2025-12-25 @ 10:48 PM
Study NCT ID:
NCT07128667
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-08-19
First Post:
2025-08-14
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Clinical Features and Long-Term Prognosis in Young Patients With Acute Myocardial Infarction
Sponsor:
China-Japan Friendship Hospital
Organization:
Study Overview
Official Title:
A Prospective Cohort Study on Clinical Characteristics, Risk Factors, and Long-Term Outcomes in Young Patients With Acute Myocardial Infarction
Status:
NOT_YET_RECRUITING
Status Verified Date:
2025-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AMI-Youth
Brief Summary:
The incidence of acute myocardial infarction (AMI) in young patients is on the rise, placing a heavy health and economic burden on individuals, families, and society. The clinical phenotype and pathophysiological mechanisms of AMI in this population exhibit significant heterogeneity compared to elderly patients, and existing risk assessment tools have limited applicability in this specific group. The core problem this study aims to address is: how to accurately identify specific risk factors in young AMI patients and build an effective risk prediction model to prevent and optimize clinical diagnosis and treatment.
This study will adopt a prospective cohort design to collect multi-dimensional clinical data from young AMI patients. It will systematically analyze their clinical characteristics, risk factors, and coronary lesion status to comprehensively map the clinical, risk factor, and pathophysiological diversity of young AMI patients. Secondly, it will delve into identifying specific risk factors that influence the onset, progression, and long-term prognosis of young AMI. Thirdly, it will combine machine learning algorithms to develop a risk prediction model for young AMI, performing internal validation in the prospective cohort and external validation in the MIMIC-IV database. Simultaneously, it will explore novel biomarkers associated with disease onset and progression. The key outcomes of this study are to establish a high-quality clinical database of young AMI patients, design a risk prediction model for young AMI based on the study results, and produce high-level academic publications.
This study will adopt a prospective cohort design to collect multi-dimensional clinical data from young AMI patients. It will systematically analyze their clinical characteristics, risk factors, and coronary lesion status to comprehensively map the clinical, risk factor, and pathophysiological diversity of young AMI patients. Secondly, it will delve into identifying specific risk factors that influence the onset, progression, and long-term prognosis of young AMI. Thirdly, it will combine machine learning algorithms to develop a risk prediction model for young AMI, performing internal validation in the prospective cohort and external validation in the MIMIC-IV database. Simultaneously, it will explore novel biomarkers associated with disease onset and progression. The key outcomes of this study are to establish a high-quality clinical database of young AMI patients, design a risk prediction model for young AMI based on the study results, and produce high-level academic publications.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: