Acute Myocardial Infarction Clinical Intelligent Decision Support System (AMI-CIDSS)

Capital Medical University

Status: Active, not recruiting

Conditions: Clinical Decision Support System; Acute Myocardial Infarction; Large Language Models

Study type: Observational

Funder types: Other

Identifiers

NCT07367399

kebingbing253

Details and patient eligibility

About

Acute Myocardial Infarction (AMI) remains the leading cause of cardiovascular mortality globally. In China, while the incidence of AMI is escalating at an annual rate of 5.2%, significant clinical challenges persist: diagnostic delays in primary care facilities exceed 40%, and the "Door-to-Balloon" (D2B) compliance rate in tertiary hospitals stagnates at a mere 65%. These figures underscore systemic deficiencies, including inefficient emergency response, regional resource disparities, and fragmented longitudinal care. Although Large Language Models (LLMs) provide a transformative technical foundation for AMI management, their clinical translation is hindered by critical bottlenecks, such as non-standardized data interfaces, limited model interpretability, inadequate hardware infrastructure at the grassroots level, and the inherent tension between data privacy and training requirements.

This research proposes a comprehensive implementation strategy for an AI-driven intelligent decision-making system for AMI. On a theoretical level, the study establishes a tripartite framework of "Technological Adaptation, Scenario Implementation, and Safeguard Mechanisms." By introducing a data governance scheme based on federated learning and multimodal fusion, and constructing a "Technical-Clinical-Economic" multidimensional evaluation model, this work bridges the theoretical divide between advanced technology and clinical practice. On a practical level, the study develops adaptive gateways and lightweight models to facilitate pervasive deployment in resource-constrained settings, optimizes the full-cycle clinical workflow to improve patient outcomes, and provides a scalable, replicable pathway for implementation.

Focusing on four core challenges-technological compatibility, clinical workflow integration, the balance between privacy and performance, and the establishment of scientific evaluation systems-this research aims to surmount existing translation barriers. It seeks to enhance the quality and efficiency of AMI care while providing a seminal reference for the clinical transformation of AI in other medical specialties.

Enrollment: 15,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Patients meeting the diagnostic criteria for acute myocardial infarction (AMI). Acute Myocardial Infarction (AMI) encompasses both ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI).

   According to the Fourth Universal Definition of Myocardial Infarction, myocardial injury is defined as the detection of an elevated cardiac troponin (cTn) value above the 99th percentile upper reference limit (URL). The injury is considered acute if there is a rise and/or fall of cTn values.

   The clinical definition of myocardial infarction (MI) requires the presence of acute myocardial injury, confirmed by abnormal cardiac biomarkers, in the setting of evidence of acute myocardial ischemia. Clinical evidence of ischemia includes at least one of the following:

   ①Symptoms of myocardial ischemia;

   ②New ischemic ECG changes;

   ③Development of pathological Q waves;

   ④Imaging evidence of new loss of viable myocardium or new regional wall motion abnormality in a pattern consistent with an ischemic etiology;

   ⑤Identification of a coronary thrombus by angiography or autopsy.

2. Patients who have provided written informed consent.

Exclusion criteria

1. patients with a life expectancy < 12 months due to non-cardiac comorbidities;
2. those with severe cognitive impairment hindering assessment;
3. individuals unable to comply with the long-term follow-up protocol