Assessment of the Diagnostic Performance of the Detection System and Establishment of an Intelligent and Rapid Triage Model

Not yet recruiting

• Conditions: Coronary Heart Disease (CHD)

• Registration Number: NCT06864676
• Lead Sponsor: China National Center for Cardiovascular Diseases

Brief Summary

The rapid triage of patients with acute chest pain remains an important issue in clinical practice. This study will establish a cohort of patients suspected of acute coronary syndrome (ACS) to construct a multi-marker dynamic combined intelligent triage model. This model will triage the risk of NSTEMI in patients with chest pain at their first visit. It will also stratify the risk of patients with chest pain using major adverse cardiovascular events (MACE) within 30 days as the endpoint.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-02
• Study Start: 2025-03
• Study First Submitted: 2025-03-04
• Study First Submitted QC: 2025-03-04
• Last Update Submitted: 2025-03-04
• Study First Posted: 2025-03-07
• Last Update Posted: 2025-03-07
• Primary Completion: 2026-08
• Study Completion: 2027-12

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 2000

Inclusion Criteria

• Adults aged 18 years or above from China, regardless of gender;
• Presence of symptoms such as chest tightness and chest pain, with the need to determine whether they are patients with acute coronary syndrome;
• Ability to obtain informed consent.

Exclusion Criteria

• • Patients with STEMI at the time of presentation;
• Patients with a clear diagnosis after tests such as troponin;
• Patients who have undergone major surgery or trauma within the past four weeks;
• Pregnant women or patients with tumors undergoing radiotherapy or chemotherapy;
• Patients who have previously participated in this study.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Myocardial infarction	30 days to 6 months	in patients suspected of NSTEMI, using the baseline (0 hours) and any one of the 1-3 hour points for the ZR iStar (POCT) hs-cTnI two-point method, combined with the time from typical chest pain to the visit, to establish a triage machine learning model for NSTEMI through the training cohort, and to establish a multi-marker dynamic combination intelligent triage model for risk stratification of chest pain patients.