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.
Study Type
OBSERVATIONAL
Enrollment
2,000
Myocardial infarction
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.
Time frame: 30 days to 6 months
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