Early Warning and Classification Model for Acute Non-traumatic Chest Pain

Condition(s) studied

Investigational drug(s) / intervention(s)

Clinical evaluation, laboratory and cardiac imaging results, medication, surgery, and any hospitalization: Examination： Electrocardiogram、 imaging examination、 X-ray, CTA, bedside echocardiography. Laboratory test results of patients, including complete blood count, D-dimer, myocardial injury markers, sST2, MPO, and other indicators. History of cardiovascular and pulmonary vascular drug therapy: Antithrombotic therapy (type, measurement) , Anticoagulation therapy (type, metering), Other drug treatments (type, measurement)

Study summary

Acute non-traumatic chest pain is one of the common causes of presentation in emergency patients, but the causes of acute non-traumatic chest pain are complex, the severity of the condition varies greatly, and the specificity of symptoms is not high. Machine learning and intelligent auxiliary models can greatly shorten the time of clinical decision-making, and improve the accuracy of etiological diagnosis in patients with chest pain, reduce the rate of misdiagnosis and missed diagnosis, and provide a clear direction for further treatment.

Eligibility

Primary outcome measure(s)

• Adverse events — 30 days after presenting to the emergency departments(ED)
  The primary outcome was a composite of adjudicated major adverse cardiovascular and cerebrovascular events (MACCE), which included cardiovascular death, all-cause mortality, nonfatal myocardial infarction, refractory angina, new onset heart failure and stroke.

Trial sites (1)

Other trials for the same condition