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.
Prospective observational studies used outpatient and follow-up information to construct an auxiliary early warning model of acute non-traumatic chest pain based on federated learning, and optimized the accuracy of early warning models through retrospective and prospective studies of large cohort data, and established an efficient and stable early warning and classification model for acute non-traumatic chest pain.
Study Type
OBSERVATIONAL
Enrollment
10,000
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)
Xiaonan He
Beijing, Chaoyang, China
RECRUITINGAdverse events
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.
Time frame: 30 days after presenting to the emergency departments(ED)
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