Prospective Multicenter Study for Early Evaluation of Acute Chest Pain
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Acute Aortic Dissection
- Acute Coronary Syndrome
- Acute Myocardial Infarction Type 1
- Chest Pain
- Pulmonary Embolism
- Design
- Observational Model: CohortTime Perspective: Prospective
Participation Requirements
- Age
- Between 18 years and 90 years
- Gender
- Both males and females
Description
In this study, acute chest pain (ACP) patients will be selected from chest pain center of nine large tertiary hospitals in China from November 1, 2019 to October 31, 2021. All the selected patients will sign the informed consent. Patients' characteristics, the first vital signs at the time of consul...
In this study, acute chest pain (ACP) patients will be selected from chest pain center of nine large tertiary hospitals in China from November 1, 2019 to October 31, 2021. All the selected patients will sign the informed consent. Patients' characteristics, the first vital signs at the time of consultation, the first arterial blood gas, complete blood count, coagulation markers, blood biochemical results and myocardial injury markers, imaging examinations and electrocardiogram will be collected within 30 minutes at admission. Meanwhile, whole blood and plasma samples will be collected and stored in - 80 ? refrigerator. After diagnosis according to the gold standard examination or related guidelines, patients will be admitted to different department for standard treatment. Medication, surgical procedures and complications will be recorded carefully. Plasma and whole blood will be used to detect proteomics and/or genomics biomarkers associated with early evaluation of ACP. Screening early evaluation indicators using novel protein biomarkers and easy-to-obtain clinical indicators, and establishing evaluation models for high-risk ACP by data analysis methods. Area under the receiver operating characteristic curves (AUROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) will be used to evaluate the prediction ability of the model.
Tracking Information
- NCT #
- NCT04122573
- Collaborators
- Not Provided
- Investigators
- Principal Investigator: Dongze Li, MBBS Emergency Department, West China Hospital, Sichuan University