coronary heart disease
Conditions
Interventions
Gold Standard:intravascular ultrasound examination
Index test:A 
prediction 
model 
based 
on 
deep 
learning 
neural 
Sponsors
General Hospital of Northern Theater Command
Eligibility
Sex/Gender
All
Age
30 Years to 70 Years
Inclusion criteria
Inclusion criteria: 1. Patients meeting all of the following criteria will be included in this study: 2. Patients who have coronary artery disease diagnosed according to clinical history, physical signs, and imaging data (presence of plaques in single or more coronary arteries with >= 50% narrowing of the arterial lumen as shown on coronary CTA; typical angina pectoris; ST segment changes?on an?electrocardiogram?(ECG); treadmill exercise electrocardiogram test positive) 20 and will be scheduled to receive treatment according to the requirements of the Chinese guideline for PCI (2016); 3. Recent emergence of irregular angina pectoris or exertional angina pectoris; 4. Willingness to participate in and cooperate with clinical trials with a sufficient understanding of research methods and purposes; 5. Receiving CTA and IVUS prior to PCI; 6. Aged > 30 years.
Exclusion criteria
Exclusion criteria: Patients meeting one or more of the following conditions will be excluded from this study: 1. Patients receiving coronary stenting or bypass grafting; 2. Patients undergoing cardiac and macrovascular surgery; 3. Patients with acute or chronic infectious diseases; 4. Patients with renal insufficiency; 5. Drug abusers; 6. Cancer patients or patients who are in the stage of radiotherapy and chemotherapy; 7. Pregnant or lactating woman; 8. Patients with poor health status (advanced malignant tumors, diabetes, poor control of hyperthyroidism, dehydration, etc.) 9. Patients with allergy-related diseases including severe asthma, urticaria, food or pollen allergy or those who are allergic to contrast media; 10. Patients with arrhythmia: atrial fibrillation, frequent atrial premature beats and ventricular premature beats, more serious sinus arrhythmia.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Sensitivity and specificity of the patch stability prediction model using training set and test set data; | — |
Secondary
| Measure | Time frame |
|---|---|
| The diagnostic accuracy, area under the ROC curve, cut-off value, positive predictive value and negative predictive value of the patch stability prediction model using training set and test set data;Coronary CTA images; | — |
Countries
China
Contacts
Public ContactBenqiang Yang
General Hospital of Northern Theater Command
Outcome results
None listed