Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT05018715 |
| Other study ID # |
XMa0001 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 22, 2021 |
| Est. completion date |
December 31, 2023 |
Study information
| Verified date |
September 2021 |
| Source |
First Affiliated Hospital of Xinjiang Medical University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Based on the clinical data of patients, a machine learning model for coronary heart disease
diagnosis was established to evaluate whether the model could improve the accuracy of
coronary heart disease diagnosis, and to evaluate its authenticity, reliability and benefits.
Description:
A total of 300 patients with CHD WHO were hospitalized in the First Affiliated Hospital of
Xinjiang Medical University from August 2021 to February 2022 were selected, all of whom met
the DIAGNOSTIC criteria of CHD formulated by the World Health Organization (WHO) and excluded
diseases such as highly severe valvular disease and congenital heart disease.A total of 300
healthy subjects from the First Affiliated Hospital of Xinjiang Medical University during the
same period were selected as controls.Observation indicators included: Clinical indicators
collected included: General conditions: gender, age, medical history;Blood biochemical
indexes, such as blood routine, liver function, kidney function, blood lipid, blood glucose,
myocardial markers, electrolyte, serum creatinine concentration, body mass index, BNP and
other indicators;Related tests such as ELECTROcardiogram, holter electrocardiogram, cardiac
ultrasound (left atrial diameter, ascending aorta, ventricular septal thickness, left
posterior wall thickness, right ventricular diameter, ejection fraction, abnormal ventricular
wall motion, evidence of infarction or ischemia, valve abnormality, congenital heart disease,
etc.);Signs include: audio data of heart sounds in nine parts of precardiac area;Medication
status.All blood biochemical indexes and examinations were completed in the laboratory
department and ultrasound department of our hospital, and the physical signs were completed
in the ward.The results of coronary angiography, pre-hospital and post-hospital
echocardiography and other related data were recorded.Machine learning model was constructed
based on clinical data to assist diagnosis of patients with coronary heart disease
