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| Name | Class |
|---|---|
| Shihezi University | OTHER |
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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.
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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| coronary heart disease | 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 |
| |
| Healthy person | .A total of 300 healthy subjects from the First Affiliated Hospital of Xinjiang Medical University during the same period were selected as controls. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Machine learning model diagnosis | Diagnostic Test | Machine learning model diagnosis |
|
| Measure | Description | Time Frame |
|---|---|---|
| make a definite diagnosis of CHD | Based on the patient's typical angina pectoris symptoms, combined with the patient's age and coronary heart disease risk factors, and excluding other causes of angina pectoris, a preliminary diagnosis can be established. Coronary CTA, coronary angiography and other examinations find direct evidence of coronary artery stenosis, which can confirm the diagnosis | 2021-2023 |
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Inclusion Criteria:
Exclusion Criteria:
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CHD patients and healthy subjects who were hospitalized in the First Affiliated Hospital of Xinjiang Medical University from August 2021 to February 2022 were selected
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The first affiliated Hospital of Xinjiang Medical University | Ürümqi | Xinjiang | 830000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 39077543 | Derived | Ainiwaer A, Hou WQ, Kadier K, Rehemuding R, Liu PF, Maimaiti H, Qin L, Ma X, Dai JG. A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease. Rev Cardiovasc Med. 2023 Jun 8;24(6):168. doi: 10.31083/j.rcm2406168. eCollection 2023 Jun. |
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| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D000787 | Angina Pectoris |
| ID | Term |
|---|---|
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D014652 | Vascular Diseases |
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| D002637 |
| Chest Pain |
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |