Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease

NCT ID: NCT05018715

Last Updated: 2021-09-02

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 600 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-08-22
• Study Completion Date: 2023-12-31

Brief 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.

Detailed 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

Conditions

Coronary Heart Disease; Acute Myocardial Infarction; Angina

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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

Machine learning model diagnosis

Intervention Type: DIAGNOSTIC_TEST

Machine learning model diagnosis

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.

No interventions assigned to this group

Interventions

Machine learning model diagnosis

Machine learning model diagnosis

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Patients who meet the diagnostic criteria for CHD set by the World Health Organization

Exclusion Criteria

• Exclude serious valvular disease, congenital heart disease, respiratory system and other diseases.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Shihezi University

OTHER

Sponsor Role: collaborator

Xiang Ma

OTHER

Sponsor Role: lead

Responsible Party

Xiang Ma

professor

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

The first affiliated Hospital of Xinjiang Medical University

Ürümqi, Xinjiang, China

Countries

China

References

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.

Reference Type: DERIVED

PMID: 39077543 (View on PubMed)

Other Identifiers

XMa0001

Identifier Type: -

Identifier Source: org_study_id