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

NCT05018715 | Unknown

• 1 other identifier: XMa0001
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

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.

Conditions

Coronary Heart Disease; Acute Myocardial Infarction; Angina

Keywords

machine learning; Deep learning; electronic patient record; auscultation; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

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

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.

Interventions

Machine learning model diagnosis DIAGNOSTIC_TEST

Machine learning model diagnosis

coronary heart disease

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Xiang Ma: lead
• Shihezi University: collaborator

Study Sites (1)

The first affiliated Hospital of Xinjiang Medical University

Ürümqi, Xinjiang, 830000, China

Location

Related Publications (1)

• 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.

  PMID: 39077543 DERIVED

MeSH Terms

Conditions

Coronary Disease; Angina Pectoris

Condition Hierarchy (Ancestors)

Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Vascular Diseases; Chest Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: professor

Study Record Dates

• First Submitted: August 22, 2021
• First Posted: August 24, 2021
• Study Start: August 22, 2021
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: September 2, 2021

Record last verified: 2021-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)