Clinical trial NCT05018715: Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
Official title: Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Coronary Heart Disease
Acute Myocardial Infarction
Angina
Keywords: machine learning
Deep learning
electronic patient record
auscultation
artificial intelligence
Lead sponsor: First Affiliated Hospital of Xinjiang Medical University
Sponsor class: OTHER
Collaborators: name: Shihezi University
First posted: 2021-08-24
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
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

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

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.

Primary outcomes:
measure: make a definite diagnosis of CHD
timeFrame: 2021-2023
Secondary outcomes:
Other outcomes:
Locations:
city: Ürümqi
country: China
facility: The first affiliated Hospital of Xinjiang Medical University
state: Xinjiang
Study officials:
References: