Coronary Heart DiseaseNCT05018715
Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
An observational study of Coronary Heart Disease and Acute Myocardial Infarction, run by Xiang Ma at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 to 100 yearsHealthy volunteers can take part
- Conditions the study is for
- Coronary Heart Disease, Acute Myocardial Infarction, Angina
- What is being tested?
- Machine learning model diagnosis
- How many people
- 600 participants
- Where
- 1 siteChina
- Study dates
- 22 Aug 2021to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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
Who may be able to join
- Patients who meet the diagnostic criteria for CHD set by the World Health Organization
Who cannot join
- Exclude serious valvular disease, congenital heart disease, respiratory system and other diseases.
Trial locations and contacts
1 site in 1 country.
China1 site
The first affiliated Hospital of Xinjiang Medical University
Ürümqi, Xinjiang, 830000
About this study
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.
Read the 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
- Being tested
- Machine learning model diagnosis
- Keywords
- machine learning, Deep learning, electronic patient record, auscultation, artificial intelligence
- Medical subject headings
- Coronary Disease, Angina Pectoris
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning model diagnosisDiagnostic test
Machine learning model diagnosis
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 2021-2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Aug 2021
- Study start22 Aug 2021
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Coronary Heart DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xiang Ma
- Sponsor type
- University, hospital or other
- Collaborators
- Shihezi University
- Responsible party
- Sponsor-investigator, Xiang Ma, First Affiliated Hospital of Xinjiang Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05018715
- Sponsor's protocol ID
- XMa0001
- Status last verified
- Sep 2021
- First submitted
- 22 Aug 2021
- Last updated
- 2 Sep 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 39077543
Study at a glance
From the registry record, last updated by the sponsor on 2 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Diagnostic Value of Machine Learning Model Based on Clinical Data in Patients With Coronary Heart Disease
- ClinicalTrials.gov ID
- NCT05018715
- Acronym / sponsor ID
- XMa0001
- Conditions
- Coronary Heart Disease, Acute Myocardial Infarction, Angina
- Keywords
- machine learning, Deep learning, electronic patient record, auscultation, artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Xiang MaUniversity, hospital or other
- Collaborators
- Shihezi University
- Study start
- 22 Aug 2021 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 24 Aug 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| coronary heart disease | – | Diagnostic Test: Machine learning model diagnosisA 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. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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
Key inclusion criteria (1 listed)
- Patients who meet the diagnostic criteria for CHD set by the World Health Organization
Key exclusion criteria (1 listed)
- Exclude serious valvular disease, congenital heart disease, respiratory system and other diseases.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning model diagnosisMachine learning model diagnosis | Diagnostic test | coronary heart disease |
Primary outcome
make a definite diagnosis of CHD
Time frame: 2021-2023
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Sep 2021.
- Derived from this study: PubMed 39077543Ainiwaer 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Coronary Heart Disease and Acute Myocardial Infarction, run by Xiang Ma at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 2 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
make a definite diagnosis of CHD (time frame: 2021-2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05018715, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Sep 2021; AzTrials last refreshed its copy on 29 Oct 2026.