Artificial Intelligence With Deep Learning and Genes on Cardiovascular Disease

NCT ID: NCT03877614

Last Updated: 2019-03-15

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 5000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-08-28
• Study Completion Date: 2022-06-30

Brief Summary

An association study with large database from electronic medical record system, images, outcome analysis and genetic single nucleotide polymorphism variations by machine learning and artificial intelligence methods in a Taiwanese and Chinese medical center based population

Detailed Description

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc... The current study is for the investigative cardiovascular team to take the advantage that in addition to the examination and treatment the participants should appropriately receive, the investigators can also analyze the individual differences and using the "deep learning methodology" to analyze the difference in physical fitness, therapeutic effectiveness and the consideration in the safety of the treatment. The additional goal of this study is to improve the quality of health care, the realization of cardiovascular "precise medicine" especially with personal difference on genetic variation.

This study will analyze the differences in the individualization of cardiovascular disease between diseases and other subjects to further improve the quality of care for clinical patients. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient's recovery, improve medical quality in the near future.

Conditions

Cardiovascular Diseases

Study Design

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

Study Groups

Cardiovascular high-risk (disease) group

A. Coronary artery disease B. Congestive heart failure with reduced ejection fraction C. Hypertrophic cardiomyopathy D. Atrial fibrillation E. Pulmonary hypertension F. Fabry's disease

ASCVD risk score

Intervention Type: OTHER

ASCVD score\< 10% will be in the control or low-risk group

Cardiovascular Low-risk (control) group

Patient with only risk factors with ASCVD score\<10% will be recognized as the comparison group

ASCVD risk score

Intervention Type: OTHER

ASCVD score\< 10% will be in the control or low-risk group

Interventions

ASCVD risk score

ASCVD score\< 10% will be in the control or low-risk group

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Patients' selection criteria and enrollment plan:

We will enroll subjects from either cardiovascular clinics or inpatients from the National Cheng Kung University Hospital from 2018 to 2021 after the signature of inform consent from patients and their families. The major enrollment criteria include one of the flowing diseases or conditions:

A. Coronary artery disease:

1. History of myocardial infarction

2. Coronary artery disease with computer tomography angiography image study with at least one vessel luminal stenosis >70%

3. Coronary artery stents implantation by hospital-based image database

4. Thallium-201 scan positive/treadmill test positive with additional 2 risk factors, including

1. Diabetes mellitus

2. Hypertension

3. Dyslipidemia

4. Family history of sudden death, coronary bypass surgery, cerebral vascular attacks (CVA), premature myocardial infarction

5. Smoking behaviors

B. Congestive heart failure with reduced ejection fraction

1. Echocardiography left ventricular ejection fraction <40%

C. Hypertrophic cardiomyopathy:

1. Left ventricle interventricular septum（IVS) >15 mm

2. Left ventricle mass index> 200gm

3. Apical hypertrophy noted on the report with 4 chamber view

D. Atrial fibrillation

1. Recorded by Holter continuous EKG

2. Recorded by standard 12 leads complete EKG

E. Pulmonary hypertension

1. Echo with systolic pulmonary pressure (sysPAP)> 40 mmHg

2. Diagnosis of idiopathic pulmonary hypertension

3. Under pulmonary hypertension medication

F. Fabry's disease

1. α-Galactosidase (a-GAL) enzyme deficiency

2. Genetic disorder

G. Patient with only risk factors (<3 risk factors), recognized as the comparison group (>500 cases)

1. Diabetes mellitus

2. Hypertension

3. Dyslipidemia

4. Family history of sudden death, coronary bypass surgery, cerebral vascular attacks, premature myocardial infarction

5. Smoking behavior

Exclusion Criteria

• Patients unwilling to be enrolled
• Concentration of DNA collection was inadequate after 3 times of collection

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Cheng-Kung University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Ping-Yen Liu

Director of Cardiology, Internal Medicine and Professor of Institute of Clinical Medicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Department of Internal Medicine, National Cheng Kung University Hospital

Tainan City, , Taiwan

Site Status: RECRUITING

Countries

Taiwan

Facility Contacts

Ping-Yen Liu, MD, PhD.

Role: primary

larry@mail.ncku.edu.tw

+88662353535 ext. 4602

Pei-Tiang Hsu

Role: backup

sz2137@yahoo.com.tw

+88662353535 ext. 2389

References

Hsu YL, Huang MS, Chang HY, Lee CH, Chen DP, Li YH, Chao TH, Liu YW, Liu PY. Application of genetic risk score for in-stent restenosis of second- and third-generation drug-eluting stents in geriatric patients. BMC Geriatr. 2023 Jul 19;23(1):443. doi: 10.1186/s12877-023-04103-w.

Reference Type: DERIVED

PMID: 37468836 (View on PubMed)

Lee PT, Huang MH, Huang TC, Hsu CH, Lin SH, Liu PY. High Burden of Premature Ventricular Complex Increases the Risk of New-Onset Atrial Fibrillation. J Am Heart Assoc. 2023 Feb 21;12(4):e027674. doi: 10.1161/JAHA.122.027674. Epub 2023 Feb 15.

Reference Type: DERIVED

PMID: 36789835 (View on PubMed)

Other Identifiers

A-ER-107-149

Identifier Type: -

Identifier Source: org_study_id