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Artificial Intelligence With Deep Learning and Genes on Cardiovascular Disease

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ClinicalTrials.gov Identifier: NCT03877614
Recruitment Status : Recruiting
First Posted : March 15, 2019
Last Update Posted : March 15, 2019
Sponsor:
Information provided by (Responsible Party):
Ping-Yen Liu, National Cheng-Kung University Hospital

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

Condition or disease Intervention/treatment
Cardiovascular Diseases Other: ASCVD risk score

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.


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Study Type : Observational
Estimated Enrollment : 5000 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Application of Artificial Intelligence Deep Learning to the Correlation Between Cardiovascular Disease and Individualized Differences
Actual Study Start Date : August 28, 2018
Estimated Primary Completion Date : December 2021
Estimated Study Completion Date : June 2022

Group/Cohort Intervention/treatment
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
Other: ASCVD risk score
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
Other: ASCVD risk score
ASCVD score< 10% will be in the control or low-risk group




Primary Outcome Measures :
  1. Major cardiovascular events [ Time Frame: 5 years ]
    The rate of myocardial infarction, stroke, death, cardiovascular death, heart failure with hospitalization


Secondary Outcome Measures :
  1. Heart function changes [ Time Frame: 5 years ]
    parameters and function changes from echocardiography

  2. Lipid profiles [ Time Frame: 5 years ]
    The percentage changes and response of lipid profile with regular lipid lowering agents

  3. Arrhythmia events [ Time Frame: 5 years ]
    The rate of arrhythmia associated complications and clinical events, stokes

  4. Recurrent acute coronary events [ Time Frame: 5 years ]
    The rate of recurrent acute coronary events with hospitalization needed or re-intervention procedures for coronary artery needed


Biospecimen Retention:   Samples With DNA
Salivary DNA with genetic polymorphism chips comparative analysis


Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population

Pipeline for case enrollment:

  1. We will enroll investigating subjects from the clinics of near 10 physicians screened by our assistant or physicians themselves, with nearly 100-120 cases/month. According to our initial experience during our previous hospital-based grant in 2018 showed feasible case number to be enrolled, nearly 1000 case within 3 months enrollment. In total, we plan to recruit 5000 subjects with cardiovascular disease or with risk factors within 3 years. (IRB approval A-ER-107-149)
  2. Our study subjects will be evaluated whether they fulfill the cases criteria by an independent physician every month.
  3. In comparison with disease, the risk factor only group (500 cases) will be the matching group on genetic background and outcome comparison.
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03877614


Locations
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Taiwan
Department of Internal Medicine, National Cheng Kung University Hospital Recruiting
Tainan, Taiwan, 704
Contact: Ping-Yen Liu, MD, PhD.    +88662353535 ext 4602    larry@mail.ncku.edu.tw   
Contact: Pei-Tiang Hsu    +88662353535 ext 2389    sz2137@yahoo.com.tw   
Sponsors and Collaborators
National Cheng-Kung University Hospital

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Responsible Party: Ping-Yen Liu, Director of Cardiology, Internal Medicine and Professor of Institute of Clinical Medicine, National Cheng-Kung University Hospital
ClinicalTrials.gov Identifier: NCT03877614     History of Changes
Other Study ID Numbers: A-ER-107-149
First Posted: March 15, 2019    Key Record Dates
Last Update Posted: March 15, 2019
Last Verified: March 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:
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Cardiovascular Diseases