UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000050501 |
|---|---|
| Receipt number | R000057520 |
| Scientific Title | Investigation of clinical application of an AI program to predict left ventricular systolic dysfunction from ECG |
| Date of disclosure of the study information | 2023/04/01 |
| Last modified on | 2023/03/07 16:25:11 |
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Basic information
Public title
Investigation of clinical application of an AI program to predict left ventricular systolic dysfunction from ECG
Acronym
Investigation of clinical application of an AI program to predict left ventricular systolic dysfunction from ECG
Scientific Title
Investigation of clinical application of an AI program to predict left ventricular systolic dysfunction from ECG
Scientific Title:Acronym
Investigation of clinical application of an AI program to predict left ventricular systolic dysfunction from ECG
Region
| Japan |
Condition
Condition
Cardiovascular disease
Classification by specialty
| Cardiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Many cardiac diseases, such as ischemic heart disease, cardiomyopathy, and myocarditis, are known to cause left ventricular systolic dysfunction as they progress. In particular, when these diseases have a chronic course, left ventricular systolic dysfunction may occur asymptomatically, and irreversible changes are often already present at the time of diagnosis. The prognosis for patients with left ventricular systolic dysfunction is also known to be poor, even in asymptomatic patients. On the other hand, it is also known that appropriate pharmacotherapy and device therapy can improve the prognosis of patients with left ventricular systolic dysfunction. Although it would be of great clinical significance if patients with left ventricular systolic dysfunction could be diagnosed at an earlier stage and linked to treatment, screening tests for left ventricular systolic dysfunction are extremely limited, and early diagnosis is known to be very difficult. The development of simple and effective screening tools is needed in this field. In recent years, deep learning has been applied to the medical field, and it is known to be particularly successful in assisting medical image diagnosis. We have developed an AI analysis program that accurately classifies patients with left ventricular systolic dysfunction by applying deep learning analysis to 12-lead electrocardiograms performed on patients suspected of having cardiac disease. In this study, we will evaluate the performance of the AI analysis program using newly collected data on patients suspected of having cardiac disease at multiple institutions in a retrospective manner, with a view to clinical application of the developed AI analysis program, and obtain regulatory approval as a medical device program by the Ministry of Health, Labour and Welfare.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Sensitivity and specificity of the AI analysis program to detect the presence of reduced left ventricular ejection fraction
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 18 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Of the cases in which echocardiography was performed or read by the relevant department at the participating institution from the date of approval through December 2023, those cases in which an electrocardiogram was performed in the 14 days prior to the echocardiography.
Key exclusion criteria
Patients under 18 years of age, post-implantation permanent pacemaker, post-implantation implantable cardioverter defibrillator, post-implantation cardiac resynchronization therapy device, and patients who are unable to provide consent or who have opted out and been denied.
Target sample size
600
Research contact person
Name of lead principal investigator
| 1st name | Satoshi |
| Middle name | |
| Last name | Kodera |
Organization
The University of Tokyo Hospital
Division name
Department of Cardiovascular Medicine
Zip code
113-8655
Address
Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan
TEL
03-3815-5411
koderasatoshi@gmail.com
Public contact
Name of contact person
| 1st name | Satoshi |
| Middle name | |
| Last name | Kodera |
Organization
The University of Tokyo Hospital
Division name
Department of Cardiovascular Medicine
Zip code
113-8655
Address
Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan
TEL
03-3815-5411
Homepage URL
koderasatoshi@gmail.com
Sponsor or person
Institute
The University of Tokyo Hospital
Institute
Department
Personal name
Funding Source
Organization
Japan Agency for Medical Research and Development
Organization
Division
Category of Funding Organization
Japanese Governmental office
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Ethics Committee, Graduate School of Medicine, The University of Tokyo
Address
Hongo 7-3-1, Bunkyo-ku, Tokyo, Japan
Tel
03-5841-0818
ethics@m.u-tokyo.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 04 | Month | 01 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Preinitiation
Date of protocol fixation
| 2023 | Year | 04 | Month | 01 | Day |
Date of IRB
Anticipated trial start date
| 2023 | Year | 04 | Month | 01 | Day |
Last follow-up date
| 2028 | Year | 12 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
None
Management information
Registered date
| 2023 | Year | 03 | Month | 06 | Day |
Last modified on
| 2023 | Year | 03 | Month | 07 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000057520
