UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000053936 |
|---|---|
| Receipt number | R000061561 |
| Scientific Title | Development of a prediction model for cardiovascular disease severity by integrating PHR (Personal Health Record) and medical data |
| Date of disclosure of the study information | 2024/03/22 |
| Last modified on | 2024/03/21 20:58:46 |
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Basic information
Public title
Development of a prediction model for cardiovascular disease severity by integrating PHR (Personal Health Record) and medical data
Acronym
AI Model to Predict Cardiovascular Disease Worsening by Integrating PHR and Medical Data
Scientific Title
Development of a prediction model for cardiovascular disease severity by integrating PHR (Personal Health Record) and medical data
Scientific Title:Acronym
AI Model to Predict Cardiovascular Disease Worsening by Integrating PHR and Medical Data
Region
| Japan |
Condition
Condition
sudden cardiac death, heart failure
Classification by specialty
| Cardiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The aim of this study is to establish a registry that integrates PHRs, which collect health information including vital signs such as blood pressure, weight, and pulse rate, and subjective symptoms of individuals on a daily basis, with background factors, diagnosis, and details of treatment ( medical data), and to create a prediction model based on factors related to cardiovascular disease severity identified in the registry.
Basic objectives2
Others
Basic objectives -Others
Integrate PHR and clinical data to build AI models that can predict sudden cardiac death and cardiovascular disease exacerbations and enable early intervention.
Trial characteristics_1
Exploratory
Trial characteristics_2
Pragmatic
Developmental phase
Assessment
Primary outcomes
Incidence of sudden cardiac death
Key secondary outcomes
Incidence of hospitalization by heart failure , acute coronary syndrome, fatal arrhythmia (including appropriate ICD activation), all-cause mortality, thromboembolic events, non-fatal arrhythmic events (atrial fibrillation, non-sustained ventricular tachycardia), syncope, non heart failure hospitalization
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
(a) Patients hospitalized with a diagnosis of acute heart failure or exacerbation of chronic heart failure, or with a history of such a diagnosis
(b) Patients admitted with a diagnosis of acute coronary syndrome or with a history of acute coronary syndrome
(c) Patients with a diagnosis of chronic heart failure and an implantable cardioverter-defibrillator or biventricular pacemaker
(d) Patients admitted to the hospital with a diagnosis of out-of-hospital cardiac arrest and saved, or with a history of out-of-hospital cardiac arrest
(e) Patients with a history of unexplained syncope
(f) Patients in which the patient is considered to be at high risk due to a close relative of sudden cardiac death
(k) Other: Patients in which the attending physician considers the patient to be at risk for sudden cardiac death.
Key exclusion criteria
Patients who do not or cannot use a smart phone
Target sample size
1000
Research contact person
Name of lead principal investigator
| 1st name | Taku |
| Middle name | |
| Last name | Iwami |
Organization
Kyoto University Graduate School of Medicine
Division name
Department of Preventive Medicine, School of Public Health
Zip code
6068501
Address
Yoshida-Konoe-cho, Sakyo-ku, Kyoto
TEL
075-753-4400
iwami.taku.8w@kyoto-u.ac.jp
Public contact
Name of contact person
| 1st name | Taku |
| Middle name | |
| Last name | Iwami |
Organization
Kyoto University Graduate School of Medicine
Division name
Department of Preventive Medicine, School of Public Health
Zip code
6068501
Address
Yoshida-Konoe-cho, Sakyo-ku, Kyoto
TEL
075-753-4400
Homepage URL
iwami.taku.8w@kyoto-u.ac.jp
Sponsor or person
Institute
Kyoto University
Institute
Department
Personal name
Funding Source
Organization
the cabinet office
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
Kyoto University
Address
Yoshida-Konoe-cho, Sakyo-ku, Kyoto
Tel
075-753-4680
ethcom@kuhp.kyoto-u.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
| 2024 | Year | 03 | Month | 22 | 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
| 2024 | Year | 03 | Month | 21 | Day |
Date of IRB
| 2024 | Year | 03 | Month | 21 | Day |
Anticipated trial start date
| 2024 | Year | 03 | Month | 22 | Day |
Last follow-up date
| 2028 | Year | 07 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Using wearable devices and other tools, daily health information is collected to create a Personal Health Record. By integrating this data with clinical information held by hospitals, a vast and detailed dataset is obtained. Analysis is then conducted to explore the relationship between events such as sudden cardiac death and exacerbation of heart failure occurring during the follow-up period, with the goal of constructing AI models capable of predicting these events in advance.
Management information
Registered date
| 2024 | Year | 03 | Month | 21 | Day |
Last modified on
| 2024 | Year | 03 | Month | 21 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000061561
