UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000045802 |
|---|---|
| Receipt number | R000052274 |
| Scientific Title | Creating a Predictive Model of Renal Failure in Type 2 Diabetes Using Machine Learning with Artificial Intelligence |
| Date of disclosure of the study information | 2021/10/25 |
| Last modified on | 2024/04/22 09:05:20 |
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Basic information
Public title
Creating a Predictive Model of Renal Failure in Type 2 Diabetes Using Machine Learning with Artificial Intelligence
Acronym
Pred(o)minance study
Scientific Title
Creating a Predictive Model of Renal Failure in Type 2 Diabetes Using Machine Learning with Artificial Intelligence
Scientific Title:Acronym
Pred(o)minance study
Region
| Japan |
Condition
Condition
Type 2 diabetes mellitus
Diabetic kidney disease
Classification by specialty
| Endocrinology and Metabolism | Nephrology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The aim of this study is to predict the future onset of renal failure in Japanese patients with type 2 diabetes using machine learning with artificial intelligence.
Basic objectives2
Others
Basic objectives -Others
The goal is to detect high-risk groups as early as possible.
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Deterioration of renal function (decrease in eGFR value from the beginning of observation to less than half)
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
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Patients who attended the outpatient clinic of the Department of Nephrology, Endocrinology and Metabolism of our hospital between 2003 and March 2015, with an eGFR of at least 60 mL/min/1.73m2 at the time of the first visit and at least two eGFR measurements every six months for at least three years.
Key exclusion criteria
None in particular
Target sample size
2533
Research contact person
Name of lead principal investigator
| 1st name | Shu |
| Middle name | |
| Last name | Meguro |
Organization
Keio University School of Medicine
Division name
Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine
Zip code
160-8582
Address
35 Shinanomachi, Shinjuku-ku, Tokyo
TEL
03-3353-1211
shumeg@keio.jp
Public contact
Name of contact person
| 1st name | Shu |
| Middle name | |
| Last name | Meguro |
Organization
Keio University School of Medicine
Division name
Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine
Zip code
160-8582
Address
35 Shinanomachi, Shinjuku-ku, Tokyo
TEL
03-3353-1211
Homepage URL
shumeg@keio.jp
Sponsor or person
Institute
Keio University School of Medicine
Division of Endocrinology, Metabolism and Nephrology, Department of Internal Medicine
Institute
Department
Personal name
Funding Source
Organization
None in particular
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Asahi Kasei Corporation Research and Development Division
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Ethics Committee, Keio University School of Medicine
Address
35 Shinanomachi, Shinjuku-ku, Tokyo
Tel
03-3353-1211
med-rinri-jimu@adst.keio.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
| 2021 | Year | 10 | Month | 25 | 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
Completed
Date of protocol fixation
| 2021 | Year | 08 | Month | 11 | Day |
Date of IRB
| 2021 | Year | 08 | Month | 31 | Day |
Anticipated trial start date
| 2021 | Year | 11 | Month | 01 | Day |
Last follow-up date
| 2024 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
| 2024 | Year | 03 | Month | 31 | Day |
Other
Other related information
Using an anonymized medical information database of patients in the Department of Nephrology, Endocrinology and Metabolism at Keio University Hospital, which has been analyzed in the past, we will investigate whether it is possible to predict renal prognosis based on patterns of eGFR variation using machine learning methods based on artificial intelligence.
Management information
Registered date
| 2021 | Year | 10 | Month | 20 | Day |
Last modified on
| 2024 | Year | 04 | Month | 22 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000052274
