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Name:
UMIN ID:

Recruitment status Preinitiation
Unique ID issued by UMIN UMIN000037476
Receipt No. R000042722
Scientific Title A machine learning-based prediction model for rapid glomerular filtration rate decline in patients with chronic kidney disease by using a big database
Date of disclosure of the study information 2019/08/01
Last modified on 2019/07/24

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Basic information
Public title A machine learning-based prediction model for rapid glomerular filtration rate decline in patients with chronic kidney disease by using a big database
Acronym A machine learning-based prediction model for rapid glomerular filtration rate decline in patients with chronic kidney disease
Scientific Title A machine learning-based prediction model for rapid glomerular filtration rate decline in patients with chronic kidney disease by using a big database
Scientific Title:Acronym A machine learning-based prediction model for rapid glomerular filtration rate decline in patients with chronic kidney disease
Region
Japan

Condition
Condition chronic kidney disease
Classification by specialty
Nephrology
Classification by malignancy Others
Genomic information NO

Objectives
Narrative objectives1 Recent studies have focused on kidney function trajectory because it might be related to the incidence of cardiovascular (CV) disease and all-cause mortality. Most of these studies included the general population or patients with CKD receiving nephrological care. We aimed to investigate risk factors for rapid GFR decline and create a machine learning-based predictive model by using one big hospital database.
Basic objectives2 Others
Basic objectives -Others machine learninig method
Trial characteristics_1
Trial characteristics_2
Developmental phase

Assessment
Primary outcomes speed of eGFR decline
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
20 years-old <=
Age-upper limit
100 years-old >
Gender Male and Female
Key inclusion criteria CKD patients whose medical records ware available
Key exclusion criteria nothing
Target sample size 20000

Research contact person
Name of lead principal investigator
1st name Daijo
Middle name
Last name Inaguma
Organization Fujita Health University School of Medicine
Division name Nephrology
Zip code 470-1192
Address Dengakugakubo 1-98, Kutsukake, Toyoake
TEL 0562-93-9245
Email daijo@fujita-hu.ac.jp

Public contact
Name of contact person
1st name Daijo
Middle name
Last name Inaguma
Organization Fujita Health University School of Medicine
Division name Nephrology
Zip code 470-1192
Address Dengakugakubo 1-98, Kutsukake, Toyoake
TEL 0562-93-9245
Homepage URL
Email daijo@fujita-hu.ac.jp

Sponsor
Institute Fujita Health University School of Medicine
Institute
Department

Funding Source
Organization Kyowa kirin. co.ltd
Organization
Division
Category of Funding Organization Profit organization
Nationality of Funding Organization

Other related organizations
Co-sponsor
Name of secondary funder(s)

IRB Contact (For public release)
Organization Nephrology
Address Dengakugakubo 1-98, Kutsukake, Toyoake
Tel 0562-93-9245
Email daijo@hujita-hu.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
2019 Year 08 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
2019 Year 08 Month 01 Day
Date of IRB
Anticipated trial start date
2019 Year 08 Month 01 Day
Last follow-up date
2019 Year 12 Month 31 Day
Date of closure to data entry
Date trial data considered complete
Date analysis concluded

Other
Other related information nothing paticular

Management information
Registered date
2019 Year 07 Month 24 Day
Last modified on
2019 Year 07 Month 24 Day


Link to view the page
URL(English) https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000042722

Research Plan
Registered date File name

Research case data specifications
Registered date File name

Research case data
Registered date File name


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