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Name:
UMIN ID:

Recruitment status No longer recruiting
Unique ID issued by UMIN UMIN000041289
Receipt No. R000047155
Scientific Title Development of an AI-based system for predicting falls and fall-related injuries in hospitals
Date of disclosure of the study information 2020/08/03
Last modified on 2021/02/02

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Basic information
Public title Development of an AI-based system for predicting falls and fall-related injuries in hospitals
Acronym Development of an AI-based system for predicting falls and fall-related injuries in hospitals
Scientific Title Development of an AI-based system for predicting falls and fall-related injuries in hospitals
Scientific Title:Acronym Development of an AI-based system for predicting falls and fall-related injuries in hospitals
Region
Japan

Condition
Condition None (All patients admitted during the study period)
Classification by specialty
Not applicable
Classification by malignancy Others
Genomic information NO

Objectives
Narrative objectives1 Development and validation of a system for predicting falls and fall-related injuries in hospital with large data sets.
Basic objectives2 Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase

Assessment
Primary outcomes Falls and fall-related injuries obtained from incident reports
Key secondary outcomes Scores of fall risk assessment tool

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 The patients who had been hospitalized in Fujita Health University Hospital from April 2012 to March 2020 and those who had been hospitalized in Fujita Health University Nanakuri Memorial Hospital from April 2016 to March 2020.
Key exclusion criteria A person who has asked to be excluded from the study to the researcher listed in the disclosure document on the website.
Target sample size 300000

Research contact person
Name of lead principal investigator
1st name Yohei
Middle name
Last name Otaka
Organization Fujita Health University
Division name Department of Rehabilitation Medicine I, School of Medicine
Zip code 470-1192
Address 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi
TEL 0562-93-2167
Email yootaka@fujita-hu.ac.jp

Public contact
Name of contact person
1st name Shin
Middle name
Last name Kitamura
Organization Fujita Health University
Division name Faculty of Rehabilitation, School of Health Sciences
Zip code 470-1192
Address 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi
TEL 0562-93-9000
Homepage URL
Email shin.kitamura@fujita-hu.ac.jp

Sponsor
Institute Fujita Health University
Institute
Department

Funding Source
Organization None
Organization
Division
Category of Funding Organization Self funding
Nationality of Funding Organization

Other related organizations
Co-sponsor FRONTEO, Inc.
Name of secondary funder(s)

IRB Contact (For public release)
Organization Fujita Health University
Address 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi
Tel 0562-93-2865
Email f-irb@fujita-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 藤田医科大学(愛知県)

Other administrative information
Date of disclosure of the study information
2020 Year 08 Month 03 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 No longer recruiting
Date of protocol fixation
2020 Year 05 Month 25 Day
Date of IRB
2020 Year 07 Month 27 Day
Anticipated trial start date
2020 Year 08 Month 03 Day
Last follow-up date
2025 Year 03 Month 31 Day
Date of closure to data entry
Date trial data considered complete
Date analysis concluded

Other
Other related information We will retrospectively analyze the medical records of patients who had been hospitalized in the two hospitals. The data will be divided into two parts. Using one of the data sets, we will develop an AI-based prediction system for the risks for falls and fall-related injuries for each patient. Then, we will validate the system against the actual incidents and the scores of the fall risk assessed tools using the other data sets.

Management information
Registered date
2020 Year 08 Month 03 Day
Last modified on
2021 Year 02 Month 02 Day


Link to view the page
URL(English) https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047155

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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