UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000044725 |
|---|---|
| Receipt number | R000051084 |
| Scientific Title | Using machine learning to create a system for predicting blood pressure decline from thermographic images during anesthesia induction |
| Date of disclosure of the study information | 2021/07/02 |
| Last modified on | 2021/07/01 14:12:22 |
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Basic information
Public title
Using machine learning to create a system for predicting blood pressure decline from thermographic images during anesthesia induction
Acronym
Using machine learning to create a system for predicting blood pressure decline from thermographic images during anesthesia induction
Scientific Title
Using machine learning to create a system for predicting blood pressure decline from thermographic images during anesthesia induction
Scientific Title:Acronym
Using machine learning to create a system for predicting blood pressure decline from thermographic images during anesthesia induction
Region
| Japan |
Condition
Condition
Elective surgery patients
Classification by specialty
| Anesthesiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The purpose of this study is to create a classifier (system) that can discriminate cases where blood pressure drops during induction of anesthesia from thermographic images at the time of entering the operating room using an image analysis system and machine learning.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Create a classifier (system) that can discriminate between drops in blood pressure, create a receiver operating characteristic curve, and determine sensitivity and specificity.
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
Patients undergoing elective surgery at Yamagata University Hospital
Key exclusion criteria
Cardiac surgery
Patients with predicted peripheral circulatory blood flow disturbances
Target sample size
500
Research contact person
Name of lead principal investigator
| 1st name | Misato |
| Middle name | |
| Last name | Kurota |
Organization
Yamagata University Faculty of Medicine
Division name
Department of Anesthesiology
Zip code
9909585
Address
2-2-2 Iida-nishi, Yamagata city, Yamagata, Japan
TEL
023-628-5400
patsykurota224@gmail.com
Public contact
Name of contact person
| 1st name | Misato |
| Middle name | |
| Last name | Kurota |
Organization
Yamagata University Faculty of Medicine
Division name
Department of Anesthesiology
Zip code
9909585
Address
2-2-2 Iida-nishi, Yamagata city, Yamagata, Japan
TEL
023-628-5400
Homepage URL
patsykurota224@gmail.com
Sponsor or person
Institute
Yamagata University Faculty of Medicine
Institute
Department
Personal name
Funding Source
Organization
Department of Anesthesiology, Yamagata University Faculty of Medicine
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
The Ethical Review Committee of Yamagata University Faculty of Medicine
Address
2-2-2, Iida-nishi, yamagata-shi, Yamagata Japan
Tel
0236285015
ikekenkyu@jm.kj.yamagata-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
| 2021 | Year | 07 | Month | 02 | 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
| 2021 | Year | 06 | Month | 01 | Day |
Date of IRB
| 2021 | Year | 06 | Month | 28 | Day |
Anticipated trial start date
| 2021 | Year | 07 | Month | 05 | Day |
Last follow-up date
| 2025 | Year | 12 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
An anesthesiologist will administer anesthesia as usual and record the degree of hypotension. Before entering the operating room, a frontal photograph of the patient and thermographic images of both hands are taken. Machine learning is performed and a classifier is created as supervised learning. After that, a receiver operating characteristic curve is created, and sensitivity and specificity are obtained.
Management information
Registered date
| 2021 | Year | 07 | Month | 01 | Day |
Last modified on
| 2021 | Year | 07 | Month | 01 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000051084
