UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000044894 |
|---|---|
| Receipt number | R000051274 |
| Scientific Title | Creation of a deep learning model to predict hypotension after induction of general anesthesia using a biometric screen during awakening - A prospective observational study |
| Date of disclosure of the study information | 2021/07/20 |
| Last modified on | 2022/01/04 09:24:59 |
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Basic information
Public title
Creation of a deep learning model to predict hypotension after induction of general anesthesia using a biometric screen during awakening - A prospective observational study
Acronym
Creation of a deep learning model to predict hypotension after induction of general anesthesia using a biometric screen during awakening - A prospective observational study
Scientific Title
Creation of a deep learning model to predict hypotension after induction of general anesthesia using a biometric screen during awakening - A prospective observational study
Scientific Title:Acronym
Creation of a deep learning model to predict hypotension after induction of general anesthesia using a biometric screen during awakening - A prospective observational study
Region
| Japan |
Condition
Condition
Surgical cases undergoing general anesthesia
Classification by specialty
| Anesthesiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The purpose of this study is to predict perioperative hypotension by using deep learning with image information. If we can intuitively predict hypotension after induction of general anesthesia by analyzing visual information obtained from images with deep learning, we may be able to prevent perioperative complications and also respond quickly.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Accuracy of Deep Learning Model Using Biometric Images Before General Anesthesia Induction for Predicting Blood Pressure Decline after General Anesthesia Induction
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
| 80 | years-old | >= |
Gender
Male and Female
Key inclusion criteria
Surgical cases undergoing general anesthesia in the operating room of Yamagata University Hospital will be included. Among them, cases in which arterial pressure measurement is performed prior to induction of general anesthesia will be considered eligible cases.
Key exclusion criteria
Patients who have been sedated prior to induction of general anesthesia.
Patients undergoing tracheal intubation prior to induction of general anesthesia.
Patients with contraindications to propofol or remimazolam.
Patients who did not give their consent to participate in the study.
Patients with aortic aneurysms or cerebral aneurysms that require management to prevent excessive blood pressure fluctuations.
Target sample size
100
Research contact person
Name of lead principal investigator
| 1st name | kaneyuki |
| Middle name | |
| Last name | kawamae |
Organization
Yamagata University Medical School Hospital
Division name
Department of Anesthesia
Zip code
9909585
Address
2-2-2, Iida-Nishi, Yamagata City
TEL
0236331122
yarimizu.kenya@gmail.com
Public contact
Name of contact person
| 1st name | kenya |
| Middle name | |
| Last name | yarimizu |
Organization
Yamagata University Medical School Hospital
Division name
Department of Anesthesia
Zip code
9909585
Address
2-2-2, Iida-Nishi, Yamagata City
TEL
0236331122
Homepage URL
yarimizu.kenya@gmail.com
Sponsor or person
Institute
Yamagata University
Institute
Department
Personal name
Funding Source
Organization
Department of Anesthesiology, Yamagata University Medical School Hospital
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
Yamagata University Medical School Hospital
Address
2-2-2, Iida-Nishi, Yamagata City
Tel
0236331122
yarimizu.kenya@gmail.com
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 | 20 | 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
Enrolling by invitation
Date of protocol fixation
| 2021 | Year | 07 | Month | 07 | Day |
Date of IRB
| 2021 | Year | 07 | Month | 07 | Day |
Anticipated trial start date
| 2021 | Year | 07 | Month | 21 | Day |
Last follow-up date
| 2023 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
After entering the operating room, non-schematic arterial pressure measurement, schematic arterial pressure measurement, transcutaneous oxygen saturation measurement, and electrocardiogram measurement should be performed, and vitals should be measured continuously until the patient leaves the operating room.
Administer propofol 1-2 mg/kg or remimazolam 12 mg/kg/hr.
Biometric images (arterial pressure, electrocardiogram, transcutaneous oxygen saturation, capnograph) from the anesthesia recorder (ORSYS, PHILIPS) will be extracted every 20-30 seconds until about 30 minutes after securing the arterial pressure line, and saved to USB with password.
The extracted images will be classified according to the following time course: T1: from before induction of general anesthesia to preoxygenation, T2: from the start of preoxygenation to the start of anesthetic administration, T3: from the start of anesthetic administration to muscle relaxation administration, T4: from muscle relaxation administration to tracheal intubation, and T5: from tracheal intubation to about 15 minutes later (about 30 minutes after securing the arterial pressure line by observation). After induction of general anesthesia (T4)
The imaging data will be categorized as positive if hypotension is observed after induction of general anesthesia (T5) and negative if hypotension is not observed. (There are two patterns each from T1 to T5, for a total of 10 patterns.)
The AI model is created using positive and negative data, with 80% of the total data used as train data. Using 20% of the total data as test data, draw ROC curve and calculate AUC.
Management information
Registered date
| 2021 | Year | 07 | Month | 18 | Day |
Last modified on
| 2022 | Year | 01 | Month | 04 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000051274
