UMIN-CTR Clinical Trial

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
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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
Email	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
Email	yarimizu.kenya@gmail.com

Sponsor
Institute	Yamagata University
Institute
Department

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
Email	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
URL(English)	https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000051274