UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000044732 |
|---|---|
| Receipt number | R000051088 |
| Scientific Title | Development of a Deep Learning Model Using Spectroscopic Arterial Pressure Waveform to Predict Hypotension after General Anesthesia Induction - A Retrospective Observational Study- |
| Date of disclosure of the study information | 2021/07/02 |
| Last modified on | 2021/07/18 12:34:50 |
* This page includes information on clinical trials registered in UMIN clinical trial registed system.
* We don't aim to advertise certain products or treatments
Basic information
Public title
Development of a Deep Learning Model Using Spectroscopic Arterial Pressure Waveform to Predict Hypotension after General Anesthesia Induction - A Retrospective Observational Study-
Acronym
Development of a Deep Learning Model Using Spectroscopic Arterial Pressure Waveform to Predict Hypotension after General Anesthesia Induction - A Retrospective Observational Study-
Scientific Title
Development of a Deep Learning Model Using Spectroscopic Arterial Pressure Waveform to Predict Hypotension after General Anesthesia Induction - A Retrospective Observational Study-
Scientific Title:Acronym
Development of a Deep Learning Model Using Spectroscopic Arterial Pressure Waveform to Predict Hypotension after General Anesthesia Induction - A Retrospective Observational Study-
Region
| Japan |
Condition
Condition
Cases in which general anesthesia is performed
Classification by specialty
| Anesthesiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
The objective is to predict hypotension after induction of general anesthesia by using deep learning with image information.
Basic objectives2
Others
Basic objectives -Others
In addition to numerical values such as test results, anesthesiologists sometimes use information such as waveforms contained in biometric images to understand the patient's condition. However, there have been few reports on predicting changes in the patient's condition using biometric imaging information. Therefore, we will create a model for predicting hypotension after induction of general anesthesia by using deep learning of biometric images, including the spectroscopic arterial pressure waveform during wakefulness, which has not been explicitly documented.
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Prediction of hypotension after induction of general anesthesia from angiographic arterial pressure waveform before induction of general anesthesia
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 in this study. From those cases, we will select those in which spectroscopic arterial pressure measurement was performed prior to the induction of general anesthesia.
Key exclusion criteria
Patients with general anesthesia administered before induction of general anesthesia Patients with tracheal intubation administered before induction of general anesthesia
Target sample size
200
Research contact person
Name of lead principal investigator
| 1st name | Kaneyuki |
| Middle name | |
| Last name | Kawamae |
Organization
Yamagata University Medical School Hospital
Division name
Anesthesiology
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
Anesthesiology
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
Yamagata university
Organization
Division
Category of Funding Organization
Other
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 | 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
Enrolling by invitation
Date of protocol fixation
| 2021 | Year | 06 | Month | 02 | Day |
Date of IRB
| 2021 | Year | 06 | Month | 02 | Day |
Anticipated trial start date
| 2021 | Year | 07 | Month | 02 | 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
From the biometric images stored in the anesthesia recording system (ORSYS, PHILIPS), we will extract the observation arterial pressure ECG images for about 10 seconds before induction of general anesthesia to a USB with a password.
We classified the image data into two groups: positive for those who showed a decrease in blood pressure after the induction of general anesthesia and negative for those who did not.
Create an AI model using the positive and negative data, using 80% of the total data as train data.
AUC was calculated by drawing ROC curve using 20% of the total data as test data.
After induction of general anesthesia, arterial pressure and electrocardiographic images will be examined in the same way. This is to confirm the prediction accuracy for hypotension occurring in real time, and to evaluate whether AI can predict changes in arterial pressure waveform before and after the induction of general anesthesia.
The following information will be obtained from the medical record.
Preoperative information, Intraoperative information,Preoperative information and the amount of anesthetics will be compared between the two groups.
Based on the obtained data, we will examine the correlation with the decrease in blood pressure after the induction of general anesthesia using deep learning.
Management information
Registered date
| 2021 | Year | 07 | Month | 01 | Day |
Last modified on
| 2021 | Year | 07 | Month | 18 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000051088
| Research Plan | |
|---|---|
| Registered date | File name |
| Research case data specifications | |
|---|---|
| Registered date | File name |
| Research case data | |
|---|---|
| Registered date | File name |
