UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000054629 |
|---|---|
| Receipt number | R000062424 |
| Scientific Title | Predicting the appropriate cuff volume for endotracheal tubes using machine learning |
| Date of disclosure of the study information | 2024/06/13 |
| Last modified on | 2025/12/12 10:37:16 |
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Basic information
Public title
Predicting the appropriate cuff volume for endotracheal tubes using machine learning
Acronym
Predicting the appropriate cuff volume for endotracheal tubes using machine learning
Scientific Title
Predicting the appropriate cuff volume for endotracheal tubes using machine learning
Scientific Title:Acronym
Predicting the appropriate cuff volume for endotracheal tubes using machine learning
Region
| Japan |
Condition
Condition
Patients who undergo surgery under general anesthesia with tracheal intubation
Classification by specialty
| Anesthesiology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Using three models, supervised learning will be conducted to predict the cuff volume needed to achieve optimal cuff pressure during endotracheal intubation. These models will be compared to determine the one with the best accuracy.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
mean squared error
Key secondary outcomes
Base
Study type
Interventional
Study design
Basic design
Single arm
Randomization
Non-randomized
Randomization unit
Blinding
Open -no one is blinded
Control
Uncontrolled
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
1
Purpose of intervention
Diagnosis
Type of intervention
| Device,equipment |
Interventions/Control_1
use of endotracheal tubes during tracheal intubation procedure
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
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Patients aged 20 yr or older, with ASA physical status I-III, scheduled to receive general anesthesia and tracheal intubation
Key exclusion criteria
Patients with pharyngeal pathology, at risk of pulmonary aspiration of gastric contents,
or predicted difficult mask ventilation
Target sample size
250
Research contact person
Name of lead principal investigator
| 1st name | Yuji |
| Middle name | |
| Last name | Soeda |
Organization
Kitakyushu General Hospital
Division name
Department of Anesthesia
Zip code
802-8517
Address
1-1 Higashijono-machi, Kokurakita-ku, Kitakyushu
TEL
093-921-0560
soecchi_y@yahoo.co.jp
Public contact
Name of contact person
| 1st name | Yuji |
| Middle name | |
| Last name | Soeda |
Organization
Kitakyushu General Hospital
Division name
Department of Anesthesia
Zip code
802-8517
Address
1-1 Higashijono-machi, Kokurakita-ku, Kitakyushu
TEL
093-921-0560
Homepage URL
soecchi_y@yahoo.co.jp
Sponsor or person
Institute
Kitakyushu General Hospital
Institute
Department
Personal name
Funding Source
Organization
Kitakyushu General 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
Ethics committee of Kitakyushu General Hospital
Address
1-1 Higashijono-machi, Kokurakita- ku, Kitakyushu
Tel
093-921-0560
y-kuga@kitakyu-hp.or.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
北九州総合病院(福岡県)
Other administrative information
Date of disclosure of the study information
| 2024 | Year | 06 | Month | 13 | Day |
Related information
URL releasing protocol
None
Publication of results
Unpublished
Result
URL related to results and publications
None
Number of participants that the trial has enrolled
250
Results
We analyzed 250 cases and compared four machine learning models to predict optimal cuff volume from preoperative data. Support vector regression showed the lowest MSE, though differences among models were small. Linear regression was statistically valid, with age, tracheal diameter, and sex as significant predictors.
Results date posted
| 2025 | Year | 12 | Month | 12 | Day |
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
The study included 250 adult patients who underwent surgery under general anesthesia with endotracheal intubation between June and October 2024. Preoperative variables collected were age, sex, height, weight, serum albumin level, and tracheal diameter. A 7.5-mm endotracheal tube was used for men and a 7.0-mm tube for women.
Participant flow
A total of 250 cases were enrolled. Among them, 200 cases were randomly allocated for model development, and the remaining 50 cases were used for validation. No participants were excluded.
Adverse events
None
Outcome measures
The primary outcome was the mean squared error of cuff volume prediction for each machine learning model. Secondary outcomes included the coefficient of determination of the linear regression model, statistical significance of predictors, and residual analyses assessing linearity, homoscedasticity, and normality.
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Preinitiation
Date of protocol fixation
| 2024 | Year | 05 | Month | 27 | Day |
Date of IRB
Anticipated trial start date
| 2024 | Year | 06 | Month | 12 | Day |
Last follow-up date
| 2024 | Year | 11 | Month | 30 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Management information
Registered date
| 2024 | Year | 06 | Month | 11 | Day |
Last modified on
| 2025 | Year | 12 | Month | 12 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000062424
