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Trial Title:
3D-printed Reconstruction Automated Matching System Predicts Size of Double-lumen Tube: a Prospective Double-blinded Randomized Controlled Trial
NCT ID:
NCT05899270
Condition:
Lung Cancer
Lung Diseases
Tracheal Intubation Morbidity
Throat Injury
Bronchus; Injury
Conditions: Official terms:
Lung Diseases
Wounds and Injuries
Study type:
Interventional
Study phase:
N/A
Overall status:
Not yet recruiting
Study design:
Allocation:
Randomized
Intervention model:
Parallel Assignment
Intervention model description:
Participates recruited from Sichuan Cancer Hospital will randomized to 3D group and
control group in 1:1 ratio via a random number list generated by a computer.
Primary purpose:
Treatment
Masking:
Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
Masking description:
Two investigators performing intubation of DLT will be blinded to the intervention. All
participates and researchers responsible for surgery, bronchoscopy assessment, follow-up,
data management and analysis will also be blinded to the grouping.
Intervention:
Intervention type:
Other
Intervention name:
3D reconstruction automatic matching system
Description:
it is an automatic comparison software for 3D reconstruction based on CT data (3DRACS).
It reconstructs the trachea and bronchus and compares them with the DLT, predicting the
most suitable size and depth of the DLT for lung isolation.
Arm group label:
3D group
Intervention type:
Other
Intervention name:
traditional method for selecting double lumen tube
Description:
In control group, the size of DLT is based on patient's sex and weight and the height is
used to guide the depth of DLT insertion.
Arm group label:
control group
Summary:
Lung isolation techniques are commonly used to facilitate surgical exposure and to
provide single-lung ventilation for patients. We have developed an automatic comparison
software for 3D reconstruction based on CT data (3DRACS). It reconstructs the trachea and
bronchus and compares them with the DLT, predicting the most suitable size and depth of
the DLT for lung isolation.The aim of this study was to compare whether the use of 3DRACS
to select a DLT size compared to conventional empirical selection methods could improve
incidence of DLT intubation success and reduce airway injury.
Detailed description:
Lung isolation techniques are commonly used to facilitate surgical exposure and to
provide single-lung ventilation for patients undergoing various intra-thoracic
procedures. Lung isolation is primarily accomplished with a double-lumen tube (DLT) or
bronchial blocker. One published study showed that residents with limited experience had
a 40% error rate in accurately placing a DLT. The accurate choice of the size of DLT is a
prerequisite for good lung isolation.Currently, There is lack of proper objective
criteria for selecting size of DLT. DLT size selection is estimated empirically using the
patient's height and sex, and studies have shown that the size of DLT according CT images
of the chest is more accurate than experience. we have developed an automatic comparison
software for 3D reconstruction based on CT data (3DRACS). It reconstructs the trachea and
bronchus and compares them with the DLT, predicting the most suitable size and depth of
the DLT for lung isolation. The aim of this study was to compare whether the use of
3DRACS to select a DLT size compared to conventional empirical selection methods could
improve incidence of DLT intubation success and reduce airway injury.
Criteria for eligibility:
Criteria:
Inclusion Criteria:
1. Aged between 18 and 75 years.
2. American Society of Anesthesiologists Physical Status (ASA-PS) I-III.
3. Planned to receive lung resection surgery during lung isolation techniques by using
DLT.
4. Signed informed written consent.
Exclusion Criteria:
The participant experiences any of the following：
1. Spinal malformation,
2. Expected difficult airway
3. Tracheal stenosis
4. Tracheal tumor
5. Bronchial tumor
6. Distorted airway anatomy
7. Tumors of the mouth or neck
Gender:
All
Minimum age:
18 Years
Maximum age:
75 Years
Healthy volunteers:
No
Start date:
November 1, 2023
Completion date:
March 30, 2024
Lead sponsor:
Agency:
Sichuan Cancer Hospital and Research Institute
Agency class:
Other
Collaborator:
Agency:
Science and Technology Department of Sichuan Province
Agency class:
Other
Source:
Sichuan Cancer Hospital and Research Institute
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05899270
