Prediction Models for Postoperative Reintubation in Patients With Acute Aortic Dissection
ADreintubate
Multiple Automated Machine-learning Prediction Models for Postoperative Reintubation in Patients With Acute Aortic Dissection
1 other identifier
observational
861
1 country
1
Brief Summary
Reintubation is an adverse postoperative complication in patients with Type A aortic dissection (AAD) that correlates to poor outcomes. This study aims to analyze the risk factors associated with reintubation and to create a fully automated score model to predict the incidence of reintubation. A total of 861 patients diagnosed with AAD and undergoing surgical procedures in a single institution between January 2018 and October 2023 were selected in wuhan Union Hospital. Preoperative and postoperative informmation was used for seeking risk factors and build prediction model for postoperative reintubation. Finally, 5 risk factors wasidentified and a nomogram was established for predicting postoperative reintubation in patients with AAD.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2018
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
October 31, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2023
CompletedFirst Submitted
Initial submission to the registry
May 9, 2024
CompletedFirst Posted
Study publicly available on registry
May 16, 2024
CompletedMay 16, 2024
May 1, 2024
5.8 years
May 9, 2024
May 9, 2024
Conditions
Outcome Measures
Primary Outcomes (1)
postoperative reintubation
reintubation incidence in patients with type A aortic dissection undergoing surgery
3 month
Study Arms (2)
training group
To determine potential predictive factors, the training group was subjected to LASSO regression analysis, which effectively eliminated several irrelevant or multicollinearity independent variables to reduce high-dimensional data.Seven models were initially constructed in the training group: multivariable logistics regression (MLR), decision-tree modeling, random forest, XGBoost, Support Vector Machines, k-nearest neighbors, and LightGBM.
testing group
Testing group was used to examine the performance of the seven prediction models, including discrimination and calibration performance. Finally, the model with best discrimination and calibration performance was used to construct nomogram for predicting reintubation.
Interventions
Patients with type A aortic dissection undergone surgery.
Eligibility Criteria
A total of 892 patients with AAD who underwent open surgery at Wuhan Union Hospital between January 2018 and October 2023 were enrolled using a convenient sampling method.
You may qualify if:
- (1) patients diagnosed with AAD admitted for open surgery; (2) aged 18 years or older.
You may not qualify if:
- (1) patients deceased during or within 24 hours after surgery; (2) patients with preoperative intubation.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430022, China
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
May 9, 2024
First Posted
May 16, 2024
Study Start
January 1, 2018
Primary Completion
October 31, 2023
Study Completion
December 31, 2023
Last Updated
May 16, 2024
Record last verified: 2024-05
Data Sharing
- IPD Sharing
- Will not share