Prediction Models for Postoperative Reintubation in Patients With Acute Aortic Dissection

Completed

• Conditions: Prediction Model; Nomogram; Postoperative Reintubation; Type A Aortic Dissection
• Interventions: Procedure: Type A aortic dissection surgery

• Registration Number: NCT06415630
• Lead Sponsor: Wuhan Union Hospital, China

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2018-01-01
• Primary Completion: 2023-10-31
• Study Completion: 2023-12-31
• Status Verified: 2024-05
• Study First Submitted: 2024-05-09
• Study First Submitted QC: 2024-05-09
• Last Update Submitted: 2024-05-09
• Study First Posted: 2024-05-16
• Last Update Posted: 2024-05-16

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 861

Inclusion Criteria

(1) patients diagnosed with AAD admitted for open surgery; (2) aged 18 years or older.

Exclusion Criteria

(1) patients deceased during or within 24 hours after surgery; (2) patients with preoperative intubation.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
testing group	Type A aortic dissection surgery	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.
training group	Type A aortic dissection surgery	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.

Primary Outcome Measures

Name	Time	Method
postoperative reintubation	3 month	reintubation incidence in patients with type A aortic dissection undergoing surgery

Trial Locations

Locations (1)

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology; 🇨🇳 Wuhan, Hubei, China