Bayesian Networks in Pediatric Cardiac Surgery

Completed

• Conditions: Cardiac Surgical Procedures; Pediatrics; Cardiopulmonary Bypass
• Interventions: Procedure: Pediatric cardiac surgery under cardiopulmonary bypass

• Registration Number: NCT05537168
• Lead Sponsor: Brugmann University Hospital

Brief Summary

Pediatric cardiac surgery with cardiopulmonary bypass is associated with significant morbidity and mortality. Also score systems for risk factors, such as Risk Adjustment for Congenital Heart surgery (RACHS 1) score or the ARISTOTLE score, have been developed, outcome prediction remains difficult. New mathematical methods using deep neural networks associated with Bayesian statistical methods have been developed to give a better understanding of the complex interaction between different risk factors, to identify risk factors and group them in related families. This method has been successfully used to predict mortality in dialysis patient as well as to better describe complex psychiatric syndromes.

The primary hypothesis of this study is that the use of these tools will give a better understanding on the factors affecting outcome after pediatric cardiac surgery.

A network analysis using Gaussian Graphical Models, Mixed Graphical models and Bayesian networks will be used to identify single or groups of risk factors for morbidity and mortality after pediatric cardiac surgery under cardiopulmonary bypass.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2022-09-08
• Study First Submitted QC: 2022-09-08
• Study First Posted: 2022-09-13
• Study Start: 2022-09-17
• Primary Completion: 2023-03-31
• Study Completion: 2023-04-30
• Status Verified: 2023-07
• Last Update Submitted: 2023-07-26
• Last Update Posted: 2023-07-27

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1364

Inclusion Criteria

• 0 to 16 years
• cardiac surgery under cardiopulmonary bypass

Exclusion Criteria

• ASA (American Society of Anesthesiologists) status 5
• Jehovah's Witness

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Pediatric cardiac surgery	Pediatric cardiac surgery under cardiopulmonary bypass	All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 will be included

Primary Outcome Measures

Name	Time	Method
Outcome predictors	28 days	All preoperative, peroperative and postoperative variables will be entered into a deep neural network with Bayesian statistics to identify groups or individual risk factors for postoperative morbidity and mortality

Trial Locations

Locations (1)

Hôpital Universitaire des Enfants Reine Fabiola; 🇧🇪 Brussels, Belgium