An observational study in Cardiac Surgical Procedures, Pediatrics and Cardiopulmonary Bypass, sponsored by Brugmann University Hospital. Completed at 1 site in Belgium. Open to participants aged Up to 16 Years. Per ClinicalTrials.gov, last updated 2023-07-27.
Sponsored by Brugmann University Hospital · Observational
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.
Brugmann University Hospital is the lead sponsor of 116 studies on the registry; 17 are open to participants now.
Counted across the registry records on this site, refreshed daily.
All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 at our institution
Exclusion Criteria:
All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 will be included
Procedure: Pediatric cardiac surgery under cardiopulmonary bypass
All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 operated at our institution
Outcome predictors
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
Time frame: 28 days
Plan to share: No
This study is completed, as verified in Jul 2023. You cannot join it, but the record below documents what was studied.
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Brugmann University Hospital