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Bayesian Networks in Pediatric Cardiac Surgery

NCT05537168 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1364

Last updated 2023-07-27

No results posted yet for this study

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.

Conditions

  • Cardiac Surgical Procedures
  • Pediatrics
  • Cardiopulmonary Bypass

Interventions

PROCEDURE

Pediatric cardiac surgery under cardiopulmonary bypass

All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 operated at our institution

Sponsors & Collaborators

  • Université Libre de Bruxelles

    collaborator OTHER

  • Brugmann University Hospital

    lead OTHER

Eligibility

Max Age
16 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-09-17
Primary Completion
2023-03-31
Completion
2023-04-30

Countries

  • Belgium

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05537168 on ClinicalTrials.gov