Bayesian Networks in Pediatric Cardiac Surgery
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Pediatric cardiac surgery 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 Measures
Primary Outcome Measures
- 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
Eligibility Criteria
Criteria
Inclusion Criteria:
-
0 to 16 years
-
cardiac surgery under cardiopulmonary bypass
Exclusion Criteria:
-
ASA (American Society of Anesthesiologists) status 5
-
Jehovah's Witness
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Hôpital Universitaire des Enfants Reine Fabiola | Brussels | Belgium | 1020 |
Sponsors and Collaborators
- Brugmann University Hospital
- Université Libre de Bruxelles
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- Briganti G, Linkowski P. Item and domain network structures of the Resilience Scale for Adults in 675 university students. Epidemiol Psychiatr Sci. 2019 Apr 22;29:e33. doi: 10.1017/S2045796019000222.
- Jenkins KJ, Gauvreau K, Newburger JW, Spray TL, Moller JH, Iezzoni LI. Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg. 2002 Jan;123(1):110-8. Review.
- Lacour-Gayet F, Clarke D, Jacobs J, Comas J, Daebritz S, Daenen W, Gaynor W, Hamilton L, Jacobs M, Maruszsewski B, Pozzi M, Spray T, Stellin G, Tchervenkov C, Mavroudis And C; Aristotle Committee. The Aristotle score: a complexity-adjusted method to evaluate surgical results. Eur J Cardiothorac Surg. 2004 Jun;25(6):911-24.
- Siga MM, Ducher M, Florens N, Roth H, Mahloul N, Fouque D, Fauvel JP. Prediction of all-cause mortality in haemodialysis patients using a Bayesian network. Nephrol Dial Transplant. 2020 Aug 1;35(8):1420-1425. doi: 10.1093/ndt/gfz295.
- Till AC, Florquin R, Delhaye M, Kornreich C, Williams DR, Briganti G. A network perspective on abnormal child behavior in primary school students. Psychol Rep. 2022 Mar 24:332941221077907. doi: 10.1177/00332941221077907. [Epub ahead of print]
- PED_CARDIAC_surg_Bayesian