Bayesian Networks in Pediatric Cardiac Surgery

Cardiopulmonary Bypass Clinical Trial

Official title:

Use of Deep Neural Networks and Bayesian Analysis to Identify Risk Factors for Poor Outcome After Pediatric Cardiac Surgery

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05537168
• Other study ID #: PED_CARDIAC_surg_Bayesian
• Status: Completed
• Start date: September 17, 2022
• Est. completion date: April 30, 2023

Study information

• Verified date: July 2023
• Source: Brugmann University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1364
• Est. completion date: April 30, 2023
• Est. primary completion date: March 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 16 Years

Eligibility

Inclusion Criteria:

• 0 to 16 years
• cardiac surgery under cardiopulmonary bypass

Exclusion Criteria:

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

Related Conditions & MeSH terms

• Cardiac Surgical Procedures
• Cardiopulmonary Bypass
• Pediatrics

Intervention

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

Locations

Country	Name	City	State
Belgium	Hôpital Universitaire des Enfants Reine Fabiola	Brussels

Sponsors (2)

Lead Sponsor	Collaborator
Brugmann University Hospital	Université Libre de Bruxelles

Country where clinical trial is conducted

Belgium, 

References & Publications (5)

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. — View Citation

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. doi: 10.1067/mtc.2002.119064. — View Citation

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. doi: 10.1016/j.ejcts.2004.03.027. — View Citation

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. — View Citation

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. 2023 Aug;126(4):1933-1953. doi: 10.1177/00332941221077907. Epub 2022 Mar 24. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	28 days