Bayesian Networks in Pediatric Cardiac Surgery

NCT ID: NCT05537168

Last Updated: 2023-07-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

1364 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-09-17

Study Completion Date

2023-04-30

Brief Summary

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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

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Conditions

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Cardiac Surgical Procedures Pediatrics Cardiopulmonary Bypass

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Pediatric cardiac surgery

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

Pediatric cardiac surgery under cardiopulmonary bypass

Intervention Type PROCEDURE

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

Interventions

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Pediatric cardiac surgery under cardiopulmonary bypass

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

Intervention Type PROCEDURE

Eligibility Criteria

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Inclusion Criteria

* 0 to 16 years
* cardiac surgery under cardiopulmonary bypass

Exclusion Criteria

* ASA (American Society of Anesthesiologists) status 5
* Jehovah's Witness
Maximum Eligible Age

16 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Université Libre de Bruxelles

OTHER

Sponsor Role collaborator

Brugmann University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Denis SCHMARTZ

Head, Département of Anesthesiology

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Hôpital Universitaire des Enfants Reine Fabiola

Brussels, , Belgium

Site Status

Countries

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Belgium

References

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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.

Reference Type BACKGROUND
PMID: 11782764 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 15144988 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 32040147 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 35331028 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 31006419 (View on PubMed)

Florquin R, Florquin R, Schmartz D, Dony P, Briganti G. Pediatric cardiac surgery: machine learning models for postoperative complication prediction. J Anesth. 2024 Dec;38(6):747-755. doi: 10.1007/s00540-024-03377-7. Epub 2024 Jul 19.

Reference Type DERIVED
PMID: 39028323 (View on PubMed)

Other Identifiers

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PED_CARDIAC_surg_Bayesian

Identifier Type: -

Identifier Source: org_study_id

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