NCT05537168

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
1,364

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Sep 2022

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

September 8, 2022

Completed
5 days until next milestone

First Posted

Study publicly available on registry

September 13, 2022

Completed
4 days until next milestone

Study Start

First participant enrolled

September 17, 2022

Completed
7 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 31, 2023

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

April 30, 2023

Completed
Last Updated

July 27, 2023

Status Verified

July 1, 2023

Enrollment Period

7 months

First QC Date

September 8, 2022

Last Update Submit

July 26, 2023

Conditions

Cardiac Surgical ProceduresPediatricsCardiopulmonary Bypass

Outcome Measures

Primary Outcomes (1)

  • 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

Study Arms (1)

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

Interventions

Pediatric cardiac surgery under cardiopulmonary bypassPROCEDURE

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

Pediatric cardiac surgery

Eligibility Criteria

AgeUp to 16 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17)
Sampling MethodNon-Probability Sample
Study Population

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

You may qualify if:

  • to 16 years
  • cardiac surgery under cardiopulmonary bypass

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Hôpital Universitaire des Enfants Reine Fabiola

Brussels, 1020, Belgium

Location

Related Publications (6)

  • 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.

    PMID: 11782764BACKGROUND

  • 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.

    PMID: 15144988BACKGROUND

  • 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.

    PMID: 32040147BACKGROUND

  • 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.

    PMID: 35331028BACKGROUND

  • 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.

    PMID: 31006419BACKGROUND

  • 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.

    PMID: 39028323DERIVED

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Head, Département of Anesthesiology

Study Record Dates

First Submitted

September 8, 2022

First Posted

September 13, 2022

Study Start

September 17, 2022

Primary Completion

March 31, 2023

Study Completion

April 30, 2023

Last Updated

July 27, 2023

Record last verified: 2023-07

Data Sharing

IPD Sharing
Will not share

Locations

BE(1)