Cardiac Surgical ProceduresNCT05537168
Bayesian Networks in Pediatric Cardiac Surgery
An observational study of Cardiac Surgical Procedures and Pediatrics, run by Brugmann University Hospital at 1 site in Belgium.
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. Read more about this study
- Who is it for?
- Ages Up to 16 years
- Conditions the study is for
- Cardiac Surgical Procedures, Pediatrics, Cardiopulmonary Bypass
- What is being tested?
- Pediatric cardiac surgery under cardiopulmonary bypass
- Type of treatment
- Surgery or procedure
- How many people
- 1,364 participants
- Where
- 1 siteBelgium
- Study dates
- 17 Sep 2022to 30 Apr 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 16 years
- All sexes
- No healthy volunteers
- Child
Study population: All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 at our institution
Who may be able to join
- 0 to 16 years
- cardiac surgery under cardiopulmonary bypass
Who cannot join
- ASA (American Society of Anesthesiologists) status 5
- Jehovah's Witness
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Use of Deep Neural Networks and Bayesian Analysis to Identify Risk Factors for Poor Outcome After Pediatric Cardiac Surgery
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.
- Being tested
- Pediatric cardiac surgery under cardiopulmonary bypass
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Pediatric cardiac surgery under cardiopulmonary bypassProcedure or surgery
All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 operated at our institution
| Study group | Role | What they receive |
|---|---|---|
| Pediatric cardiac surgery | All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 will be included Receives: Procedure: Pediatric cardiac surgery under cardiopulmonary bypass |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 28 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Sep 2022
- Study start17 Sep 2022
- Primary completion31 Mar 2023
- Study completion30 Apr 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Cardiac Surgical ProceduresStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,364 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brugmann University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Université Libre de Bruxelles
- Responsible party
- Principal investigator, Denis SCHMARTZ, Brugmann University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05537168
- Sponsor's protocol ID
- PED_CARDIAC_surg_Bayesian
- Status last verified
- Jul 2023
- First submitted
- 8 Sep 2022
- Last updated
- 27 Jul 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 11782764Lacour-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.
PubMed 15144988Siga 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.
PubMed 32040147Till 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.
PubMed 35331028Briganti 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.
PubMed 31006419Florquin 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.
PubMed 39028323
Study at a glance
From the registry record, last updated by the sponsor on 27 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Use of Deep Neural Networks and Bayesian Analysis to Identify Risk Factors for Poor Outcome After Pediatric Cardiac Surgery
- ClinicalTrials.gov ID
- NCT05537168
- Acronym / sponsor ID
- PED_CARDIAC_surg_Bayesian
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,364 participants (actual)
- Lead sponsor
- Brugmann University HospitalUniversity, hospital or other
- Collaborators
- Université Libre de Bruxelles
- Study start
- 17 Sep 2022 (actual)
- Primary completion
- 31 Mar 2023 (actual)
- Study completion
- 30 Apr 2023 (actual)
- First posted
- 13 Sep 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pediatric cardiac surgery | – | Procedure: Pediatric cardiac surgery under cardiopulmonary bypassAll patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 will be included |
Who may qualify
- Ages
- Up to 16 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 at our institution
Key inclusion criteria (2 listed)
- 0 to 16 years
- cardiac surgery under cardiopulmonary bypass
Key exclusion criteria (2 listed)
- ASA (American Society of Anesthesiologists) status 5
- Jehovah's Witness
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Pediatric cardiac surgery under cardiopulmonary bypassAll patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 operated at our institution | Procedure or surgery | Pediatric cardiac surgery |
Primary outcome
Outcome predictors
Time frame: 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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jul 2023.
- Background: PubMed 11782764Jenkins 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.
- Background: PubMed 15144988Lacour-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.
- Background: PubMed 32040147Siga 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.
- Background: PubMed 35331028Till 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.
- Background: PubMed 31006419Briganti 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.
- Derived from this study: PubMed 39028323Florquin 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.
- Individual participant dataSharing planned: No
Related trials for Cardiac Surgical Procedures
Not yet recruitingPhase 1/2
A Study Of Surgical Repair Of Discontinuous Branching Pulmonary Arteries
Absent Pulmonary Artery
Active, not recruitingObservational study
Associations of Postoperative Hemoglobin With AKI-AKD Trajectories
Patients Undergoing Valvular Heart Surgery
Not yet recruiting
Starry-Sky Projection and Maternal Voice for Sleep After Pediatric Cardiac Surgery
Congenital Heart Disease (CHD), Postoperative Sleep
Recruiting
I-ASV in Cardiac Surgery
Ventilator Lung
RecruitingPhase 1/2
Butyric Acid Supplementation for Gut Improvement After Cardiac Surgery in Kids
Congenital Heart Disease (CHD)
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Cardiac Surgical Procedures and Pediatrics, run by Brugmann University Hospital at 1 site in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Jul 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Outcome predictors (time frame: 28 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05537168, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jul 2023.