Ignite Creation Date:
2025-12-24 @ 6:28 PM
Ignite Modification Date:
2025-12-30 @ 4:14 PM
Study NCT ID:
NCT05537168
Status:
COMPLETED
Last Update Posted:
2023-07-27
First Post:
2022-09-08
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Bayesian Networks in Pediatric Cardiac Surgery
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 1364}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-09-17', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-07', 'completionDateStruct': {'date': '2023-04-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-07-26', 'studyFirstSubmitDate': '2022-09-08', 'studyFirstSubmitQcDate': '2022-09-08', 'lastUpdatePostDateStruct': {'date': '2023-07-27', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-09-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-03-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Outcome predictors', 'timeFrame': '28 days', 'description': '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'}]}, 'oversightModule': {'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Cardiac Surgical Procedures', 'Pediatrics', 'Cardiopulmonary Bypass']}, 'referencesModule': {'references': [{'pmid': '11782764', 'type': 'BACKGROUND', '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.'}, {'pmid': '15144988', 'type': 'BACKGROUND', '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.'}, {'pmid': '32040147', 'type': 'BACKGROUND', '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.'}, {'pmid': '35331028', 'type': 'BACKGROUND', '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.'}, {'pmid': '31006419', 'type': 'BACKGROUND', 'citation': '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': '39028323', 'type': 'DERIVED', 'citation': '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.'}]}, 'descriptionModule': {'briefSummary': '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.\n\nThe 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.\n\nA 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '16 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 at our institution', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* 0 to 16 years\n* cardiac surgery under cardiopulmonary bypass\n\nExclusion Criteria:\n\n* ASA (American Society of Anesthesiologists) status 5\n* Jehovah's Witness"}, 'identificationModule': {'nctId': 'NCT05537168', 'briefTitle': 'Bayesian Networks in Pediatric Cardiac Surgery', 'organization': {'class': 'OTHER', 'fullName': 'Brugmann University Hospital'}, 'officialTitle': 'Use of Deep Neural Networks and Bayesian Analysis to Identify Risk Factors for Poor Outcome After Pediatric Cardiac Surgery', 'orgStudyIdInfo': {'id': 'PED_CARDIAC_surg_Bayesian'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Pediatric cardiac surgery', 'description': 'All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 will be included', 'interventionNames': ['Procedure: Pediatric cardiac surgery under cardiopulmonary bypass']}], 'interventions': [{'name': 'Pediatric cardiac surgery under cardiopulmonary bypass', 'type': 'PROCEDURE', 'description': 'All patients with pediatric cardiac surgery under cardiopulmonary bypass between 2008 and 2018 operated at our institution', 'armGroupLabels': ['Pediatric cardiac surgery']}]}, 'contactsLocationsModule': {'locations': [{'zip': '1020', 'city': 'Brussels', 'country': 'Belgium', 'facility': 'Hôpital Universitaire des Enfants Reine Fabiola', 'geoPoint': {'lat': 50.85045, 'lon': 4.34878}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Brugmann University Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'Université Libre de Bruxelles', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Head, Département of Anesthesiology', 'investigatorFullName': 'Denis SCHMARTZ', 'investigatorAffiliation': 'Brugmann University Hospital'}}}}