Congenital Heart Disease in ChildrenFPMODS2NCT05284500
Predictive Pre and Peroperative Factors for MODS-2 in Pediatric Cardiac Surgery
An observational study of Congenital Heart Disease in Children and Cardiac Surgical Procedures, run by Brugmann University Hospital at 1 site in Belgium.
Trial summary
Pediatric cardiac surgery has a relatively high morbi-mortality. Despite great advances in surgical techniques, today the mortality rate is about 3% and morbidity is about 30-40%. Read more about this study
- Who is it for?
- Ages Up to 16 years
- Conditions the study is for
- Congenital Heart Disease in Children, Cardiac Surgical Procedures, Outcome Assessment
- What is being tested?
- Pediatric cardiac surgery with cardiopulmonary bypass
- Type of treatment
- Surgery or procedure
- How many people
- 152 participants
- Where
- 1 siteBelgium
- Study dates
- 18 Mar 2022to 8 Jul 2022
- 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 undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution
Who may be able to join
- patients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution
- accepting blood transfusions
- ASA score 1-4
Who cannot join
- Jehova's witness
- ASA 5 status
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Predictive Pre and Peroperative Factors for Multiple Organ Dysfunction Score 2 (MODS-2) in Pediatric Cardiac Surgery
Pediatric cardiac surgery has a relatively high morbi-mortality. Despite great advances in surgical techniques, today the mortality rate is about 3% and morbidity is about 30-40%. Outcome has been related to demographic factors, like age; peroperative factors, like duration of cardiopulmonary bypass as well as postoperative factors like positive fluid balance. Willems et al defined a new score (MODS2), an outcome score combining either patient's death or a high postoperative morbidity. This morbidity is defined as minimum of 2 organ failures: either respiratory insufficiency, prolonged use of inotropic agents or renal insufficiency. The aim of this study is to identify pre and peroperative factors which are predictors of MODS2. Patients operated between 2008 and 2018 for pediatric cardiac surgery with cardiopulmonary bypass will be included. Variables extracted from our database will be: sex, ASA score, cyanotic cardiac pathology, redo surgery, RACH1 score, use of antifibrinolytic agents, aortic cross-clamping, deep hypothermic circulatory arrest, selective cerebral perfusion, red cell transfusion in the operating room, administration of fresh frozen plasma in the operating room, age, preoperative weight, weight difference between preop weight and weight at postop day 2, emergency surgery, duration of aortic cross clamping, duration of selective cerebral perfusion, duration of cardiopulmonary bypass, duration off deep hypothermic circulatory arrest, duration of surgery, minimal core temperature, cardiopulmonary priming volume, calculated hemodilution, use of red blood cells in the cardiopulmonary bypass priming, preoperative hemoglobin, preoperative hematocrit, preoperative platelet count, preop international normalized ratio, preop fibrinogen, preop creatinin, toal fluid balance, blood loss during surgery. A statistical analysis (see detailed description) will be used to establish a prediction model for MODS2. The variables describing best the MODS2 outcome will be retained.
Read the detailed description
Detailed statistical analysis:
5 multiple imputations via the mice R package will be performed, which is the most appropriate method for a risk model. We then take the mean of the imputed datasets in order to start the data mining models on one single dataset. All of the variables will be entered in the model. Before to run the data mining models, we will perform three transformations on the continuous variables: 1) standardization; 2) best normalisation via the bestNormalize R package and 3) taking the variable from its power 2 to its power 10. The dataset will be split into a training set (75% of the cases) and test set (25% of the cases). For all tested data mining models, we will use a 10-fold cross-validation method on the training set before applying the retained model on the test set. The following data mining models will be tested: 1) a regression tree, 2) a logistic regression (GLM), 3) a Neural Network (NN); 4) a Support Vector Machine (SVM); 5) a Random Forest (RF); 6) a Multivariate Adaptive Regression Spline model (MARS) and 7) a Non-Linear Support Vector Machine (SVM NL). The models will be drawn with the caret R package. The confusion matrix, reporting the sensibilities, specificities, accuracies will be drawn on the test set based on the models developed on the training set, and the calibration plot will be drawn for three model competitors. The R software (R Core Team, 2019), version 3.6.1. will be used to produce the results.
- Conditions
- Congenital Heart Disease in Children, Cardiac Surgical Procedures, Outcome Assessment, Organ Dysfunction Scores
- Being tested
- Pediatric cardiac surgery with cardiopulmonary bypass
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Pediatric cardiac surgery with cardiopulmonary bypassProcedure or surgery
All patients undergoing pediatric cardiac surgery with cardiopulmonary bypass will be extracted from our database
| Study group | Role | What they receive |
|---|---|---|
| Pediatric cardiac surgery patients | Patients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution. Receives: Procedure: Pediatric cardiac surgery with cardiopulmonary bypass |
What is being measured
The primary outcome is the main question the study is designed to answer.
MODS2
Occurence of MODS2 (as percentage) will be extracted. A predictive model will be established from the observed preoperative and peroperative variables. A statistical analysis (see detailed description) will be used to establish a prediction model for MODS2. The variables describing best the MODS2 outcome will be retained.
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 posted17 Mar 2022
- Study start18 Mar 2022
- Primary completion30 Jun 2022
- Study completion8 Jul 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (13)Papers the sponsor has linked to this study.
- Other trials for Congenital Heart Disease in ChildrenStudies 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
- 152 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brugmann University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Denis SCHMARTZ, Brugmann University Hospital
- Investigators
- Denis Schmartz, MD, Study director, CHU Brugmann
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05284500
- Sponsor's protocol ID
- FPMODS2
- Acronym
- FPMODS2
- Status last verified
- Jul 2022
- First submitted
- 9 Mar 2022
- Last updated
- 20 Jul 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Ambler G, Omar RZ, Royston P. A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome. Stat Methods Med Res. 2007 Jun;16(3):277-98. doi: 10.1177/0962280206074466.
PubMed 17621472Hickey PA, Pasquali SK, Gaynor JW, He X, Hill KD, Connor JA, Gauvreau K, Jacobs ML, Jacobs JP, Hirsch-Romano JC. Critical Care Nursing's Impact on Pediatric Patient Outcomes. Ann Thorac Surg. 2016 Oct;102(4):1375-80. doi: 10.1016/j.athoracsur.2016.03.019. Epub 2016 May 10.
PubMed 27173065Hill KD, Baldwin HS, Bichel DP, Ellis AM, Graham EM, Hornik CP, Jacobs JP, Jaquiss RDB, Jacobs ML, Kannankeril PJ, Li JS, Torok R, Turek JW, O'Brien SM. Overcoming underpowering: Trial simulations and a global rank end point to optimize clinical trials in children with heart disease. Am Heart J. 2020 Aug;226:188-197. doi: 10.1016/j.ahj.2020.05.011. Epub 2020 May 20.
PubMed 32599259Jenkins 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 11782764Agarwal HS, Wolfram KB, Saville BR, Donahue BS, Bichell DP. Postoperative complications and association with outcomes in pediatric cardiac surgery. J Thorac Cardiovasc Surg. 2014 Aug;148(2):609-16.e1. doi: 10.1016/j.jtcvs.2013.10.031. Epub 2013 Nov 23.
PubMed 24280709Wilder NS, Yu S, Donohue JE, Goldberg CS, Blatt NB. Fluid Overload Is Associated With Late Poor Outcomes in Neonates Following Cardiac Surgery. Pediatr Crit Care Med. 2016 May;17(5):420-7. doi: 10.1097/PCC.0000000000000715.
PubMed 27028790Willems A, Van Lerberghe C, Gonsette K, De Ville A, Melot C, Hardy JF, Van der Linden P. The indication for perioperative red blood cell transfusions is a predictive risk factor for severe postoperative morbidity and mortality in children undergoing cardiac surgery. Eur J Cardiothorac Surg. 2014 Jun;45(6):1050-7. doi: 10.1093/ejcts/ezt548. Epub 2014 Jan 14.
PubMed 24431174Baltsavias I, Faraoni D, Willems A, El Kenz H, Melot C, De Hert S, Van der Linden P. Blood storage duration and morbidity and mortality in children undergoing cardiac surgery. A retrospective analysis. Eur J Anaesthesiol. 2014 Jun;31(6):310-6. doi: 10.1097/EJA.0000000000000024.
PubMed 24492183Long JB, Engorn BM, Hill KD, Feng L, Chiswell K, Jacobs ML, Jacobs JP, Goswami D. Postoperative Hematocrit and Adverse Outcomes in Pediatric Cardiac Surgery Patients: A Cross-Sectional Study From the Society of Thoracic Surgeons and Congenital Cardiac Anesthesia Society Database Collaboration. Anesth Analg. 2021 Nov 1;133(5):1077-1088. doi: 10.1213/ANE.0000000000005416.
PubMed 33721876Marshall JC, Cook DJ, Christou NV, Bernard GR, Sprung CL, Sibbald WJ. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med. 1995 Oct;23(10):1638-52. doi: 10.1097/00003246-199510000-00007.
PubMed 7587228Slater A, Shann F, Pearson G; Paediatric Index of Mortality (PIM) Study Group. PIM2: a revised version of the Paediatric Index of Mortality. Intensive Care Med. 2003 Feb;29(2):278-85. doi: 10.1007/s00134-002-1601-2. Epub 2003 Jan 23.
PubMed 12541154Slater A, Shann F; ANZICS Paediatric Study Group. The suitability of the Pediatric Index of Mortality (PIM), PIM2, the Pediatric Risk of Mortality (PRISM), and PRISM III for monitoring the quality of pediatric intensive care in Australia and New Zealand. Pediatr Crit Care Med. 2004 Sep;5(5):447-54. doi: 10.1097/01.PCC.0000138557.31831.65.
PubMed 15329160Despotis G, Avidan M, Eby C. Prediction and management of bleeding in cardiac surgery. J Thromb Haemost. 2009 Jul;7 Suppl 1:111-7. doi: 10.1111/j.1538-7836.2009.03412.x.
PubMed 19630781
Study at a glance
From the registry record, last updated by the sponsor on 20 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predictive Pre and Peroperative Factors for Multiple Organ Dysfunction Score 2 (MODS-2) in Pediatric Cardiac Surgery
- ClinicalTrials.gov ID
- NCT05284500
- Acronym / sponsor ID
- FPMODS2 / FPMODS2
- Conditions
- Congenital Heart Disease in Children, Cardiac Surgical Procedures, Outcome Assessment, Organ Dysfunction Scores
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 152 participants (actual)
- Lead sponsor
- Brugmann University HospitalUniversity, hospital or other
- Study start
- 18 Mar 2022 (actual)
- Primary completion
- 30 Jun 2022 (actual)
- Study completion
- 8 Jul 2022 (actual)
- First posted
- 17 Mar 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 has a relatively high morbi-mortality. Despite great advances in surgical techniques, today the mortality rate is about 3% and morbidity is about 30-40%. Outcome has been related to demographic factors, like age; peroperative factors, like duration of cardiopulmonary bypass as well as postoperative factors like positive fluid balance. Willems et al defined a new score (MODS2), an outcome score combining either patient's death or a high postoperative morbidity. This morbidity is defined as minimum of 2 organ failures: either respiratory insufficiency, prolonged use of inotropic agents or renal insufficiency. The aim of this study is to identify pre and peroperative factors which are predictors of MODS2. Patients operated between 2008 and 2018 for pediatric cardiac surgery with cardiopulmonary bypass will be included. Variables extracted from our database will be: sex, ASA score, cyanotic cardiac pathology, redo surgery, RACH1 score, use of antifibrinolytic agents, aortic cross-clamping, deep hypothermic circulatory arrest, selective cerebral perfusion, red cell transfusion in the operating room, administration of fresh frozen plasma in the operating room, age, preoperative weight, weight difference between preop weight and weight at postop day 2, emergency surgery, duration of aortic cross clamping, duration of selective cerebral perfusion, duration of cardiopulmonary bypass, duration off deep hypothermic circulatory arrest, duration of surgery, minimal core temperature, cardiopulmonary priming volume, calculated hemodilution, use of red blood cells in the cardiopulmonary bypass priming, preoperative hemoglobin, preoperative hematocrit, preoperative platelet count, preop international normalized ratio, preop fibrinogen, preop creatinin, toal fluid balance, blood loss during surgery. A statistical analysis (see detailed description) will be used to establish a prediction model for MODS2. The variables describing best the MODS2 outcome will be retained.
Detailed description
Detailed statistical analysis:
5 multiple imputations via the mice R package will be performed, which is the most appropriate method for a risk model. We then take the mean of the imputed datasets in order to start the data mining models on one single dataset. All of the variables will be entered in the model. Before to run the data mining models, we will perform three transformations on the continuous variables: 1) standardization; 2) best normalisation via the bestNormalize R package and 3) taking the variable from its power 2 to its power 10. The dataset will be split into a training set (75% of the cases) and test set (25% of the cases). For all tested data mining models, we will use a 10-fold cross-validation method on the training set before applying the retained model on the test set. The following data mining models will be tested: 1) a regression tree, 2) a logistic regression (GLM), 3) a Neural Network (NN); 4) a Support Vector Machine (SVM); 5) a Random Forest (RF); 6) a Multivariate Adaptive Regression Spline model (MARS) and 7) a Non-Linear Support Vector Machine (SVM NL). The models will be drawn with the caret R package. The confusion matrix, reporting the sensibilities, specificities, accuracies will be drawn on the test set based on the models developed on the training set, and the calibration plot will be drawn for three model competitors. The R software (R Core Team, 2019), version 3.6.1. will be used to produce the results.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pediatric cardiac surgery patients | – | Procedure: Pediatric cardiac surgery with cardiopulmonary bypassPatients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution. |
Who may qualify
- Ages
- Up to 16 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- All patients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution
Key inclusion criteria (3 listed)
- patients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution
- accepting blood transfusions
- ASA score 1-4
Key exclusion criteria (2 listed)
- Jehova's witness
- ASA 5 status
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 with cardiopulmonary bypassAll patients undergoing pediatric cardiac surgery with cardiopulmonary bypass will be extracted from our database | Procedure or surgery | Pediatric cardiac surgery patients |
Primary outcome
MODS2
Time frame: 28 days
Occurence of MODS2 (as percentage) will be extracted. A predictive model will be established from the observed preoperative and peroperative variables. A statistical analysis (see detailed description) will be used to establish a prediction model for MODS2. The variables describing best the MODS2 outcome will be retained.
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 20 Jul 2022.
- Background: PubMed 17621472Ambler G, Omar RZ, Royston P. A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome. Stat Methods Med Res. 2007 Jun;16(3):277-98. doi: 10.1177/0962280206074466.
- Background: PubMed 27173065Hickey PA, Pasquali SK, Gaynor JW, He X, Hill KD, Connor JA, Gauvreau K, Jacobs ML, Jacobs JP, Hirsch-Romano JC. Critical Care Nursing's Impact on Pediatric Patient Outcomes. Ann Thorac Surg. 2016 Oct;102(4):1375-80. doi: 10.1016/j.athoracsur.2016.03.019. Epub 2016 May 10.
- Background: PubMed 32599259Hill KD, Baldwin HS, Bichel DP, Ellis AM, Graham EM, Hornik CP, Jacobs JP, Jaquiss RDB, Jacobs ML, Kannankeril PJ, Li JS, Torok R, Turek JW, O'Brien SM. Overcoming underpowering: Trial simulations and a global rank end point to optimize clinical trials in children with heart disease. Am Heart J. 2020 Aug;226:188-197. doi: 10.1016/j.ahj.2020.05.011. Epub 2020 May 20.
- 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 24280709Agarwal HS, Wolfram KB, Saville BR, Donahue BS, Bichell DP. Postoperative complications and association with outcomes in pediatric cardiac surgery. J Thorac Cardiovasc Surg. 2014 Aug;148(2):609-16.e1. doi: 10.1016/j.jtcvs.2013.10.031. Epub 2013 Nov 23.
- Background: PubMed 27028790Wilder NS, Yu S, Donohue JE, Goldberg CS, Blatt NB. Fluid Overload Is Associated With Late Poor Outcomes in Neonates Following Cardiac Surgery. Pediatr Crit Care Med. 2016 May;17(5):420-7. doi: 10.1097/PCC.0000000000000715.
- Background: PubMed 24431174Willems A, Van Lerberghe C, Gonsette K, De Ville A, Melot C, Hardy JF, Van der Linden P. The indication for perioperative red blood cell transfusions is a predictive risk factor for severe postoperative morbidity and mortality in children undergoing cardiac surgery. Eur J Cardiothorac Surg. 2014 Jun;45(6):1050-7. doi: 10.1093/ejcts/ezt548. Epub 2014 Jan 14.
- Background: PubMed 24492183Baltsavias I, Faraoni D, Willems A, El Kenz H, Melot C, De Hert S, Van der Linden P. Blood storage duration and morbidity and mortality in children undergoing cardiac surgery. A retrospective analysis. Eur J Anaesthesiol. 2014 Jun;31(6):310-6. doi: 10.1097/EJA.0000000000000024.
- Background: PubMed 33721876Long JB, Engorn BM, Hill KD, Feng L, Chiswell K, Jacobs ML, Jacobs JP, Goswami D. Postoperative Hematocrit and Adverse Outcomes in Pediatric Cardiac Surgery Patients: A Cross-Sectional Study From the Society of Thoracic Surgeons and Congenital Cardiac Anesthesia Society Database Collaboration. Anesth Analg. 2021 Nov 1;133(5):1077-1088. doi: 10.1213/ANE.0000000000005416.
- Background: PubMed 7587228Marshall JC, Cook DJ, Christou NV, Bernard GR, Sprung CL, Sibbald WJ. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med. 1995 Oct;23(10):1638-52. doi: 10.1097/00003246-199510000-00007.
- Background: PubMed 12541154Slater A, Shann F, Pearson G; Paediatric Index of Mortality (PIM) Study Group. PIM2: a revised version of the Paediatric Index of Mortality. Intensive Care Med. 2003 Feb;29(2):278-85. doi: 10.1007/s00134-002-1601-2. Epub 2003 Jan 23.
- Background: PubMed 15329160Slater A, Shann F; ANZICS Paediatric Study Group. The suitability of the Pediatric Index of Mortality (PIM), PIM2, the Pediatric Risk of Mortality (PRISM), and PRISM III for monitoring the quality of pediatric intensive care in Australia and New Zealand. Pediatr Crit Care Med. 2004 Sep;5(5):447-54. doi: 10.1097/01.PCC.0000138557.31831.65.
- Individual participant dataSharing planned: No
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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.
Denis Schmartz, MD study director
CHU Brugmann
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 Congenital Heart Disease in Children and Cardiac Surgical Procedures, 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 3 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 20 Jul 2022. 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?
MODS2 (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 NCT05284500, which also lists 13 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 20 Jul 2022.