Predictive Pre and Peroperative Factors for MODS-2 in Pediatric Cardiac Surgery

Cardiac Surgical Procedures Clinical Trial

— FPMODS2  

Official title:

Predictive Pre and Peroperative Factors for Multiple Organ Dysfunction Score 2 (MODS-2) in Pediatric Cardiac Surgery

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05284500
• Other study ID #: FPMODS2
• Status: Completed
• Start date: March 18, 2022
• Est. completion date: July 8, 2022

Study information

• Verified date: July 2022
• Source: Brugmann University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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%. 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 152
• Est. completion date: July 8, 2022
• Est. primary completion date: June 30, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 16 Years

Eligibility

Inclusion Criteria:

• patients undergoing pediatric cardiac surgery with cardiopulmonary bypass between 2008 and 2018 at our institution

• accepting blood transfusions

• ASA score 1-4

Exclusion Criteria:

• Jehova's witness

• ASA 5 status

Related Conditions & MeSH terms

• Cardiac Surgical Procedures
• Congenital Heart Disease in Children
• Heart Defects, Congenital
• Heart Diseases
• Outcome Assessment

Intervention

Procedure:
• Pediatric cardiac surgery with cardiopulmonary bypass
All patients undergoing pediatric cardiac surgery with cardiopulmonary bypass will be extracted from our database

Locations

Country	Name	City	State
Belgium	Hôpital Universitaire des Enfants Reine Fabiola	Brussels

Sponsors (1)

Lead Sponsor	Collaborator
Brugmann University Hospital

Country where clinical trial is conducted

Belgium, 

References & Publications (13)

Agarwal 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. — View Citation

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. — View Citation

Baltsavias 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. — View Citation

Despotis 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. Review. — View Citation

Hickey 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. — View Citation

Hill 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. — View 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. Review. — View Citation

Long 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. — View Citation

Marshall 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. Review. — View Citation

Slater 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. Epub 2003 Jan 23. — View Citation

Slater 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. — View Citation

Wilder 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. — View Citation

Willems A, Van Lerberghe C, Gonsette K, De Villé 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. — View Citation

* Note: There are 13 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	28 days