Pediatric Cardiac Surgery Clinical Trial
Official title:
Does the Choice of Perioperative Inotropes Has Influence on the Morbidity and Mortality of Infants Operated for Complex Heart Defects?
Our study is based on the observation that management of the cardiopulmonary by-pass
withdrawal in pediatric heart surgery is quite variable between medical teams.
Indeed, in our center (HUDERF), the most common inotropic plan is dobutamine only or
combination of milrinone and adrenalin but their uses seem to be very anesthesiologist
dependent.
Despite the lack of sufficient evidence, the choice is probably multi-factorial, based mainly
on the inotrope pharmacology, the patient physiopathology and the anesthesiologist
preference.
The aim of this study is to determine if one of these inotropic strategy (dobutamine VS
milrinone + adrenaline) is better compared to the other in terms of morbi-mortality.
In order to do that, the investigators will realize a retrospective study based on files from
January 2008 to December 2018 concerning 250 selected patients.
As use of inotropes is highly variable between anesthesiologists, the investigators want to
study if the choice of the inotropic drugs have an impact on morbid-mortality in paediatric
cardiac surgery.
The investigators will retrospectively review the files from patients operated from january
2008 to December 2018. The investigators will concentrate on high risk patients defined as
age ≤ 1 year, RACHS score ≥ 3 and complex heart surgery (Switch procedure, atrioventricular
canal defect, aortic coarctation or aortic arch interruption, truncus arteriosus, mitral
valvuloplasty). The investigators aim for a study population of about 250 patients. Primary
outcome will be MODS score defined by death or 2 organ dysfunction (circulatory, respiratory
or renal failure).
Statistical analysis:
One propensity score will be performed on two groups: Dobu and Milri. After 15 multiple
imputations of the datasets using the mice R package, the CBPS R package will be used to
perform the propensity score, estimating an Average Treatment Effect (ATE), using covariate
balancing and requesting an exact match, which has been showed to be superior to traditional
logistic regression approaches and boosted classification and regression trees . An absolute
standardized difference less than 10-15% will be considered to support the assumption of
balance between the groups because it is not affected by the sample size, unlike P-values,
and it may be used to compare the relative balance of variables measured in different units.
The mean and standard deviation obtained after matching for continuous variables, and the
percentage for categorical variables will be presented. After the propensity score, the
investigators will use the survey R package to perform logistic regressions for binary
outcome variables and linear regressions for continuous outcomes, which will include the
treatment group effect, the weight resulting from the matching and variables present in the
propensity score in order to obtain a doubly-robust estimator which will correct the last
remaining possible imbalance between the covariates and produces an unbiased treatment
effect. The survey R package includes the Huber-White corrected standard errors, which
maintains the standard errors unbiased even under heterogeneity of the residuals. Last, the
advantage of a doubly-robust estimator is that it needs only one of the two models
(propensity score and logistic regression after the propensity score) to be correctly
specified. The so-called 'within approach', that is averaging the treatment effects on the 15
logistic and linear regressions has been showed to produce less biased estimates than the
'Across approach' - that is, performing linear or logistic regressions on averaged imputed
matched datasets - especially when missing data is related to the treatment group. The R
software (R Core Team, 2019), version 3.6.2. will be used to produce the results.
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