Primary outcome analysis
lactatemia, death, chronic kidney disease
CHRONIC HEART FAILURE, DIABETES MELLITUS, REGRESSION, HYPERTENSION, ACUTE HEART FAILURE
The time to successful weaning from VA-ECMO was assessed for each copy of patient. Follow-up started at beginning of ECMO support and ended at first ECMO removal or death. The 30-day period after ECMO removal was only used to qualify ECMO weaning success but not included in the calculation of the time-to-event. Patients that were still on ECMO support at 30 days were censored at 30 days. As the primary objective of the study was to estimate the etiologic effect of levosimendan on ECMO weaning success, we first took into account competing risk of death and of ECMO weaning failure after ECMO removal by using a cause-specific hazard model, as proposed by Austin, Lee, and Fine [The effect of levosimendan on the primary outcome was then estimated on pooled nested trials data using a univariable and a multivariable Cox model to adjust for confounders measured at baseline of each nested trial, as the decision to prescribe levosimendan was at the discretion of the medical team in this cohort, and some confounders may be imbalanced between the two treatment groups at baseline of nested trials. Covariates entered into the model were as follows: age, sex, SAPS II at ICU admission, history of chronic heart failure, previous sternotomy, hypertension, diabetes mellitus, type of acute heart failure, indication for surgery, chronic kidney disease, BMI, SOFA score, lactatemia, ECMO blood flow rate indexed to body surface area, and VIS. SOFA score, ECMO output, lactatemia, and VIS were measured at baseline of each nested trial, all other covariates were measured at ICU admission or at beginning of ECMO support. All confounders were entered into the model based on clinical expertise and prior knowledge [Mortality at 30 days and at 1 year after assignment to the nested trials was estimated in a univariable analysis by calculating the odds ratio and its confidence interval. Multivariable analyses with logistic regression were then performed to adjust for confounders. See Additional file 