Of note, 97% of the acute coronary syndrome patients underwent percutaneous coronary intervention (PCI) either before ECLS implantation or under ECLS support. Extracorporeal life support implantation was performed on scene (Emergency Department, Cath Lab, Intensive Care Unit) by a senior cardiac surgeon and a trained perfusionist, in 8 cases (20%) in the referring hospital.

Thirty-day mortality was 51% [21 patients, due to MOF (n = 14), cerebral complications (n = 6) and heart failure (n = 1)]. Logistic regression analysis identified 6-h pH values as an independent risk factor of 30-day mortality (P < 0.001, OR = 0.000, 95% CI 0.000-0.042). Neither CPR nor implantation under

ongoing CPR resulted in significant differences. In 26 cases (63%), the ECLS system could be explanted, after mean support of 169 +/- 67 h. Seven of these patients received cardiac surgery [ventricular assist device 3-deazaneplanocin A price implantation (n = 4), heart transplantation (n = 1), other procedures (n = 2)].

Due to the evolution of transportable ECLS systems and percutaneous techniques implantation on scene is feasible. Extracorporeal life support may serve as a bridge-to-decision and bridge-to-treatment device. Neurological evaluation

before ventricular assist device implantation and PCI under stable conditions are possible. Despite substantial mortality, ECLS implantation in selected Tyrosine Kinase Inhibitor Library patients by an experienced team offers additional support to conventional therapy as well as CPR and allows survival in patients that otherwise most likely Bcl 2 inhibitor would have died. This concept has to be implemented in cardiac survival networks in the future.”
“Background: When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model.

Methods: An analytical expression was derived under the assumption

that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition.

Results: Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects).