Comparison of Four Intensive Care Scores in Prediction of VA-ECMO Survival.

Overview

VA-ECMO is increasingly utilized in the setting of cardiogenic shock or cardiac arrest to restore hemodynamic stability and end organ function. VA-ECMO serves as a short-term ventricular assist device that can be rapidly placed at the bedside in the emergency room, intensive care unit, cardiac catheterization suite, or operating room. A bridge to decision approach allows for the assessment of neurologic status, renal and hepatic function, and subsequent recovery of cardiac function. Despite its life-saving potential, VA-ECMO is fraught with complications including vascular complications from cannulation. In addition, neurologic injury, renal failure, liver failure, and sepsis are all well described sequelae of the post-cardiogenic shock or cardiac arrest VA-ECMO patient. For these reasons, identifying early prognostic indicators and developing a score with regard to the outcome of this special patient population is of high interest. The proposed study aims to analyze 4 ICU scores/Survival prediction models \[APACHE-II (acute physiology and chronic health evaluation II), SAVE (Survival after VA ECMO) , SOFA (sequential organ failure assessment )and CASUS (Cardiac Surgery Score)\] and evaluate their performance in predicting survival after VA ECMO insertion. This will be a clinical retrospective study which will be conducted in the Cardiothoracic Intensive Care unit (CTICU) in the Department of Cardiothoracic Surgery, Heart Hospital, Doha. Patients admitted to the CTICU after institution of VA ECMO in the period between 1st January 2015 to 31st October 2022 will be screened retrospectively. Patients who stay for at least 12 hours in the CTICU would be eligible for inclusion in the study. All charts of patients who were admitted to CTICU after institution of VA ECMO during the afore mentioned period will be included in the review. All the risk scores would be calculated separately for all the enrolled patients and then subjected to statistical analysis to determine predictive accuracy for survival.

Conditions