[The Value of Using SCAI Cardiogenic Shock Stages in Predicting Mortality in CICU Patients].

Objective: To study the value of using the cardiogenic shock (CS) stages developed by the Society of Cardiovascular Imaging and Intervention (SCAI) in predicting the mortality of CS patients in cardiac intensive care unit (CICU).

Methods: We retrospectively collected (Jan., 2011-Jan., 2018) the information of inpatients who were admitted to the CICU of West China Hospital of Sichuan University on consecutive days, and conducted analysis on those with CS. The patients were divided into groups C, D and E, according to the corresponding SCAI stages, and the primary outcome indicator was in-hospital mortality. Logistic regression was done to determine the association between SCAI staging and in-hospital mortality before and after multivariate adjustment. The receiver operating characteristic curve was used to assess the value of SCAI stages of CS in predicting in-hospital mortality.

Results: We studies 839 CS patients who met our inclusion criteria. The proportions of patients of SCAI stages C (Classic), D (Deteriorating), and E (Extremis) were 43.3% (363 cases), 38.7% (325 cases) and 18.0% (151 cases), respectively. The unadjusted in-hospital mortality rates were 22.9% (83 cases), 44.0% (143 cases) and 53.6% (81 cases), respectively ( <0.001). The SCAI stages had an (area under the curve) of 0.640 for predicting in-hospital mortality among CS patients in CICU. After multivariate adjustment, the increased to 0.776 ( <0.001). In patients with acute coronary syndrome, the Global Registry of Acute Coronary Events (GRACE) scores had an of 0.644 for predicting in-hospital mortality, while a combination of the GRACE score with SCAI staging yielded an increased of 0.702 ( <0.001).

Conclusion: In CICU patients with CS, the SCAI stages of CS can be used as a stratified method for rapid assessment of disease risks upon admission. In patients with acute coronary syndrome and CS, SCAI stages combined with GRACE scores improved the ability to predict risks of death.