ABSTRACT
BACKGROUND AND OBJECTIVES: The application of prognostic scoring systems to identify risk of death within 24 h of CICU admission has significant consequences for clinical decision-making. Previous score of parameters collected after 24 h was considered too late to predict mortality. As a result, we attempted to develop a CICU admission risk score to predict hospital mortality using indicators collected within 24 h. METHODS: Data were obtained from SCIENCE registry from January 1, 2021 to December 21, 2021. Outcomes of 657 patients (mean age 58.91 ± 12.8 years) were recorded retrospectively. Demography, risk factors, comorbidities, vital signs, laboratory and echocardiography data at 24-h of patient admitted to CICU were analysed by multivariate logistic regression to create two models of scoring system (probability and cut-off model) to predict in-hospital mortality of any cause. RESULTS: From a total of 657 patients, the hospital mortality was 15%. The significant predictors of mortality were male, acute heart failure, hemodynamic instability, pneumonia, baseline creatinine ≥1.5 mg/dL, TAPSE <17 mm, and the use of mechanical ventilator within first 24-h of CICU admission. Based on Receiver Operating Characteristic (ROC) curve analysis a cut off of ≥3 is considered to be a high risk of in-hospital mortality (sensitivity 75% and specificity 65%). CONCLUSION: The initial 24-h SCIENCE admission risk rating system can be used to predict in-hospital mortality in patients admitted to the CICU with a high degree of sensitivity and specificity.
