The Assessment of Risk in Cardiothoracic Intensive Care (ARCtIC): prediction of hospital mortality after admission to cardiothoracic critical care

Abstract

The models used to predict outcome after adult general critical care may not be applicable to cardiothoracic critical care. Therefore, we analysed data from the Case Mix Programme to identify variables associated with hospital mortality after admission to cardiothoracic critical care units and to develop a risk-prediction model. We derived predictive models for hospital mortality from variables measured in 17,002 patients within 24 h of admission to five cardiothoracic critical care units. The final model included 10 variables: creatinine; white blood count; mean arterial blood pressure; functional dependency; platelet count; arterial pH; age; Glasgow Coma Score; arterial lactate; and route of admission. We included additional interaction terms between creatinine, lactate, platelet count and cardiac surgery as the admitting diagnosis. We validated this model against 10,238 other admissions, for which the c index (95% CI) was 0.904 (0.89-0.92) and the Brier score was 0.055, while the slope and intercept of the calibration plot were 0.961 and -0.183, respectively. The discrimination and calibration of our model suggest that it might be used to predict hospital mortality after admission to cardiothoracic critical care units.

Keywords: cardiothoracic critical care; hospital mortality; risk assessment; risk prediction tool.