Mortality Prediction After Cardiac Surgery: Higgins’ Intensive Care Unit Admission Score Revisited

Abstract

This study was performed to develop and validate a cardiac surgical intensive care risk adjustment model for mixed cardiac surgery based on a few preoperative laboratory tests, extracorporeal circulation time, and measurements at arrival to the intensive care unit. This was a retrospective study of admissions to 5 cardiac surgical intensive care units in Sweden that submitted data to the Swedish Intensive Care Registry. Admissions from 2008 to 2014 (n= 21,450) were used for model development, whereas admissions from 2015 to 2016 (n= 6463) were used for validation. Models were built using logistic regression with transformation of raw values or categorization into groups. The final model showed good performance, with an area under the receiver operating characteristics curve of 0.86 (95% confidence interval, 0.83-0.89), a Cox calibration intercept of -0.16 (95% confidence interval, -0.47 to 0.19), and a slope of 1.01 (95% confidence interval, 0.89-1.13) in the validation cohort. Eleven variables available on admission to the intensive care unit can be used to predict 30-day mortality after cardiac surgery. The model performance was better than those of general intensive care risk adjustment models used in cardiac surgical intensive care and also avoided the subjective assessment of the cause of admission. The standardized mortality ratio improves over time in Swedish cardiac surgical intensive care.