Mortality prediction after cardiac surgery: Higgins' intensive care unit admission score revisited.

Abstract Background 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 (ICU). Methods A retrospective study of admissions to five cardiac surgical intensive care units in Sweden which submitted data to the Swedish Intensive Care Registry. Admissions from 2008-2014 (n=21,450) were used for model development, while admissions from 2015-2016 (n=6,463) were used for validation. Models were built using logistic regression with transformation of raw values or categorization into groups. Results The final model showed good performance, with an area under the receiver operating characteristics curve of 0.86 (95% confidence interval [CI]: 0.83 to 0.89), a Cox calibration intercept of -0.16 (-0.47 to 0.19), and slope of 1.01 (0.89 to 1.13) in the validation cohort. Conclusions Eleven variables available on admission to the ICU can be used for the prediction of 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.