Inclusion of ‘ICU-Day’ in a Logistic Scoring System Improves Mortality Prediction in Cardiac Surgery
Fabian Doerr, Matthias B. Heldwein, Ole Bayer, Anton Sabashnikov, Alexander Weymann, Pascal M. Dohmen, Thorsten Wahlers, Khosro Hekmat
Department of Cardiothoracic Surgery, University of Cologne, Cologne, Germany
Med Sci Monit Basic Res 2015; 21:145-152
Prolonged intensive care unit (ICU) stay is a predictor of mortality. The length of ICU stay has never been considered as a variable in an additive scoring system. How could this variable be integrated into a scoring system? Does this integration improve mortality prediction?
MATERIAL AND METHODS: The ‘modified CArdiac SUrgery Score’ (CASUS) was generated by implementing the length of stay as a new variable to the ‘additive CASUS’. The ‘logistic CASUS’ already considers this variable. We defined outcome as ICU mortality and statistically compared the three CASUS models. Discrimination, comparison of receiver operating characteristic curves (DeLong’s method), and calibration (observed/expected ratio) were analyzed on days 1–13.
RESULTS: Between 2007 and 2010, we included 5207 cardiac surgery patients in this prospective study. The mean age was 67.2±10.9 years. The mean length of ICU stay was 4.6±7.0 days and ICU mortality was 5.9%. All scores had good discrimination, with a mean area under the curve of 0.883 for the additive and modified, and 0.895 for the ‘logistic CASUS’. DeLong analysis showed superiority in favor of the logistic model as from day 5. The calibration of the logistic model was good. We identified overestimation (days 1–5) and accurate (days 6–9) calibration for the additive and ‘modified CASUS’. The ‘modified CASUS’ remained accurate but the ‘additive CASUS’ tended to underestimate the risk of mortality (days 10–13).
CONCLUSIONS: The integration of length of ICU stay as a variable improves mortality prediction significantly. An ‘ICU-day’ variable should be included into a logistic but not an additive model.
Keywords: Cardiac Surgical Procedures - statistics & numerical data, Intensive Care Units - statistics & numerical data, Models, Statistical, Predictive Value of Tests, Prospective Studies, Risk Factors