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External Validation and Updating of the Cardiac Surgery Score for Prediction of Mortality in a Cardiac Surgery
Brock Wilson1, Diem T T Tran1, Jean-Yves Dupuis1
1Division of Cardiac Anesthesiology, Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa Heart Institute, Ottawa, ON, Canada.
Insights
The Cardiac Surgery Score (CASUS) reliably predicts mortality in intensive care units after cardiac surgery. Recalibration improved its accuracy, making it a dependable tool for assessing patient risk.
Area of Science:
- Critical Care Medicine
- Cardiovascular Surgery
- Health Outcomes Research
Background:
- The Cardiac Surgery Score (CASUS) is a severity of illness score for predicting mortality in cardiac surgery intensive care units.
- External validation of CASUS predictive performance is necessary to confirm its reliability across different settings.
Purpose of the Study:
- To externally validate the predictive performance of the logistic and additive Cardiac Surgery Score (CASUS).
- To assess the accuracy of CASUS in predicting mortality for patients in the cardiac surgery intensive care unit.
Main Methods:
- Retrospective analysis of prospectively collected data from a single Canadian university cardiac surgery intensive care unit (n=4,519).
- Validation of logistic and additive CASUS models using original equations and logistic regression.
- Assessment of predictive performance using observed mortality, calibration (Hosmer-Lemeshow test), and discrimination (area under the ROC curve).
Main Results:
- Observed mortality was 1.8%.
- Uncalibrated logistic CASUS overestimated mortality, with overestimation decreasing over time (29%-78%).
- Recalibrated logistic CASUS provided mortality estimates comparable to observed mortality, with good discrimination (AUC > 0.7). Additive CASUS showed similar performance.
Conclusions:
- Recalibrated logistic CASUS reliably predicts mortality in the intensive care unit after cardiac surgery.
- Logistic regression models derived from additive CASUS perform comparably to logistic CASUS.
Objective:
To externally validate the predictive performance of the logistic and additive Cardiac Surgery Score (CASUS), a postoperative severity of illness score designed specifically for prediction of mortality in the cardiac surgery intensive care unit.
Design:
A retrospective analysis of prospectively collected data between July 1, 2012, and September 30, 2015.
Setting:
Single university cardiac surgery intensive care unit in Canada.
Participants:
Consecutive adult patients (n = 4,519) admitted to the intensive care unit after cardiac surgery.
Intervention:
None.
Measurements And Main Results:
The mortality predicted by logistic CASUS was calculated for each patient on admission day 0 and postoperative days 2, 4, 7, and 10 using the original model equation. The mortality predicted by additive CASUS was determined on each day with separate logistic regression models, using the total score as a single variable. The observed mortality was 1.8%. Logistic CASUS overestimated mortality by 78%, 59%, 51%, 52%, and 29% on days 0, 2, 4, 7, and 10, respectively. After model updating with logistic calibration, logistic CASUS consistently provided estimates of death comparable with the observed mortality, as determined with the Hosmer-Lemeshow goodness-of-fit test. The stability of those estimates was confirmed by bootstrapping. Similar calibration results were obtained with additive CASUS. Logistic and additive CASUS had good discrimination with areas under the receiver operating characteristic curve greater than 0.7 on each study day.
Conclusions:
Recalibrated logistic CASUS reliably predicts mortality in the intensive care unit after cardiac surgery. Logistic regression models derived from additive CASUS perform as well as logistic CASUS.
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