Mortality Prediction in Cardiac Surgery Patients

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Abstract
Background—Our purpose was to assess the performance of general severity systems (Acute Physiology and Chronic Health Evaluation [APACHE], Simplified Acute Physiology Score [SAPS], and Mortality Probability Models [MPM]) and to compare them with the Parsonnet score to predict mortality after cardiac surgery. Methods and Results—This was a prospective observational study of 465 cardiac surgery patients in a tertiary referral center. Probabilities of hospital death for patients were estimated by applying the 4 models and were compared with actual mortality rates. Performance of the 4 systems was assessed by evaluating calibration with the Hosmer-Lemeshow goodness-of-fit test and discrimination with receiver operating characteristic (ROC) curves. χ2 values were 3.71 for Parsonnet, 4.52 for MPM II0, 4.30 for MPM II24, 5.16 for SAPS II, and 10.57 for APACHE II. The area under the ROC curve was 0.857 for Parsonnet, 0.783 for MPM II0, 0.796 for MPM II24, 0.771 for SAPS II, and 0.803 for APACHE II. Conclusions—In our experience, the Parsonnet score performs very well, with calibration and discrimination very high, better than general severity systems, and it is an appropriate tool to assess mortality in cardiac surgery patients. In our experience, the general severity systems perform well to predict mortality after cardiac surgery, with high calibration of MPM II24, MPM II0, and SAPS II; minor calibration for APACHE II; and high discrimination for 3 general systems, but not as well as the Parsonnet score.