Mortality from isolated coronary bypass surgery: a comparison of the Society of Thoracic Surgeons and the EuroSCORE risk prediction algorithms

Abstract

We compared the performances of the additive European System for Cardiac Operative Risk Evaluation, EuroSCORE (AES) and logistic EuroSCORE (LES) with the Society of Thoracic Surgeons' risk prediction algorithm in terms of discrimination and calibration in predicting mortality in patients undergoing isolated coronary artery bypass grafting (CABG) at a single institution in Pakistan. Both models were applied to 380 patients, operated upon at the Aga Khan University Hospital from August 2009 to July 2010. The actual mortality was 2.89%. The mean AES of all patients was 4.36 ± 3.58%, the mean LES was 5.96 ± 9.18% and the mean Society of Thoracic Surgeons' (STS) score was 2.30 ± 4.16%. The Hosmer-Lemeshow goodness-of-fit test gave a P-value of 0.801 for AES, 0.699 for LES and 0.981 for STS. The area under the receiver operating characteristic curve was 0.866 for AES, 0.842 for LES and 0.899 for STS. STS outperformed AES and LES both in terms of calibration and discrimination. STS, however, underestimated mortality in the top 20% of patients having an STS score >2.88, thus overall STS estimates were lower than actual mortality. We conclude that STS is a more accurate model for risk assessment as compared to additive and logistic EuroSCORE models in the Pakistani population.

Figures

Figure 1:

Predicted mortality (on the basis of additive EuroSCORE) plotted against (a) actual mortality (b) additive EuroSCORE (c) logistic EuroSCORE and (d) STS scores.

Figure 2:

Receiver operating characteristic curve graphs for the AES, LES and STS scores.

Figure 3:

(a) Additive EuroSCORE calibration. (b) Logistic EuroSCORE calibration. (c) STS thirty-day operative mortality risk model calibration. (d) Stroke risk model calibration. (e) Reoperation risk model calibration. (f) Prolonged ventilation risk model calibration. (g) Renal failure risk model calibration. (h) Deep sternal wound infection risk model calibration.