Objective: Risk algorithms were used to identify a high-risk population for transcatheter aortic valve implantation instead of standard aortic valve replacement in patients with aortic stenosis. We evaluated the efficacy of these methods for predicting outcomes in high-risk patients undergoing aortic valve replacement.
Methods: Data were collected on 638 patients identified as having isolated aortic valve replacement between January 1, 1998 and December 31, 2006, using The Society of Thoracic Surgeons (STS) database. Long-term survival was determined from the Social Security Death Index or family contact. Operative risk was calculated using the STS Predicted Risk of Mortality, the EuroSCORE logistic and additive algorithms, and the Ambler Risk Score. Patients at or above the 90th percentile of risk (8.38% for STS, 33.47% for logistic, 12% for additive, 14.3% for Ambler) were identified as high risk. We then compared actual with predicted mortality and each algorithm's ability to identify patients with the worst long-term survival.
Results: Operative mortality was 24 of 638 (3.76%). An additional 121 (19.0%) patients died during the follow-up study period (mean 4.2 +/- 2.7 years). Overall mortality was 145 of 638 (22.7%). Expected versus observed mortality for the high-risk group by algorithm was 13.3% versus 18.8% for STS, 50.9% versus 15.6% for logistic, 14.0% versus 11.9% for additive, and 19.0% versus 13.4% by Ambler. Long-term mortality, per high-risk group, was 64.1% in the STS Predicted Risk of Mortality, 45.3% in the logistic, 45.2% in the additive, and 40.2% in Ambler Risk Score. Logistic regression showed that the STS algorithm was the most sensitive in defining the patients most at risk for long-term mortality.
Conclusion: The STS Predicted Risk of Mortality most accurately predicted perioperative and long-term mortality for the highest risk patients having aortic valve replacement.