Prediction of Poor Outcome After Transcatheter Aortic Valve Replacement

Abstract

BackgroundA series of models have been developed to identify patients at high risk for poor outcomes after transcatheter aortic valve replacement (TAVR) to help guide treatment choices, offer patients realistic expectations of long-term outcomes, and support decision making. ObjectivesThis study examined the performance of the previously developed TAVR Poor Outcome risk models in an external dataset and explored the incremental contribution of geriatric domains to model performance. MethodsPoor outcome after TAVR was defined as death, poor quality of life (QOL), or decline in QOL, as assessed using the Kansas City Cardiomyopathy Questionnaire. We tested 4 TAVR Poor Outcome risk models: 6-month and 1-year full and clinical (reduced) models. We examined each model’s discrimination and calibration in the CoreValve trial dataset, and then tested the incremental contribution of frailty and disability markers to the model’s discrimination using the incremental discrimination index. ResultsAmong 2,830 patients who underwent TAVR in the CoreValve US Pivotal Extreme and High Risk trials and associated continued access registries, 31.2% experienced a poor outcome at 6 months following TAVR (death, 17.6%; very poor QOL, 11.6%; QOL decline, 2.0%) and 50.8% experienced a poor outcome at 1 year (death, 30.2%; poor QOL, 19.6%; QOL, decline 1.0%). The models demonstrated similar discrimination as in the Placement of Aortic Transcatheter Valves Trial cohorts (c-indexes, 0.637 to 0.665) and excellent calibration. Adding frailty as a syndrome increased the c-indexes by 0.000 to 0.004 (incremental discrimination index, p < 0.01 for all except the 1-year clinical model), with the most important individual components being disability and unintentional weight loss. ConclusionsAlthough discrimination of the TAVR Poor Outcome risk models was generally moderate, calibration was excellent among patients with different risk profiles and treated with a different TAVR device. These findings demonstrated the value of these models for individualizing outcome predictions in high-risk patients undergoing TAVR.