Predictors of poor outcomes after transcatheter aortic valve replacement: results from the PARTNER (Placement of Aortic Transcatheter Valve) trial.

BACKGROUND Transcatheter aortic valve replacement (TAVR) is a less invasive option for treatment of high-risk patients with severe aortic stenosis. We sought to identify patients at high risk for poor outcome after TAVR using a novel definition of outcome that integrates quality of life with mortality. METHODS AND RESULTS Among 2137 patients who underwent TAVR in the PARTNER (Placement of Aortic Transcatheter Valve) trial or its associated continued access registry, quality of life was assessed with the Kansas City Cardiomyopathy Questionnaire-Overall Summary Scale (KCCQ-OS; range 0-100, where a higher score equates to a better quality of life) at baseline and at 1, 6, and 12 months after TAVR. A poor 6-month outcome (defined as death, KCCQ-OS score <45, or ≥10-point decrease in KCCQ-OS score compared with baseline) occurred in 704 patients (33%). Using a split-sample design, we developed a multivariable model to identify a parsimonious set of covariates to identify patients at high risk for poor outcome. The model demonstrated moderate discrimination (c-index=0.66) and good calibration with the observed data, performed similarly in the separate validation cohort (c-index=0.64), and identified 211 patients (10% of the population) with a ≥50% likelihood of a poor outcome after TAVR. A second model that explored predictors of poor outcome at 1 year identified 1102 patients (52%) with ≥50% likelihood and 178 (8%) with ≥70% likelihood of a poor 1-year outcome after TAVR. CONCLUSIONS Using a large, multicenter cohort, we have developed and validated predictive models that can identify patients at high risk for a poor outcome after TAVR. Although model discrimination was moderate, these models may help guide treatment choices and offer patients realistic expectations of outcomes based on their presenting characteristics. CLINICAL TRIAL REGISTRATION URL http://www.clinicaltrials.gov. Unique identifier: NCT00530894.