Predictors of One-Year Mortality After Transcatheter Aortic Valve Implantation for Severe Symptomatic Aortic Stenosis

Transcatheter aortic valve implantation (TAVI) is already an accepted option to treat elderly patients with severe symptomatic aortic stenosis who are inoperable or at high surgical risk. However, short- and long-term mortality after TAVI remains an important issue, raising the need to further improve the technology of TAVI as well as to identify patients who will not benefit from TAVI. A total of 1,391 patients treated with TAVI at 27 hospitals were included in the German Transcatheter Aortic Valve Interventions - Registry. One-year follow-up data were available for 1,318 patients (94.8%), with a mean follow-up period of 12.9 +/- 4.5 months. One-year mortality was 19.9%. Survivors and nonsurvivors showed multiple differences in patient characteristics, indications for interventions, preintervention and interventional characteristics, and postintervention events. A higher logistic European System for Cardiac Operative Risk Evaluation score was associated with higher 1-year mortality (p <0.0001). Cox proportional-hazards analysis revealed the following independent predictors of mortality: among preintervention findings: previous mitral insufficiency >= II degrees (p = 0.0005), low-gradient aortic stenosis (p = 0.0008), previous decompensation (p = 0.0061), previous myocardial infarction (p = 0.0138), renal failure (p = 0.0180), previous New York Heart Association class IV (p = 0.0254), and female gender (p = 0.0346); among procedural factors: intraprocedural conversion to surgery (p = 0.0009), pen-intervention stroke (p = 0.0003), and residual aortic insufficiency >= II degrees (p = 0.0022); and among postprocedural events: postintervention myocardial infarction (p = 0.0009) and postintervention pulmonary embolism (p = 0.0025). In conclusion, 1-year mortality after TAVI was 19.9% in this series. Patient characteristics and procedural as well as postintervention factors associated with mortality were identified, which may allow better patient selection and better care for these critically ill patients. (C) 2013 Elsevier Inc. All rights reserved.