Long-term outcomes after transcatheter aortic valve implantation: derivation and validation of the long-TAVI prediction model

Abstract Background Identification of high-risk individuals after transcatheter aortic valve implantation (TAVI) is essential to personalized care. Established risk models require a plethora of variables, fail to account for biomarker dynamics and imaging parameters, mostly focus on the short term, and offer limited predictive performance. Purpose We aimed to develop an easily applicable prediction model for long-term mortality or heart failure (HF) admission following TAVI. Methods Patients undergoing TAVI at a high-volume university centre in the United Kingdom (n = 990) were recruited into a prospective cohort study. Enrolled patients were divided into a development cohort (07/2007 – 12/2016) and a validation cohort (01/2017 – 11/2018) and followed for up to 15.1 (median 5.3, interquartile range [IQR] 2.8 – 7.4) years. Patient characteristics along with pre- and post-procedural imaging results and brain natriuretic peptide (BNP) levels before the procedure and at 3 months were recorded. A clinical risk score for long-term mortality and HF admission (i.e., Long-TAVI risk score) was derived using Cox regression and evaluated using time-dependent area under the receiver operating characteristics curve (AUC). Results Age at presentation was 79.8 (standard deviation [SD] 8.9) years, 961 (99.0%) patients had signs of heart failure, left ventricular ejection fraction was 52 (SD 11.2), and pre-TAVI BNP levels were 298.0 (interquartile range [IQR] 159.0 – 571.5) pg/mL. High BNP levels at presentation (higher vs. lower than 400 pg/mL: HR 1.50, 95% CI 1.17 – 1.92, P < 0.001) and dynamic changes in BNP with the first 3 months (low/high vs. low/low: 2.61, 95% CI 1.17 – 5.86, P = 0.020; high/high vs. low/low: 2.41, 95% CI 1.26 – 4.64, P = 0.008) were independent predictors mortality or HF admission. The simple 8-item Long-TAVI score accounts for periprocedural BNP dynamics and shows good discrimination (AUC 0.76, 95% CI 0.72 – 0.79) and calibration in the development cohort performing similarly well in the external validation cohort (AUC 0.70, 95% CI 0.63 – 0.77). Long-TAVI significantly outperformed the logistic EURO score (AUC 0.56, 95% CI 0.52 – 0.59, P < 0.001). Conclusion Both pre-TAVI BNP levels and periprocedural BNP trajectories are independently associated with long-term outcomes after TAVI. Accounting for clinical characteristics, imaging parameters, and circulating BNP levels, the newly developed Long-TAVI risk score achieves high performance and provides the first prediction model for long-term mortality or HF admission. Long-TAVI represents a novel tool for personalized risk assessment in patients undergoing TAVI.Correlation at baseline A) and FUP B)Score performance A) and risk groups B)