Risk modeling in transcatheter aortic valve replacement remains unsolved: an external validation study in 2946 German patients

BACKGROUND: Surgical risk prediction models are routinely used to guide decision-making for transcatheter aortic valve replacement (TAVR). New and updated TAVR-specific models have been developed to improve risk stratification; however, the best option remains unknown. OBJECTIVE: To perform a comparative validation study of six risk models for the prediction of 30-day mortality in TAVR METHODS AND RESULTS: A total of 2946 patients undergoing transfemoral (TF, n = 2625) or transapical (TA, n = 321) TAVR from 2008 to 2018 from the German Rhine Transregio Aortic Diseases cohort were included. Six surgical and TAVR-specific risk scoring models (LogES I, ES II, STS PROM, FRANCE-2, OBSERVANT, GAVS-II) were evaluated for the prediction of 30-day mortality. Observed 30-day mortality was 3.7% (TF 3.2%; TA 7.5%), mean 30-day mortality risk prediction varied from 5.8 ± 5.0% (OBSERVANT) to 23.4 ± 15.9% (LogES I). Discrimination performance (ROC analysis, c-indices) ranged from 0.60 (OBSERVANT) to 0.67 (STS PROM), without significant differences between models, between TF or TA approach or over time. STS PROM discriminated numerically best in TF TAVR (c-index 0.66; range of c-indices 0.60 to 0.66); performance was very similar in TA TAVR (LogES I, ES II, FRANCE-2 and GAVS-II all with c-index 0.67). Regarding calibration, all risk scoring models—especially LogES I—overestimated mortality risk, especially in high-risk patients. CONCLUSIONS: Surgical as well as TAVR-specific risk scoring models showed mediocre performance in prediction of 30-day mortality risk for TAVR in the German Rhine Transregio Aortic Diseases cohort. Development of new or updated risk models is necessary to improve risk stratification. GRAPHIC ABSTRACT: [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00392-020-01731-9) contains supplementary material, which is available to authorized users.