In:
Clinical Research in Cardiology, Springer Science and Business Media LLC, Vol. 110, No. 3 ( 2021-03), p. 368-376
Abstract:
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
Type of Medium:
Online Resource
ISSN:
1861-0684
,
1861-0692
DOI:
10.1007/s00392-020-01731-9
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2021
detail.hit.zdb_id:
2218331-0
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