Redefining cardiac damage staging in aortic stenosis: the value of GLS and RVAc

医学 心脏病学 狭窄 内科学 阀门更换 二尖瓣反流 接收机工作特性 逻辑回归 心室 主动脉瓣狭窄 置信区间 比例危险模型 放射科
作者
Eva Gutiérrez,Carmen Olmos,Irene Carrión-Sánchez,Pilar Jiménez‐Quevedo,Luis Nombela‐Franco,Rocío Párraga,Sandra Gil-Abizanda,Patricia Mahía,María Luaces,José Agustín,Fabián Islas
出处
期刊:European Journal of Echocardiography [Oxford University Press]
卷期号:24 (12): 1608-1617 被引量:10
标识
DOI:10.1093/ehjci/jead140
摘要

Cardiac damage staging has been postulated as a prognostic tool in patients undergoing transcatheter aortic valve replacement (TAVR). The aims of our study are (i) to validate cardiac damage staging systems previously described to stratify patients with aortic stenosis (AS), (ii) to identify independent risk factors for 1-year mortality in patients with severe AS undergoing TAVR, and (iii) to develop a novel staging model and compare its predictive performance to that of the above mentioned.Patients undergoing TAVR from 2017 to 2021 were included in a single-centre prospective registry. Transthoracic echocardiography was performed in all patients before TAVR. Logistic and Cox's regression analysis were used to identify predictors of 1-year all-cause mortality. In addition, patients were classified based on previously published cardiac damage staging systems, and the predictive performance of the different scores was measured.Four hundred and ninety-six patients (mean age 82.1 ± 5.9 years, 53% female) were included. Mitral regurgitation (MR), left ventricle global longitudinal strain (LV-GLS) and right ventricular-arterial coupling (RVAc) were independent predictors of all-cause 1-year mortality. A new classification system with four different stages was developed using LV-GLS, MR, and RVAc. The area under the receiver operating characteristic curve was 0.66 (95% confidence interval 0.63-0.76), and its predictive performance was superior compared with the previously published systems (P < 0.001).Cardiac damage staging might have an important role in patients' selection and better timing for TAVR. A model that includes LV-GLS, MR, and RVAc may help to improve prognostic stratification and contribute to better selection of patients undergoing TAVR.
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