Clin Res Cardiol (2021)
DOI DOI https://doi.org/10.1007/s00392-021-01843-w
|A risk model to predict outcome after successful percutaneous edge-to-edge-repair for severe mitral valve regurgitation|
|A. Ben Ammar1, R. Osteresch1, K. Diehl1, P. Dierks1, S. Rühle1, J. Schmucker1, A. Fach1, H. Wienbergen1, R. Hambrecht1|
|1Bremer Institut für Herz- und Kreislaufforschung (BIHKF), Bremen;|
Background: Several studies identified various predictors of worse clinical outcome after transcatheter mitral valve repair (TMVR). Nevertheless, there is a lack of specific tools for risk stratification in patients undergoing TMVR.
Objective: To combine preprocedural variables with prognostic impact into a specific risk model for long-term outcome in patients undergoing TMVR.
Method Consecutive patients with severe mitral regurgitation (MR) who underwent successful TMVR (MR ?2+ at discharge) were included and followed prospectively. Primary endpoint was defined as a composite of all-cause mortality and re-hospitalization for heart failure during a median follow-up period of 16±4 months.
A multivariable Cox-proportional hazard regression analysis was performed to identify independent risk factors for primary endpoint. The prognostic value of a specific risk model (0-15 points) incorporating independent predictors from Cox regression analysis was tested using Kaplan-Meier and Receiver operator characteristic (ROC) analysis.
Results: 144 patients (median age 76±7.7years, 68.1% male) were enrolled. At long-term follow-up, primary endpoint occured in 74 patients (51.0%). The rate of all-cause mortality and rehospitalization for heart failure was 32.4% and 36.6%, respectively. In Cox regression analysis, preexisting NYHA functional class IV (HR 2.41; 95% CI 1.30-4.48; p<0.005), left ventricular stroke work index (HR 0.96; 95% CI 0.93-0.98; p=0.005), severe tricuspid valve regurgitation (HR 1.94; 95% CI 1.01-3.72; p=0.048) and increased creatinine levels (HR 1.68; 95% CI 1.20-2.34; p=0.002) were independent predictors for combined primary endpoint. A treatment-specific risk model incorporating this independent variables showed good discrimination (area under the ROC curve of 0.81; 95% CI 0.71-0.90; p<0.001). Thus, Kaplan Meier analysis showed that freedom from combined primary outcome was lowest in patients with the highest tertile of the risk model (26.8% vs. 44.1% vs. 64.3%; log-rank p=0.004).
Conclusions: A treatment-specific risk model incorporating independend risk factors from multivariable Cox regression analysis might be useful for risk stratification in patients undergoing TMVR.