Differential prognostic accuracy of right ventricular dysfunction, the Seattle Heart Failure Model and MAGGIC Score in patients with severe mitral regurgitation undergoing the MitraClip® procedure
Authors:   , Heyl Andreas Stephan 
Published: December 2021

The global incidence and prevalence of valvular heart disease like mitral regurgitation (MR) are increasing due to changes in the population age structure, leading to decreased quality of life and premature death for the affected individuals and a high economic burden placed on the healthcare system. The standard of care is surgical reconstruction or replacement of the mitral valve, however, due to age and comorbidities (especially chronic heart failure (CHF)) up to 50% of patients are deemed unsuitable for mitral valve surgery. For these patients, transcatheter reconstruction of the valve using the MitraClip system is an established alternative leading to a decrease in hospitalizations and mortality on some, but not all patient cohorts. Especially patients with right ventricular dysfunction (RVD) seem to derive less benefit. To avoid exposing patients to unnecessary risk and decrease health care spending identifying patients who benefit from the procedure and those with an unfavorable risk- benefit ratio beforehand would be advantageous. Between June 2013 and February 2017 119 patients were treated with the MitraClip- system after interdisciplinary assessment (Heart Team). After obtaining written consent, they were included in the Mitral Valve Registry Frankfurt with follow up until 31. 12. 2017. Aim of the study was to determine the prognostic accuracy of established heart failure risk scores and whether the Seattle Heart Failure Model (SHFM) and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) Score differ in their accuracy in patients with RVD undergoing the MitraClip®- Procedure. RVD was diagnosed in the echocardiogram using an established parameter, the tricuspid annular plane systolic excursion (TAPSE), which measures the maximum distance traveled of the tricuspid annulus during systole. Area under receiver operating curves (AUROC) were calculated for SHFM and MAGGIC with all-cause mortality within one year after MC as an endpoint to determine prognostic accuracy. 29 patients died within one year after Mitraclip therapy (28.2%). The 1- year mortality rate in patients with functional mitral regurgitation (FMR) was 23.3% and 31.7% in patients with degenerative mitral regurgitation (DMR) 31,7%. In KaplanMeier analysis, one- year mortality was significantly higher among patients with RVD than in patients without RVD (34.8 vs 22.8%; p= 0.009). Patients with FMR and concurrent RVD had a higher all-cause mortality then those without RVD (38.1% vs 9.1%). We did not see this association in patients with DMR and concurrent RVD (32% with RVD vs. 34.3%). In our patient cohort, prognostic accuracy of the SHFM and MAGGIC score were similar (SHFM: 0.704, MAGGIC: 0.692). This held true for separate analysis in FMR/ DMR patients (FMR: SHFM 0.696, MAGGIC 0.722; DMR: SHFM 0.727, MAGGIC 0.629). In pts without RVD, however, the SHFM displayed a significantly higher AUROC value and therefore better diagnostic accuracy compared to the MAGGIC score (SHFM: 0.775; MAGGIC: 0.551, p <0.05). There was no significant difference in patients with RVD (SHFM: 0.615; MAGGIC: 0.799, p>0.05), with a nonsignificant trend favoring the MAGGIC score. RVD is an important prognostic marker in MR pts undergoing MC and should be taken into consideration by the heart team. The SHFM and MAGGIC displayed adequate overall prognostic power in our patient cohort. Accuracy of these models differed in pts with and without RVD, indicating higher predictive power of the SHFM score in pts without RVD and a comparable overall sensitivity of the MAGGIC score in pts with RVD. Possibly due to the heterogenicity of mitral valve disease and comorbidities, both scores display only moderate accuracy on an individual patient level. In the future, a more accurate score for patients with severe MR might be created by including a broad range of clinical, anatomical, demographic and laboratory data using a machine learning approach.