Mixed aortic valve disease: association with paravalvular leak and reduced survival after transcatheter aortic valve replacement.

AIMS: Transcatheter aortic valve replacement (TAVR) revolutionized the therapy of severe aortic stenosis (AS) with rising numbers. Mixed aortic valve disease (MAVD) treated by TAVR is gaining more interest, as those patients represent a more complex cohort as compared with isolated AS. However, concerning long-term outcome for this cohort only, limited data are available. The aim of the study is to assess the prevalence of MAVD in TAVR patients, investigate its association with paravalvular regurgitation (PVR), and analyse its impact on long-term mortality after TAVR. METHODS AND RESULTS: We conducted a registry-based cohort study using the Vienna TAVR registry, enrolling patients who underwent TAVR at Medical University of Vienna between January 2007 and May 2020 with available transthoracic echocardiography before and after TAVR (n = 880). Data analysis included PVR incidence and long-term survival outcomes. A total of 647 (73.52%) out of 880 patients had ≥ mild aortic regurgitation next to severe AS. MAVD was associated with PVR compared with isolated AS with an odds ratio of 2.06, 95% confidence interval (CI): 1.51-2.81 (P = <0.001). More than mild PVR after TAVR (n = 168 out of 880: 19.09%) was related to higher mortality compared with the absence of PVR with a hazard ratio (HR) of 1.33, 95% CI: 1.05- 1.67 (P = 0.016). MAVD patients developing ≥ mild PVR after TAVR were also associated with higher mortality compared with the absence of PVR with an HR of 1.30 and 95% CI: 1.04-1.62 (P = 0.022). CONCLUSION: MAVD is prevalent among TAVR patients and presents unique challenges, with increased PVR risk and worse outcomes compared with isolated AS. Long-term survival for MAVD patients, not limited to those developing PVR post-TAVR, is compromised. Earlier intervention before the occurrence of structural myocardial damage or surgical valve replacement might be a potential workaround to improve outcomes.

Amyloid Burden Correlates with Electrocardiographic Findings in Patients with Cardiac Amyloidosis-Insights from Histology and Cardiac Magnetic Resonance Imaging.

Cardiac amyloidosis (CA) is associated with several distinct electrocardiographic (ECG) changes. However, the impact of amyloid depositions on ECG parameters is not well investigated. We therefore aimed to assess the correlation of amyloid burden with ECG and test the prognostic power of ECG findings on outcomes in patients with CA. Consecutive CA patients underwent ECG assessment and cardiac magnetic resonance imaging (CMR), including the quantification of extracellular volume (ECV) with T1 mapping. Moreover, seven patients underwent additional amyloid quantification using immunohistochemistry staining of endomyocardial biopsies. A total of 105 CA patients (wild-type transthyretin: 74.3%, variant transthyretin: 8.6%, light chain: 17.1%) were analyzed for this study. We detected correlations of total QRS voltage with histologically quantified amyloid burden (r = -0.780, p = 0.039) and ECV (r = -0.266, p = 0.006). In patients above the ECV median (43.9%), PR intervals were significantly longer (p = 0.016) and left anterior fascicular blocks were more prevalent (p = 0.025). In our survival analysis, neither Kaplan-Meier curves (p = 0.996) nor Cox regression analysis detected associations of QRS voltage with adverse patient outcomes (hazard ratio: 0.995, p = 0.265). The present study demonstrated that an increased amyloid burden is associated with lower voltages in CA patients. However, baseline ECG findings, including QRS voltage, were not associated with adverse outcomes.

Native skeletal muscle T1-time on cardiac magnetic resonance: A predictor of outcome in patients with heart failure with preserved ejection fraction.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is associated with heart failure (HF) hospitalizations and death. Previous studies have shown that altered muscle composition is associated with higher risk of adverse outcome in HFpEF patients. AIM: The purpose of our study was to investigate the association between skeletal muscle composition, as measured by skeletal muscle T1-times on cardiac magnetic resonance (CMR) imaging, and adverse outcome. METHODS: We measured skeletal muscle T1-times of the back muscles on standard CMR images in a prospective cohort of HFpEF patients. Cox regression models were used to test the association of skeletal muscle T1-times and adverse outcome defined as hospitalization for HF and/or cardiovascular death. RESULTS: We included 101 patients (mean age 72±7 years, 71 % female) in our study. The median skeletal muscle T1-times were 842 ms (IQR 806-881 ms). In univariate analysis high muscle T1-time was associated with adverse outcome (HR=1.96 [95 % CI, 1.31-2.94] per every 100 ms increase; p=.001). After adjustment for age, sex, body mass index, left- and right ventricular ejection fraction, N-terminal pro-brain natriuretic peptide and myocardial native T1-times, native skeletal muscle T1-time remained an independent predictor for adverse outcome (HR=1.94 [95 % CI, 1.24-3.03] per every 100 ms increase; p=.004). CONCLUSION: In patients with HFpEF, high skeletal muscle T1-times on standard CMR scans are associated with higher rates of HF hospitalizations and cardiovascular death. CONDENSED ABSTRACT: Skeletal muscle abnormalities are common in patients with heart failure with preserved ejection fraction (HFpEF). The present study evaluates skeletal muscle composition, as quantified by native skeletal muscle T1-times of the back muscles on standard cardiac magnetic resonance imaging, and assessed the association with adverse outcome, defined as hospitalization for heart failure and/or cardiovascular death. In a prospective cohort of 101 patients with HFpEF, we found that high native skeletal muscle T1-times are associated with an increased risk for adverse outcome. These findings suggest that native skeletal muscle T1-time may serve as marker for improved risk prediction.

Tailored Risk Stratification in Severe Mitral Regurgitation and Heart Failure Using Supervised Learning Techniques.

Background: Secondary mitral regurgitation (sMR) in the setting of heart failure (HF) has considerable impact on quality of life, HF rehospitalizations, and mortality. Identification of high-risk cohorts is essential to understand disease trajectories and for risk stratification. Objectives: This study aimed to provide a structured decision tree-like approach to risk stratification in patients with severe sMR and HF. Methods: This observational study included 1,317 patients with severe sMR from the entire HF spectrum. Clinical, echocardiographic, and laboratory data were extracted for all patients. The primary end point was all-cause mortality. Survival tree analysis, a supervised learning technique, was applied to identify patient subgroups at risk of mortality and further stratified by HF subtype (preserved, mildly reduced, and reduced ejection fraction). Results: Using supervised learning (survival tree method), 8 distinct subgroups were identified that differed significantly in long-term survival. Subgroup 7, characterized by younger age (≤66 years), higher hemoglobin (>12.7 g/dL), and higher albumin levels (>40.6 g/L) had the best survival. In contrast, subgroup 5 displayed a 20-fold risk of mortality (hazard ratio: 20.38 [95% CI: 10.78-38.52]); P < 0.001 and had older age (>68 years), low serum albumin (≤40.6 g/L), and higher NT-proBNP levels (≥9,750 pg/mL). Unique subgroups were further identified for each type of HF subtypes. Conclusions: Supervised machine learning reveals heterogeneity in the sMR risk spectrum, highlighting the clinical variability in the population. A decision tree-like model can help identify differences in outcomes among subgroups and can help provide tailored risk stratification.