Transcatheter aortic valve implantation in patients with bicuspid aortic valve stenosis utilizing the next-generation fully retrievable and repositionable valve system: mid-term results from a prospective multicentre registry

Mid-term outcomes of patients with Lotus and Evolut transcatheter valves

Introduction

Long-term data on the Lotus^® (Boston Scientific, USA) valve are lacking.

Aim

To evaluate mid-term outcomes of aortic stenosis patients treated with either Lotus or Evolut R^® valves (Medtronic, USA).

Material and methods

Our study sample comprised 190 patients (71 Lotus and 119 Evolut valves). The mean clinical follow-up was 2.0 ±0.9 years. Information on mortality was obtained from the National Institutes of Health Information and Statistics.

Results

No significant differences existed in baseline characteristics between the groups. The rate of procedural complications was low and without significant differences between groups. The log rank test showed higher mortality in the Lotus group for cardiovascular mortality (p = 0.02; RR = 2.4, 95% CI: 1.123–5.075). Multivariable analysis revealed that the Lotus valve was independently associated with cardiovascular mortality (p = 0.03). At the end of echocardiography follow-up (4.1 ±0.9 years), we found a significantly higher mean aortic valve gradient (AVGm) in the Lotus group than in the Evolut group (17.9 ±9.5 vs. 10.2 ±3.5 mm Hg; p = 0.0006), and 3 (10%) patients from the Lotus group suffered from symptomatic re-stenosis requiering re-intervention.

Conclusions

The results of our study suggest that higher cardiovascular mortality rates during mid-term follow-up were associated with Lotus compared with Evolut valves. Higher AVGm in the Lotus valves suggests the possibility of accelerated prosthesis degeneration.

Predictors of Permanent Pacemaker Implantation in Patients Undergoing Transcatheter Aortic Valve Replacement - A Systematic Review and Meta-Analysis

Background

As transcatheter aortic valve replacement (TAVR) technology expands to healthy and lower‐risk populations, the burden and predictors of procedure‐related complications including the need for permanent pacemaker (PPM) implantation needs to be identified.

Methods and Results

Digital databases were systematically searched to identify studies reporting the incidence of PPM implantation after TAVR. A random‐ and fixed‐effects model was used to calculate unadjusted odds ratios (OR) for all predictors. A total of 78 studies, recruiting 31 261 patients were included in the final analysis. Overall, 6212 patients required a PPM, with a mean of 18.9% PPM per study and net rate ranging from 0.16% to 51%. The pooled estimates on a random‐effects model indicated significantly higher odds of post‐TAVR PPM implantation for men (OR, 1.16; 95% CI, 1.04–1.28); for patients with baseline mobitz type‐1 second‐degree atrioventricular block (OR, 3.13; 95% CI, 1.64–5.93), left anterior hemiblock (OR, 1.43; 95% CI, 1.09–1.86), bifascicular block (OR, 2.59; 95% CI, 1.52–4.42), right bundle‐branch block (OR, 2.48; 95% CI, 2.17–2.83), and for periprocedural atriorventricular block (OR, 4.17; 95% CI, 2.69–6.46). The mechanically expandable valves had 1.44 (95% CI, 1.18–1.76), while self‐expandable valves had 1.93 (95% CI, 1.42–2.63) fold higher odds of PPM requirement compared with self‐expandable and balloon‐expandable valves, respectively.

Conclusions

Male sex, baseline atrioventricular conduction delays, intraprocedural atrioventricular block, and use of mechanically expandable and self‐expanding prosthesis served as positive predictors of PPM implantation in patients undergoing TAVR.

Transcatheter Aortic Valve Replacement for Bicuspid Aortic Valve Stenosis: A Practical Operative Overview

The bicuspid aortic valve (BAV) represents a complex anatomic scenario for transcatheter aortic valve replacement (TAVR) because of its unique technical challenges. As TAVR is moving towards younger and lower-risk populations, the proportion of BAV patients undergoing TAVR is expected to rise. Initial experiences of TAVR with first-generation transcatheter heart valves in high surgical risk patients with BAV stenosis showed higher rates of device failure and periprocedural complications as compared to tricuspid anatomy. The subsequent advances in imaging techniques and understanding of BAV anatomy, new iterations of transcatheter heart valves, and growing operators' experience yielded better outcomes. However, in the lack of randomized trials and rigorous evidence, the field of TAVR in BAV has been driven by empirical observations, with wide variability in transcatheter heart valve sizing and implantation techniques across different centers and operators. Thus, in this review article, we provide a fully illustrated overview of operative periprocedural steps for TAVR in BAV stenosis, though recognizing that it still remains anecdotal.

Comparison of the results of transcatheter aortic valve implantation in patients with bicuspid and tricuspid aortic valve

Introduction

Indications for transcatheter aortic valve implantation (TAVI) are constantly expanding, including younger patients. Bicuspid aortic valves (BAV) often occur in this group. In order to achieve optimal treatment results in younger patients, it is necessary to develop an effective method for selecting the size of implanted valves.

Aim

To compare the results of TAVI with use of a self-expanding prosthesis in patients with a BAV and a tricuspid aortic valve (TAV) with valve selection based on annular sizing.

Material and methods

The diagnosis of BAV and TAV and measurements (annular sizing) were based on multi-slice computed tomography scans. Eighty-three patients received a self-expanding CoreValve or Evolut R prosthesis. In group I (BAV) there were 21 (25.3%) patients and in group II (TAV) there were 62 (74.7%) patients.

Results

The groups did not differ in terms of baseline clinical characteristics. Device success was achieved in 16 (76.2%) and 55 (88.7%) (p = NS) in group I and II respectively. Composite endpoints: early safety occurred in 5 (23.8%) and 11 (17.7%) patients (p =NS) in group I and II respectively; clinical efficacy occurred in 10 (47.6%) and 28 (45.2%) patients (p = NS) in group I and II respectively. 30-day mortality was 4.8% vs 9.7%, 1-year mortality was 28.6% vs 17.7% (p = NS) in group I and II respectively.

Conclusions

TAVI in patients with severe aortic stenosis and BAV is as effective as in patients with TAV using self-expanding prostheses if the valve selection is based on annular sizing.

Kidney injury as post-interventional complication of TAVI

Transcatheter aortic valve implantation (TAVI) is an accepted treatment approach of aortic stenosis. In the beginning, this technique was executed in high-risk patients only. Today, intermediate-risk patients are also amenable for TAVI, as long as the transfemoral approach is chosen. Numerous predictors have been identified that could lead to periprocedural complications and are defined by patient co-morbidities as well as being inherent to the technical approach. Although vascular complications and postinterventional paravalvular regurgitation have been minimized over the past years by revised technologies and techniques, there is a prevailing individual risk brought about by the specific pathophysiology of the cardiorenal syndrome.

Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

BACKGROUND

Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations.

METHODS AND RESULTS

The dataset consists of 451 consecutive patients undergoing elective TAVI between February 2014 and June 2016. The applied machine learning methods were neural networks, support vector machines, and random forests. Their performance was evaluated using five-fold nested cross-validation. Considering all 83 features, the performance of all machine learning models in predicting all-cause intrahospital mortality (AUC 0.94-0.97) was significantly higher than both the STS risk score (AUC 0.64), the STS/ACC TAVR score (AUC 0.65), and all machine learning models using baseline characteristics only (AUC 0.72-0.82). Using an extreme boosting gradient, baseline troponin T was found to be the most important feature among all input variables. Overall, after feature selection, there was a slightly inferior performance. Stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations could not be accurately predicted.

CONCLUSIONS

Machine learning has the potential to improve patient selection and risk management of interventional cardiovascular procedures, as it is capable of making superior predictions compared to current logistic risk scores.

Current and Future Aspects of Multimodal Imaging, Diagnostic, and Treatment Strategies in Bicuspid Aortic Valve and Associated Aortopathies

Bicuspid aortic valve (BAV) is the most frequent congenital cardiac abnormality leading to premature aortic valve apparatus dysfunction and is often associated with aortopathy. Therefore, current guidelines recommend a surgical aortic valve replacement (SAVR), even if many patients are deemed inoperable owing to their comorbidities and require alternatives such as transcatheter aortic valve replacement (TAVR). However, BAV variations remain challenging for procedural success. Therefore, the latest development in different imaging modalities (echocardiography, multislice-computertomographie, cardiovascular magnetic resonance) allows in-depth analysis for preprocedural risk stratification, follow up, and patient selection. Furthermore, we shed light on the latest developments in pre- and periprocedural fusion imaging as well as on current and future treatment options.