Optimizations of stent and tissue leaflets in a new surgical bovine pericardial valve

Five-year outcomes of surgical aortic valve replacement with a novel bovine pericardial bioprosthesis

OBJECTIVES

The short-term performance of the Cingular bovine pericardial aortic valve was proven. This study evaluated its 5-year safety and haemodynamic outcomes.

METHODS

It enrolled 148 patients who underwent surgical aortic valve replacement with the Cingular bovine pericardial aortic valve between March 2016 and October 2017 in 5 clinical centres in China. Safety and haemodynamic outcomes were followed up to 5 years. The incidence of all-cause mortality, structural valve deterioration and reintervention was estimated by Kaplan-Meier analysis.

RESULTS

The mean age of patients was 67.7 [standard deviation (SD) 5.1] years, and 36.5% of patients were female. The mean follow-up was 5.3 (SD 1.2) years. Five-year freedom from all-cause mortality, structural valve deterioration and all-cause reintervention were 91.2%, 100% and 99.3%, respectively. At 5 years, the mean gradient and effective orifice area of all sizes combined were 14.0 (SD 5.5) mmHg and 1.9 (SD 0.3) cm2, respectively. For 19- and 21-mm sizes of aortic prostheses, the mean gradients and effective orifice area at 5 years were 17.5 (SD 7.0) mmHg and 1.6 (SD 0.2) cm2 and 13.7 (SD 6.7) mmHg and 1.8 (SD 0.3) cm2, respectively. The incidence of moderate or severe patient-prosthesis mismatch was 4.1% and 0.0% patients at 5 years, respectively.

CONCLUSIONS

The 5-year safety and haemodynamic outcomes of Cingular bovine pericardial aortic valve are encouraging. Longer-term follow-up is warranted to assess its true durability.

Perfect prosthetic heart valve: generative design with machine learning, modeling, and optimization

Majority of modern techniques for creating and optimizing the geometry of medical devices are based on a combination of computer-aided designs and the utility of the finite element method This approach, however, is limited by the number of geometries that can be investigated and by the time required for design optimization. To address this issue, we propose a generative design approach that combines machine learning (ML) methods and optimization algorithms. We evaluate eight different machine learning methods, including decision tree-based and boosting algorithms, neural networks, and ensembles. For optimal design, we investigate six state-of-the-art optimization algorithms, including Random Search, Tree-structured Parzen Estimator, CMA-ES-based algorithm, Nondominated Sorting Genetic Algorithm, Multiobjective Tree-structured Parzen Estimator, and Quasi-Monte Carlo Algorithm. In our study, we apply the proposed approach to study the generative design of a prosthetic heart valve (PHV). The design constraints of the prosthetic heart valve, including spatial requirements, materials, and manufacturing methods, are used as inputs, and the proposed approach produces a final design and a corresponding score to determine if the design is effective. Extensive testing leads to the conclusion that utilizing a combination of ensemble methods in conjunction with a Tree-structured Parzen Estimator or a Nondominated Sorting Genetic Algorithm is the most effective method in generating new designs with a relatively low error rate. Specifically, the Mean Absolute Percentage Error was found to be 11.8% and 10.2% for lumen and peak stress prediction respectively. Furthermore, it was observed that both optimization techniques result in design scores of approximately 95%. From both a scientific and applied perspective, this approach aims to select the most efficient geometry with given input parameters, which can then be prototyped and used for subsequent in vitro experiments. By proposing this approach, we believe it will replace or complement CAD-FEM-based modeling, thereby accelerating the design process and finding better designs within given constraints. The repository, which contains the essential components of the study, including curated source code, dataset, and trained models, is publicly available at https://github.com/ViacheslavDanilov/generative_design.

Two-Year Clinical Follow-Up Assessment of the Novel Cingular Surgical Bovine Pericardial Valve

Objectives: To evaluate the 2-year clinical safety and hemodynamic outcomes of the Cingular bovine pericardial bioprosthesis. Methods: A prospective, multicenter, single-arm trial was conducted in patients who required aortic or mitral valve replacement. From March 2016 to October 2017, 197 patients were implanted with the Cingular bovine pericardial valve at five sites in China. The clinical outcomes and hemodynamic performance were assessed through a 2-year follow-up. Clinical safety events were reviewed by an independent clinical events committee, and echocardiographic data were assessed by an independent core laboratory. Results: The mean age was 66.9 ± 4.9 years. The 2-year survival rate was 96.4%. A complete 2-year clinical follow-up was achieved in 189 of 190 survivors. No case of structural valve deterioration, major perivalvular leak, prosthetic valve endocarditis, or valve-related reoperation was seen. For the aortic valve, the mean pressure gradient observed was 12.5 ± 4.0 mm Hg, and the effective orifice area (EOA) was 2.0 ± 0.3 cm2. For the smaller size aortic valves, 19 mm and 21 mm, respective mean EOA values of 1.7 ± 0.2 cm2 and 1.8 ± 0.2 cm2 were found. The values for mean pressure gradient and mean EOA for mitral bioprostheses were 4.0 ± 1.4 mm Hg and 2.2 ± 0.3 cm2, respectively. There was no significant change between 1-year and 2-year hemodynamic performance. Conclusions: The Cingular bovine pericardial valve showed favorable clinical safety and hemodynamic outcomes over a 2-year follow-up. Further follow-up is required to validate the long-term durability.