Despite the rapid expansion of transcatheter approaches for aortic valve implantation, the treatment of choice in patients presenting with multiple valvular heart disease remains surgical aortic valve replacement. Nonetheless, it is well known that cardiopulmonary bypass time and aortic cross-clamp time are important independent predictors of mortality in patients undergoing multivalve procedures (1).Approaches enabling a reduction in ischemia-reperfusion injury during valve procedures are very desirable, especially

Introduction: Primary cardiac sarcomas (PCSs) are an extremely rare and aggressive type of malignancies that have been described only by a limited number of observational studies. This study aimed to evaluate the currently existing evidence comparing surgical to multimodality treatment of PCS.Methods: We systematically reviewed Embase, MEDLINE, Cochrane Database, and Google Scholar, from inception to December 2020, for original articles about surgical and multimodality treatment of PCS. The outcomes included were mortality at various time points, resection margin status, and mean estimated survival. The pooled treatment effects were calculated using a random-effects model.Results: Ten studies including a total of 1570 patients met our inclusion criteria. Surgery was associated with significantly lower mortality when compared to conservative treatment at 1, 2, and 3 years, whereas no significant difference was found at 5 years. Furthermore, multimodality treatment showed significantly lower mortality at 1 year when compared to surgery alone, but not at 2 and 5 years. We found no difference in mortality between angiosarcomas and other PCS subtypes. Conclusion:Overall, surgery was found to provide a significant mortality advantage to PCS patients up to 3 years following treatment. Multimodality treatment might be of additional benefit, although only within the first year. Prospective randomized studies are needed to further explore these differences in the treatment of PCS.

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Medical soft robotics constitutes a rapidly developing field in the treatment of cardiovascular diseases, with a promising future for millions of patients suffering from heart failure worldwide. Herein, the present state and future direction of artificial muscle‐based soft robotic biomedical devices in supporting the inotropic function of the heart are reviewed, focusing on the emerging electrothermally artificial heart muscles (AHMs). Artificial muscle powered soft robotic devices can mimic the action of complex biological systems such as heart compression and twisting. These artificial muscles possess the ability to undergo complex deformations, aiding cardiac function while maintaining a limited weight and use of space. Two very promising candidates for artificial muscles are electrothermally actuated AHMs and biohybrid actuators using living cells or tissue embedded with artificial structures. Electrothermally actuated AHMs have demonstrated superior force generation while creating the prospect for fully soft robotic actuated ventricular assist devices. This review will critically analyze the limitations of currently available devices and discuss opportunities and directions for future research. Last, the properties of the cardiac muscle are reviewed and compared with those of different materials suitable for mechanical cardiac compression.

Background This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. Methods A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021. Results Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk. Conclusion ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.