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BACKGROUND: Tricuspid regurgitation (TR) is common following heart transplantation and has been shown to adversely influence patient outcomes. The aim of this study was to identify causes of progression to moderate-severe TR in the first 2 y after transplantation. METHODS: This was a retrospective, single-center study of all patients who underwent heart transplantation over a 6-y period. Transthoracic echocardiogram (TTE) was performed at month 0, between 6 and 12 mo, and 1-2 y postoperatively to determine the presence and severity of TR. RESULTS: A total of 163 patients were included, of whom 142 underwent TTE before first endomyocardial biopsy. At month 0, 127 (78%) patients had nil-mild TR before first biopsy, whereas 36 (22%) had moderate-severe TR. In patients with nil-mild TR, 9 (7%) progressed to moderate-severe TR by 6 mo and 1 underwent tricuspid valve (TV) surgery. Of patients with moderate-severe TR before first biopsy, by 2 y, 3 had undergone TV surgery. The use of postoperative extracorporeal membrane oxygenation (ECMO) in the latter group was significant (78%; P < 0.05) as was rejection profile ( P = 0.02). Patients with late progressive moderate-severe TR had a significantly higher 2-y mortality than those who had moderate-severe TR immediately. CONCLUSIONS: Overall, our study has shown that in the 2 main groups of interest (early moderate-severe TR and progression from nil-mild to moderate-severe TR), TR is more likely to be the result of significant underling graft dysfunction rather than the cause of it.

Heart transplantation from donation after circulatory death (DCD) donors has the potential to substantially increase overall heart transplant activity. The aim of this report is to review the first 8 y of our clinical heart transplant program at St Vincent's Hospital Sydney, to describe how our program has evolved and to report the impact that changes to our retrieval protocols have had on posttransplant outcomes. Since 2014, we have performed 74 DCD heart transplants from DCD donors utilizing a direct procurement protocol followed by normothermic machine perfusion. Changes to our retrieval protocol have resulted in a higher retrieval rate from DCD donors and fewer rejections of DCD hearts during normothermic machine perfusion. Compared with our previously reported early experience in the first 23 transplants, we have observed a significant reduction in the incidence of severe primary graft dysfunction from 35% (8/23) to 8% (4/51) in the subsequent 51 transplant recipients ( P < 0.01). The only withdrawal time interval significantly associated with severe primary graft dysfunction was the asystolic warm ischemic time: 15 (12-17) versus 13 (11-14) min ( P < 0.05). One- and 5-y survival of DCD heart transplant recipients was 94% and 88%, comparable to that of a contemporary cohort of donation after brain death recipients: 87 and 81% ( P -value was not significant). In conclusion, heart transplantation from DCD donors has become a major contributor to our overall transplant activity accounting for almost 30% of all transplants performed by our program in the last 2 y, with similar DCD and donation after brain death outcomes.

BACKGROUND: Frailty is associated with adverse outcomes in advanced heart failure. We studied the impact of frailty on postoperative outcomes in bridge to transplant (BTT) durable mechanical circulatory support (MCS) recipients. METHODS: Patients undergoing left ventricular assist device (LVAD, n = 96) or biventricular support (BiV, n = 11) as BTT underwent frailty assessment. Frailty was defined as ≥ 3 physical domains of the Fried's Frailty Phenotype (FFP) or ≥ 2 physical domains of the FFP plus cognitive impairment on the Montreal Cognitive Assessment (MoCA). RESULTS: No difference in mortality at 360 days was observed in frail (n = 6/38, 15.8%) vs non-frail (n = 4/58, 6.9%) LVAD supported patients, p = 0.19. However, there was a significant excess mortality in frail BiV (n = 4/5) vs non-frail BiV (n = 0/6) supported patients, p = 0.013. In all patients, frail patients compared to non-frail patients experienced longer intensive care unit stay, 12 vs 6 days (p < 0.0001) and hospital length of stay, 48 vs 27 days (p < 0.0001). There was no difference in hemocompatibility and infection related adverse events. The majority (n = 22/29, 75.9%) of frail patients became non-frail following MCS; contrastingly, a minority (n = 3/42, 7.1%) became frail from being non-frail (p = 0.0003). CONCLUSIONS: Abnormal markers of frailty are common in patients undergoing BTT-MCS support and those used herein predict mortality in BiV-supported patients, but not in LVAD patients. These findings may help us better identify patients who will benefit most from BiV-BTT therapy.

Introduction: Coronavirus disease 2019 (COVID-19) is a contagious disease that is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Health care workers are at risk of infection from aerosolisation of respiratory secretions, droplet and contact spread. There are a number of procedures that represent a high risk of aerosol generation during cardiothoracic surgery. It is important that adequate training, equipment and procedures are in place to reduce that risk. Recommendations: We provide a number of key recommendations, which reduce the risk of aerosol generation during cardiothoracic surgery and help protect patients and staff. These include general measures such as patient risk stratification, appropriate use of personal protective equipment, consideration to delay surgery in positive patients, and careful attention to theatre planning and preparation. There are also recommended procedural interventions during airway management, transoesophageal echocardiography, cardiopulmonary bypass, chest drain management and specific cardiothoracic surgical procedures. Controversies exist regarding the management of low risk patients undergoing procedures at high risk of aerosol generation, and recommendations for these patients will change depending on the regional prevalence, risk of community transmission and the potential for asymptomatic patients attending for these procedures. Changes in management as a result of this statement: This statement reflects changes in management based on expert opinion, national guidelines and available evidence. Our knowledge with regard to COVID-19 continues to evolve and with this, guidance may change and develop. Our colleagues are urged to follow national guidelines and institutional recommendations regarding best practices to protect their patients and themselves. Endorsed by: Australian and New Zealand Society of Cardiac and Thoracic Surgeons and the Anaesthetic Continuing Education Cardiac Thoracic Vascular a

Deep learning (DL) models have been successfully applied in video-based affective computing, allowing to recognize emotions and mood, or to estimate the intensity of pain or stress based on facial expressions. Despite the advances with state-of-the-art DL models for spatio-temporal recognition of facial expressions associated with depression, some challenges remain in the cost-effective application of 3D-CNNs: (1) 3D convolutions employ structures with fixed temporal depth that decreases the potential to extract discriminative representations due to the usually small difference of spatio-temporal variations along different depression levels; and (2) the computationally complexity of these models with consequent susceptibility to overfitting. To address these challenges, we propose a novel DL architecture called the Maximization and Differentiation Network (MDN) in order to effectively represent facial expression variations that are relevant for depression assessment. The MDN operates without 3D convolutions, by exploring multiscale temporal information using a maximization block that captures smooth facial variations, and a difference block to encode sudden facial variations. Extensive experiments using our proposed MDN result in improved performance while reducing the number of parameters by more than 3 when compared with 3D-ResNet models. Our model also outperforms other 3D models and achieves state-of-the-art results for depression detection. Code available at: https://github.com/wheidima/MDN.

BACKGROUND Frailty is prevalent in the patients with advanced heart failure; however, its impact on clinical outcomes after heart transplantation (HTx) is unclear. The aim of this study was to assess the impact of pre-transplant frailty on mortality and the duration of hospitalization after HTx. METHODS We retrospectively reviewed the post-transplant outcomes of 140 patients with advanced heart failure who had undergone frailty assessment within the 6-month interval before HTx: 43 of them were frail (F) and 97 were non-frail (NF). RESULTS Post-transplant survival rates for the NF cohort at 1 and 12 months were 97% (93–100) and 95% (91–99) (95% CI), respectively. In contrast, post-transplant survival rates for the F cohort at the same time points were 86% (76–96) and 74% (60–84) (p < 0.0008 vs NF cohort), respectively. The Cox proportional hazards regression analysis demonstrated that pre-transplant frailty was an independent predictor of post-transplant mortality with a hazard ratio of 3.8 (95% CI: 1.4–10.5). Intensive care unit and hospital length of stay were 2 and 7 days longer in the F cohort (both p < 0.05), respectively, than in the NF cohort. CONCLUSIONS Frailty within 6 months before HTx is independently associated with increased mortality and prolonged hospitalization after transplantation. Future research should focus on the development of strategies to mitigate the adverse effects of pre-transplant frailty.

Recently, deep learning models have been successfully employed in video-based affective computing applications. One key application is automatic depression recognition from facial expressions. State-of-the-art approaches to recognize depression typically explore spatial and temporal information individually, by using convolutional neural networks (CNNs) to analyze appearance information, and then by either mapping feature variations or averaging the depression level over video frames. This approach has limitations to represent dynamic information that can help to discriminate between depression levels. In contrast, 3D CNN-based models can directly encode the spatio-temporal relationships, although these models rely on fixed-range temporal information and single receptive field. This approach limits the ability to capture facial expression variations with diverse ranges, and the exploitation of diverse facial areas. In this paper, a novel 3D CNN architecture the Multiscale Spatiotemporal Network (MSN) is introduced to effectively represent facial information related to depressive behaviours. The basic structure of the model is composed of parallel convolutional layers with different temporal depths and sizes of receptive field, which allows the MSN to explore a wide range of spatio-temporal variations in facial expressions. Experimental results on two benchmark datasets show that our MSN is effective, outperforming state-of-the-art methods in automatic depression recognition.

Depression is a mental illness that may be harmful to an individual’s health. Using deep learning models to recognize the facial expressions of individuals captured in videos has shown promising results for automatic depression detection. Typically, depression levels are recognized using 2D-Convolutional Neural Networks (CNNs) that are trained to extract static features from video frames, which impairs the capture of dynamic spatio-temporal relations. As an alternative, 3D-CNNs may be employed to extract spatiotemporal features from short video clips, although the risk of overfitting increases due to the limited availability of labeled depression video data. To address these issues, we propose a novel temporal pooling method to capture and encode the spatio-temporal dynamic of video clips into an image map. This approach allows fine-tuning a pre-trained 2D CNN to model facial variations, and thereby improving the training process and model accuracy. Our proposed method is based on two-stream model that performs late fusion of appearance and dynamic information. Extensive experiments on two benchmark AVEC datasets indicate that the proposed method is efficient and outperforms the state-of-the-art schemes.