Coronary Artery Disease in Patients Undergoing Transvalvular Aortic Valve Implantation.

Coronary artery disease (CAD) is common in patients with severe aortic stenosis. With the advent of transcatheter aortic valve implantation (TAVI) as a therapeutic option, management of CAD in such patients has undergone a revolution. Younger patients are now candidates for treatment, and have a greater life-time probability of requiring post-TAVI coronary access. Considerations include pre-procedural assessment and revascularisation, procedural planning to avoid coronary obstruction as well as optimisation of post-procedural coronary access. The authors review the challenges of managing CAD in TAVI patients, shed light on the evidence base, and provide guidance on how to optimise management.

Transcatheter aortic valve replacement for aortic regurgitation in Asians: TAVR for aortic regurgitation in Asians.

AIMS: Although surgical aortic valve replacement (SAVR) is currently the recommended intervention for patients with native AR without aortic stenosis, a significant proportion of Asian patients undergo transcatheter aortic valve replacement (TAVR), which has not been studied fully for safety and outcomes. This systematic review aims to examine the characteristics and outcomes of Asian patients with pure native aortic regurgitation (AR) undergoing TAVR. METHODS AND RESULTS: PubMed, Embase, Scopus, Web of Science and Cochrane CENTRAL were systematically searched for randomised controlled trials, observational studies and case reports published  from inception to 2 April 2020, involving patients of Asian ethnicity with pure native aortic regurgitation who had undergone TAVR. Our primary outcome was all-cause mortality, with secondary outcomes including all major complications. Five studies (n=274 patients) and eight case reports were included. Device success was reported in 94.9% of the patients, the all-cause mortality rate was 4.4%, 2.5% were converted to SAVR, 1.7% had post-operative paravalvular leak and 6.7% required permanent pacemaker implantation. CONCLUSIONS: TAVR has demonstrated acceptable safety and efficacy in Asian patients with pure AR displaying low mortality rates and few adverse outcomes.

Sleep apnoea and cardiovascular outcomes after coronary artery bypass grafting.

OBJECTIVE: Patients with advanced coronary artery disease are referred for coronary artery bypass grafting (CABG) and it remains unknown if sleep apnoea is a risk marker. We evaluated the association between sleep apnoea and major adverse cardiac and cerebrovascular events (MACCE) in patients undergoing non-emergent CABG. METHODS: This was a prospective cohort study conducted between November 2013 and December 2018. Patients from four public hospitals referred to a tertiary cardiac centre for non-emergent CABG were recruited for an overnight sleep study using a wrist-worn Watch-PAT 200 device prior to CABG. RESULTS: Among the 1007 patients who completed the study, sleep apnoea (defined as apnoea-hypopnoea index ≥15 events per hour) was diagnosed in 513 patients (50.9%). Over a mean follow-up period of 2.1 years, 124 patients experienced the four-component MACCE (2-year cumulative incidence estimate, 11.3%). There was a total of 33 cardiac deaths (2.5%), 42 non-fatal myocardial infarctions (3.7%), 50 non-fatal strokes (4.9%) and 36 unplanned revascularisations (3.2%). The crude incidence of MACCE was higher in the sleep apnoea group than the non-sleep apnoea group (2-year estimate, 14.7% vs 7.8%; p=0.002). Sleep apnoea predicted the incidence of MACCE in unadjusted Cox regression analysis (HR 1.69; 95% CI 1.18 to 2.43), and remained statistically significant (adjusted HR 1.57; 95% CI 1.09 to 2.25), after adjustment for age, sex, body mass index, left ventricular ejection fraction, diabetes mellitus, hypertension, chronic kidney disease and excessive daytime sleepiness. CONCLUSION: Sleep apnoea is independently associated with increased MACCE in patients undergoing CABG. TRIAL REGISTRATION NUMBER: NCT02701504.

Distributed Newton Methods for Deep Neural Networks.

Deep learning involves a difficult nonconvex optimization problem with a large number of weights between any two adjacent layers of a deep structure. To handle large data sets or complicated networks, distributed training is needed, but the calculation of function, gradient, and Hessian is expensive. In particular, the communication and the synchronization cost may become a bottleneck. In this letter, we focus on situations where the model is distributedly stored and propose a novel distributed Newton method for training deep neural networks. By variable and feature-wise data partitions and some careful designs, we are able to explicitly use the Jacobian matrix for matrix-vector products in the Newton method. Some techniques are incorporated to reduce the running time as well as memory consumption. First, to reduce the communication cost, we propose a diagonalization method such that an approximate Newton direction can be obtained without communication between machines. Second, we consider subsampled Gauss-Newton matrices for reducing the running time as well as the communication cost. Third, to reduce the synchronization cost, we terminate the process of finding an approximate Newton direction even though some nodes have not finished their tasks. Details of some implementation issues in distributed environments are thoroughly investigated. Experiments demonstrate that the proposed method is effective for the distributed training of deep neural networks. Compared with stochastic gradient methods, it is more robust and may give better test accuracy.