Real Time Non-Invasive Hemodynamic Assessment of Ventricular Tachycardia

Hemodynamically unstable ventricular tachycardia (VT) is a critical cardiac arrhythmia associated with hemodynamic compromise that requires immediate cardioversion to prevent sudden cardiac death. Since unnecessary cardioverter defibrillators shocks damage the heart and increase the risk of mortality, the discrimination between unstable (i.e. requiring cardioversion) and stable (i.e. not requiring cardioversion) VT is of paramount importance. The aim of this study was to propose and assess non-invasive identification of hemodynamically unstable VT using photoplethysmography (PPG). Seventy-five (n = 75) episodes of VT were recorded in 14 patients undergoing invasive electrophysiological studies for VT catheter ablation. Invasive continuous arterial blood pressure (ABP), PPG and electrocardiogram (ECG) were simultaneously recorded. VTs were classified as unstable if during the first 10 seconds from onset, the mean ABP (PVT < 60PVT) was PVT < 60PVT <60 mmHg or if PVT dropped more than 30% with respect to a 10 seconds baseline (i.e. ratio RABP <0.70). Five PPG morphological features were derived and compared to the heart rate from the ECG. PPG markers detected hemodynamically unstable VT with accuracy as high as 86% and were more accurate than the heart rate. The mean absolute slope was the best PPG parameter for classification of PVT< 60PVT <60PVT < 60 mmHg (AUC = 0.85, Sensitivity = 72%, Specificity = 86%) and RABP <0.70RABP< 0.70 (AUC = 0.90, Sensitivity = 83%, Specificity = 89%) and it was automatically selected in the best two-variables logistic regression, for which AUC = 0.94. In conclusion, PPG analysis can accurately identify haemodynamically unstable VTs and has potential to enable optimization of VT therapy and reduce unnecessary and harmful cardioversion shocks

Russia’s Digital Economy Program : An Effective Strategy for Digital Transformation?

Peer reviewe

Denoising diffusion models for out-of-distribution detection

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://github.com/marksgraham/ddpm-ood

Hierarchical Brain Parcellation with Uncertainty

Many atlases used for brain parcellation are hierarchically organised, progressively dividing the brain into smaller sub-regions. However, state-of-the-art parcellation methods tend to ignore this structure and treat labels as if they are ‘flat’. We introduce a hierarchically-aware brain parcellation method that works by predicting the decisions at each branch in the label tree. We further show how this method can be used to model uncertainty separately for every branch in this label tree. Our method exceeds the performance of flat uncertainty methods, whilst also providing decomposed uncertainty estimates that enable us to obtain self-consistent parcellations and uncertainty maps at any level of the label hierarchy. We demonstrate a simple way these decision-specific uncertainty maps may be used to provided uncertainty-thresholded tissue maps at any level of the label tree

Limitations and Challenges in Mapping Ventricular Tachycardia: New Technologies and Future Directions

Recurrent episodes of ventricular tachycardia in patients with structural heart disease are associated with increased mortality and morbidity, despite the life-saving benefits of implantable cardiac defibrillators. Reducing implantable cardiac defibrillator therapies is important, as recurrent shocks can cause increased myocardial damage and stunning, despite the conversion of ventricular tachycardia/ventricular fibrillation. Catheter ablation has emerged as a potential therapeutic option either for primary or secondary prevention of these arrhythmias, particularly in post-myocardial infarction cases where the substrate is well defined. However, the outcomes of catheter ablation of ventricular tachycardia in structural heart disease remain unsatisfactory in comparison with other electrophysiological procedures. The disappointing efficacy of ventricular tachycardia ablation in structural heart disease is multifactorial. In this review, we discuss the issues surrounding this and examine the limitations of current mapping approaches, as well as newer technologies that might help address them

Measuring maternal mortality : an overview of opportunities and options for developing countries

Background:There is currently an unprecedented expressed need and demand for estimates of maternal mortality in developing countries. This has been stimulated in part by the creation of a Millennium Development Goal that will be judged partly on the basis of reductions in maternal mortality by 2015. Methods: Since the launch of the Safe Motherhood Initiative in 1987, new opportunities for data capture have arisen and new methods have been developed, tested and used. This paper provides a pragmatic overview of these methods and the optimal measurement strategies for different developing country contexts. Results: There are significant recent advances in the measurement of maternal mortality, yet also room for further improvement, particularly in assessing the magnitude and direction of biases and their implications for different data uses. Some of the innovations in measurement provide efficient mechanisms for gathering the requisite primary data at a reasonably low cost. No method, however, has zero costs. Investment is needed in measurement strategies for maternal mortality suited to the needs and resources of a country, and which also strengthen the technical capacity to generate and use credible estimates. Conclusion: Ownership of information is necessary for it to be acted upon: what you count is what you do. Difficulties with measurement must not be allowed to discourage efforts to reduce maternal mortality. Countries must be encouraged and enabled to count maternal deaths and act.WJG is funded partially by the University of Aberdeen. OMRC is partially funded by the London School of Hygiene and Tropical Medicine. CS and SA are partially funded by Johns Hopkins University. CAZ is funded by the Health Metrics Network at the World Health Organization. WJG, OMRC, CS and SA are also partially supported through an international research program, Immpact, funded by the Bill & Melinda Gates Foundation, the Department for International Development, the European Commission and USAID

Time-resolved tomographic quantification of the microstructural evolution of ice cream

Ice cream is a complex multi-phase colloidal soft-solid and its three-dimensional microstructure plays a critical role in determining the oral sensory experience or mouthfeel. Using in-line phase contrast synchrotron X-ray tomography, we capture the rapid evolution of the ice cream microstructure during heat shock conditions in situ and operando, on a time scale of minutes. The further evolution of the ice cream microstructure during storage and abuse was captured using ex situ tomography on a time scale of days. The morphology of the ice crystals and unfrozen matrix during these thermal cycles was quantified as an indicator for the texture and oral sensory perception. Our results reveal that the coarsening is due to both Ostwald ripening and physical agglomeration, enhancing our understanding of the microstructural evolution of ice cream during both manufacturing and storage. The microstructural evolution of this complex material was quantified, providing new insights into the behavior of soft-solids and semi-solids, including many foodstuffs, and invaluable data to both inform and validate models of their processing

An Appraisal of the Maternal Mortality Decline in Nepal

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