Does time of surgery influence the rate of false-negative appendectomies?:A retrospective observational study of 274 patients

Background Multiple disciplines have described an “after-hours effect” relating to worsened mortality and morbidity outside regular working hours. This retrospective observational study aimed to evaluate whether diagnostic accuracy of a common surgical condition worsened after regular hours. Methods Electronic operative records for all non-infant patients (age > 4 years) operated on at a single centre for presumed acute appendicitis were retrospectively reviewed over a 56-month period (06/17/2012–02/01/2017). The primary outcome measure of unknown diagnosis was compared between those performed in regular hours (08:00–17:00) or off hours (17:01–07:59). Pre-clinical biochemistry and pre-morbid status were recorded to determine case heterogeneity between the two groups, along with secondary outcomes of length of stay and complication rate. Results Out of 289 procedures, 274 cases were deemed eligible for inclusion. Of the 133 performed in regular hours, 79% were appendicitis, compared to 74% of the 141 procedures performed off hours. The percentage of patients with an unknown diagnosis was 6% in regular hours compared to 15% off hours (RR 2.48; 95% CI 1.14–5.39). This was accompanied by increased numbers of registrars (residents in training) leading procedures off hours (37% compared to 24% in regular hours). Pre-morbid status, biochemistry, length of stay and post-operative complication rate showed no significant difference. Conclusions This retrospective study suggests that the rate of unknown diagnoses for acute appendicitis increases overnight, potentially reflecting increased numbers of unnecessary procedures being performed off hours due to poorer diagnostic accuracy. Reduced levels of staffing, availability of diagnostic modalities and changes to workforce training may explain this, but further prospective work is required. Potential solutions may include protocolizing the management of common acute surgical conditions and making more use of non-resident on call senior colleagues

Natural hazards in Australia: heatwaves

As part of a special issue on natural hazards, this paper reviews the current state of scientific knowledge of Australian heatwaves. Over recent years, progress has been made in understanding both the causes of and changes to heatwaves. Relationships between atmospheric heatwaves and large-scale and synoptic variability have been identified, with increasing trends in heatwave intensity, frequency and duration projected to continue throughout the 21st century. However, more research is required to further our understanding of the dynamical interactions of atmospheric heatwaves, particularly with the land surface. Research into marine heatwaves is still in its infancy, with little known about driving mechanisms, and observed and future changes. In order to address these knowledge gaps, recommendations include: focusing on a comprehensive assessment of atmospheric heatwave dynamics; understanding links with droughts; working towards a unified measurement framework; and investigating observed and future trends in marine heatwaves. Such work requires comprehensive and long-term collaboration activities. However, benefits will extend to the international community, thus addressing global grand challenges surrounding these extreme events

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications

Cardiovascular magnetic resonance (CMR) tagging has been established as an essential technique for measuring regional myocardial function. It allows quantification of local intramyocardial motion measures, e.g. strain and strain rate. The invention of CMR tagging came in the late eighties, where the technique allowed for the first time for visualizing transmural myocardial movement without having to implant physical markers. This new idea opened the door for a series of developments and improvements that continue up to the present time. Different tagging techniques are currently available that are more extensive, improved, and sophisticated than they were twenty years ago. Each of these techniques has different versions for improved resolution, signal-to-noise ratio (SNR), scan time, anatomical coverage, three-dimensional capability, and image quality. The tagging techniques covered in this article can be broadly divided into two main categories: 1) Basic techniques, which include magnetization saturation, spatial modulation of magnetization (SPAMM), delay alternating with nutations for tailored excitation (DANTE), and complementary SPAMM (CSPAMM); and 2) Advanced techniques, which include harmonic phase (HARP), displacement encoding with stimulated echoes (DENSE), and strain encoding (SENC). Although most of these techniques were developed by separate groups and evolved from different backgrounds, they are in fact closely related to each other, and they can be interpreted from more than one perspective. Some of these techniques even followed parallel paths of developments, as illustrated in the article. As each technique has its own advantages, some efforts have been made to combine different techniques together for improved image quality or composite information acquisition. In this review, different developments in pulse sequences and related image processing techniques are described along with the necessities that led to their invention, which makes this article easy to read and the covered techniques easy to follow. Major studies that applied CMR tagging for studying myocardial mechanics are also summarized. Finally, the current article includes a plethora of ideas and techniques with over 300 references that motivate the reader to think about the future of CMR tagging

On the adaptability of unsupervised CNN-based deformable image registration to unseen image domains

Deformable image registration is a fundamental problem in medical image analysis. During the last years, several methods based on deep convolutional neural networks (CNN) proved to be highly accurate to perform this task. These models achieved state-of-the-art accuracy while drastically reducing the required computational time, but mainly focusing on images of specific organs and modalities. To date, no work has reported on how these models adapt across different domains. In this work, we ask the question: can we use CNN-based registration models to spatially align images coming from a domain different than the one/s used at training time? We explore the adaptability of CNN-based image registration to different organs/modalities. We employ a fully convolutional architecture trained following an unsupervised approach. We consider a simple transfer learning strategy to study the generalisation of such model to unseen target domains, and devise a one-shot learning scheme taking advantage of the unsupervised nature of the proposed method. Evaluation on two publicly available datasets of X-Ray lung images and cardiac cine magnetic resonance sequences is provided. Our experiments suggest that models learned in different domains can be transferred at the expense of a decrease in performance, and that one-shot learning in the context of unsupervised CNN-based registration is a valid alternative to achieve consistent registration performance when only a pair of images from the target domain is available