The need to streamline patient management for coronavirus disease-19 (COVID-19) has become more pressing than ever. Chest X-rays (CXRs) provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge the severity of COVID-19 lung infections (and pneumonia in general) that can be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. Methods Images from a public COVID-19 database were scored retrospectively by three blinded experts in terms of the extent of lung involvement as well as the degree of opacity. A neural network model that was pre-trained on large (non-COVID-19) chest X-ray datasets is used to construct features for COVID-19 images which are predictive for our task. Results This study finds that training a regression model on a subset of the outputs from this pretrained chest X-ray model predicts our geographic extent score (range 0-8) with 1.14 mean absolute error (MAE) and our lung opacity score (range 0-6) with 0.78 MAE. Conclusions These results indicate that our model's ability to gauge the severity of COVID-19 lung infections could be used for escalation or de-escalation of care as well as monitoring treatment efficacy, especially in the ICU. To enable follow up work, we make our code, labels, and data available online.

Across the world’s coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main imaging tools, chest X-rays (CXRs) are common, fast, non-invasive, relatively cheap, and potentially bedside to monitor the progression of the disease. This paper describes the first public COVID-19 image data collection as well as a preliminary exploration of possible use cases for the data. This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19. It was manually aggregated from publication figures as well as various web based repositories into a machine learning (ML) friendly format with accompanying dataloader code. We collected frontal and lateral view imagery and metadata such as the time since first symptoms, intensive care unit (ICU) status, survival status, intubation status, or hospital location. We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient’s trajectory during treatment. Data can be accessed here: https://github.com/ieee8023/covid-chestxray-dataset

Background: In May 2019, Health Canada released a national recall of all macrotextured breast implants that later became international in July 2019 regarding increasing accounts of suspected breast implant–associated anaplastic large cell lymphoma. In Canada, this recall targeted Allergan’s Biocell implants. This report presents the postmortem of this comprehensive single-center recall, which had to be undertaken in a limited time. Methods: Four months after the beginning of the recall, the authors analyzed the transcript of meetings to characterize the team assembled during the recall. Then, to reconstruct the systemic work plan as well as the crucial steps and actors of the recall process, a chronologic table of the 5 meetings held during the recall, agendas and transcripts of every meeting, electronic correspondences, and other documents created during the recall were consulted. Results: Between 1996 and 2018, 1260 women were affected by the recall, meaning that they received Allergan’s macrotextured implants. Ninety-two patients underwent explantation of the device or will undergo implant explantation. To this day, no patient was diagnosed with breast implant–associated anaplastic large cell lymphoma. Conclusions: Our center’s experience highlights the utmost importance of building a national breast implants registry. We recommend breast centers to develop preestablished crisis centers and train staff to better prepare for future device recalls and minimize waste of time. Finally, we believe that implants should be identified based on the characteristics rather than their brand name.

Summary: Acellular dermal matrices have become a mandatory tool in reconstructive breast surgery. Since their introduction, they have been considered to be nonreactive and nonimmunogenic scaffolds. However, some patients who undergo implant-based breast reconstruction with acellular dermal matrices develop postoperative cutaneous erythema overlying their matrices, a condition commonly referred to as red breast syndrome. The aim of this study was to gain a better understanding of this phenomenon. An analysis was conducted on consecutive patients who underwent acellular dermal matrix– and implant-based breast reconstructions and developed red breast syndrome that was treated surgically between April of 2017 and June of 2018 at the authors’ institution. During surgery, 1-cm2 specimens of acellular dermal matrix were sampled and analyzed by scanning electron microscopy. Observations were charted to score and record the presence and thickness of biofilm, and for identification of bacteria. These measurements were performed using Adobe Photoshop CS6 Extended software. Six postmastectomy breast reconstruction patients were included, all with AlloDerm Ready-to-Use–based reconstructions. All specimens were colonized by various bacteria ranging from Gram-negative bacilli to Gram-positive microorganisms. Biofilm was present in all studied specimens. The cause of skin erythema overlying acellular dermal matrix grafts, and the so-called red breast syndrome, may be related to contamination with various bacteria. Although contamination was omnipresent in analyzed samples, its clinical significance is variable. Even if acellular dermal matrix–based reconstructions are salvaged, this could come at the price of chronic local inflammation.