Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Defining the scope of the European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet): A bottom-up and One Health approach

Background: Building the European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet) was proposed to strengthen the European One Health antimicrobial resistance (AMR) surveillance approach. Objectives: To define the combinations of animal species/production types/age categories/bacterial species/specimens/antimicrobials to be monitored in EARS-Vet. Methods: The EARS-Vet scope was defined by consensus between 26 European experts. Decisions were guided by a survey of the combinations that are relevant and feasible to monitor in diseased animals in 13 European countries (bottom-up approach). Experts also considered the One Health approach and the need for EARS-Vet to complement existing European AMR monitoring systems coordinated by the ECDC and the European Food Safety Authority (EFSA). Results: EARS-Vet plans to monitor AMR in six animal species [cattle, swine, chickens (broilers and laying hens), turkeys, cats and dogs], for 11 bacterial species (Escherichia coli, Klebsiella pneumoniae, Mannheimia haemolytica, Pasteurella multocida, Actinobacillus pleuropneumoniae, Staphylococcus aureus, Staphylococcus pseudintermedius, Staphylococcus hyicus, Streptococcus uberis, Streptococcus dysgalactiae and Streptococcus suis). Relevant antimicrobials for their treatment were selected (e.g. tetracyclines) and complemented with antimicrobials of more specific public health interest (e.g. carbapenems). Molecular data detecting the presence of ESBLs, AmpC cephalosporinases and methicillin resistance shall be collected too. Conclusions: A preliminary EARS-Vet scope was defined, with the potential to fill important AMR monitoring gaps in the animal sector in Europe. It should be reviewed and expanded as the epidemiology of AMR changes, more countries participate and national monitoring capacities improve

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation : The MMs Challenge

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (MMs) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field

Toward Tumor Fight and Tumor Microenvironment Remodeling: PBA Induces Cell Cycle Arrest and Reduces Tumor Hybrid Cells' Pluripotency in Bladder Cancer.

Bladder cancer (BC) is the second most frequent cancer of the genitourinary system. The most successful therapy since the 1970s has consisted of intravesical instillations of Bacillus Calmette-Guérin (BCG) in which the tumor microenvironment (TME), including macrophages, plays an important role. However, some patients cannot be treated with this therapy due to comorbidities and severe inflammatory side effects. The overexpression of histone deacetylases (HDACs) in BC has been correlated with macrophage polarization together with higher tumor grades and poor prognosis. Herein we demonstrated that phenylbutyrate acid (PBA), a HDAC inhibitor, acts as an antitumoral compound and immunomodulator. In BC cell lines, PBA induced significant cell cycle arrest in G1, reduced stemness markers and increased PD-L1 expression with a corresponding reduction in histone 3 and 4 acetylation patterns. Concerning its role as an immunomodulator, we found that PBA reduced macrophage IL-6 and IL-10 production as well as CD14 downregulation and the upregulation of both PD-L1 and IL-1β. Along this line, PBA showed a reduction in IL-4-induced M2 polarization in human macrophages. In co-cultures of BC cell lines with human macrophages, a double-positive myeloid-tumoral hybrid population (CD11b+EPCAM+) was detected after 48 h, which indicates BC cell-macrophage fusions known as tumor hybrid cells (THC). These THC were characterized by high PD-L1 and stemness markers (SOX2, NANOG, miR-302) as compared with non-fused (CD11b-EPCAM+) cancer cells. Eventually, PBA reduced stemness markers along with BMP4 and IL-10. Our data indicate that PBA could have beneficial properties for BC management, affecting not only tumor cells but also the TME

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI:The M&Ms challenge

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms