COVIDIAGNOSTIX: health technology assessment of serological tests for SARS-CoV-2 infection.

OBJECTIVE: In vitro diagnostic tests for SARS-COV-2, also known as serological tests, have rapidly spread. However, to date, mostly single-center technical and diagnostic performance's assessments have been carried out without an intralaboratory validation process and a health technology assessment (HTA) systematic approach. Therefore, the rapid HTA for evaluating antibody tests for SARS-COV-2 was applied. METHODS: The use of rapid HTA is an opportunity to test innovative technology. Unlike traditional HTA (which evaluates the benefits of new technologies after being tested in clinical trials or have been applied in practice for some time), the rapid HTA is performed during the early stages of developing new technology. A multidisciplinary team conducted the rapid HTA following the HTA Core Model® (version 3.0) developed by the European Network for Health Technology Assessment. RESULTS: The three methodological and analytical steps used in the HTA applied to the evaluation of antibody tests for SARS-COV-2 are reported: the selection of the tests to be evaluated; the research and collection of information to support the adoption and appropriateness of the technology; and the preparation of the final reports and their dissemination. Finally, the rapid HTA of serological tests for SARS-CoV-2 is summarized in a report that allows its dissemination and communication. CONCLUSIONS: The rapid-HTA evaluation method, in addition to highlighting the characteristics that differentiate the tests from each other, guarantees a timely and appropriate evaluation, becoming a tool to create a direct link between science and health management.

Deep learning and lung ultrasound for Covid-19 pneumonia detection and severity classification.

The Covid-19 European outbreak in February 2020 has challenged the world's health systems, eliciting an urgent need for effective and highly reliable diagnostic instruments to help medical personnel. Deep learning (DL) has been demonstrated to be useful for diagnosis using both computed tomography (CT) scans and chest X-rays (CXR), whereby the former typically yields more accurate results. However, the pivoting function of a CT scan during the pandemic presents several drawbacks, including high cost and cross-contamination problems. Radiation-free lung ultrasound (LUS) imaging, which requires high expertise and is thus being underutilised, has demonstrated a strong correlation with CT scan results and a high reliability in pneumonia detection even in the early stages. In this study, we developed a system based on modern DL methodologies in close collaboration with Fondazione IRCCS Policlinico San Matteo's Emergency Department (ED) of Pavia. Using a reliable dataset comprising ultrasound clips originating from linear and convex probes in 2908 frames from 450 hospitalised patients, we conducted an investigation into detecting Covid-19 patterns and ranking them considering two severity scales. This study differs from other research projects by its novel approach involving four and seven classes. Patients admitted to the ED underwent 12 LUS examinations in different chest parts, each evaluated according to standardised severity scales. We adopted residual convolutional neural networks (CNNs), transfer learning, and data augmentation techniques. Hence, employing methodological hyperparameter tuning, we produced state-of-the-art results meeting F1 score levels, averaged over the number of classes considered, exceeding 98%, and thereby manifesting stable measurements over precision and recall.

Preparing for the Maximum Emergency with a Simulation: A Table-Top Test to Evaluate Bed Surge Capacity and Staff Compliance with Training.

INTRODUCTION: The sudden increase in the number of critically ill patients following a disaster can be overwhelming. STUDY OBJECTIVE: The main objective of this study was to assess the real number of available and readily freeable beds ("bed surge capacity") and the availability of emergency operating rooms (OR) in a maximum emergency using a theoretical simulation. PATIENTS AND METHODS: The proportion of dismissible patients in four areas (Medical Area, Surgical Area, Sub-intensive Care Units, Intensive Care Units) and three emergency OR was assessed at 2 and 24 hours after a simulated maximum emergency. Four scenarios were modeled. Hospitalization and surgical capacities were assessed on weekdays and holidays. The creation of new beds was presumed by the possibility of moving patients to a lower level of care than that provided at the time of detection, of dislocation of patients to a discharge room, with care transferred to lower-intensity hospitals, rehabilitation, or discharge facilities. The Phase 1 table-top simulations were conducted during the weekday morning hours. In particular, the 24-hour table-top simulations of a hypothetical event lasted about 150 minutes compared to those conducted at 2 hours, which were found to be longer (about 195 minutes). Phase 2 was conducted on two public holidays and a quick response time was observed within the first 40 minutes of the start of the test (about 45% of departments). RESULTS: The availability of simulated beds was greater than that indicated in the maximum emergency plans (which was based solely on the census of beds). Patients admitted to Intensive Care and The Sub-Intensive Area may be more difficult to move than those in low-intensity care. The availability of emergency OR was not problematic. Age influenced the possibility of remitting/transferring patients. CONCLUSION: Simulation in advance of a maximum emergency is helpful in designing an efficient response plan.

[COVID-19 emergency management activities promoted by an university hospital in Northern Italy]. / Attività di prevenzione e protezione nell'ambito della gestione dell'emergenza COVID-19 promosse da un Ospedale Universitario nel Nord Italia.

SUMMARY: Background. In December 2019, a Coronavirus 2019 epidemic (COVID-19) was reported, caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which occurred in the city of Wuhan, Hubei province, China. Perceived risk of contracting diseases has led many Governments and Healthcare Organizations to implement a variety of control and protection measures for the population, in particular for health professionals who have made contact with positive Covid-19 patients. In this publication, we have carried out a review of the information available, in order to share the prevention and protection measures for health and safety at work, which a University Hospital of Pavia, in Northern Italy, has remodulated, according to the changed scenario in which professionals finds themselves carrying out their profession in the post lockdown, in account to the specificity of processes and methods of work organizing, which overall, they serve to characterize risks, in order to be able to prevent them in the best possible way for patients, visitors and healthcare professionals.

A method for morphological characterization of dural ectasia in Marfan syndrome.

In this paper we reported a novel method to detect and quantify dural ectasia in Marfan syndrome. Firstly, the dural sacs of 8 Marfan patients were segmented by applying an unsupervised Fuzzy C-Means method on T2-weighed magnetic resonance images. Then, for each patient a tubular model of the dural sac was extracted by detecting and removing the existent pathological extrusions. The segmented images together with the resulting tube were then rendered using a marching cubes algorithm. The proposed algorithm represents a first attempt to quantify and to morphologically characterize the pathological ectasia that usually accompanies the Marfan disorder. The generated 3D reconstruction and the opportunity to overlap them with a physiological model provides the clinician with a tool for a panoramic view of the structures and a means for a more accurate inspection of ectasia. In addition the extracted parameters furnish quantitative and reproducible measures that could be useful as discriminative indexes for an automatic and more objective diagnosis.