COVID-19 associated multisystem inflammatory syndrome in children mimicking acute appendicitis - how to differentiate and conduct pediatric patients during the pandemic? - Proposal of a management flowchart.

INTRODUCTION: the new coronavirus pandemic has been a reality throughout 2020, and it has brought great challenges. The virus predominantly manifests in the pediatric population with mild symptoms. However, an increase in the incidence of Multisystemic Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 has been described in the literature. MIS-C manifests mainly with fever and gastrointestinal symptoms and may mimic acute abdomen due to acute appendicitis. The objective of this study is to propose a care flowchart for suspected cases of acute appendicitis in the initial phase in pandemic times, considering the possibility of MIS-C. This situation was brought up by a patient treated in a pediatric hospital in Brazil. DISCUSSION: It was possible to identify common signs and symptoms in the reported patient and those published cases that may serve as alerts for early identification of MIS-C cases. Based on the literature review and on the similarities between the syndrome and the inflammatory acute abdomen in children, we elaborated an initial approach for these cases to facilitate the identification, early diagnosis, and management. The flowchart considers details of the clinical history, physical examination, and complementary exams prior to the indication of appendectomy in patients with initial phase symptoms. CONCLUSION: MIS-C, although rare and of poorly known pathophysiology, is most often severe and has a high mortality risk. The use of the proposed flowchart can help in the diagnosis and early treatment of MIS-C.

Imaging of Cardiac Infections: A Comprehensive Review and Investigation Flowchart for Diagnostic Workup.

Infections of the cardiovascular system may present with nonspecific symptoms, and it is common for patients to undergo multiple investigations to arrive at the diagnosis. Echocardiography is central to the diagnosis of endocarditis and pericarditis. However, cardiac computed tomography (CT) and magnetic resonance imaging also play an additive role in these diagnoses; in fact, magnetic resonance imaging is central to the diagnosis of myocarditis. Functional imaging (fluorine-18 fluorodeoxyglucose-positron emission tomography/CT and radiolabeled white blood cell single-photon emission computed tomography/CT) is useful in the diagnosis in prosthesis-related and disseminated infection. This pictorial review will detail the most commonly encountered cardiovascular bacterial and viral infections, including coronavirus disease-2019, in clinical practice and provide an evidence basis for the selection of each imaging modality in the investigation of native tissues and common prostheses.

Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images.

Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.

Improving Patient Outreach by Defining Telehealth Suitability in a Tertiary Huntington's Disease Clinic.

BACKGROUND: The COVID-19 pandemic has increased the need for remote healthcare options among patients with Huntington's disease (HD). However, since not every HD patient is suitable for telehealth, it is important to differentiate who can be seen virtually from who should remain as in-person. Unfortunately, there are no clinical guidelines on how to evaluate HD patients for telehealth eligibility. OBJECTIVE: To standardize the teleneurology selection process in HD by implementing a screening tool that accounts for patient-specific factors. METHODS: We organized various indications and contraindications to teleneurology into a flowchart. If any indications or contraindications were met, patients were assigned to telehealth or maintained as in-person, respectively. If no indications or contraindications were met, patients were given the option of telehealth or in-person for their upcoming appointments. In two implementation cycles, we tested this screening tool among all HD patients scheduled for clinic visits, aided by chart review and phone interview. RESULTS: In a cohort of 81 patients, telehealth acceptance among eligible patients increased from 45.0%to 83.3%. Frequency of telehealth visits increased from a pre-intervention baseline of 12.8%to 28.2%. CONCLUSION: Teleneurology utilization among HD patients more than doubled across our study. Our intervention promotes consistency and patient-centeredness in HD clinical care and streamlines the overall telehealth selection process. Future studies can seek to reduce telehealth no-shows and also evaluate the utility of the motor and psychiatric criteria included in our screening tool.

Emergency Department Utilization by In-hospital Healthcare Workers after COVID-19 Vaccination.

BACKGROUND: This is an observational study to analyze an emergency department (ED) utilization pattern of coronavirus disease 2019 (COVID-19) vaccinated in-hospital healthcare workers (HCWs). METHODS: We included 4,703 HCWs who were administered the first dose of the COVID-19 vaccine between March 4 and April 2, 2021, in a tertiary hospital in Korea where fast-track and post-vaccination cohort zone (PVCZ) were introduced in ED. We analyzed data of participants' age, sex, occupation, date and type of vaccination, and their clinical information using SPSS v25.0. RESULTS: The sample comprised HCWs, who received either the ChAdOx1 (n = 4,458) or the BNT162B2 (n = 245) vaccines; most participants were female (73.5%), and 81.1% were under 50 years old. Further, 153 (3.3%) visited the ED and reported experiencing fever (66.9%) and myalgia (56.1%). Additionally, 91 (59.5%) of them were in their 20s, and 106 (67.5%) were assigned to the PVCZ. Lastly, 107 (68.2%) of the patients received parenteral management. No patient required hospitalization. CONCLUSION: In conclusion, vaccinated HCWs who visited the ED with adverse events had a high incidence of fever and a low likelihood of developing serious illnesses. As the COVID-19 vaccination program for Korean citizens continues to expand, strategies to minimize unnecessary ED overcrowding should be put into effect.

Implementation of a visual raise hand function in PowerPoint®.

Objective: Replacing face-to-face lessons by remote teaching due to COVID-19 led to a markedly reduced interaction between students and lecturers. In our opinion, one of the main reasons for this is the raise hand function of the respective web conference systems, which (independent of the system used) results in an unobtrusive signal that can easily be missed by the lecturer. Given the necessary focus on one's own presentation, questions can therefore only be perceived with a considerable time delay and can only be integrated into the lessons to a limited extent. Thus, the idea arose to display question requests of the auditorium by a clear visual signal in PowerPoint® itself. Methodology: With Visual Basic for Applications (VBA), Microsoft PowerPoint® holds an integrated programming language that extends its functionality. Accordingly, VBA was used to program a routine running in the background of the presentation, which periodically retrieves the contents of a web-based "signal file" in a cycle of a few seconds. The content of this signal file, in turn, can be modified by the students by calling up an URL (i.e. from any Internet-capable device) - this results in a (customizable) visual signal in PowerPoint® that is temporarily visible and does not further interfere with the presentation. Conclusion: With the concept presented here, a raise hand function was realized in PowerPoint®, which manifests itself as a clear visual signal independent of the web conferencing system used. This enables the lecturers to respond instantly to questions from the audience during live transmission of lectures.

COVID-19 pandemic: an Italian single institution's experience and lessons learned by public health residents' workforce.

Methods: We hereby provide a systematic description of the response actions in which the public health residents' workforce was pivotal, in a large tertiary hospital. Background: The Coronavirus Disease 2019 pandemic has posed incredible challenges to healthcare workers worldwide. The residents have been affected by an almost complete upheaval of the previous setting of activities, with a near total focus on service during the peak of the emergency. In our Institution, residents in public health were extensively involved in leading activities in the management of Coronavirus Disease 2019 pandemic. Results: The key role played by residents in the response to Coronavirus Disease 2019 pandemic is highlighted by the diversity of contributions provided, from cooperation in the rearrangement of hospital paths for continuity of care, to establishing and running new services to support healthcare professionals. Overall, they constituted a workforce that turned essential in governing efficiently such a complex scenario. Conclusions: Despite the difficulties posed by the contingency and the sacrifice of many training activities, Coronavirus Disease 2019 pandemic turned out to be a unique opportunity of learning and measuring one's capabilities and limits in a context of absolute novelty and uncertainty.

COVID-19 Outcome Prediction and Monitoring Solution for Military Hospitals in South Korea: Development and Evaluation of an Application.

BACKGROUND: COVID-19 has officially been declared as a pandemic, and the spread of the virus is placing sustained demands on public health systems. There are speculations that the COVID-19 mortality differences between regions are due to the disparities in the availability of medical resources. Therefore, the selection of patients for diagnosis and treatment is essential in this situation. Military personnel are especially at risk for infectious diseases; thus, patient selection with an evidence-based prognostic model is critical for them. OBJECTIVE: This study aims to assess the usability of a novel platform used in the military hospitals in Korea to gather data and deploy patient selection solutions for COVID-19. METHODS: The platform's structure was developed to provide users with prediction results and to use the data to enhance the prediction models. Two applications were developed: a patient's application and a physician's application. The primary outcome was requiring an oxygen supplement. The outcome prediction model was developed with patients from four centers. A Cox proportional hazards model was developed. The outcome of the model for the patient's application was the length of time from the date of hospitalization to the date of the first oxygen supplement use. The demographic characteristics, past history, patient symptoms, social history, and body temperature were considered as risk factors. A usability study with the Post-Study System Usability Questionnaire (PSSUQ) was conducted on the physician's application on 50 physicians. RESULTS: The patient's application and physician's application were deployed on the web for wider availability. A total of 246 patients from four centers were used to develop the outcome prediction model. A small percentage (n=18, 7.32%) of the patients needed professional care. The variables included in the developed prediction model were age; body temperature; predisease physical status; history of cardiovascular disease; hypertension; visit to a region with an outbreak; and symptoms of chills, feverishness, dyspnea, and lethargy. The overall C statistic was 0.963 (95% CI 0.936-0.99), and the time-dependent area under the receiver operating characteristic curve ranged from 0.976 at day 3 to 0.979 at day 9. The usability of the physician's application was good, with an overall average of the responses to the PSSUQ being 2.2 (SD 1.1). CONCLUSIONS: The platform introduced in this study enables evidence-based patient selection in an effortless and timely manner, which is critical in the military. With a well-designed user experience and an accurate prediction model, this platform may help save lives and contain the spread of the novel virus, COVID-19.

Forecast UTI: application for predicting intensive care unit beds in the context of the COVID-19 pandemic. / Forecast UTI: aplicativo para previsão de leitos de unidades de terapia intensiva no contexto da pandemia de COVID-19.

In view of the need to manage and forecast the number of Intensive Care Unit (ICU) beds for critically ill COVID-19 patients, the Forecast UTI open access application was developed to enable hospital indicator monitoring based on past health data and the temporal dynamics of the Coronavirus epidemic. Forecast UTI also enables short-term forecasts of the number of beds occupied daily by COVID-19 patients and possible care scenarios to be established. This article presents the functions, mode of access and examples of uses of Forecast UTI, a computational tool intended to assist managers of public and private hospitals within the Brazilian National Health System by supporting quick, strategic and efficient decision-making.

Frente à necessidade de gerenciamento e previsão do número de leitos de unidades de terapia intensiva (UTIs) para pacientes graves de COVID-19, foi desenvolvido o Forecast UTI, um aplicativo de livre acesso, que permite o monitoramento de indicadores hospitalares com base em dados históricos do serviço de saúde e na dinâmica temporal da epidemia por coronavírus. O Forecast UTI também possibilita realizar previsões de curto prazo do número de leitos ocupados pela doença diariamente, e estabelecer possíveis cenários de atendimento. Este artigo apresenta as funções, modo de acesso e exemplos de uso do Forecast UTI, uma ferramenta computacional destinada a auxiliar gestores de hospitais da rede pública e privada do Sistema Único de Saúde (SUS) no subsídio à tomada de decisão, de forma rápida, estratégica e eficiente.

En vista de la necesidad de administrar y prever el número de camas en la Unidad de Cuidados Intensivos para pacientes graves de COVID-19, se desarrolló Forecast UTI: una aplicación de acceso abierto que permite el monitoreo de indicadores hospitalarios basados en datos históricos del servicio salud y la dinámica temporal de esta epidemia por coronavirus También es posible hacer pronósticos a corto plazo del número de camas ocupadas diariamente por la enfermedad y establecer posibles escenarios de atención. Este artículo presenta las funciones, el modo de acceso y ejemplos de uso de Forecast UTI, una herramienta computacional capaz de ayudar a los gestores de hospitales públicos y privados en el Sistema Único de Salud, ya que apoyan la toma de decisiones de manera rápida, estratégica y eficiente.

Mathematical model describing CoViD-19 in São Paulo, Brazil - evaluating isolation as control mechanism and forecasting epidemiological scenarios of release.

In São Paulo, Brazil, the first case of coronavirus disease 2019 (CoViD-19) was confirmed on 26 February, the first death due to CoViD-19 was registered on 16 March, and on 24 March, São Paulo implemented the isolation of persons in non-essential activities. A mathematical model was formulated based on non-linear ordinary differential equations considering young (60 years old or less) and elder (60 years old or more) subpopulations, aiming to describe the introduction and dissemination of the new coronavirus in São Paulo. This deterministic model used the data collected from São Paulo to estimate the model parameters, obtaining R0 = 6.8 for the basic reproduction number. The model also allowed to estimate that 50% of the population of São Paulo was in isolation, which permitted to describe the current epidemiological status. The goal of isolation implemented in São Paulo to control the rapid increase of the new coronavirus epidemic was partially succeeded, concluding that if isolation of at least 80% of the population had been implemented, the collapse in the health care system could be avoided. Nevertheless, the isolated persons must be released one day. Based on this model, we studied the potential epidemiological scenarios of release by varying the proportions of the release of young and elder persons. We also evaluated three different strategies of release: All isolated persons are released simultaneously, two and three releases divided in equal proportions. The better scenarios occurred when young persons are released, but maintaining elder persons isolated for a while. When compared with the epidemic without isolation, all strategies of release did not attain the goal of reducing substantially the number of hospitalisations due to severe CoViD-19. Hence, we concluded that the best decision must be postponing the beginning of the release.

The global experience of digital health interventions in COVID-19 management.

Digital health interventions are globally playing a significant role to combat coronavirus disease 2019 (COVID-19), which is an infectious disease caused by Severe Acute Respiratory Syndrome coronavirus 2. Here, we present a very brief overview of the multifaceted digital interventions, globally, and in India, for maintaining health and health-care delivery, in the context of the Covid-19 pandemic.