The reliability and integrity of overall survival data based on follow-up records only and potential solutions to the challenges

Overall survival (OS) is considered the standard clinical endpoint to support effectiveness claims in new drug applications globally, particularly for lethal conditions such as cancer. However, the source and reliability of OS in the setting of clinical trials have seldom been doubted and discussed. This study first raised the common issue that data integrity and reliability are doubtful when we collect OS information or other time-to-event endpoints based solely on simple follow-up records by investigators without supporting material, especially since the 2019 COVID-19 pandemic. Then, two rounds of discussions with 30 Chinese experts were held and 12 potential source scenarios of three methods for obtaining the time of death of participants, including death certificate, death record and follow-up record, were sorted out and analysed. With a comprehensive assessment of the 12 scenarios by legitimacy, data reliability, data acquisition efficiency, difficulty of data acquisition, and coverage of participants, both short-term and long-term recommended sources, overall strategies and detailed measures for improving the integrity and reliability of death date are presented. In the short term, we suggest integrated sources such as public security systems made available to drug inspection centres appropriately as soon as possible to strengthen supervision. Death certificates provided by participants' family members and detailed standard follow-up records are recommended to investigators as the two channels of mutual compensation, and the acquisition of supporting materials is encouraged as long as it is not prohibited legally. Moreover, we expect that the sharing of electronic medical records and the legal disclosure of death records in established health registries can be realized with the joint efforts of the whole industry in the long-term. The above proposed solutions are mainly based on the context of China and can also provide reference for other countries in the world. © 2022 The Authors

Use of Virtual Reality for Training on Procedures in an Intensive Care Unit during the COVID-19 Pandemic

The COVID-19 pandemic has underscored the importance of awareness and preparedness on the part of medical personnel. Virtual reality (VR) may be viewed as a useful tool in enabling knowledge and ability of medical practitioners in protecting themselves during the pandemic. This research describes the details of a VR application developed to train healthcare personnel in an intensive care unit (ICU) environment on some of the critical procedures related to COVID-19 patients. We discuss design considerations of VR for healthcare training as well as a process of selecting, based on expert opinion, key ICU training modules. The process of creating a 3D model of the ICU is outlined, and the major components and the use of the VR application are discussed. We find that a practical VR training program should apply a suitable VR platform and simulation techniques, while placing emphasis on addressing emerging problems and needs of healthcare personnel during the pandemic. © 2021 The Authors.

The changing role of BIM-facilitated facilities management in the post-COVID era: A systematic literature review

COVID-19 has become a long-term pandemic and pushed the re-interpretation of normality. Fighting against the pandemic and building a new normal in the post-COVID era requires constant and innovative approaches. Building information modeling (BIM) facilitated facilities management (FM) has been advocated as a powerful method to overcome the huge public health-related challenges. However, studies investigating the role of BIM-facilitated FM in the pandemic era are fragmented, and holistic knowledge is limited. Research objectives of this study are twofold: (1) to identify varying FM requirements after the outbreak of COVID-19, (2) to investigate how BIM-facilitated FM copes with changing requests in the pandemic period. To achieve the objectives, a systematic literature review was conducted. Currently, FM tends to be public health-centric, and highly emphasized requirements encompass the flexibility of space transformation for emergency purpose, effective and sufficient ventilation, reliable biosecurity, and strict anti-infection management. Based on centralized database and visualized model, BIM-facilitated FM enables space optimization, simulation and prediction of infection risk, monitoring and managing equipment operation, and effective information management, which enhances both hard and soft FM. This paper sheds light on the applications and directions of future BIM-facilitated FM research in protecting public health in the post-COVID era.

Modeling the Impact of Social Determinants of Health on COVID Behaviors in Older Adults using the All of Us Dataset

Non-pharmaceutical interventions such as hand-washing hygiene, avoiding large gatherings, and avoiding visiting nursing homes remain important in mitigating risks of COVID infection among at-risk populations such as older adults. The NIH's All of Us Research Program offers a unique dataset which contains detailed survey data that medical records often lack. Leveraging this dataset and impact scores, we were able to compare deep neural network (DNN) models to more conventional logistic regression and XGBoost models in the task of examining the relationships between social determinants of health and COVID-related behaviors in older adults. LR and DNN models found that African American participants were more likely than White participants to report adherence to guidelines regarding attending large social gatherings, abiding by stay-at-home recommendations and practicing pandemic-related hygiene. Both models also showed that respondents who were employed were less likely than their unemployed/retired counterparts to avoid large social gatherings or participate in activities outside their homes but were more likely to report practice pandemic-related hygiene. DNN models combined with impact scores to explain their output present an alternate approach to modeling outcomes in large, multi-variate cohorts which can outperform conventional statistical modeling. © 2022 IEEE.

An Agent-Based Modeling and Virtual Reality Application Using Distributed Simulation: Case of a COVID-19 Intensive Care Unit

Hospitals and other healthcare settings use various simulation methods to improve their operations, management, and training. The COVID-19 pandemic, with the resulting necessity for rapid and remote assessment, has highlighted the critical role of modeling and simulation in healthcare, particularly distributed simulation (DS). DS enables integration of heterogeneous simulations to further increase the usability and effectiveness of individual simulations. This article presents a DS system that integrates two different simulations developed for a hospital intensive care unit (ICU) ward dedicated to COVID-19 patients. AnyLogic has been used to develop a simulation model of the ICU ward using agent-based and discrete event modeling methods. This simulation depicts and measures physical contacts between healthcare providers and patients. The Unity platform has been utilized to develop a virtual reality simulation of the ICU environment and operations. The high-level architecture, an IEEE standard for DS, has been used to build a cloud-based DS system by integrating and synchronizing the two simulation platforms. While enhancing the capabilities of both simulations, the DS system can be used for training purposes and assessment of different managerial and operational decisions to minimize contacts and disease transmission in the ICU ward by enabling data exchange between the two simulations.

Use of two topic modeling methods to investigate covid vaccine hesitancy

COVID vaccine hesitancy in the face of a pandemic is a concern for public health researchers and policy makers who aim to achieve herd immunity. We investigated the COVID vaccine hesitancy by analyzing Twitter posts (tweets) using two topic modeling methods: Latent Dirichlet Allocation (LDA) and Top2Vec. Of the two methods, Top2Vec was able to reveal topics which directly discussed Vaccine Hesitancy and thus offered more utility for this research topic. Common reasons for vaccine hesitancy found in the dataset included concerns about recent (at the time of tweet collection) news regarding side effects associated with the COVID vaccines, and a mixture of scientific and government skepticism related to vaccine development and distribution. © 14th International Conference on ICT, Society, and Human Beings, ICT 2021, 18th International Conference on Web Based Communities and Social Media, WBC 2021 and 13th International Conference on e-Health, EH 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

Research progress in anti-coronavirus effect and safety of chloroquine

Coronavirus, identified as a zoonotic pathogen, can cause pulmonary infections and even a pandemic. Corona Virus Disease 2019 (COVID-19), which was induced by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), struck the world in December 2019. There is no specific drug for coronavirus. As a classical antimalarial drug, chloroquine has been proved to have antiviral activity by changing the pH of endocytosis, via autophagy reactions and by changing the glycosylation mode of the virus envelope. At the cellular level, chloroquine has inhibitory effects on SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-2. Recent clinical research results are beginning to show that chloroquine can improve the success rate and prognosis. Chloroquine has been used less clinically in recent years due to the many adverse reactions it causes. The safety of chloroquine used in coronavirus infection requires in-depth evaluation. This article summarizes research progress in the anti-coronavirus effect and safety of chloroquine in order to provide reference for its clinical application. © 2020 Chinese Journal of Pharmacology and Toxicology. All rights reserved.

Anesthetic management of a patient with COVID-19 in incubation period undergoing total hip arthroplasty

One of the characteristics of coronavirus disease 2019 (COVID-19) epidemiology is that asymptomatic infection is also a source of infection. There was no fever and/or respiratory symptoms in this patient before the operation, and the results of routine blood test showed decreased lymphocyte count and lymphocyte percentage, and novel coronavirus pathogen detection was not carried out in the hospital, and the detection was not performed, chest imaging examination showed no obvious ground glass shadow, so viral pneumonia was not considered. The medical staff involved in the operation all adopted standard secondary medical protection measures. Five days after the operation, the patient was tested positive for novel coronavirus nucleic acid, which was a confirmed case of COVID-19. The results of epidemiological investigation and analysis showed that the patient was in the incubation period of COVID-19 before operation, however, none of the medical staff involved in surgical anesthesia and none of the patients in the next operation were infected. The reason is that the author′s hospital has strict training for all staff in infection control, and surgical anesthesiology medical staff have mastered the relevant basic knowledge and operation skills, have a good sense of infection control in the operating room, and can standardize the implementation. This case also suggested that attention should be paid to the patients with significant preoperative simple lymphocyte level decline, and measures should be taken to identify asymptomatic infection early. © 2020 Chinese Medical Association

Analysis of the factors associated with negative conversion of severe acute respiratory syndrome coronavirus 2 rna of coronavirus disease 2019

AIM: To understand the factors associated with negative conversion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, targeted surveillance and control measures can be taken to provide scientific basis for the treatment of the disease and to improve the prognosis of the disease. METHODS: Using the method of retrospective cohort study, we collected the data of Coronavirus Disease 2019 (COVID-19) patients in Tongji Hospital of Wuhan, China from 10 January to 25 March, 2020. Among the data of 282 cases, 271 patients, according to whether the negative conversion happened, were divided into negative conversion group and control group. We made the quantitative variables into classification;Chi-square test single-factor and Cox regression were used in univariate analysis and extracted 30 meaningful variables, then through the collinearity diagnosis, excluded the existence of collinear variables. Finally, 22 variables were included in Cox regression analysis. RESULTS: The gender distribution was statistically significant between two groups (p < 0.05). While in the negative conversion group, the patients of non-severe group occupied a large proportion (p < 0.001). The median time for the negative conversion group was 17 days, and at the end of the observation period, the virus duration in control group was 24 days (p < 0.05). A total of 55 variables were included in univariate analysis, among which 30 variables were statistically different between the two groups. After screening variables through collinearity diagnosis, 22 variables were included in the Cox regression analysis. Last, lactate dehydrogenase (LDH), age, fibrinogen (FIB), and disease severity were associated with negative conversion of SARS-CoV-2 RNA. CONCLUSION: Our results suggest that in the treatment of COVID-19, focus on the age of more than 65 years old, severe, high level of LDH, FIB patients, and take some targeted treatment, such as controlling of inflammation, reducing organ damage, so as to provide good conditions for virus clearance in the body.