Hospital Admission and Hospital Course Predictors of Severity of Post-covid-19 Condition (preprint)

BACKGROUND Prediction of severity of Post-COVID-19 condition is not clear. We determined whether hospital course variables correlated with objective and subjective outcomes 3- and 6-months post-COVID-19 onset.METHODS Adults admitted to hospital with acute COVID-19 were recruited (Feb 15, 2020-April 1, 2021) from two tertiary hospitals in Vancouver, Canada. We recorded baseline characteristics, COVID severity score, organ function, ICU and hospital length of stay. Survivors were evaluated at 3- and 6-months post- COVID-19 onset: subjective: University of California San Diego Shortness of Breath Score, EuroQol 5D Visual Analogue Scale, Frailty Index; Objective: functional capacity (6-minute walk test), pulmonary (pulmonary function tests), cardiac (echocardiography, BNP), renal (creatinine), hepatic (AST, ALT, bilirubin), and coagulation (aPTT, INR, D-dimers, fibrinogen).RESULTS In 133 post-COVID-19 patients (age 62 ± 14 years, 65% male with frequent comorbidities [hypertension (42%), diabetes (29%), chronic cardiac (22%)), 42% were admitted to Intensive Care Unit: 23%, 23% and 2% required ventilation, vasopressors or renal support, respectively. One-third had restrictive lung function (< 80% predicted FVC %, TLC%, or DLCO%) at 3 months. Novel findings were correlations of (1) acute hepatic dysfunction with restrictive lung function at 3 months post COVID-19 onset; (2) more severe acute COVID-19 with later pro-thrombotic phenotype and (3) greater acute organ dysfunction with later objective organ dysfunction at 3- and 6-months. No hospital course variables were associated with subjective outcomes at 3- and 6-months.CONCLUSIONS Early organ dysfunction predicts later objective pathology especially restrictive lung disease and a pro-coagulant state. These hypothesis-generating findings require further validation in additional patients.

The role of IOT in transforming community pharmacy

During Covid-19 Pandemic the entire world experiences the role and importance of Internet of Things (IoT) in healthcare system and especially in Community Pharmacy. Internet of Things can be described as a network architecture incorporating an abundance of sensors software hardware, computing devices, technologies, machines and many more utilities, assisting us in our relevant domains as per the needful. Over the yesteryears it has been observed that the footprints of Internet of Things over the healthcare sector have increased eloquently. This observation leads us to the fact that with the escalating population and the healthcare plight Internet of Things can really prove itself to be serious breakthrough in the community pharmacy. By revising the current healthcare system in the community pharmacies in a more socio-economic fashion. This paper reviews the technological advancements in Internet of Things, for the medical management of public health so that it can be vitally made use of in the domain of community pharmacy. There is a demand of efficient Internet of Things framework that would seriously be able to challenge the abysmal state of community pharmacy mostly in the remote areas and the distant regions. © 2022 MEDIC SCIENTIFIC. All Rights Reserved.

Supporting the bereaved following mass bereavement events: A systematic review of disaster response interventions with lessons for covid-19

Background: The COVID-19 pandemic represents a global mass bereavement event, on a scale seldom witnessed. National health and social care systems are challenged with supporting large numbers of bereaved people whilst also negotiating the ongoing restrictions to provide this support safely. This review aimed to synthesise the evidence regarding system-level responses to mass bereavement events, including natural and human-made disasters, to inform service provision and policy during the pandemic. Methods: A rapid systematic review was conducted in April 2020, with narrative synthesis of results. MEDLINE, Global Health, PsycINFO and Scopus databases were searched for studies published between 2000 and 2020, reporting evidence on system-level responses to mass bereavement events in OECD countries (plus Singapore, China and Taiwan). Citation and reference tracking was conducted and study quality assessed. Results: Six studies were included, reporting on system responses to man-made disasters (e.g. terror attacks in the US and Norway), as well as natural disasters (e.g. Hurricane Katrina and the South-East Asian Tsunami). Despite differences across disaster types, common approaches were identified and positive impacts were reported across a range of individual and group-based support interventions. Key features of service delivery included: a proactive outreach approach, centrally organised but locally delivered interventions, event-specific professional competencies and an emphasis on psycho-educational content. However, study quality was generally low and reliant on data from retrospective evaluation designs. Conclusion: Co-ordinated responses to bereavement support which include the features we identified are required to meet the needs of bereaved people during and beyond the pandemic. Rigorous primary studies investigating the experiences of the bereaved and the services that support them are essential to inform current and future disaster response efforts.

A realist synthesis of mobile application-enabled communication for patients in end-of-life care and in light of the COVID-19 pandemic

Background: The COVID-19 pandemic restrictions have led to extended usage of virtual communication technology in healthcare, especially via mobile applications. Understanding the adoption of mobile applicationenabled communication in end-of-life care in particular is important to improve its usage for patients and carers. This is a pertinent topic in light of the pandemic and the higher health risks of this population group. Aims: To explore the use of mobile application-enabled communication for end-of-life care patients and their carers. Methods (design, data collection, analysis): A realist synthesis approach is being adopted, using six databases and other information sources from 2005 to present. Data screening and quality assessment will be carried out by two reviewers independently. Discrepancies and disagreements will be discussed and resolved by a third reviewer. Data abstraction will use the context, mechanism, and outcome approach to capture specific information about the theories around the development, adoption, and use of mobile application-enabled technologies in end-of-life care. (https:// www.crd.york.ac.uk/prospero/display-record.php?RecordID=231535) Results: A total of 666 abstracts were screened and 218 full texts are being analysed. The project is ongoing, and the results will be presented at the meeting. A summary table of the theories around the development, adoption, and use of mobile application-enabled technologies in end-of-life care will be used to create a logic model of the link between empirical data and theories. Conclusion / Discussion: The study will highlight the theories which explain the adoption of technology use in end-of-life care. The realist synthesis uses the available theories as a starting point and the empirical findings to build on these. The results will help to inform the applicability of a blended model of face-to-face and virtual communication after the end of the pandemic.

Effectiveness of Big Data in Early Prediction and Measure for COVID-19 Using Data Science

COVID-19 originates from a group of infections related to severe acute respiratory syndrome (SARS). Through data analysis, frameworks have demonstrated to have the option to approximate how patterns may progress. Data analysis assumes a key role, as does arithmetic, which, together with data science, permits us to have a top to bottom comprehension of the subtleties of nature and how things are made. As in the previous years, the pioneers of data science have had a mind-blowing effect on the reality where information and examination have been utilized to drive critical change throughout the spread of the sickness. One of the primary recorded uses of information examination was in 1852, during a cholera episode in London. John Snow, one of the primary information-driven disease transmission specialists, had the option to geospatially examine the passing that happened in London and in this manner segregate the wellspring of the infection. Depending on his investigation, specialists had the option to focus on their interventions to quickly check the spread of the pandemic. During this pandemic, information can be an exceptional factor as far as quality and consistency. Complexities of this sort incorporate instances of false-positive patients. Large data and data science can be utilized to check consistency with isolate and can be utilized for tranquilizing investigation. These are only a portion of the arrangements offered by new advanced innovations in the field of data science to confront the coronavirus crisis. We provide here early prediction and early survey, with the genuine and potential commitment of data science to the battle against COVID-19, just as the present requirements on these commitments. It expects to draw brisk take-away from a quick extending conversation and developing a collection of work, to fill in as a contribution for fast reactions in research, approach, and clinical examination. The expense of the pandemic as far as lives and monetary harm will be horrible;at the hour of composing, incredible vulnerability encompassed appraisals of exactly how awful and of how fruitful both non-pharmaceutical and pharmaceutical reactions can be. Improving data science, one of the most encouraging information expository apparatuses to have been created over the previous decade or something like that, to help decrease these vulnerabilities, is a beneficial interest. Data science isn’t yet assuming a huge job in the battle against COVID-19, at any rate from the epidemiological, demonstrative, and pharmaceutical perspectives. Its utilization is compelled by an absence of data and by an excess of noisy data. Enthusiastically, data analysts and researchers have responded to the challenge. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.