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1.
COVID ; 2(3):419-432, 2022.
Article in English | MDPI | ID: covidwho-1753447

ABSTRACT

This study describes the implementation and utility of a standalone device designed, developed, and 3D-printed by PwC Singapore and Southeast Asia Consulting as a response to Corona Virus Disease 2019 (COVID-19), in the Emergency Department (ED) of the National University Hospital in Singapore. Over a 2-week period, all staff used the devices for the duration of their shifts, with the device additionally tagged to patients who were swabbed on suspicion of or surveillance for COVID-19 in the subsequent two weeks. Additional control hardware was placed in the ED to analyze (1) time-intervals of greatest interaction, (2) clusters of close physical distance among staff, (3) areas with high traffic, and (4) potential use of a rapid contact tracing capability. Time-day trends indicated the greatest interaction time-intervals during the beginning of the day, with Monday hosting the greatest average daily interactions across the first two weeks. Social cluster trends indicated the greatest average daily interactions between nurses–nurses during Phase 1, and patients–patients during Phase 2. User-location trends revealed the greatest average daily interaction counts at the intermediate care areas, isolation outdoor tent, pantry, and isolation holding units relative to other areas. Individual-level visualization and contact tracing capabilities were not utilized as nobody contracted COVID-19 during either phase. While congregation in intermediate and resuscitation areas are unavoidable within the ED context, the findings of this study were acted upon, improving social distancing within the pantry and between healthcare groups. This real-time solution addresses multiple privacy concerns while rapidly facilitating contact tracing.

2.
Sci Rep ; 12(1): 889, 2022 01 18.
Article in English | MEDLINE | ID: covidwho-1630723

ABSTRACT

Predicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.


Subject(s)
COVID-19 , Gene Expression Regulation , RNA, Messenger/blood , SARS-CoV-2/metabolism , Acute Disease , COVID-19/blood , COVID-19/mortality , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Retrospective Studies
3.
BMJ Open ; 11(1): e044497, 2021 01 06.
Article in English | MEDLINE | ID: covidwho-1013055

ABSTRACT

INTRODUCTION: Accurate triage is an important first step to effectively manage the clinical treatment of severe cases in a pandemic outbreak. In the current COVID-19 global pandemic, there is a lack of reliable clinical tools to assist clinicians to perform accurate triage. Host response biomarkers have recently shown promise in risk stratification of disease progression; however, the role of these biomarkers in predicting disease progression in patients with COVID-19 is unknown. Here, we present a protocol outlining a prospective validation study to evaluate the biomarkers' performance in predicting clinical outcomes of patients with COVID-19. METHODS AND ANALYSIS: This prospective validation study assesses patients infected with COVID-19, in whom blood samples are prospectively collected. Recruited patients include a range of infection severity from asymptomatic to critically ill patients, recruited from the community, outpatient clinics, emergency departments and hospitals. Study samples consist of peripheral blood samples collected into RNA-preserving (PAXgene/Tempus) tubes on patient presentation or immediately on study enrolment. Real-time PCR (RT-PCR) will be performed on total RNA extracted from collected blood samples using primers specific to host response gene expression biomarkers that have been previously identified in studies of respiratory viral infections. The RT-PCR data will be analysed to assess the diagnostic performance of individual biomarkers in predicting COVID-19-related outcomes, such as viral pneumonia, acute respiratory distress syndrome or bacterial pneumonia. Biomarker performance will be evaluated using sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operating characteristic curve. ETHICS AND DISSEMINATION: This research protocol aims to study the host response gene expression biomarkers in severe respiratory viral infections with a pandemic potential (COVID-19). It has been approved by the local ethics committee with approval number 2020/ETH00886. The results of this project will be disseminated in international peer-reviewed scientific journals.


Subject(s)
Biomarkers/metabolism , COVID-19/metabolism , Critical Illness/epidemiology , Emergency Service, Hospital/statistics & numerical data , Pandemics , SARS-CoV-2 , Triage/methods , Adult , COVID-19/epidemiology , Female , Follow-Up Studies , Humans , Male , Middle Aged , Prognosis , Prospective Studies , Time Factors
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