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Early Detection and Assessment of Covid-19

Background: Since the Covid-19 global pandemic emerged, developing countries have been facing multiple challenges over its diagnosis. We aimed to establish a relationship between the signs and symptoms of COVID-19 for early detection and assessment to reduce the transmission rate of SARS-Cov-2. Meth...

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Autores principales: Hashmi, Hafiz Abdul Sattar, Asif, Hafiz Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296153/
https://www.ncbi.nlm.nih.gov/pubmed/32582748
http://dx.doi.org/10.3389/fmed.2020.00311
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author Hashmi, Hafiz Abdul Sattar
Asif, Hafiz Muhammad
author_facet Hashmi, Hafiz Abdul Sattar
Asif, Hafiz Muhammad
author_sort Hashmi, Hafiz Abdul Sattar
collection PubMed
description Background: Since the Covid-19 global pandemic emerged, developing countries have been facing multiple challenges over its diagnosis. We aimed to establish a relationship between the signs and symptoms of COVID-19 for early detection and assessment to reduce the transmission rate of SARS-Cov-2. Methods: We collected published data on the clinical features of Covid-19 retrospectively and categorized them into physical and blood biomarkers. Common features were assigned scores by the Borg scoring method with slight modifications and were incorporated into a newly-developed Hashmi-Asif Covid-19 assessment Chart. Correlations between signs and symptoms with the development of Covid-19 was assessed by Pearson correlation and Spearman Correlation coefficient (rho). Linear regression analysis was employed to assess the highest correlating features. The frequency of signs and symptoms in developing Covid-19 was assessed through Chi-square test two tailed with Cramer's V strength. Changes in signs and symptoms were incorporated into a chart that consisted of four tiers representing disease stages. Results: Data from 10,172 Covid-19 laboratory confirmed cases showed a correlation with Fever in 43.9% (P = 0.000) cases, cough 54.08% and dry mucus 25.68% equally significant (P = 0.000), Hyperemic pharyngeal mucus membrane 17.92% (P = 0.005), leukopenia 28.11% (P = 0.000), lymphopenia 64.35% (P = 0.000), thrombopenia 35.49% (P = 0.000), elevated Alanine aminotransferase 50.02% (P = 0.000), and Aspartate aminotransferase 34.49% (P = 0.000). The chart exhibited a maximum scoring of 39. Normal tier scoring was ≤ 12/39, mild state scoring was 13–22/39, and star values scoring was ≥7/15; this latter category on the chart means Covid-19 is progressing and quarantine should be adopted. Moderate stage scored 23–33 and severe scored 34–39 in the chart. Conclusion: The Hashmi-Asif Covid-19 Chart is significant in assessing subclinical and clinical stages of Covid-19 to reduce the transmission rate.
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spelling pubmed-72961532020-06-23 Early Detection and Assessment of Covid-19 Hashmi, Hafiz Abdul Sattar Asif, Hafiz Muhammad Front Med (Lausanne) Medicine Background: Since the Covid-19 global pandemic emerged, developing countries have been facing multiple challenges over its diagnosis. We aimed to establish a relationship between the signs and symptoms of COVID-19 for early detection and assessment to reduce the transmission rate of SARS-Cov-2. Methods: We collected published data on the clinical features of Covid-19 retrospectively and categorized them into physical and blood biomarkers. Common features were assigned scores by the Borg scoring method with slight modifications and were incorporated into a newly-developed Hashmi-Asif Covid-19 assessment Chart. Correlations between signs and symptoms with the development of Covid-19 was assessed by Pearson correlation and Spearman Correlation coefficient (rho). Linear regression analysis was employed to assess the highest correlating features. The frequency of signs and symptoms in developing Covid-19 was assessed through Chi-square test two tailed with Cramer's V strength. Changes in signs and symptoms were incorporated into a chart that consisted of four tiers representing disease stages. Results: Data from 10,172 Covid-19 laboratory confirmed cases showed a correlation with Fever in 43.9% (P = 0.000) cases, cough 54.08% and dry mucus 25.68% equally significant (P = 0.000), Hyperemic pharyngeal mucus membrane 17.92% (P = 0.005), leukopenia 28.11% (P = 0.000), lymphopenia 64.35% (P = 0.000), thrombopenia 35.49% (P = 0.000), elevated Alanine aminotransferase 50.02% (P = 0.000), and Aspartate aminotransferase 34.49% (P = 0.000). The chart exhibited a maximum scoring of 39. Normal tier scoring was ≤ 12/39, mild state scoring was 13–22/39, and star values scoring was ≥7/15; this latter category on the chart means Covid-19 is progressing and quarantine should be adopted. Moderate stage scored 23–33 and severe scored 34–39 in the chart. Conclusion: The Hashmi-Asif Covid-19 Chart is significant in assessing subclinical and clinical stages of Covid-19 to reduce the transmission rate. Frontiers Media S.A. 2020-06-09 /pmc/articles/PMC7296153/ /pubmed/32582748 http://dx.doi.org/10.3389/fmed.2020.00311 Text en Copyright © 2020 Hashmi and Asif. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Hashmi, Hafiz Abdul Sattar
Asif, Hafiz Muhammad
Early Detection and Assessment of Covid-19
title Early Detection and Assessment of Covid-19
title_full Early Detection and Assessment of Covid-19
title_fullStr Early Detection and Assessment of Covid-19
title_full_unstemmed Early Detection and Assessment of Covid-19
title_short Early Detection and Assessment of Covid-19
title_sort early detection and assessment of covid-19
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296153/
https://www.ncbi.nlm.nih.gov/pubmed/32582748
http://dx.doi.org/10.3389/fmed.2020.00311
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