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<Articles JournalTitle="Frontiers in Emergency Medicine">
  <Article>
    <Journal>
      <PublisherName>Tehran University of Medical Sciences</PublisherName>
      <JournalTitle>Frontiers in Emergency Medicine</JournalTitle>
      <Issn>2717-3593</Issn>
      <Volume>7</Volume>
      <Issue>4</Issue>
      <PubDate PubStatus="epublish">
        <Year>2023</Year>
        <Month>11</Month>
        <Day>24</Day>
      </PubDate>
    </Journal>
    <title locale="en_US">Scoring system for mortality prediction of in-hospital COVID-19 patients in resource-limited settings: a single center cohort study during Delta and Omicron waves</title>
    <FirstPage>e37</FirstPage>
    <LastPage>e37</LastPage>
    <AuthorList>
      <Author>
        <FirstName>Phey</FirstName>
        <LastName>Liana</LastName>
        <affiliation locale="en_US">Clinical Pathology Department, Faculty of Medicine, Universitas Sriwijaya-Dr. Mohammad Hoesin Hospital, Palembang, Indonesia.</affiliation>
      </Author>
      <Author>
        <FirstName>Krisna</FirstName>
        <LastName>Murti</LastName>
        <affiliation locale="en_US">Anatomic Pathology Department, Faculty of Medicine, Universitas Sriwijaya-Dr. Mohammad Hoesin Hospital, Palembang, Indonesia.</affiliation>
      </Author>
      <Author>
        <FirstName>Iche Andriyani</FirstName>
        <LastName>Liberty</LastName>
        <affiliation locale="en_US">Public Health Department, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia.</affiliation>
      </Author>
      <Author>
        <FirstName>Zen</FirstName>
        <LastName>Hafy</LastName>
        <affiliation locale="en_US">Biomedical Department, Faculty of Medicine, Universitas Sriwijaya, Palembang, Indonesia.</affiliation>
      </Author>
      <Author>
        <FirstName>Tungki Pratama</FirstName>
        <LastName>Umar</LastName>
        <affiliation locale="en_US">UCL Centre for Nanotechnology and Regenerative Medicine, Division of Surgery and Interventional Science, University College London, London, UK.</affiliation>
      </Author>
    </AuthorList>
    <History>
      <PubDate PubStatus="received">
        <Year>2023</Year>
        <Month>07</Month>
        <Day>18</Day>
      </PubDate>
      <PubDate PubStatus="accepted">
        <Year>2023</Year>
        <Month>10</Month>
        <Day>08</Day>
      </PubDate>
    </History>
    <abstract locale="en_US">Objective: Coronavirus Disease 2019 (COVID-19)-related mortality includes several risk variables that are country-specific in nature. The development of a scoring system is necessary regarding&#xA0;the appearance of novel virus variants. The objective of this research is to develop a prognostic score for COVID-19 patients in resource-constrained settings.&#xA0;Methods: This study used a retrospective and prospective cohort design to identify variables that influence COVID-19 patients' in-hospital mortality. The receiver operating characteristic (ROC) curve analysis was utilized to determine the laboratory variables cut-off. Cox regression analysis was undertaken to determine the exact variables influencing the survival of COVID-19 patients. A scoring system was created using the best model based on the Hosmer-Lemeshow test (calibration) and the area under the curve (AUC) (discrimination ability).&#xA0;Results: Based on calibration and discrimination testing, model 2 (immune disorders, unconsciousness, cerebrovascular disease, onset, and oxygen saturation) was rated as the most advantageous model. Model 2 (without age adjustment) had a superior AUC than model 2A (with age). Cut-off was determined at 2, and calculated for onset &#x2265;7 days (AUC=0.816, 95% CI: 0.742,0.890) and &lt;7 days (AUC=0.850, 95% CI: 0.784,0.916). There was no difference in scoring system utilization for subjects recruited during Delta or Omicron waves (P=0.527).&#xA0;Conclusion: The model (cut-off value &#x2265;2) which incorporated age &#x2265;65 years, immune disorders, decreased consciousness, increased respiratory rate, and oxygen saturation &lt;95% is the best model in our study to predict COVID-19 patient mortality.</abstract>
    <web_url>https://fem.tums.ac.ir/index.php/fem/article/view/1250</web_url>
    <pdf_url>https://fem.tums.ac.ir/index.php/fem/article/download/1250/458</pdf_url>
  </Article>
</Articles>
