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    <title>Combination of inflammatory and vascular markers in the febrile phase of dengue is
      associated with more severe outcomes</title>
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        <h1 itemprop="headline"
          content="Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more sever…">
          Combination of inflammatory and vascular markers in the febrile phase of dengue is
          associated with more severe outcomes</h1>
        <meta itemprop="image"
          content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=Combination%20of%20inflammatory%20and%20vascular%20markers%20in%20the%20febrile%20phase%20of%20dengue%20is%20associated%20with%20more%20sever%E2%80%A6">
        <ol data-itemprop="authors">
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Nguyen Lam Vuong"><span data-itemprop="givenNames"><span
                itemprop="givenName">Nguyen</span><span itemprop="givenName">Lam</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Vuong</span></span><span
              data-itemprop="emails"><a itemprop="email"
                href="mailto:vuongnl@oucru.org">vuongnl@oucru.org</a></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a><a itemprop="affiliation"
                href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Phung Khanh Lam"><span data-itemprop="givenNames"><span
                itemprop="givenName">Phung</span><span itemprop="givenName">Khanh</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Lam</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a><a itemprop="affiliation"
                href="#author-organization-2">2</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Damien Keng Yen Ming"><span
              data-itemprop="givenNames"><span itemprop="givenName">Damien</span><span
                itemprop="givenName">Keng</span><span itemprop="givenName">Yen</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Ming</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-3">3</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Huynh Thi Le Duyen"><span
              data-itemprop="givenNames"><span itemprop="givenName">Huynh</span><span
                itemprop="givenName">Thi</span><span itemprop="givenName">Le</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Duyen</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Nguyet Minh Nguyen"><span
              data-itemprop="givenNames"><span itemprop="givenName">Nguyet</span><span
                itemprop="givenName">Minh</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Nguyen</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Dong Thi Hoai Tam"><span data-itemprop="givenNames"><span
                itemprop="givenName">Dong</span><span itemprop="givenName">Thi</span><span
                itemprop="givenName">Hoai</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Tam</span></span><span data-itemprop="affiliations"><a
                itemprop="affiliation" href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Kien Duong Thi Hue"><span
              data-itemprop="givenNames"><span itemprop="givenName">Kien</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Duong</span><span
                itemprop="familyName">Thi</span><span itemprop="familyName">Hue</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-1">1</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Nguyen VV Chau"><span data-itemprop="givenNames"><span
                itemprop="givenName">Nguyen</span><span itemprop="givenName">VV</span></span><span
              data-itemprop="familyNames"><span itemprop="familyName">Chau</span></span><span
              data-itemprop="affiliations"><a itemprop="affiliation"
                href="#author-organization-4">4</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Ngoun Chanpheaktra"><span
              data-itemprop="givenNames"><span itemprop="givenName">Ngoun</span></span><span
              data-itemprop="familyNames"><span
                itemprop="familyName">Chanpheaktra</span></span><span
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                href="#author-organization-5">5</a></span>
          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Lucy Chai See Lum"><span data-itemprop="givenNames"><span
                itemprop="givenName">Lucy</span><span itemprop="givenName">Chai</span><span
                itemprop="givenName">See</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Lum</span></span><span data-itemprop="affiliations"><a
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          </li>
          <li itemscope="" itemtype="http://schema.org/Person" itemprop="author">
            <meta itemprop="name" content="Ernesto Pleités"><span data-itemprop="givenNames"><span
                itemprop="givenName">Ernesto</span></span><span data-itemprop="familyNames"><span
                itemprop="familyName">Pleités</span></span><span data-itemprop="affiliations"><a
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            <meta itemprop="name" content="Cameron P Simmons"><span data-itemprop="givenNames"><span
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            <meta itemprop="name" content="Kerstin D Rosenberger"><span
              data-itemprop="givenNames"><span itemprop="givenName">Kerstin</span><span
                itemprop="givenName">D</span></span><span data-itemprop="familyNames"><span
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            <meta itemprop="name" content="Thomas Jaenisch"><span data-itemprop="givenNames"><span
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            <meta itemprop="name" content="David Bell"><span data-itemprop="givenNames"><span
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            <meta itemprop="name" content="Christine Halleux"><span data-itemprop="givenNames"><span
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            <meta itemprop="name" content="Bridget A Wills"><span data-itemprop="givenNames"><span
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            id="author-organization-1"><span itemprop="name">Oxford University Clinical Research
              Unit (OUCRU)</span><address itemscope="" itemtype="http://schema.org/PostalAddress"
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                itemprop="addressCountry">Viet Nam</span></address></li>
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                itemprop="addressCountry">Viet Nam</span></address></li>
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              Imperial College London</span><address itemscope=""
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                itemprop="addressLocality">London</span><span itemprop="addressCountry">United
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                itemprop="addressCountry">Viet Nam</span></address></li>
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                itemprop="addressCountry">Cambodia</span></address></li>
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            id="author-organization-6"><span itemprop="name">University of Malaya Medical
              Centre</span><address itemscope="" itemtype="http://schema.org/PostalAddress"
              itemprop="address"><span itemprop="addressLocality">Kuala Lumpur</span><span
                itemprop="addressCountry">Malaysia</span></address></li>
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            id="author-organization-7"><span itemprop="name">Hospital Nacional de Niños Benjamin
              Bloom</span><address itemscope="" itemtype="http://schema.org/PostalAddress"
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                itemprop="addressCountry">El Salvador</span></address></li>
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            id="author-organization-8"><span itemprop="name">Centre for Tropical Medicine and Global
              health, Nuffield Department of Clinical Medicine, University of Oxford</span><address
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                Kingdom</span></address></li>
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            id="author-organization-9"><span itemprop="name">Institute for Vector-Borne Disease,
              Monash University</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Clayton</span><span
                itemprop="addressCountry">Australia</span></address></li>
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              itemprop="name">Section Clinical Tropical Medicine, Department for Infectious
              Diseases, Heidelberg University Hospital</span><address itemscope=""
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                itemprop="addressLocality">Heidelberg</span><span
                itemprop="addressCountry">Germany</span></address></li>
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            itemid="#author-organization-11" id="author-organization-11"><span
              itemprop="name">Heidelberg Institute of Global Health (HIGH), Heidelberg University
              Hospital</span><address itemscope="" itemtype="http://schema.org/PostalAddress"
              itemprop="address"><span itemprop="addressLocality">Heidelberg</span><span
                itemprop="addressCountry">Germany</span></address></li>
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            itemid="#author-organization-12" id="author-organization-12"><span
              itemprop="name">Independent consultant</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Issaquah</span><span itemprop="addressCountry">United
                States</span></address></li>
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            itemid="#author-organization-13" id="author-organization-13"><span
              itemprop="name">Consultant, Intellectual Ventures, Global Good Fund</span><address
              itemscope="" itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Bellevue</span><span itemprop="addressCountry">United
                States</span></address></li>
          <li itemscope="" itemtype="http://schema.org/Organization"
            itemid="#author-organization-14" id="author-organization-14"><span
              itemprop="name">UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training
              in Tropical Diseases, World Health Organization</span><address itemscope=""
              itemtype="http://schema.org/PostalAddress" itemprop="address"><span
                itemprop="addressLocality">Geneva</span><span
                itemprop="addressCountry">Switzerland</span></address></li>
        </ol><span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher">
          <meta itemprop="name" content="Unknown"><span itemscope=""
            itemtype="http://schema.org/ImageObject" itemprop="logo">
            <meta itemprop="url"
              content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown">
          </span>
        </span><time itemprop="datePublished" datetime="2021-06-22">2021-06-22</time>
        <ul data-itemprop="genre">
          <li itemprop="genre">Research Article</li>
        </ul>
        <ul data-itemprop="about">
          <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span
              itemprop="name">Medicine</span></li>
          <li itemscope="" itemtype="http://schema.org/DefinedTerm" itemprop="about"><span
              itemprop="name">Microbiology and Infectious Disease</span></li>
        </ul>
        <ul data-itemprop="keywords">
          <li itemprop="keywords">dengue</li>
          <li itemprop="keywords">biomarkers</li>
          <li itemprop="keywords">prognostic</li>
          <li itemprop="keywords">Virus</li>
        </ul>
        <ul data-itemprop="identifiers">
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/publisher-id"><span
              itemprop="name">publisher-id</span><span itemprop="value"
              data-itemtype="http://schema.org/Number">67460</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID" content="https://registry.identifiers.org/registry/doi">
            <span itemprop="name">doi</span><span itemprop="value">10.7554/eLife.67460</span>
          </li>
          <li itemscope="" itemtype="http://schema.org/PropertyValue" itemprop="identifier">
            <meta itemprop="propertyID"
              content="https://registry.identifiers.org/registry/elocation-id"><span
              itemprop="name">elocation-id</span><span itemprop="value">e67460</span>
          </li>
        </ul>
        <section data-itemprop="description">
          <h2 data-itemtype="http://schema.stenci.la/Heading">Abstract</h2>
          <meta itemprop="description"
            content="Early identification of severe dengue patients is important regarding patient management and resource allocation. We investigated the association of 10 biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) with the development of severe/moderate dengue (S/MD). We performed a nested case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated dengue cases were included. On days 1–3 from symptom onset, higher levels of any biomarker increased the risk of developing S/MD. When assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association from positive to negative; others showed weaker associations. The best combinations associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults. Our findings assist the development of biomarker panels for clinical use and could improve triage and risk prediction in dengue patients. This study was supported by the EU's Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and Melinda Gates Foundation.">
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Early identification of
            severe dengue patients is important regarding patient management and resource
            allocation. We investigated the association of 10 biomarkers (VCAM-1, SDC-1, Ang-2,
            IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin, CRP) with the development of
            severe/moderate dengue (S/MD).</p>
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We performed a nested
            case-control study from a multi-country study. A total of 281 S/MD and 556 uncomplicated
            dengue cases were included.</p>
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">On days 1–3 from symptom
            onset, higher levels of any biomarker increased the risk of developing S/MD. When
            assessing together, SDC-1 and IL-1RA were stable, while IP-10 changed the association
            from positive to negative; others showed weaker associations. The best combinations
            associated with S/MD comprised IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 for
            children, and SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 for adults.</p>
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Our findings assist the
            development of biomarker panels for clinical use and could improve triage and risk
            prediction in dengue patients.</p>
          <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This study was supported by
            the EU&#39;s Seventh Framework Programme (FP7-281803 IDAMS), the WHO, and the Bill and
            Melinda Gates Foundation.</p>
        </section>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="introduction">Introduction
        </h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Dengue is the most common
          arboviral disease to affect humans globally. In 2019, the World Health Organization (WHO)
          identified dengue as one of the top 10 threats to global health <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib50"><span>50</span><span>World
                Health Organization</span><span>2019</span></a></cite>. Transmission occurs in 129
          countries, with an estimated 3.9 billion people being at risk <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib51"><span>51</span><span>World
                Health Organization</span><span>2020</span></a></cite>. Over the last two decades,
          the number of reported cases per year has increased more than eight-fold <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib51"><span>51</span><span>World Health
                Organization</span><span>2020</span></a></cite>, and in 2020 the annual number of
          dengue virus (DENV) infections was estimated to be 105 million, with 51 million cases
          being clinically apparent <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib4"><span>4</span><span>Cattarino et al.</span><span>2020</span></a></cite>.
          With climate change, increased travel and urbanization, this rise is forecasted to
          continue over the coming decades <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib48"><span>48</span><span>Whitehorn and
                  Yacoub</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib52"><span>52</span><span>Yacoub
                  et al.</span><span>2011</span></a></cite></span>. Despite the large disease
          burden, there is still no specific treatment for dengue, and the only licensed vaccine is
          recommended only in individuals with earlier dengue infection <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib31"><span>31</span><span>Redoni et
                al.</span><span>2020</span></a></cite>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In many dengue-endemic
          settings, seasonal epidemics can rapidly overwhelm fragile health systems. Although most
          symptomatic dengue infections are self-limiting, a small proportion of patients develop
          complications, most of which manifest at around 4–6 days from symptom onset. Thus, large
          numbers of patients require regular assessments to identify complications should they
          arise. The accurate and early identification of such patients, particularly within the
          first 3 days of illness in the febrile phase, should allow for appropriate care to be
          provided and potentially increase health system effectiveness. Although the 2009 WHO
          dengue guidelines set out specific warning signs for use in patient triage, utility of
          these guidelines at identifying those at risk for complications remains limited <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib21"><span>21</span><span>Morra et al.</span><span>2018</span></a></cite>.
        </p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The pathogenesis of dengue
          involves a complex interplay between viral factors and the host response. It is
          hypothesized that an excessive immune response acting through inflammatory mediators can
          lead to the observed manifestations of bleeding, shock, and organ dysfunction. Studies
          have shown that in secondary infections, adaptive immune activation can result in high
          circulating levels of plasma cytokines and chemokines <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib15"><span>15</span><span>Katzelnick et
                  al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib19"><span>19</span><span>Midgley
                  et al.</span><span>2011</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib37"><span>37</span><span>Screaton
                  et al.</span><span>2015</span></a></cite></span>. Binding of viral NS1 protein
          onto endothelial cells can act in concert with vasoactive substances, cytokines, and
          chemokines, to result in endothelial activation and glycocalyx disruption, and these
          processes likely underlie the increased vascular permeability and coagulopathy <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib18"><span>18</span><span>McBride
                  and Khanh Lam</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib20"><span>20</span><span>Modhiran
                  et al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib40"><span>40</span><span>St John
                  et al.</span><span>2013</span></a></cite></span>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The role of blood biomarkers in
          predicting severe outcomes has been investigated in many studies, but mostly at later
          time-points or at hospital admission and many of these biomarkers either peak too late in
          the disease course or have too short a half-life to be clinically useful <span
            itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Ab-Rahman
                  et al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib13"><span>13</span><span>John et
                  al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Oliveira
                  et al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib27"><span>27</span><span>Puerta-Guardo et
                  al.</span><span>2019</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib30"><span>30</span><span>Rathore
                  et al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib32"><span>32</span><span>Robinson
                  and Einav</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib34"><span>34</span><span>S S et
                  al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib38"><span>38</span><span>Soo et
                  al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib44"><span>44</span><span>Vasey et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib55"><span>55</span><span>Yacoub
                  et al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib54"><span>54</span><span>Yacoub
                  et al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib56"><span>56</span><span>Yong et
                  al.</span><span>2017</span></a></cite></span>. Acknowledging these
          characteristics, we selected 10 candidate biomarkers from the vascular, immunological, and
          inflammatory pathways with good evidence supporting their involvement in the pathogenesis
          of dengue infection – focusing on those likely to be increased early in the disease
          course. We included vascular cell adhesion molecule-1 (VCAM-1), syndecan-1 (SDC-1), and
          angiopoietin-2 (Ang-2) because they represent endothelial activation and glycocalyx
          integrity <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib7"><span>7</span><span>Han et al.</span><span>2019</span></a></cite><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib17"><span>17</span><span>Mapalagamage et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib41"><span>41</span><span>Suwarto
                  et al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib53"><span>53</span><span>Yacoub
                  et al.</span><span>2016</span></a></cite></span>. For markers of immune
          activation, we measured interleukin-8 (IL-8) and interferon gamma-induced protein-10
          (IP-10) as these are associated with disease severity <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib23"><span>23</span><span>Oliveira
                  et al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib24"><span>24</span><span>Pandey
                  et al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib29"><span>29</span><span>Rathakrishnan et
                  al.</span><span>2012</span></a></cite></span>, and IL-1 receptor antagonist
          (IL-1RA), soluble cluster of differentiation 163 (sCD163), and soluble triggering receptor
          expressed on myeloid cells-1 (sTREM-1) as these are activation markers of monocytes and
          macrophages, the major targets for dengue replication <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Ab-Rahman
                  et al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib13"><span>13</span><span>John et
                  al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib34"><span>34</span><span>S S et
                  al.</span><span>2017</span></a></cite></span>. For markers of general
          inflammation, we included ferritin and C-reactive protein (CRP) <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib1"><span>1</span><span>Ab-Rahman
                  et al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib6"><span>6</span><span>Finkelstein et
                  al.</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib22"><span>22</span><span>Mukherjee and
                  Tripathi</span><span>2020</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib39"><span>39</span><span>Soundravally et
                  al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib45"><span>45</span><span>Vuong et
                  al.</span><span>2020</span></a></cite></span>.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The aims of this study were:
          (1) to investigate the association of these ten biomarkers with development of more severe
          dengue outcomes, (2) to find the best combination of biomarkers associated with more
          severe dengue outcomes. The results of the second aim could help in developing multiplex
          panels for use in outpatient settings to rapidly identify patients who require
          hospitalization.</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="materials-and-methods">
          Materials and methods</h2>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="study-design">Study design
        </h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We conducted a nested
          case-control study using the samples and clinical information from a large multi-country
          observational study named ‘Clinical evaluation of dengue and identification of risk
          factors for severe disease’ (IDAMS study, NCT01550016) <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib12"><span>12</span><span>Jaenisch
                et al.</span><span>2016</span></a></cite>. The IDAMS study and the blood sample
          analysis were approved by the Scientific and Ethics Committees of all study sites
          (Hospital for Tropical Diseases [Ho Chi Minh City, Vietnam] Ref No 03/HDDD-05/01/2018;
          Angkor Hospital for Children [Siem Reap, Cambodia] Ref No 0146/18-AHC; University of
          Malaya Medical Centre [Kuala Lumpur, Malaysia] Ref No 201865–6361) and by the Oxford
          Tropical Research Ethics Committee (OxTREC Ref No 502–18). There were 7428 participants in
          eight countries across Asia and Latin America enrolled in the IDAMS study. Patients were
          eligible for inclusion if they were aged 5 years or older, had fever or history of fever
          for less than 72 hr, and had symptoms consistent with dengue, with no features strongly
          suggestive of another disease. Participants were followed daily with a standard schedule
          of clinical examination and blood samples. Individual management (including
          hospitalization) was in accordance with routine practice at each study site. All
          diagnostic samples were processed and stored following specific protocols, and later
          transferred to designated sites for diagnostic testing in order to ensure consistency.
          Laboratory-confirmed dengue was defined by a positive reverse transcriptase polymerase
          chain reaction (RT-PCR) or a positive NS1 enzyme-linked immunosorbent assay (ELISA)
          result. Immune status was classified based on capture IgG results on paired samples. A
          probable primary infection was defined by two negative IgG results on two consecutive
          specimens taken at least 2 days apart, with at least one specimen obtained during the
          convalescent phase (after illness day 5). A probable secondary infection was defined by a
          positive IgG result identified during either or both the febrile and convalescent phases.
          In all other cases with the absence of suitable specimens at the appropriate time points
          immune status was classified as inconclusive. Each participant was given an overall
          severity grade (severe, moderate, or uncomplicated dengue), using all available
          information and a grading system in line with current guidelines and recommendations to
          classify clinical endpoints in dengue clinical trials <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib43"><span>43</span><span>Tomashek
                et al.</span><span>2018</span></a></cite>.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="study-population">Study
          population</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Of the 2694
          laboratory-confirmed dengue cases in the IDAMS study, 38 and 266 cases were classified as
          severe and moderate dengue respectively. For this study, we selected all severe and
          moderate cases from five study sites in four countries (Vietnam, Cambodia, Malaysia, and
          El Salvador), as residual plasma from these countries’ sample sets was available at the
          Oxford University Clinical Research Unit (OUCRU) in Ho Chi Minh City, Vietnam. For the
          control group, we selected patients with uncomplicated dengue with similar geographic and
          demographic characteristics at a 2:1 ratio. In total 281 cases and 556 controls were
          included in the analysis (<a href="#fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 1</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig1" title="Figure 1.">
          <label data-itemprop="label">Figure 1.</label><img src="index.html.media/fig1.jpg" alt=""
            itemscope="" itemtype="http://schema.org/ImageObject">
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading" id="study-flowchart">Study
              flowchart.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">*The IDAMS study was
              performed in eight countries across Asia and Latin America. For this study, we
              selected cases in four countries (Vietnam, Cambodia, Malaysia, and El Salvador) as the
              blood samples were stored at the laboratory of the Oxford University Clinical Research
              Unit in Ho Chi Minh City, Vietnam.</p>
          </figcaption>
        </figure>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="laboratory-evaluation-details-in-appendix-1">Laboratory evaluation (details in
          Appendix 1)</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The biomarkers were measured at
          two time points: at enrollment (illness day 1–3) and after recovery (day 10–31
          post-symptom onset), if available. Eight biomarkers (CRP and ferritin excepted) were
          combined in a premixed magnetic bead panel (Cat No. LXSAHM; R and D). CRP was measured
          using a separate commercial magnetic bead panel (Cat. No. HCVD3MAG-67K; EMD Millipore
          Corporation). These panels were analyzed using the Luminex200 analyzer with the Luminex
          calibration (Cat. No. LX200-CAL-K25) and verification kits (Cat. No. LX200-CON-K25).
          Ferritin was measured using the Human Ferritin ELISA kit (Cat. No. ARG80501, Arigo). All
          tests were done according to the manufacturer’s specifications.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="study-endpoints-details-in-appendix-2">Study endpoints (details in Appendix 2)</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The primary endpoint was
          combined severe and moderate dengue (S/MD), defined by the development of severe or
          moderate grades of any of the following – plasma leakage, haemorrhage, or organ impairment
          (including neurologic, hepatic, or cardiac involvement) (<a href="#app2table1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 2—table 1</a>). We
          combined severe and moderate dengue to form the primary endpoint (S/MD) as severe dengue
          events were rare; this combined endpoint is relevant to clinical practice since the
          moderate group is likely to develop complications and therefore may also require medical
          intervention and hospitalization. We studied three secondary endpoints: severe dengue
          alone, severe dengue or dengue with warning signs according to the 2009 WHO
          classification, and hospitalization. These endpoints were selected as they also reflect
          the disease burden and severity and are generalizable across different settings. The
          decision to hospitalize was based only on clinical judgement and local guidelines
          particular to each study site, without use of any biomarker information.</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="statistical-analysis-details-in-appendix-3">Statistical analysis (details in Appendix
          3)</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Plasma levels of all biomarkers
          were transformed to the base-2 logarithm (log-2) before analysis as a right skewed
          distribution was apparent. We used a logistic regression model for all endpoints. We
          investigated the non-linear effects of all biomarkers and age on the endpoints, using
          restricted cubic splines with three knots at the 10th, 50th, and 90th percentiles.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For the first aim, that is to
          investigate the association of all biomarkers with the primary and secondary endpoints, we
          performed two different analyses: (1) fitting models for each biomarker separately
          (‘single models’) and (2) fitting models including all biomarkers together (‘global
          models’). In the ‘single models’ for a particular biomarker, only that biomarker along
          with age and their interaction were included, whereas in the ‘global models’ all the
          biomarkers along with their interactions with age were included. We performed the ‘global
          model’ in order to investigate the influence of the biomarkers when considering them
          together and this was also the initial step to develop models for the second aim. Results
          are reported as odds ratio (OR) and presented graphically.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For the second aim to find the
          best combination of biomarkers associated with the primary endpoint, we built upon the
          results from the first aim to fit separate models for children and adults (&lt;15
          versus ≥15 years of age), as differences were apparent by age. We used variable selection
          based on the ‘best subset’ approach <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib9"><span>9</span><span>Hastie et
                  al.</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib11"><span>11</span><span>Hocking
                  and Leslie</span><span>1967</span></a></cite></span>. Briefly, this approach
          screened all possible combinations of biomarkers and selected the best based on the Akaike
          information criterion (AIC). We chose AIC as a ranking measurement because it quantifies
          goodness of fit, while guarding against over-fitting. The marker combination with the
          lowest AIC was taken as the best. From an ‘initial model’ including all biomarkers, we
          determined the best general combination and the best combinations of 2, 3, 4, and 5
          biomarkers. We then performed a bootstrap procedure to check the robustness (stability) of
          the selected models. For this we resampled 1000 times with replacement from the original
          dataset. For each of these 1000 bootstrap samples, we performed the ‘best subset’
          procedure similar to above to determine the best combination. We calculated the selection
          frequency of each marker combination over the 1000 samples. The frequency of the
          combination that was selected when using the original dataset in relation to the other
          combinations characterizes robustness of the selection.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We carried out several
          sensitivity analyses. First, we fitted the single and global models taking into account
          potential differences between serotypes by including serotype variable along with its
          interaction with the biomarkers. Second, we included viremia (viral RNA measured by
          RT-PCR) levels as an additional biomarker and performed the single model, global model and
          best subset procedure. Higher viremia levels have been associated with worse disease
          outcomes; however, viral load was not considered in the main analysis as the focus was on
          host markers with the potential for combining in a biomarker rapid test.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">All analyses were done using
          the statistical software R version 3.6.3 <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib28"><span>28</span><span>R Core
                Development Team</span><span>2020</span></a></cite> and the packages ‘rms’ <cite
            itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib8"><span>8</span><span>Harrell Jr</span><span>2019</span></a></cite>,
          ‘MuMIn’ <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib2"><span>2</span><span>Bartoń</span><span>2020</span></a></cite> and
          ‘ggplot2’ <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib49"><span>49</span><span>Wickham</span><span>2016</span></a></cite>. The
          code is available on <a
            href="https://github.com/Nguyenlamvuong/eLife_Biomarkers_Dengue_2021" itemscope=""
            itemtype="http://schema.stenci.la/Link">GitHub</a> (<cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a
              href="#bib47"><span>47</span><span>Vuong</span><span>2021</span></a></cite>; copy
          archived at <a
            href="https://archive.softwareheritage.org/swh:1:dir:2208b3484f7b7568f4ecde57bb8f0f641194a6b0;origin=https://github.com/Nguyenlamvuong/eLife_Biomarkers_Dengue_2021;visit=swh:1:snp:531311172177ecad060ca11b9c3752edb33ce261;anchor=swh:1:rev:847d8e0f564eeb3f075b443205fb3384598bc2b4"
            itemscope=""
            itemtype="http://schema.stenci.la/Link">swh:1:rev:847d8e0f564eeb3f075b443205fb3384598bc2b4</a>). 
        </p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="results">Results</h2>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="patient-characteristics">
          Patient characteristics</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The majority of the patients
          were from Vietnam (640 cases, 76%). Median (1st, 3rd quartiles) age of the case and
          control groups were 12 (9, 22) and 16 (10, 24) years. Among the S/MD group, 127 cases
          (45%) were children and 154 cases (55%) were adults. Male gender was predominant (60% and
          54% in the case and control groups respectively). Serotype distribution was similar
          between the S/MD and control groups, with DENV-1 predominating (42%), particularly in
          children (48%). Host immune status however differed: there was a higher proportion of
          secondary infections in the S/MD group compared with controls (78% versus 64%,
          respectively) and this was consistent in both children and adults. The S/MD had a slightly
          lower percentage of obese patients than the control group (10% versus 14%). As expected,
          hospitalization was more common in the S/MD group (57% versus 31%) (<a href="#table1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Table 1</a>). Overall, 38 patients
          developed severe dengue, most were severe plasma leakage (33/38 cases, 87%) and 29/38
          (76%) were children. Most of the moderate dengue cases were plasma leakage and/or hepatic
          involvement (<a href="#app4table1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 4—table 1</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" title="General setup"><label
            data-itemprop="label">General setup</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-warning="FALSE" data-message="FALSE" data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># All codes are in my GitHub at https://github.com/Nguyenlamvuong/eLife_Biomarkers_Dengue_2021
# Load packages
library(tidyverse)
library(gtsummary) # need to install package &#39;flextable&#39;
library(rms)
library(MuMIn) # for best subset selection
library(facetscales) # need to install package &#39;facetscales&#39; from devtools::install_github(&quot;zeehio/facetscales&quot;)
source(&quot;Elife ERA functions.R&quot;) # to include my functions
options(gtsummary.tbl_summary.percent_fun = function(x) sprintf(x * 100, fmt=&#39;%1.0f&#39;)) # to report percentages without decimal
options(knitr.kable.NA = &#39;&#39;) # to set NA to &#39;&#39; in kable results
theme_set(theme_bw()) # I love black &amp; white theme
options(na.action = &quot;na.fail&quot;) # for &#39;dredge&#39; function [MuMIn]

# Load full data
dat0 &lt;- read_csv(&quot;Dengue_Biomarkers_data_27Jul2021.csv&quot;) %&gt;%
  mutate(group2 = factor(sev.or.inte, levels=c(0,1), labels=c(&quot;Uncomplicated dengue&quot;,&quot;Severe/moderate dengue&quot;)),
         Country = as.factor(Country),
         Serotype = as.factor(Serotype),
         Serology = factor(Serology, levels = c(&quot;Probable primary&quot;, &quot;Probable secondary&quot;, &quot;Inconclusive&quot;)),
         WHO2009 = factor(WHO2009, levels = c(&quot;Mild dengue&quot;, &quot;Dengue with warning signs&quot;, &quot;Severe dengue&quot;, &quot;Unknown&quot;)))

# Data at enrollment (for Table 1 &amp; Appendix 4-table 1)
dat &lt;- dat0 %&gt;% filter(Time == &quot;Enrolment&quot;)

# Data at enrollment for models
## with inverse probability weights (IPW) for inclusion probability by countries (for the analysis of secondary outcomes)
## transform the biomarker&#39;s levels to log-2 and viremia to log-10
## set all biomarker&#39;s levels under the limit of detection (u...=1) to the limit of detection
dat1 &lt;- dat %&gt;%
  mutate(age15 = factor(age15, levels=c(&quot;No&quot;,&quot;Yes&quot;), labels=c(&quot;Under 15&quot;,&quot;15 and above&quot;)),
         Serotype = ifelse(Serotype==&quot;Unknown&quot;, NA, as.character(Serotype)),
         Serotype = ifelse(Serotype==&quot;DENV-1&quot;, 1, 2),
         Serotype2 = Serotype, # for getting results for Appendix5-tables 1, 2
         Serotype = factor(Serotype, levels=c(1,2), labels=c(&quot;DENV-1&quot;,&quot;Others&quot;)), # set Serotype to DENV-1 and others
         ipw = ifelse(sev.or.inte==1, 1,
                      ifelse(Country==&quot;Vietnam&quot;, (1505-204)/436,
                             ifelse(Country==&quot;Malaysia&quot;, (259-29)/58,
                                    ifelse(Country==&quot;El Salvador&quot;, (306-18)/23,
                                           ifelse(Country==&quot;Cambodia&quot;, (302-30)/39, NA))))),
         ipwsd = ipw * 837/2372,
         VCAM = ifelse(uVCAM==1, log2(0.028), log2(VCAM1)),
         SDC = log2(SDC1),
         Ang = ifelse(uAng==1, log2(17.1), log2(Ang2)),
         IL8 = ifelse(uIL8==1, log2(1.8), log2(IL8)),
         IP10 = ifelse(uIP10==1, log2(1.18), log2(IP10)),
         IL1RA = ifelse(uIL1RA==1, log2(18), log2(IL1RA)),
         CD163 = log2(CD163),
         TREM = ifelse(uTREM==1, log2(10.65), log2(TREM1)),
         Fer = log2(Fer),
         CRP = log2(CRP), 
         Vir = log10(Viremia)) %&gt;%
  select(Code, Age, age15, Serotype, Serotype2, sev.or.inte, sev.only, sev.or.ws, hospital, VCAM, SDC, Ang, IL8, IP10, IL1RA, CD163, TREM, Fer, CRP, Vir, uVCAM, uAng, uIL8, uIP10, uCD163, uTREM, uVir, ipw, ipwsd)

dat1c &lt;- dat1 %&gt;% filter(age15 == &quot;Under 15&quot;) ## Data for children
dat1a &lt;- dat1 %&gt;% filter(age15 == &quot;15 and above&quot;) ## Data for adults

# Set references for calculating the ORs and 95% CIs
ref0 &lt;- c(median(dat1$VCAM), median(dat1$SDC), median(dat1$Ang), median(dat1$IL8), median(dat1$IP10), median(dat1$IL1RA), median(dat1$CD163), median(dat1$TREM), median(dat1$Fer), median(dat1$CRP))

# Create data for plotting biomarkers (for Figure 2 &amp; Appendix 4-figure 1)
## Enrollment
tmp1 &lt;- dat0 %&gt;%
  filter(Time == &quot;Enrolment&quot;) %&gt;%
  select(Code, group2, Day, Daygr, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %&gt;%
  gather(., &quot;Biomarker&quot;, &quot;Result&quot;, 5:14)

## Follow up
tmp2 &lt;- dat0 %&gt;%
  filter(Time == &quot;Follow up&quot;) %&gt;%
  select(Code, group2, Day, Daygr, VCAM1, SDC1, Ang2, IL8, IP10, IL1RA, CD163, TREM1, Fer, CRP) %&gt;%
  gather(., &quot;Biomarker&quot;, &quot;Result&quot;, 5:14)

## Merge long data for biomarkers
dat_plot &lt;- bind_rows(tmp1, tmp2) %&gt;%
  mutate(Daygr = factor(Daygr, levels = c(&quot;Day 1&quot;, &quot;Day 2&quot;, &quot;Day 3&quot;, &quot;Day 10-20&quot;, &quot;Day &gt;20&quot;),
                        labels = c(&quot;1&quot;, &quot;2&quot;, &quot;3&quot;, &quot;10-20&quot;, &quot;&gt;20&quot;)),
         Biomarker = factor(Biomarker, levels=c(&quot;VCAM1&quot;, &quot;SDC1&quot;, &quot;Ang2&quot;, &quot;IL8&quot;, &quot;IP10&quot;, &quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM1&quot;, &quot;Fer&quot;, &quot;CRP&quot;), 
                            labels=c(&quot;VCAM-1 (ng/ml)&quot;, &quot;SDC-1 (pg/ml)&quot;, &quot;Ang-2 (pg/ml)&quot;, &quot;IL-8 (pg/ml)&quot;, &quot;IP-10 (pg/ml)&quot;, 
                                     &quot;IL-1RA (pg/ml)&quot;, &quot;sCD163 (ng/ml)&quot;, &quot;sTREM-1 (pg/ml)&quot;, &quot;Ferritin (ng/ml)&quot;, &quot;CRP (mg/l)&quot;)))</code></pre>
          </stencila-code-chunk>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="table1" title="Table 1.">
          <label data-itemprop="label">Table 1.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Codes in &#39;General setup&#39; need to be run first
# All patients
t1 &lt;- dat %&gt;%
  select(group2, Country, Age, Sex, Day, Serotype, Serology, Obesity, Diabetes, WHO2009, hospital) %&gt;%
  tbl_summary(by = group2,
              statistic = list(all_continuous() ~ &quot;{median} ({p25}, {p75})&quot;, all_categorical() ~ &quot;{n} ({p})&quot;),
              value = list(Sex ~ &quot;Male&quot;),
              digits = list(all_continuous() ~ c(0,0)),
              label = list(Age ~ &quot;Age (years)&quot;, Sex ~ &quot;Gender male&quot;, Day ~ &quot;Illness day at enrolment&quot;,
                           Serology ~ &quot;Immune status&quot;, WHO2009 ~ &quot;WHO 2009 classification&quot;, hospital ~ &quot;Hospitalization&quot;)) %&gt;%
  add_stat_label(label = c(all_categorical() ~ &quot;n (%)&quot;, Age ~ &quot;median (1st, 3rd quartiles)&quot;)) %&gt;%
  modify_header(label = &quot;&quot;, stat_by = &quot;**{level} (N={n})**&quot;)

# Children (&lt;15 years of age)
t2 &lt;- dat %&gt;%
  filter(age15==&quot;No&quot;) %&gt;%
  select(group2, Country, Age, Sex, Day, Serotype, Serology, Obesity, Diabetes, WHO2009, hospital) %&gt;%
  tbl_summary(by = group2,
              statistic = list(all_continuous() ~ &quot;{median} ({p25}, {p75})&quot;, all_categorical() ~ &quot;{n} ({p})&quot;),
              value = list(Sex ~ &quot;Male&quot;),
              digits = list(all_continuous() ~ c(0,0)),
              label = list(Age ~ &quot;Age (years)&quot;, Sex ~ &quot;Gender male&quot;, Day ~ &quot;Illness day at enrolment&quot;,
                           Serology ~ &quot;Immune status&quot;, WHO2009 ~ &quot;WHO 2009 classification&quot;, hospital ~ &quot;Hospitalization&quot;)) %&gt;%
  add_stat_label(label = c(all_categorical() ~ &quot;n (%)&quot;, Age ~ &quot;median (1st, 3rd quartiles)&quot;)) %&gt;%
  modify_header(label = &quot;&quot;, stat_by = &quot;**{level} (N={n})**&quot;)

# Adults (&gt;=15 years of age)
t3 &lt;- dat %&gt;%
  filter(age15==&quot;Yes&quot;) %&gt;%
  select(group2, Country, Age, Sex, Day, Serotype, Serology, Obesity, Diabetes, WHO2009, hospital) %&gt;%
  tbl_summary(by = group2,
              statistic = list(all_continuous() ~ &quot;{median} ({p25}, {p75})&quot;, all_categorical() ~ &quot;{n} ({p})&quot;),
              value = list(Sex ~ &quot;Male&quot;),
              digits = list(all_continuous() ~ c(0,0)),
              label = list(Age ~ &quot;Age (years)&quot;, Sex ~ &quot;Gender male&quot;, Day ~ &quot;Illness day at enrolment&quot;,
                           Serology ~ &quot;Immune status&quot;, WHO2009 ~ &quot;WHO 2009 classification&quot;, hospital ~ &quot;Hospitalization&quot;)) %&gt;%
  add_stat_label(label = c(all_categorical() ~ &quot;n (%)&quot;, Age ~ &quot;median (1st, 3rd quartiles)&quot;)) %&gt;%
  modify_header(label = &quot;&quot;, stat_by = &quot;**{level} (N={n})**&quot;)

# Merge tables
tbl1 &lt;- tbl_merge(tbls = list(t1, t2, t3),
          tab_spanner = c(&quot;**All patients**&quot;, &quot;**Children**&quot;, &quot;**Adults**&quot;))

# Convert the table to a dataframe for output as an HTML table
tbl1_formatted &lt;- data.frame(
    var = tbl1$table_body$label,
	  al1 = tbl1$table_body$stat_1_1,
    al2 = tbl1$table_body$stat_2_1,
    ch1 = tbl1$table_body$stat_1_2,
    ch2 = tbl1$table_body$stat_2_2,
    ad1 = tbl1$table_body$stat_1_3,
    ad2 = tbl1$table_body$stat_2_3
)

for (i in c(2:5,9:11,13:17,19:21,25:28)) {tbl1_formatted$var[[i]] &lt;- paste(&quot; - &quot;, tbl1_formatted$var[[i]])}

names(tbl1_formatted) &lt;- c(
    &quot;Variable&quot;,
    &quot;All, uncomplicated dengue (N=556)&quot;,
    &quot;All, severe/moderate dengue (N=281)&quot;,
    &quot;Children, uncomplicated dengue (N=337)&quot;,
    &quot;Children, severe/moderate dengue (N=127)&quot;,
    &quot;Adults, uncomplicated dengue (N=219)&quot;,
    &quot;Adults, severe/moderate dengue (N=154)&quot;
)
tbl1_formatted</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="summary-of-clinical-data-by-primary-outcome">Summary of clinical data by primary
              outcome.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Obesity is defined as body
              mass index of higher than 30 kg/m2 (for patients of older than 18 years) or two
              standard deviations of the median of body mass index for age (for patients of 18 years
              or below). WHO: World Health Organization.</p>
          </figcaption>
        </figure>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="biomarker-levels">Biomarker
          levels</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">On average, the patients who
          progressed to S/MD had higher levels of the biomarkers in both children and adult
          patients, both at enrollment and at follow-up (<a href="#fig2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 2</a>, <a href="#app4table2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 4—table 2</a>). For most individuals,
          the levels of five biomarkers (VCAM-1, IL-8, IP-10, IL-1RA, and CRP) decreased between
          enrollment and follow-up, whereas SDC-1 increased slightly and the other markers showed no
          clear trends (<a href="#app4fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 4—figure 1</a>). In some of the cases
          the biomarkers did not return to normal at convalescence. Moderate-to-strong positive
          correlations were evident for some markers, in particular IP-10 and IL-1RA, and IP-10 and
          VCAM-1, both with Spearman’s rank correlation coefficients above 0.6 (<a href="#app4fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 4—figure 2</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig2" title="Figure 2.">
          <label data-itemprop="label">Figure 2.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#&#39; @width 28
#&#39; @height 20
# Codes in &#39;General setup&#39; need to be run first

tick &lt;- c(0,1,4,10,40,100,200,400,1000,4000,10000,20000,40000,70000) # for y-axis tick labels

dat_plot %&gt;%
  ggplot(., aes(Daygr, Result^(1/4), fill=group2, color=group2)) +
  geom_boxplot(alpha=.5, outlier.size=.9, lwd=.4, fatten=1) +
  geom_boxplot(alpha=0, outlier.color=NA, color=&quot;black&quot;, lwd=.4, fatten=1) +
  facet_wrap(~ Biomarker, scales=&quot;free&quot;, ncol=5) +
  scale_y_continuous(breaks=tick^(1/4), labels=tick) +
  xlab(&quot;Illness day (day 1 [n=140]; day 2 [n=390]; day 3 [n=307]; day 10-20 [n=625]; day &gt;20 [n=43])&quot;) +
  theme(axis.title.y=element_blank(), legend.position=&quot;top&quot;, legend.title=element_blank(),
        axis.text.y=element_text(size=rel(.8)))</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="biomarker-levels-by-groups">Biomarker levels by groups.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">VCAM-1: vascular cell
              adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8;
              IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1 receptor antagonist;
              sCD163: soluble cluster of differentiation 163; sTREM-1: soluble triggering receptor
              expressed on myeloid cells-1; CRP: C-reactive protein. Y-axes are transformed using
              the fourth root transformation.</p>
          </figcaption>
        </figure>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="associations-between-biomarker-levels-and-the-endpoints">Associations between
          biomarker levels and the endpoints</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In the single models, higher
          levels of each biomarker on illness days 1, 2, or 3 increased the risk of developing S/MD,
          with the exception of ferritin in adults where there was a downward trend at higher values
          (<a href="#fig3" itemscope="" itemtype="http://schema.stenci.la/Link">Figure 3</a>, <a
            href="#table2" itemscope="" itemtype="http://schema.stenci.la/Link">Table 2</a>). We
          observed differences between children and adults for several biomarkers, the most
          pronounced being SDC-1, IL-8, ferritin, and IL-1RA. Associations between SDC-1 and IL-8
          and the S/MD endpoint were stronger in adults than children, while the effects of IL-1RA
          and ferritin were stronger in children than adults.</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="fig3" title="Figure 3.">
          <label data-itemprop="label">Figure 3.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code>#&#39; @width 28
#&#39; @height 20
# Codes in &#39;General setup&#39; need to be run first

# Set datadist for &#39;lrm&#39; function [rms]
dd &lt;- datadist(dat1); options(datadist=&quot;dd&quot;)

# Get results from models for children with my functions &#39;get_pred1&#39; and &#39;get_pred2&#39;
dd$limits[&quot;Adjust to&quot;,&quot;Age&quot;] &lt;- 10

dat_m1 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;VCAM&quot;, age=10, dat=dat1) %&gt;% rename(value=VCAM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m2 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;SDC&quot;, age=10, dat=dat1) %&gt;% rename(value=SDC) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m3 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;Ang&quot;, age=10, dat=dat1) %&gt;% rename(value=Ang) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m4 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;IL8&quot;, age=10, dat=dat1) %&gt;% rename(value=IL8) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m5 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;IP10&quot;, age=10, dat=dat1) %&gt;% rename(value=IP10) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m6 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;IL1RA&quot;, age=10, dat=dat1) %&gt;% rename(value=IL1RA) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m7 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;CD163&quot;, age=10, dat=dat1) %&gt;% rename(value=CD163) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m8 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;TREM&quot;, age=10, dat=dat1) %&gt;% rename(value=TREM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m9 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;Fer&quot;, age=10, dat=dat1) %&gt;% rename(value=Fer) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_m10 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;CRP&quot;, age=10, dat=dat1) %&gt;% rename(value=CRP) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))

dat_mg1 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;VCAM&quot;, age=10, dat=dat1) %&gt;% rename(value=VCAM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg2 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;SDC&quot;, age=10, dat=dat1) %&gt;% rename(value=SDC) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg3 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;Ang&quot;, age=10, dat=dat1) %&gt;% rename(value=Ang) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg4 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;IL8&quot;, age=10, dat=dat1) %&gt;% rename(value=IL8) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg5 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;IP10&quot;, age=10, dat=dat1) %&gt;% rename(value=IP10) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg6 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;IL1RA&quot;, age=10, dat=dat1) %&gt;% rename(value=IL1RA) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg7 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;CD163&quot;, age=10, dat=dat1) %&gt;% rename(value=CD163) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg8 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;TREM&quot;, age=10, dat=dat1) %&gt;% rename(value=TREM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg9 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;Fer&quot;, age=10, dat=dat1) %&gt;% rename(value=Fer) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))
dat_mg10 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;CRP&quot;, age=10, dat=dat1) %&gt;% rename(value=CRP) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;Under 15&quot;))

dat_p1 &lt;- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %&gt;%
  mutate(age = &quot;10 years&quot;)

# Get results from models for adults
dd$limits[&quot;Adjust to&quot;,&quot;Age&quot;] &lt;- 25

dat_m1 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;VCAM&quot;, age=25, dat=dat1) %&gt;% rename(value=VCAM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m2 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;SDC&quot;, age=25, dat=dat1) %&gt;% rename(value=SDC) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m3 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;Ang&quot;, age=25, dat=dat1) %&gt;% rename(value=Ang) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m4 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;IL8&quot;, age=25, dat=dat1) %&gt;% rename(value=IL8) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m5 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;IP10&quot;, age=25, dat=dat1) %&gt;% rename(value=IP10) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m6 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;IL1RA&quot;, age=25, dat=dat1) %&gt;% rename(value=IL1RA) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m7 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;CD163&quot;, age=25, dat=dat1) %&gt;% rename(value=CD163) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m8 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;TREM&quot;, age=25, dat=dat1) %&gt;% rename(value=TREM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m9 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;Fer&quot;, age=25, dat=dat1) %&gt;% rename(value=Fer) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_m10 &lt;- get_pred1(out=&quot;sev.or.inte&quot;, bio=&quot;CRP&quot;, age=25, dat=dat1) %&gt;% rename(value=CRP) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))

dat_mg1 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;VCAM&quot;, age=25, dat=dat1) %&gt;% rename(value=VCAM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg2 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;SDC&quot;, age=25, dat=dat1) %&gt;% rename(value=SDC) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg3 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;Ang&quot;, age=25, dat=dat1) %&gt;% rename(value=Ang) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg4 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;IL8&quot;, age=25, dat=dat1) %&gt;% rename(value=IL8) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg5 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;IP10&quot;, age=25, dat=dat1) %&gt;% rename(value=IP10) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg6 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;IL1RA&quot;, age=25, dat=dat1) %&gt;% rename(value=IL1RA) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg7 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;CD163&quot;, age=25, dat=dat1) %&gt;% rename(value=CD163) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg8 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;TREM&quot;, age=25, dat=dat1) %&gt;% rename(value=TREM) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg9 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;Fer&quot;, age=25, dat=dat1) %&gt;% rename(value=Fer) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))
dat_mg10 &lt;- get_pred2(out=&quot;sev.or.inte&quot;, bio=&quot;CRP&quot;, age=25, dat=dat1) %&gt;% rename(value=CRP) %&gt;% 
  select(value, yhat, lower, upper, biomarker, model) %&gt;% slice(which(dat1$age15==&quot;15 and above&quot;))

dat_p2 &lt;- rbind(dat_m1,dat_mg1, dat_m2,dat_mg2, dat_m3,dat_mg3, dat_m4,dat_mg4, dat_m5,dat_mg5, dat_m6,dat_mg6, dat_m7,dat_mg7, dat_m8,dat_mg8, dat_m9,dat_mg9, dat_m10,dat_mg10) %&gt;%
  mutate(age = &quot;25 years&quot;)

# Merge results for children and adults
vis1 &lt;- rbind(dat_p1, dat_p2) %&gt;% mutate(outcome = &quot;outcome 1&quot;)

# Merge data for plots
tmp0 &lt;- dat1 %&gt;% arrange(age15) %&gt;% select(sev.or.inte)
tmp &lt;- data.frame(sev.or.inte = rep(tmp0$sev.or.inte, 20))
  
tmp_p1 &lt;- vis1 %&gt;%
  arrange(biomarker, model) %&gt;%
  mutate(value1 = 2^value,
         age = factor(age, levels=c(&quot;10 years&quot;, &quot;25 years&quot;)),
         gr = ifelse(model==&quot;Single model&quot; &amp; age==&quot;10 years&quot;, 1,
                     ifelse(model==&quot;Single model&quot; &amp; age==&quot;25 years&quot;, 2,
                            ifelse(model==&quot;Global model&quot; &amp; age==&quot;10 years&quot;, 3, 4))),
         gr = factor(gr, levels=c(1:4), labels=c(&quot;Single children&quot;, &quot;Single adults&quot;, &quot;Global children&quot;, &quot;Global adults&quot;)),
         model = factor(model, levels=c(&quot;Single model&quot;, &quot;Global model&quot;))) %&gt;%
  bind_cols(., tmp)

vline &lt;- tmp_p1 %&gt;%
  filter(model==&quot;Single model&quot;) %&gt;%
  filter(yhat==0) %&gt;%
  select(biomarker, value, value1) %&gt;%
  rename(vline=value, vline1=value1)

dat_p &lt;- left_join(tmp_p1, vline, by=&quot;biomarker&quot;)

# Alternative data to limit to 5th - 95th of the values
dat_alt &lt;- dat_p %&gt;%
  group_by(biomarker, model) %&gt;%
  mutate(up = quantile(value, .95),
         lo = quantile(value, .05)) %&gt;%
  ungroup() %&gt;%
  mutate(is.outlier = value&lt;lo | value&gt;up | value&lt;0,
         value = ifelse(is.outlier, NA, value),
         value1 = ifelse(is.outlier, NA, value1),
         yhat = ifelse(is.outlier, NA, yhat),
         lower = ifelse(is.outlier, NA, lower),
         upper = ifelse(is.outlier, NA, upper))

dat_1_5 &lt;- dat_alt %&gt;% 
  filter(biomarker %in% c(&quot;VCAM&quot;, &quot;SDC&quot;, &quot;Ang&quot;, &quot;IL8&quot;, &quot;IP10&quot;)) %&gt;%
  mutate(biomarker = factor(biomarker, levels=c(&quot;VCAM&quot;, &quot;SDC&quot;, &quot;Ang&quot;, &quot;IL8&quot;, &quot;IP10&quot;)))

dat_6_10 &lt;- dat_alt %&gt;% 
  filter(biomarker %in% c(&quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM&quot;, &quot;Fer&quot;, &quot;CRP&quot;)) %&gt;%
  mutate(biomarker = factor(biomarker, levels=c(&quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM&quot;, &quot;Fer&quot;, &quot;CRP&quot;)))

# Modify facets&#39; scales
require(facetscales)
xVCAM &lt;- c(1,4,15,60,250,1000,4000)
xSDC &lt;- c(1400,2000,2800,4000,5600)
xAng &lt;- c(50,100,250,500,1000,2000)
xIL8 &lt;- c(5,7,10,14,20,28,40)
xIP10 &lt;- c(25,100,400,1600,6400)
xIL1RA &lt;- c(1000,2000,4000,8000,16000)
xCD163 &lt;- c(75,150,300,600)
xTREM &lt;- c(35,50,70,100,140,200)
xFer &lt;- c(50,100,200,400,800)
xCRP &lt;- c(2.5,5,10,20,40,80)

scales_x &lt;- list(
  `VCAM` = scale_x_continuous(breaks = log2(xVCAM), labels = xVCAM),
  `SDC` = scale_x_continuous(breaks = log2(xSDC), labels = xSDC),
  `Ang` = scale_x_continuous(breaks = log2(xAng), labels = xAng), 
  `IL8` = scale_x_continuous(breaks = log2(xIL8), labels = xIL8), 
  `IP10` = scale_x_continuous(breaks = log2(xIP10), labels = xIP10), 
  `IL1RA` = scale_x_continuous(breaks = log2(xIL1RA), labels = xIL1RA), 
  `CD163` = scale_x_continuous(breaks = log2(xCD163), labels = xCD163), 
  `TREM` = scale_x_continuous(breaks = log2(xTREM), labels = xTREM), 
  `Fer` = scale_x_continuous(breaks = log2(xFer), labels = xFer), 
  `CRP` = scale_x_continuous(breaks = log2(xCRP), labels = xCRP)
)

ybreak1 &lt;- c(.125, .25, .5, 1, 2, 4, 8)
ybreak2 &lt;- c(.125, .25, .5, 1, 2, 4)

scales_y1 &lt;- list(
  `Single model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1),
  `Global model` = scale_y_continuous(limits = c(log(.124), log(8.01)), breaks = log(ybreak1), labels = ybreak1)
)

scales_y2 &lt;- list(
  `Single model` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2),
  `Global model` = scale_y_continuous(limits = c(log(.124), log(4.01)), breaks = log(ybreak2), labels = ybreak2)
)

# Set facets&#39; names
models &lt;- c(`Single model` = &quot;Single model&quot;,
            `Global model` = &quot;Global model&quot;)

biomarkers &lt;- c(`VCAM` = &quot;VCAM-1 (ng/ml)&quot;,
                `SDC`= &quot;SDC-1 (pg/ml)&quot;,
                `Ang` = &quot;Ang-2 (pg/ml)&quot;,
                `IL8` = &quot;IL-8 (pg/ml)&quot;,
                `IP10` = &quot;IP-10 (pg/ml)&quot;,
                `IL1RA` = &quot;IL-1RA (pg/ml)&quot;,
                `CD163` = &quot;sCD163 (ng/ml)&quot;,
                `TREM` = &quot;sTREM-1 (pg/ml)&quot;,
                `Fer` = &quot;Ferritin (ng/ml)&quot;,
                `CRP` = &quot;CRP (mg/l)&quot;)

# Plot for the first 5 biomarkers
p.1.5 &lt;- ggplot(dat_1_5, aes(x=value)) +
  geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) +
  geom_vline(aes(xintercept=vline), color=&quot;black&quot;, linetype=&quot;dashed&quot;, alpha=.4) +
  geom_hline(yintercept=0, color=&quot;black&quot;, linetype=&quot;dashed&quot;, alpha=.4) +
  geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) +
  geom_rug(data=filter(dat_1_5, model==&quot;Single model&quot; &amp; sev.or.inte==0 &amp; age==&quot;10 years&quot;), sides=&quot;b&quot;, alpha=.2, color=&quot;red&quot;) +
  geom_rug(data=filter(dat_1_5, model==&quot;Single model&quot; &amp; sev.or.inte==1 &amp; age==&quot;10 years&quot;), sides=&quot;t&quot;, alpha=.2, color=&quot;red&quot;) +
  geom_rug(data=filter(dat_1_5, model==&quot;Global model&quot; &amp; sev.or.inte==0 &amp; age==&quot;25 years&quot;), sides=&quot;b&quot;, alpha=.2, color=&quot;blue&quot;) +
  geom_rug(data=filter(dat_1_5, model==&quot;Global model&quot; &amp; sev.or.inte==1 &amp; age==&quot;25 years&quot;), sides=&quot;t&quot;, alpha=.2, color=&quot;blue&quot;) +
  scale_color_manual(values=c(&quot;red&quot;,&quot;blue&quot;), labels=c(&quot;Children&quot;,&quot;Adults&quot;)) +
  scale_fill_manual(values=c(&quot;red&quot;,&quot;blue&quot;), labels=c(&quot;Children&quot;,&quot;Adults&quot;)) +
  ylab(&quot;Odds ratio&quot;) +
  theme(legend.position=&quot;top&quot;, legend.title=element_blank(), axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) +
  facet_grid_sc(cols=vars(biomarker), rows=vars(model), 
                scales=list(x=scales_x, y=scales_y1),
                labeller = as_labeller(c(models, biomarkers)))

# Plot for the last 5 biomarkers
p.6.10 &lt;- ggplot(dat_6_10, aes(x=value)) +
  geom_ribbon(aes(ymin=lower, ymax=upper, fill=age), alpha=.15) +
  geom_vline(aes(xintercept=vline), color=&quot;black&quot;, linetype=&quot;dashed&quot;, alpha=.4) +
  geom_hline(yintercept=0, color=&quot;black&quot;, linetype=&quot;dashed&quot;, alpha=.4) +
  geom_line(aes(y=yhat, color=age), size=.5, alpha=.7) +
  geom_rug(data=filter(dat_6_10, model==&quot;Single model&quot; &amp; sev.or.inte==0 &amp; age==&quot;10 years&quot;), sides=&quot;b&quot;, alpha=.2, color=&quot;red&quot;) +
  geom_rug(data=filter(dat_6_10, model==&quot;Single model&quot; &amp; sev.or.inte==1 &amp; age==&quot;10 years&quot;), sides=&quot;t&quot;, alpha=.2, color=&quot;red&quot;) +
  geom_rug(data=filter(dat_6_10, model==&quot;Global model&quot; &amp; sev.or.inte==0 &amp; age==&quot;25 years&quot;), sides=&quot;b&quot;, alpha=.2, color=&quot;blue&quot;) +
  geom_rug(data=filter(dat_6_10, model==&quot;Global model&quot; &amp; sev.or.inte==1 &amp; age==&quot;25 years&quot;), sides=&quot;t&quot;, alpha=.2, color=&quot;blue&quot;) +
  scale_color_manual(values=c(&quot;red&quot;,&quot;blue&quot;), labels=c(&quot;Children&quot;,&quot;Adults&quot;)) +
  scale_fill_manual(values=c(&quot;red&quot;,&quot;blue&quot;), labels=c(&quot;Children&quot;,&quot;Adults&quot;)) +
  ylab(&quot;Odds ratio&quot;) +
  theme(legend.position=&quot;none&quot;, axis.title.x=element_blank(), axis.text.x=element_text(angle=45)) +
  facet_grid_sc(cols=vars(biomarker), rows=vars(model), 
                scales=list(x=scales_x, y=scales_y2),
                labeller = as_labeller(c(models, biomarkers)))

# Merge plots
gridExtra::grid.arrange(p.1.5, p.6.10, nrow=2, heights=c(1,.86))</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="results-from-models-for-the-primary-endpoint-severe-or-moderate-dengue">Results
              from models for the primary endpoint (severe or moderate dengue).</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The odds ratio of
              severe/moderate dengue (the red and blue lines) and 95% confidence interval (the red
              and blue regions) are estimated from multivariable logistic regression models allowing
              for a non-linear relation of log-2 of the biomarker level with severe/moderate dengue
              using restricted cubic splines. Each single model contains the corresponding
              biomarker, age and their interaction, while the global model contains all biomarkers
              and their interaction with age. The reference values for the odds ratios (where the
              odds ratio is equal to 1) are represented by the vertical gray dashed lines. They are
              chosen as the median of the biomarker levels of the whole study population (VCAM-1:
              1636 ng/ml; SDC-1: 2519 pg/ml; Ang-2: 1204 pg/ml; IL-8: 14 pg/ml; IP-10: 3093 pg/ml;
              IL-1RA: 6434 pg/ml; sCD163: 295 ng/ml; sTREM-1: 85 ng/ml; ferritin: 243 ng/ml; and
              CRP: 28 mg/l). The x-axis represents biomarker levels; it is transformed using log-2
              and its range truncated by the 5th and 95th percentiles of the biomarker levels of the
              whole study population. The rug plot on the x-axis represents the distribution of
              individual cases; the bottom rug plot represents the uncomplicated dengue cases and
              the top rug plot represents the severe/moderate dengue cases (children [&lt;15 years
              of age] are in red and adults [≥15 years of age] are in blue). The red line and region
              represent children; results are shown for children at age of 10 years. The blue line
              and region represents adults; results are shown for adults at age of 25 years. VCAM-1:
              vascular cell adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8:
              interleukin-8; IP-10: interferon gamma-induced protein-10; IL-1RA: interleukin-1
              receptor antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble
              triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein.</p>
          </figcaption>
        </figure>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="table2" title="Table 2.">
          <label data-itemprop="label">Table 2.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Codes in &#39;General setup&#39; need to be run first
# Set datadist for &#39;lrm&#39; function [rms]
dd &lt;- datadist(dat1); options(datadist=&quot;dd&quot;)

# Use my function &#39;get_est1&#39; and &#39;get_est2&#39; to get ORs and CIs from models
tmp1 &lt;- data.frame(
  sort1 = rep(c(1:10), 2),
  sort2 = c(rep(2,10), rep(3,10)),
  bio = rep(c(&quot;VCAM&quot;, &quot;SDC&quot;, &quot;Ang&quot;, &quot;IL8&quot;, &quot;IP10&quot;, &quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM&quot;, &quot;Fer&quot;, &quot;CRP&quot;), 2),
  ref1 = c(ref0-1, ref0),
  ref2 = c(ref0, ref0+1)
) %&gt;%
  arrange(sort1, sort2) %&gt;%
  group_by(sort1, sort2) %&gt;%
  do(cbind(.,
           # Children - single models
           or1c = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;OR&quot;, age=10, dat=dat1),
           lo1c = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;loCI&quot;, age=10, dat=dat1),
           up1c = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;upCI&quot;, age=10, dat=dat1),
           # Children - global model
           or2c = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;OR&quot;, age=10, dat=dat1),
           lo2c = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;loCI&quot;, age=10, dat=dat1),
           up2c = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;upCI&quot;, age=10, dat=dat1),
           # Adults - single models
           or1a = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;OR&quot;, age=25, dat=dat1),
           lo1a = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;loCI&quot;, age=25, dat=dat1),
           up1a = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;upCI&quot;, age=25, dat=dat1),
           # Adults - global model
           or2a = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;OR&quot;, age=25, dat=dat1),
           lo2a = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;loCI&quot;, age=25, dat=dat1),
           up2a = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, ref1=.$ref1, ref2=.$ref2, est=&quot;upCI&quot;, age=25, dat=dat1))) %&gt;%
  ungroup()
for (i in 6:17) {tmp1[[i]] &lt;- sprintf(&quot;%.2f&quot;, round(tmp1[[i]],2))}

# Use my function &#39;get_est1&#39; and &#39;get_est2&#39; to get p-values from models
tmp2 &lt;- data.frame(
  sort1 = c(1:10),
  sort2 = rep(1,10),
  bio = c(&quot;VCAM&quot;, &quot;SDC&quot;, &quot;Ang&quot;, &quot;IL8&quot;, &quot;IP10&quot;, &quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM&quot;, &quot;Fer&quot;, &quot;CRP&quot;)
) %&gt;%
  group_by(sort1) %&gt;%
  do(cbind(.,
           p.s1 = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, est=&quot;p&quot;, dat=dat1), # P overall from single models
           p.s1_int = get_est1(out=&quot;sev.or.inte&quot;, bio=.$bio, est=&quot;p int&quot;, dat=dat1), # P interaction from single models
           p.g1 = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, est=&quot;p&quot;, dat=dat1), # P overall from global models
           p.g1_int = get_est2(out=&quot;sev.or.inte&quot;, bio=.$bio, est=&quot;p int&quot;, dat=dat1))) %&gt;% # P interaction from global models
  ungroup()
for (i in 4:7) {tmp2[[i]] &lt;- ifelse(tmp2[[i]]&lt;0.001, &quot;&lt;0.001&quot;, sprintf(&quot;%.3f&quot;, round(tmp2[[i]],3)))}  

# Combine results into a table
res1 &lt;- bind_rows(tmp1, tmp2) %&gt;%
  arrange(sort1, sort2) %&gt;%
  mutate(bio = ifelse(!is.na(ref1), paste(&quot; -&quot;, round(2^ref2,0), &quot;vs&quot;, round(2^ref1,0), sep=&quot; &quot;), as.character(bio)),
         or.sc1 = ifelse(is.na(lo1c), NA, paste(or1c, &quot; (&quot;, lo1c, &quot;-&quot;, up1c, &quot;)&quot;, sep=&quot;&quot;)), # s: single model; c: children
         or.gc1 = ifelse(is.na(lo2c), NA, paste(or2c, &quot; (&quot;, lo2c, &quot;-&quot;, up2c, &quot;)&quot;, sep=&quot;&quot;)), # g: global model
         or.sa1 = ifelse(is.na(lo1a), NA, paste(or1a, &quot; (&quot;, lo1a, &quot;-&quot;, up1a, &quot;)&quot;, sep=&quot;&quot;)), # a: adults
         or.ga1 = ifelse(is.na(lo2a), NA, paste(or2a, &quot; (&quot;, lo2a, &quot;-&quot;, up2a, &quot;)&quot;, sep=&quot;&quot;))) %&gt;%
  select(bio, or.sc1, or.sa1, p.s1, p.s1_int, or.gc1, or.ga1, p.g1, p.g1_int)

names(res1) &lt;- c(&quot;Biomarker&quot;, &quot;OR (95% CI) [children - single model]&quot;, &quot;OR (95% CI) [adults - single model]&quot;, 
                 &quot;P-overall [single model]&quot;, &quot;P-interaction [single model]&quot;,
                 &quot;OR (95% CI) [children - global model]&quot;, &quot;OR (95% CI) [adults - global model]&quot;, 
                 &quot;P-overall [global model]&quot;, &quot;P-interaction [global model]&quot;)

# Report the results
res1</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="results-from-models-for-the-primary-endpoint-severe-or-moderate-dengue-1">Results
              from models for the primary endpoint (severe or moderate dengue).</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><em itemscope=""
                itemtype="http://schema.stenci.la/Emphasis">P<sub itemscope=""
                  itemtype="http://schema.stenci.la/Subscript">overall</sub></em> is derived from
              Wald test for the overall association of the biomarker with the endpoint; <em
                itemscope="" itemtype="http://schema.stenci.la/Emphasis">P<sub itemscope=""
                  itemtype="http://schema.stenci.la/Subscript">interaction</sub></em> is from the
              test for the interaction between the biomarker and age. The odds ratios are estimated
              at age of 10 and 25 years, represented as children and adults respectively.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In the global model there were
          some differences compared to the single models (<a href="#fig3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Figure 3</a>, <a href="#table2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Table 2</a>). The biomarkers SDC-1 and IL-1RA
          were the most stable relative to the single models for both children and adults. However,
          for IP-10 the trend of the association with S/MD changed from positive to negative in both
          children and adults. In children, VCAM-1 changed the trend from positive to weakly
          negative and IL-8 changed the trend from weakly positive to negative. Other biomarkers
          showed weaker associations with the endpoint in the global model based on the ORs. In
          addition, the differences of the associations between children and adults were more
          marked, particularly for Ang-2, IL-8, and ferritin.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The sensitivity analysis showed
          that the association between the biomarkers and S/MD did not differ between DENV-1 and
          other serotypes (<a href="#app5fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 5—figure 1</a>; <a href="#app5fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 5—figure 2</a>; <a
            href="#app5table1" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 5—table
            1</a>; <a href="#app5table2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 5—table 2</a>). Similar patterns were
          observed in the various analyses related to the secondary endpoints, as described in
          detail in the Appendix 6 (<a href="#app6fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 6—figure 1</a>; <a href="#app6fig2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 6—figure 2</a>, <a
            href="#app6table1" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 6—table
            1</a>; <a href="#app6table2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 6—table 2</a>, <a href="#app6table3"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 6—table 3</a>).</p>
        <h3 itemscope="" itemtype="http://schema.stenci.la/Heading"
          id="best-combinations-of-biomarkers-associated-with-the-primary-endpoint">Best
          combinations of biomarkers associated with the primary endpoint</h3>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For children, the best subset
          that showed the clearest association with S/MD was the combination of the six markers
          IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1 with an AIC of 465.9. This model was
          selected most often in the bootstrap procedure, but was not highly robust (it was selected
          in 134 of the 1000 samples) (<a href="#table3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Table 3</a>, <a href="#app7table1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 7—table 1</a>). Over the 1000 samples,
          the six variables had an inclusion frequency ranging from 73.5% for SDC-1 to 100% for
          IL-1RA. The most important biomarkers in order were IL-1RA, Ang-2, IL-8, and ferritin (<a
            href="#app7table2" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 7—table
            2</a>). The best combination of two biomarkers was IL-1RA and ferritin, the best of
          three added Ang-2, the best of four added IP-10, and the best of five added IL-8. The best
          combinations of two and five variables were most robust with a selection percentage of
          43.7% and 44%. The best of five had almost the same AIC as the best subset of six markers
          (467.6 versus 465.9) (<a href="#table3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Table 3</a>). The coefficients of the selected
          biomarkers were similar to the initial model estimates (<a href="#app7table2" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 7—table 2</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="table3" title="Table 3.">
          <label data-itemprop="label">Table 3.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Codes in &#39;General setup&#39; need to be run first
# EPV --------------------------------------------------------------
pred &lt;- c(&quot;VCAM&quot;, &quot;SDC&quot;, &quot;Ang&quot;, &quot;IL8&quot;, &quot;IP10&quot;, &quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM&quot;, &quot;Fer&quot;, &quot;CRP&quot;)

# Estimate full model ----------------------------------------------
full_mod &lt;- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + IP10 + IL1RA + CD163 + TREM + Fer + CRP, 
                family=binomial, data=dat1c, x=T, y=T)

# Selected model ---------------------------------------------------
sel_var &lt;- matrix(0, ncol=length(pred)+1, nrow=5, dimnames=list(NULL, c(pred, &quot;aic&quot;)))

for (i in 1:5) {
  if (i==1) {
    bs &lt;- dredge(full_mod, rank=&quot;AIC&quot;)
  } else {
    bs &lt;- dredge(full_mod, rank=&quot;AIC&quot;, m.lim=c(i,i))
  }
  bs_var &lt;- attr(get.models(bs, 1)[[1]]$terms, &quot;term.labels&quot;)

  for (j in 1:(ncol(sel_var)-1)) {sel_var[i,j] &lt;- ifelse(names(sel_var[i,j]) %in% bs_var, 1, 0)}
  formula &lt;- paste(&quot;sev.or.inte~&quot;, paste(names(sel_var[i,][sel_var[i,]==1]), collapse = &quot;+&quot;))
  sel_mod &lt;- glm(formula, data = dat1c, family = binomial, x = T, y = T)
  sel_var[i, ncol(sel_var)] &lt;- AIC(sel_mod)
}

# Report results --------------------------------------------------
out1 &lt;- as.data.frame(sel_var) %&gt;% mutate(AIC = round(aic,1)) %&gt;% select(-aic)
for (i in 1:(ncol(out1)-1)) {out1[,i] &lt;- ifelse(out1[,i]==0, NA, &quot;+&quot;)}

out2 &lt;- as.data.frame(t(out1))
colnames(out2) &lt;- c(&quot;Best of all combinations&quot;, &quot;Best combination of 2 variables&quot;, &quot;Best combination of 3 variables&quot;,
                    &quot;Best combination of 4 variables&quot;, &quot;Best combination of 5 variables&quot;)
rownames(out2) &lt;- c(&quot;- VCAM-1&quot;, &quot;- SDC-1&quot;, &quot;- Ang-2&quot;, &quot;- IL-8&quot;, &quot;- IP-10&quot;, &quot;- IL-1RA&quot;, &quot;- sCD163&quot;, &quot;- sTREM-1&quot;,
                    &quot;- Ferritin&quot;, &quot;- CRP&quot;, &quot;AIC of the selected model&quot;)

out2

# For bootstrap results please look at Appendix 7-tables 1, 2 (for the best of all combinations)
# and the bootstrap codes for the best combinations of 2, 3, 4, and 5 variables
# All codes are in my GitHub at https://github.com/Nguyenlamvuong/eLife_Biomarkers_Dengue_2021</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="best-combinations-of-biomarkers-associated-with-severe-or-moderate-dengue-for-children">
              Best combinations of biomarkers associated with severe or moderate dengue for
              children.</h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">VCAM-1: vascular cell
              adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8;
              IP-10: interferon gamma-induced protein-10; IL- 1RA: interleukin-1 receptor
              antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble
              triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein; AIC: Akaike
              information criterion.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">For adults, the best subset
          included the seven markers SDC-1, IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163. This
          model was selected 79 times among 1000 bootstrap samples, but still was selected more
          often than the other models (<a href="#table4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Table 4</a>, <a href="#app7table3" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 7—table 3</a>). Over the 1000 samples,
          the seven variables had a bootstrap inclusion frequency ranging from 59.1% for sCD163 to
          99.2% for SDC-1. The three most important biomarkers in order were SDC-1, IL-8, and
          ferritin (<a href="#app7table4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 7—table 4</a>). The best combination of
          two biomarkers included SDC-1 and IL-8, the best of three added ferritin, the best of four
          added IL-1RA, and the best of five added sTREM-1. The best combination of two was the most
          robust with a selection percentage of 56.7%, followed by the best of three variables
          (43.2%) (<a href="#table4" itemscope="" itemtype="http://schema.stenci.la/Link">Table
            4</a>). The coefficients of the selected markers were also similar to the initial model
          estimates (<a href="#app7table4" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 7—table 4</a>).</p>
        <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="table4" title="Table 4.">
          <label data-itemprop="label">Table 4.</label>
          <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk"
            data-programminglanguage="r">
            <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock"
              slot="text"><code># Codes in &#39;General setup&#39; need to be run first
# EPV --------------------------------------------------------------
pred &lt;- c(&quot;VCAM&quot;, &quot;SDC&quot;, &quot;Ang&quot;, &quot;IL8&quot;, &quot;ns1(IP10)&quot;, &quot;IL1RA&quot;, &quot;CD163&quot;, &quot;TREM&quot;, &quot;Fer&quot;, &quot;CRP&quot;)

# Estimate full model ----------------------------------------------
full_mod &lt;- glm(sev.or.inte ~ VCAM + SDC + Ang + IL8 + ns1(IP10) + IL1RA + CD163 + TREM + Fer + CRP, 
                family=binomial, data=dat1a, x=T, y=T)

# Selected model ---------------------------------------------------
sel_var &lt;- matrix(0, ncol=length(pred)+1, nrow=5, dimnames=list(NULL, c(pred, &quot;aic&quot;)))

for (i in 1:5) {
  if (i==1) {
    bs &lt;- dredge(full_mod, rank=&quot;AIC&quot;)
  } else {
    bs &lt;- dredge(full_mod, rank=&quot;AIC&quot;, m.lim=c(i,i))
  }
  bs_var &lt;- attr(get.models(bs, 1)[[1]]$terms, &quot;term.labels&quot;)

  for (j in 1:(ncol(sel_var)-1)) {sel_var[i,j] &lt;- ifelse(names(sel_var[i,j]) %in% bs_var, 1, 0)}
  formula &lt;- paste(&quot;sev.or.inte~&quot;, paste(names(sel_var[i,][sel_var[i,]==1]), collapse = &quot;+&quot;))
  sel_mod &lt;- glm(formula, data = dat1a, family = binomial, x = T, y = T)
  sel_var[i, ncol(sel_var)] &lt;- AIC(sel_mod)
}

# Report results --------------------------------------------------
out1 &lt;- as.data.frame(sel_var) %&gt;% mutate(AIC = round(aic,1)) %&gt;% select(-aic)
for (i in 1:(ncol(out1)-1)) {out1[,i] &lt;- ifelse(out1[,i]==0, NA, &quot;+&quot;)}

out2 &lt;- as.data.frame(t(out1))
colnames(out2) &lt;- c(&quot;Best of all combinations&quot;, &quot;Best combination of 2 variables&quot;, &quot;Best combination of 3 variables&quot;,
                    &quot;Best combination of 4 variables&quot;, &quot;Best combination of 5 variables&quot;)
rownames(out2) &lt;- c(&quot;- VCAM-1&quot;, &quot;- SDC-1&quot;, &quot;- Ang-2&quot;, &quot;- IL-8&quot;, &quot;- IP-10&quot;, &quot;- IL-1RA&quot;, &quot;- sCD163&quot;, &quot;- sTREM-1&quot;,
                    &quot;- Ferritin&quot;, &quot;- CRP&quot;, &quot;AIC of the selected model&quot;)

out2

# For bootstrap results please look at Appendix 7-tables 3, 4 (for the best of all combinations)
# and the bootstrap codes for the best combinations of 2, 3, 4, and 5 variables
# All codes are in my GitHub at https://github.com/Nguyenlamvuong/eLife_Biomarkers_Dengue_2021</code></pre>
          </stencila-code-chunk>
          <figcaption>
            <h4 itemscope="" itemtype="http://schema.stenci.la/Heading"
              id="best-combinations-of-biomarkers-associated-with-severe-or-moderate-dengue-for-adults">
              Best combinations of biomarkers associated with severe or moderate dengue for adults.
            </h4>
            <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">VCAM-1: vascular cell
              adhesion molecule-1; SDC-1: syndecan-1; Ang-2: angiopoietin-2; IL-8: interleukin-8;
              IP-10: interferon gamma-induced protein-10; IL- 1RA: interleukin-1 receptor
              antagonist; sCD163: soluble cluster of differentiation 163; sTREM-1: soluble
              triggering receptor expressed on myeloid cells-1; CRP: C-reactive protein; AIC: Akaike
              information criterion. *Variable is kept as non-linear effect using natural cubic
              splines with three knots.</p>
          </figcaption>
        </figure>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In the sensitivity analysis,
          viremia was not selected in any of the best combinations for children, and the marker
          combinations remained the same as the main analysis. For adults, the best subset included
          five markers SDC-1, IL-8, ferritin, viremia and sCD163. The best of two and three were the
          same as the main analysis; viremia was selected in the best of four and five (<a
            href="#app8fig1" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 8—figure
            1</a>; <a href="#app8table1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 8—table 1</a>; <a href="#app8table2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 8—table 2</a>; <a
            href="#app8table3" itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 8—table
            3</a>).</p>
        <h2 itemscope="" itemtype="http://schema.stenci.la/Heading" id="discussion">Discussion</h2>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This nested case-control study
          has shown that a range of endothelial, immune activation and inflammatory biomarkers
          measured during the early febrile phase of dengue are associated with progression to worse
          clinical outcomes in both children and adults. In children we found IL-1RA to have the
          most robust association with S/MD, whereas in adults we found SDC-1 and IL-8 to have the
          most robust association. For children, the best combination (ordered by robustness)
          included six biomarkers IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults the
          best combination identified comprised seven biomarkers SDC-1, IL-8, ferritin, sTREM-1,
          IL-1RA, IP-10, and sCD163.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Our results add to the current
          literature on biomarkers in severe/moderate dengue compared with uncomplicated dengue, by
          including early time-points prior to the development of the severe manifestations, as well
          as providing data on the use of biomarker combinations, which takes into consideration the
          complex inflammatory-vascular pathogenesis of severe dengue. We observed that there were
          marked changes in the associations between individual biomarkers and outcomes when
          considering them together, while other biomarkers showed consistent associations.
          Particularly, the association of IP-10 with S/MD changed significantly from the single to
          global model, which may be because another biomarker in our model is a mediator or
          confounder of IP-10 in the pathway to the outcome. This could be IL-1RA as its association
          with S/MD was similar between the single and global model, and the correlation between
          IP-10 and IL-1RA was strong (Spearman’s rank correlation coefficient was 0.75).
          Nonetheless, changing the direction of the association from the single to global model
          does not diminish the possibility of that biomarker being selected in the best
          combinations.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Our study also demonstrates
          some key differences between pediatric and adult dengue. Clinical phenotypes of dengue in
          children and adults differ, with children experiencing more shock and adults more organ
          impairment and bleeding, with distinct clinical management guidelines published by the
          WHO. Our results imply dengue pathogenesis may differ by age, with distinct combinations
          of immune-activation and vascular markers demonstrated between children and adults.
          Specifically, the association of IL-8 and ferritin differed between children and adults,
          which is likely to be due to the composite endpoint of severe and moderate dengue. As
          shown in the analysis of severe dengue alone (<a href="#app4fig1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 4—figure 1</a>, <a href="#app4table1"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 4—table 1</a>), the
          effects of IL-8 and ferritin were similar in children and adults, which suggests these
          biomarkers are still associated with severe disease in all age groups and that the
          difference is driven by the moderate dengue group. In addition, uncomplicated dengue in
          adults have higher ferritin levels compared to in children, with increasing age and
          chronic conditions in adults likely contributing to this observation. Hence patients’ age
          should be considered when developing biomarker panels for dengue risk prediction.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The use of biomarker panels for
          the prediction of severe outcomes in dengue has been investigated in previous studies,
          using several statistical approaches <span itemscope=""
            itemtype="http://schema.stenci.la/CiteGroup"><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib3"><span>3</span><span>Brasier et
                  al.</span><span>2012</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib5"><span>5</span><span>Conroy et
                  al.</span><span>2015</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib14"><span>14</span><span>Ju and
                  Brasier</span><span>2013</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib16"><span>16</span><span>Lee et
                  al.</span><span>2016</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib25"><span>25</span><span>Pang et
                  al.</span><span>2016</span></a></cite></span>. However, because of small sample
          size and differences in the biomarkers assessed, the associations found vary between
          studies and as yet there are no validated prognostic panels for dengue. Dengue cases are
          forecasted to increase over the next few decades and, given the limited healthcare
          resources available in many endemic settings, particularly during epidemics, there is an
          urgent need to develop innovative methods to rapidly identify patients likely to develop
          complications and require hospital care <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a
              href="#bib33"><span>33</span><span>Rodriguez-Manzano et
                al.</span><span>2018</span></a></cite>. Previously, we showed that CRP as a single
          biomarker was useful for early dengue diagnosis and risk identification, which is
          currently easy to use in all settings <cite itemscope=""
            itemtype="http://schema.stenci.la/Cite"><a href="#bib45"><span>45</span><span>Vuong et
                al.</span><span>2020</span></a></cite>. Recently, we also showed that higher plasma
          viremia was associated with increased dengue severity regardless of age, serotype and
          immune status of patients <cite itemscope="" itemtype="http://schema.stenci.la/Cite"><a
              href="#bib46"><span>46</span><span>Vuong et al.</span><span>2021</span></a></cite>.
          However, future point-of-care testing could be improved by using a combination of
          biomarkers outlined in this study. Our results are applicable to the development of
          point-of-care panels capable of multiplex analysis and suited for use in outpatient
          settings for dengue prognosis, with scope for incorporation with innovative point-of-care
          technologies. To be more applicable by balancing model fit, robustness, and parsimony, we
          suggest the combination of five biomarkers IL-1RA, Ang-2, IL-8, ferritin, and IP-10 for
          children, and the combination of three biomarkers SDC-1, IL-8, and ferritin for adults to
          be used in practice. These combinations had a similar AIC with the best combinations (the
          difference of AIC was less than 5), but they required fewer number of biomarkers in a test
          panel. With the advent of novel technologies including microarray platforms and multiplex
          lateral flow assays, the cost is likely to come down in the future, allowing for
          wide-spread use in low-to-middle-income countries.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Methods of variable selection
          have been discussed previously but there remains no clear consensus regarding the best
          approach <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib10"><span>10</span><span>Heinze et
                  al.</span><span>2018</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib35"><span>35</span><span>Sauerbrei et
                  al.</span><span>2020</span></a></cite></span>. We adopted a data-driven ‘best
          subset’ approach which we think offers advantages over other methods, given the complexity
          of the biomarkers involved and their interactions. We also explored other approaches for
          variable selection <span itemscope="" itemtype="http://schema.stenci.la/CiteGroup"><cite
              itemscope="" itemtype="http://schema.stenci.la/Cite"><a
                href="#bib10"><span>10</span><span>Heinze et
                  al.</span><span>2018</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a href="#bib26"><span>26</span><span>Piironen
                  and Vehtari</span><span>2017</span></a></cite><cite itemscope=""
              itemtype="http://schema.stenci.la/Cite"><a
                href="#bib35"><span>35</span><span>Sauerbrei et
                  al.</span><span>2020</span></a></cite></span> and the results were very similar to
          the best subset procedure (<a href="#app9table1" itemscope=""
            itemtype="http://schema.stenci.la/Link">Appendix 9—table 1</a><a href="#app9table2"
            itemscope="" itemtype="http://schema.stenci.la/Link">Appendix 9—table 2</a>).</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Strengths of our study include
          the large sample size and use of a nested case-control dataset from a prospective
          multi-country cohort study with consistent data collection and standardized outcome
          definitions and laboratory methodologies. The biomarker panel we selected was guided by
          pathogenesis studies, focusing on pathways activated early in the disease course, thus
          ensuring clinical relevance.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">There are some limitations in
          our study. One being we analysed the biomarkers at only one time-point in the early phase;
          limited financial resources did not allow us to evaluate the full range of biomarkers
          across the whole IDAMS population and at more time-points. Secondly, this study was not
          designed to build prediction models so we did not use a measure of predictive value as a
          criterion, which was motivated by the nested case-control design. Our findings need to be
          validated in new studies.</p>
        <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">In conclusion, higher levels of
          the ten biomarkers (VCAM-1, SDC-1, Ang-2, IL-8, IP-10, IL-1RA, sCD163, sTREM-1, ferritin,
          and CRP), when considered individually, are associated with increased risk of adverse
          clinical outcomes in both children and adults with dengue. The best biomarker combination
          for children includes IL-1RA, Ang-2, IL-8, ferritin, IP-10, and SDC-1; for adults, SDC-1,
          IL-8, ferritin, sTREM-1, IL-1RA, IP-10, and sCD163 were selected. These findings serve to
          assist the development of biomarker panels to improve future triage and early assessment
          of dengue patients. This would aid not only individual patient management and facilitate
          healthcare allocation which would be of major public health benefit especially in outbreak
          settings, but could also serve as potential biological endpoints for dengue clinical
          trials.</p>
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