<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.146814.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Research Article</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio predicting hospital length of stay and mortality in young COVID-19 patients: A retrospective study</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 2 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>El-Menyar</surname>
                        <given-names>Ayman</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-2584-953X</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Khan</surname>
                        <given-names>Naushad A.</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Asim</surname>
                        <given-names>Mohammad</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Formal Analysis</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Al-Thani</surname>
                        <given-names>Hassan</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a3">3</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Abukhattab</surname>
                        <given-names>Mohammed</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <xref ref-type="aff" rid="a4">4</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Al Maslamani</surname>
                        <given-names>Muna</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <xref ref-type="aff" rid="a4">4</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Clinical Medicine, Weill Cornell Medical College, Doha, Qatar</aff>
                <aff id="a2">
                    <label>2</label>Clinical Research, Trauma &amp; Vascular Surgery Section, Hamad Medical Corporation, Doha, Doha, Qatar</aff>
                <aff id="a3">
                    <label>3</label>Department of Surgery, Hamad Medical Corporation, Doha, Doha, Qatar</aff>
                <aff id="a4">
                    <label>4</label>Department of Medicine, Hamad Medical Corporation, Doha, Doha, Qatar</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:aymanco65@yahoo.com">aymanco65@yahoo.com</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>3</day>
                <month>5</month>
                <year>2024</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2024</year>
            </pub-date>
            <volume>13</volume>
            <elocation-id>446</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>25</day>
                    <month>4</month>
                    <year>2024</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 El-Menyar A et al.</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/13-446/pdf"/>
            <abstract>
                <sec>
                    <title>Background</title>
                    <p>This study investigated the utility of platelet-to-lymphocyte ratio (PLR) and Neutrophil-to-Lymphocyte ratio (NLR) in patients with COVID-19 with respect to age, early (a week) vs. delayed recovery (&gt; a week) and mortality.</p>
                </sec>
                <sec>
                    <title>Methods</title>
                    <p>This was a retrospective study including 1,016 COVID-19 patients. The discriminatory power and multivariate logistic regression analysis were performed.</p>
                </sec>
                <sec>
                    <title>Results</title>
                    <p>The mean age of patients was 45 (&#x00b1; 13.9), and 75.7% were males. Older patients had elevated NLR, PLR, D-dimer, CRP, and Interleukin-6 levels and longer hospital stay than the younger group (p &lt; 0.001). In-hospital mortality was higher in older adults (26.9% vs. 6.6%, p =0.001). On-admission NLR (5.8 vs. 3.2; 
                        <italic toggle="yes">P</italic>= 0.001) and PLR (253.9&#x00b1;221.1 vs. 192.2&#x00b1;158.5; 
                        <italic toggle="yes">p</italic> = 0.004) were higher in the non-survivors than survivors. Both PLR and NLR displayed significant discriminatory ability for mortality. NLR had a higher AUC and specificity, while PLR exhibited slightly higher sensitivity. In individuals aged &#x2264;55, NLR showed superior discrimination (AUC=0.717) compared to PLR (AUC=0.620). Conversely, for older adults, PLR displayed enhanced discrimination (AUC=0.710), while NLR showed AUC=0.693.</p>
                </sec>
                <sec>
                    <title>Conclusion</title>
                    <p>Higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients.</p>
                </sec>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>COVID-19; Inflammation; Mortality; Neutrophil-to-lymphocyte ratio</kwd>
                <kwd>platelet-to-lymphocyte ratio</kwd>
                <kwd>Hospital stay</kwd>
                <kwd>age</kwd>
            </kwd-group>
            <funding-group>
                <funding-statement>The author(s) declared that no grants were involved in supporting this work.</funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec5">
            <title>Key messages</title>
            <p>
                <list list-type="bullet">
                    <list-item>
                        <label>-</label>
                        <p>Simple, instant bedside laboratory tests on admission are of utmost value for patients&#x2019; stratification during a pandemic.</p>
                    </list-item>
                    <list-item>
                        <label>-</label>
                        <p>COVID-19 patients with elevated NLR and PLR levels are associated with delayed recovery, more ICU admissions, and intubation.</p>
                    </list-item>
                    <list-item>
                        <label>-</label>
                        <p>A greater NLR values are associated with higher mortality in older COVID-19 patients.</p>
                    </list-item>
                    <list-item>
                        <label>-</label>
                        <p>However, none of these two parameters alone is an independent predictor of death.</p>
                    </list-item>
                </list>
            </p>
        </sec>
        <sec id="sec6" sec-type="intro">
            <title>Introduction</title>
            <p>The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) causing Coronavirus disease 2019 (COVID-19) has exhausted the healthcare infrastructure worldwide by causing recurrent waves.
                <sup>
                    <xref ref-type="bibr" rid="ref1">1</xref>
                </sup> The SARS-CoV-2 infection has wide clinical variations, ranging from asymptomatic infection to moderate upper respiratory tract sickness to severe viral pneumonia with respiratory failure and death.
                <sup>
                    <xref ref-type="bibr" rid="ref2">2</xref>
                </sup> Of note, reliable laboratory parameters of the severity of the disease, treatment response, and outcome were not thoroughly investigated during the early phase of the pandemic due to rapid onset and spread. As a result, early identification of clinical and laboratory variables linked with poor outcomes is critical for identifying low- and high-risk patients for triage and appropriate management.</p>
            <p>Infectious diseases are associated with inflammation, and existing data supports its central contribution to the progression and pathogenesis of COVID-19.
                <sup>
                    <xref ref-type="bibr" rid="ref2">2</xref>
                </sup> Because of SARS-COV-2 viral replication, cellular destruction leads to cytokines and chemokines from the activated macrophages.
                <sup>
                    <xref ref-type="bibr" rid="ref3">3</xref>
                </sup> As a result, they set off immunological responses, which in turn cause cytokine storms and aggravate the situation. As a result, they elicit immune responses, which create cytokine storms and exacerbate the problem. This imbalance arises because the adaptive immune response depends on the inflammatory response&#x2019;s strength.
                <sup>
                    <xref ref-type="bibr" rid="ref4">4</xref>
                </sup> Therefore, patients with a pre-existing chronic inflammatory status might be vulnerable to a severe form of COVID-19 disease.</p>
            <p>The Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are straightforwardly obtainable ratios from complete blood count (CBC) panels. Emerging evidence suggests that peripheral NLR and PLR can be used as markers of systemic inflammation in various disease processes.
                <sup>
                    <xref ref-type="bibr" rid="ref5">5</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup> Several studies have reported the prognostic role of NLR in differentiating mild/moderate cases from severe COVID-19 cases and have proposed that NLR can be a reliable predictor of COVID-19 progression associated with high mortality in COVID-19.
                <sup>
                    <xref ref-type="bibr" rid="ref10">10</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref16">16</xref>
                </sup> Moreover, several studies have also suggested PLR to be a promising and reliable indicator of disease severity, exhibiting good predictive values on progression and clinical outcomes in patients with COVID-19.
                <sup>
                    <xref ref-type="bibr" rid="ref17">17</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref22">22</xref>
                </sup>
            </p>
            <p>However, an imitating factor of these ratios is the inability to collate with ethnic differences.
                <sup>
                    <xref ref-type="bibr" rid="ref23">23</xref>
                </sup> Also, they can be profoundly influenced by age and gender,
                <sup>
                    <xref ref-type="bibr" rid="ref24">24</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref25">25</xref>
                </sup> whose dependence has not yet been fully explored in COVID-19 disease. Moreover, Qatar has a distinct demographic profile, with around 88% of the expatriate workforce of Qatar&#x2019;s 2.8 million citizens. While the bulk of the population (75%) is male gender, the pyramid shape of population distribution is disproportionately concentrated in the 20&#x2013;50-year age group.
                <sup>
                    <xref ref-type="bibr" rid="ref25">25</xref>
                </sup> COVID-19 affects males disproportionately, and older adults tend to have worse outcomes.
                <sup>
                    <xref ref-type="bibr" rid="ref26">26</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref27">27</xref>
                </sup>
            </p>
            <p>We sought to evaluate the association of NLR and PLR and the recovery and mortality in patients with COVID-19. Also, to assess age-stratified differences in these outcomes.</p>
        </sec>
        <sec id="sec7" sec-type="methods">
            <title>Methods</title>
            <sec id="sec8">
                <title>Study population and data collection</title>
                <p>A retrospective observational study was conducted, including patients with COVID-19 admitted to the different affiliated Hospitals of Hamad Medical Corporation (HMC) in Qatar at the beginning of the coronavirus pandemic (from March 01 to June 01, 2020). The subjects included in the study were laboratory-confirmed cases of COVID-19 disease (&gt;18 years old) of both genders. Patients with an inconclusive diagnosis of COVID-19 by RT-PCR testing, undefined diagnosis, and missing data were excluded from the study. Data were extracted from the electronic medical record (CERNER), which included patients&#x2019; demographics such as (age, gender, nationality); recent exposure history, clinical symptoms and signs, comorbidities (Hypertension, diabetes mellitus, cancer, renal failure, chronic obstructive pulmonary disease, and others), initial vitals (Systolic blood pressure, diastolic blood pressure, pulse, respiratory rate, blood oxygen saturation), routine laboratory findings (initial and repeated readings) including CBC, blood chemistry and C-reactive protein (CRP), chest X-ray and computed tomographic scans, treatment, mechanical ventilation, hospital and intensive critical care (ICU) length of stay, speed of recovery (within one week, and more than one week), discharge from hospital and mortality.</p>
                <p>TaqPath COVID-19 Combo Kit
                    <sup>TM</sup> (Thermo Fisher Scientific, Waltham, Massachusetts, USA) or Cobas SARS-CoV-2 Test
                    <sup>&#x00ae;</sup> (Roche Diagnostics, Rotkreuz, Switzerland) were used to identify SARS-CoV-2 infection utilizing Nasopharyngeal and throat samples. All COVID-19 testing was performed at the central laboratory of the HMC, which manages over 85% of the country&#x2019;s inpatient bed capacity and is responsible for delivering public healthcare.</p>
            </sec>
            <sec id="sec9">
                <title>Study definitions</title>
                <p>
                    <list list-type="bullet">
                        <list-item>
                            <label>-</label>
                            <p>Every patient who experienced COVID-19-like manifestations and at the same time tested positive for COVID-19 in respiratory samples using a real-time reverse-transcription polymerase chain reaction (RT-PCR) assay was deemed a confirmed COVID-19 case.</p>
                        </list-item>
                        <list-item>
                            <label>-</label>
                            <p>The platelet-to-lymphocyte ratio (PLR) was defined as the ratio between absolute Platelet counts to absolute lymphocyte count, and the neutrophil-to-lymphocyte ratio (NLR) was defined as the ratio between absolute neutrophil counts to absolute lymphocyte count.</p>
                        </list-item>
                        <list-item>
                            <label>-</label>
                            <p>Recovery referred to two negative swab tests done consecutively.</p>
                        </list-item>
                    </list>
                </p>
            </sec>
            <sec id="sec10">
                <title>Statistical analysis</title>
                <p>The data were collated in Microsoft Excel, and statistical analysis was performed using SPSS, version 28.0. for Windows (Armonk, NY: IBM Corp, USA). Data were expressed as proportions, means &#x00b1; standard deviations, or medians as appropriate for continuous variables or as absolute counts and percentages for categorical variables. Data were compared using the student-t-test for continuous variables and the Pearson &#x03c7;
                    <sup>2</sup> test for categorical variables. The Fisher exact test was used if the expected cell frequencies were below five. For skewed continuous data, a nonparametric Mann-Whitney test was performed. The independent predictors of mortality were identified using multivariable logistic regression analysis after adjusting for age, gender, comorbidities, complications, NLR, and PLR as covariates of interest.</p>
                <p>Areas under the curve (AUC) of ROC curves were employed to determine the ratios&#x2019; performance in age discrimination regarding NLR and PLR. The best cut-off points of the ratios were the points on the curves with the highest sensitivity and specificity. The sample size for the current study was not determined a priori as we intended to include all the laboratory-confirmed COVID-19 cases during the study period. A two-sided 
                    <italic toggle="yes">P</italic>-value &lt; 0.05 was considered statistically significant.</p>
                <p>This observational study was conducted in accordance with the STROBE principles. The study was authorized by the Institutional Review Board and Medical Research Council (MRC-01-20-672 &amp; MRC-05-213) of Hamad Medical Corporation. A waiver of consent was granted for this retrospective study as there was no direct contact was made with the participants, and the data were collected anonymously.</p>
            </sec>
        </sec>
        <sec id="sec11" sec-type="results">
            <title>Results</title>
            <p>During the study period, 1016 persons tested positive for SARS-CoV-2. The mean age of the cohort was 45 &#x00b1;13.9 years, and an overwhelming majority of infected persons were male (75.7 %). The most common chronic medical conditions were hypertension (40.3%), followed by diabetes mellitus (39.0%), chronic kidney disease (14.0%), cancer (5.4%) and chronic obstructive pulmonary disease (4.8%).</p>
            <p>
                <xref ref-type="table" rid="T1">Table 1</xref> outlines the comparison of clinical characteristics, in-hospital complications, comorbidities, and outcomes of COVID-19 patients according to hospital length of stay. Patients in the long-stay group were older (45.9&#x00b1;13.9 vs.40.6&#x00b1;13.2), had significantly lower SpO
                <sub>2</sub> (97.1&#x00b1;3.6 vs. 98.4&#x00b1;2.0), and were more likely to have significant medical comorbidities compared to &#x2018;short stay&#x2019; group. Compared with the short-stay group, patients in the long-stay group were presented with lower lymphocyte and platelet counts and higher inflammation-related indices (CRP, IL-6). Further significant elevations in NLR [3.7 (0.3-72.0) vs. 2.8 (0.6(0.6-53.0); 
                <italic toggle="yes">P</italic>=0.002] and the PLR indices was found [205.4&#x00b1;178.8 vs. 199.6&#x00b1;168.2; 
                <italic toggle="yes">P</italic>=0.001). Concerning the major in-hospital complications, patients in the long-hospital stay group were more likely to have renal failure (16.7% vs. 5.1%; 
                <italic toggle="yes">P</italic>=0.001) and ARDS (3.7% vs. 0.0%; 
                <italic toggle="yes">P</italic>=0.009) than patients in the short-stay group. The in-hospital mortality rate was 11.9% (121/1016). Patients in the long-stay group had higher in-hospital mortality than those in the short-stay group (12.8% vs.7.9%; 
                <italic toggle="yes">P</italic>&lt;0.06).</p>
            <table-wrap id="T1" orientation="portrait" position="float">
                <label>Table 1. </label>
                <caption>
                    <title>Comparisons of clinical characteristics, and outcomes of COVID-19 patients according to hospital length of stay.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top">Variables</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">Length of hospital stays</th>
                            <th align="left" colspan="1" rowspan="2" valign="top">
                                <italic toggle="yes">P</italic>-value</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Short stay (&#x2264; 1 week) (n =178, 17.5%)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Long stay (&gt; 1 week) (n = 838, 82.5%)</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Age (years)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">40.6&#x00b1;13.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">45.9&#x00b1;13.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Males</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">105 (59.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">664 (79.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Number of admissions</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2 (1-14)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1 (1-57)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Initial vital signs</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Systolic blood pressure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">125.9&#x00b1;20.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">127.1&#x00b1;18.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.45</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diastolic blood pressure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">75.6&#x00b1;11.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">76.8&#x00b1;11.8</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.21</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Pulse</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">90.3&#x00b1;15.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">90.8&#x00b1;15.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.73</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Respiratory rate</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">19.3&#x00b1;2.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20.8&#x00b1;5.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Oxygen saturation</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">98.4&#x00b1;2.0</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">97.1&#x00b1;3.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Comorbidities</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Hypertension</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">50 (28.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">359 (42.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diabetes Mellitus</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">50 (28.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">346 (41.3%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Cancer</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6 (3.4%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">49 (5.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chronic Kidney Disease</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">17 (9.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">125 (14.9%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.06</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">COPD</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10 (5.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">39 (4.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.58</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Initial laboratory findings</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Creatinine (&#x03bc;mol/L) (n=950)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">75 (22-1254)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">85 (20-1891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">CRP (mg/L) (n=891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.8 (0.3-318.9)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">42.4 (0.3-444.8)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">D-Dimer (mg/L FEU) (n=532)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.97 (0.19-64.5)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.89 (0.19-91.6)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.86</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Ferritin (&#x03bc;g/L) (n=623)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">344.8 (9.0-28677)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">590 (4.2-45878)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">IL-6 (pg/mL) (n=209)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">79 (15-1923)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">112.5 (2-4021)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.61</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Lymphocytes (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.72&#x00b1;0.74</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.44&#x00b1;0.75</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophils (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.7&#x00b1;3.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.8&#x00b1;4.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.91</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">250.7&#x00b1;83.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">231.3&#x00b1;85.4</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.006</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Troponin (ng/L) (n=421)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20 (3-1278)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11 (3-2979)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.50</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">WBC (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.2&#x00b1;3.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7.9&#x00b1;4.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.47</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet-to-lymphocyte ratio (PLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">172.4&#x00b1;101.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">205.4&#x00b1;178.8</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophil-to-lymphocyte ratio (NLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.8 (0.6-53.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.7 (0.3-72.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ECMO</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1 (0.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">26 (3.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.05</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Intubation</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11 (6.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">233 (27.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ICU admission</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">19 (10.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">335 (40.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ICU length of stay (Days)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.2 (0.16-45.1)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13.6 (0.1-83.4)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Ventilatory days</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.8 (0.3-5.3)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11.9 (0.1-87.8)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Complications</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARDS</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0 (0.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">31 (3.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.009</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Renal Failure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9 (5.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">140 (16.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Pulmonary embolism</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0 (0.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8 (1.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.19</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Sepsis</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2 (1.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (0.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">DVT</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0 (0.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (0.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.35</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Mortality</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14 (7.9%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">107 (12.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.06</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>COPD: Chronic Obstructive Pulmonary Disease; CRP: C-reactive Protein; IL-6: Interleukin-6; WBC: White Blood Cells; ECMO: Extracorporeal Membrane Oxygenation; ICU: Intensive Care Unit; ARDS: Acute Respiratory Distress Syndrome; DVT: Deep vein thrombosis.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>
                <xref ref-type="table" rid="T2">Table 2</xref> summarizes the impact of age. Of the total COVID-19 patients, 74% were aged &#x2264;55, and 26% were &gt;55. Hypertension (75% vs. 8.1%), diabetes mellitus (DM) (70.8% vs. 27.8%), and chronic kidney disease (30.3% vs. 8.2%) were more evident in older subjects than in the younger group. Regarding vital signs, the older patients had significantly lower diastolic blood pressure (DBP), pulse rate, and oxygen saturation than the younger patients. The initial laboratory results showed that, compared with the younger patients, older patients had significantly higher NLR, PLR, creatinine, CRP, IL-6, and D-dimer levels. Intubation was performed more in older patients (42% vs.17.7%; 
                <italic toggle="yes">P</italic>=0.001). Besides, the median length of ICU [14.1 (0.1-74.3) vs. 11.7 (0.16-83.4) days] and ventilatory days [13.7 (0.4-74.7) vs. 9.3 (0.1-87.8)] were significantly longer in the older group. The older patient group experienced a higher frequency of renal failure (29.2% vs. 9.6%), ARDS (4.2% vs.2.7%), pulmonary embolism (1.5% vs. 0.5%), and a higher mortality rate than the younger group (26.9% vs. 6.6 %, 
                <italic toggle="yes">P</italic>&lt;0.001).</p>
            <table-wrap id="T2" orientation="portrait" position="float">
                <label>Table 2. </label>
                <caption>
                    <title>Comparisons of clinical characteristics, complications, and outcomes among COVID-19 patients according to age.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Variables</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Age &#x2264;55 (n = 752, 74.0%)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Age &gt;55 (n = 264, 26.0%)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">P-value</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Age (years)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">38.6&#x00b1;9.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">63.4&#x00b1;5.4</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Males</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">564 (75.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">205 (77.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.38</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Number of admissions</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2 (1-14)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1 (1-57)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Initial vital signs</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Systolic blood pressure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">125.2&#x00b1;17.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">131.6&#x00b1;21.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diastolic blood pressure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">76.8&#x00b1;11.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">75.7&#x00b1;11.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.18</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Pulse</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">91.3&#x00b1;15.8</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">88.8&#x00b1;14.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Respiratory rate</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20.2&#x00b1;5.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">21.3&#x00b1;5.4</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Oxygen saturation</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">97.7&#x00b1;3.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">96.5&#x00b1;3.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Comorbidities</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Hypertension</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">211 (28.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">198 (75.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diabetes Mellitus</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">209 (27.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">187 (70.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Cancer</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">35 (4.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20 (7.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.07</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chronic Kidney Disease</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">62 (8.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">80 (30.3%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">COPD</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">27 (3.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">22 (8.3%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Initial laboratory findings</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Creatinine (&#x03bc;mol/L) (n=950)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">80 (20-1891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">97 (32-1401)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">CRP (mg/L) (n=891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">26.0 (0.3-1891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">60.3 (0.3-387.6)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">D-Dimer (mg/L FEU) (n=532)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.79 (0.19-91.6)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.06 (0.22-84.4)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Ferritin (&#x03bc;g/L) (n=623)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">520 (4.2-28677)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">659.5 (18.3-45878)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">IL-6 (pg/mL) (n=209)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">94.5 (2.0-4021.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">133 (3-2351)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.04</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Lymphocytes (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.58&#x00b1;0.78</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.24&#x00b1;0.64</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophils (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.9&#x00b1;4.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.6&#x00b1;3.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.30</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">241.6&#x00b1;83.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">215.1&#x00b1;88.6</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Troponin (ng/L) (n=421)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9 (3-2979)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">19 (3-2351)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">WBC (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.3&#x00b1;4.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7.4&#x00b1;4.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.006</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet-to-lymphocyte ratio (PLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">194.2&#x00b1;168.2</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">214.8&#x00b1;167.7</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.08</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophil-to-lymphocyte ratio (NLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.3 (0.27-72.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4.0 (0.4-53.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ECMO</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">22 (2.9%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5 (1.9%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.37</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Intubation</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">133 (17.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">111 (42.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ICU admission</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">205 (27.3%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">149 (56.4%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ICU length of stay (Days)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11.7 (0.16-83.4)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">14.1 (0.1-74.3)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.06</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Ventilatory days</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9.3 (0.1-87.8)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">13.7 (0.4-74.7)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.04</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Short stay (&#x2264; 1 week)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">152(20.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">26 (9.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Long-stay (&gt; 1 week)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">600(79.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">238(90.2%)</td>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Complications</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARDS</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">20 (2.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">11 (4.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.22</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Renal Failure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">72 (9.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">77 (29.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Pulmonary embolism</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (0.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (1.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.12</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Sepsis</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5 (0.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1 (0.4%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.60</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Deep vein thrombosis</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (0.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0 (0.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.23</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Mortality</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">50 (6.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">71 (26.9%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>COPD: Chronic Obstructive Pulmonary Disease; CRP: C-reactive Protein; IL-6: Interleukin-6; WBC: White Blood Cells; ECMO: Extracorporeal Membrane Oxygenation; ICU: Intensive Care Unit; ARDS: Acute Respiratory Distress Syndrome; DVT: Deep vein thrombosis.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>
                <xref ref-type="table" rid="T3">Table 3</xref> compares clinical characteristics, laboratory results, and complications among COVID-19 patients stratified according to survival status. The deceased patients were significantly older than those who survived (56.4&#x00b1;11.4 vs. 43.5&#x00b1;13.25 years, respectively, 
                <italic toggle="yes">P</italic>&lt;0.001) with more comorbidities as well. Creatinine, CRP, D-dimer, ferritin, IL-6, neutrophil, troponin, PLR, and NLR were significantly higher, whereas lymphocyte and platelet counts were significantly lower in the deceased patients.</p>
            <table-wrap id="T3" orientation="portrait" position="float">
                <label>Table 3. </label>
                <caption>
                    <title>Comparisons of clinical characteristics, complications, and outcomes among COVID-19 patients according to mortality.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Variables</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Survivors (n=895)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Non-survivors (n=121)</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
                                <italic toggle="yes">P</italic>-value</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Age (years)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">43.5&#x00b1;13.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">56.4&#x00b1;11.4</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Males</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">664 (74.2%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">105 (86.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Comorbidities</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Hypertension</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">336 (37.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">73 (60.3%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diabetes Mellitus</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">318 (35.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">78 (64.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Cancer</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">43 (4.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">12 (9.9%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Chronic Kidney Disease</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">115 (12.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">27 (22.3%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.005</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">COPD</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">43 (4.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6 (5.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.94</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Initial laboratory findings</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Creatinine (&#x03bc;mol/L) (n=950)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">82 (20-1891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">101 (32-1131)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">CRP (mg/L) (n=891)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">28.0 (0.3-444.8)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">94.2 (0.4-387.6)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">D-Dimer (mg/L FEU) (n=532)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.82 (0.19-91.6)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.24 (0.3-84.4)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Ferritin (&#x03bc;g/L) (n=623)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">527 (4.2-45878)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">868.5 (66.5-39695)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">IL-6 (pg/mL) (n=209)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">87 (2-4021)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">185.5 (4-2599)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.006</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Lymphocytes (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.55&#x00b1;0.77</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.03&#x00b1;0.52</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophils (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.7&#x00b1;3.8</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">7.0&#x00b1;5.3</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.008</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">238.6&#x00b1;83.9</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">206.2&#x00b1;91.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Troponin (ng/L) (n=421)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">9 (3-2351)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">27 (3-2979)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">WBC (&#x00d7;10
                                <sup>9</sup>/L)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">117 (36.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">65 (67.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet-to-lymphocyte ratio (PLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">192.2&#x00b1;158.5</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">253.9&#x00b1;221.1</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.004</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophil-to-lymphocyte ratio (NLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.2 (0.27-72.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5.8 (0.9-53.0)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>ICU length of stay (days)</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10.9 (0.1-72)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">16.6 (0.16-83.4)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Ventilatory days</bold>
                            </td>
                            <td align="left" colspan="1" rowspan="1" valign="top">8.0 (0.1-78.5)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">16.4 (0.3-87.8)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Complications</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">ARDS</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">16 (1.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">15 (12.4%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Renal failure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">90 (10.1%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">59 (48.8%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Pulmonary embolism</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">5 (0.6%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3 (2.5%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.02</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Sepsis</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (0.4%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2 (1.7%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.10</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Deep vein thrombosis</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">4 (0.4%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0 (0.0%)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.46</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>COPD: Chronic Obstructive Pulmonary Disease; CRP: C-reactive Protein; IL-6: Interleukin-6; ICU: Intensive Care Unit; ARDS: Acute Respiratory Distress Syndrome; DVT: Deep vein thrombosis.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>
                <xref ref-type="fig" rid="f1">Figure 1(a)</xref> and 
                <xref ref-type="fig" rid="f1">(b)</xref> show the result of the ROC analysis plotting the sensitivity and specificity of the PLR and NLR and their discriminatory ability to predict overall mortality and their performance by age categories in COVID-19 patients, respectively.</p>
            <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                <label>Figure 1. </label>
                <caption>
                    <title>Receiver operating characteristic (ROC) curves analyses for predicting discriminatory power analysis of initial Platelet-to-Lymphocyte Ratio and Neutrophil-to-Lymphocyte Ratio for the prediction of mortality in COVID-19 patients (a) overall mortality (b) mortality by age groups.</title>
                </caption>
                <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/160935/a8b7365a-a8aa-41c3-a787-06efc5c1fcf3_figure1a.gif"/>
                <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/160935/a8b7365a-a8aa-41c3-a787-06efc5c1fcf3_figure1b.gif"/>
            </fig>
            <p>The area under the curve (AUC) for NLR was 0.710, indicating a good discriminatory performance, and for PLR, 0.614 suggesting a fair discriminatory capacity, respectively. The optimal cut-off for NLR and PLR were 5.03 (Sensitivity 66.9% and specificity 46.5%) and 150.16 (Sensitivity 61.2% and specificity 68.4%). In individuals aged &#x2264;55 years, the PLR demonstrated moderate discrimination with an AUC of 0.620, while the NLR exhibited a higher AUC of 0.717, signifying superior discrimination compared to the PLR. Conversely, for individuals aged &gt;55 years, PLR showed an increased higher AUC of 0.710 in comparison to those &#x2264;55 years, implying enhanced discrimination in this age group, while NLR exhibited moderate discrimination with an AUC of 0.693 (
                <xref ref-type="fig" rid="f1">Figure 1(b)</xref>).</p>
            <p>
                <xref ref-type="table" rid="T4">Table 4</xref> shows the association of PLR and NLR in predicting mortality and delayed recovery in COVID-19 patients. The crude odd ratio for NLR was 1.078 (95% CI 1.049-1.109; 
                <italic toggle="yes">P</italic>=0.001), and PLR was 1.001 (95% CI, 1.001-1.002; 
                <italic toggle="yes">P</italic>=0.002) for mortality. The crude odd ratio for NLR was 1.034 (95% CI 0.996-1.072; 
                <italic toggle="yes">P</italic>=0.078), and PLR was 1.002 (95% CI, 1.000-1.004; 
                <italic toggle="yes">P</italic>=0.021) for delayed recovery.</p>
            <table-wrap id="T4" orientation="portrait" position="float">
                <label>Table 4. </label>
                <caption>
                    <title>Association of PLR and NLR with mortality and delayed recovery.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top">Variables</th>
                            <th align="left" colspan="1" rowspan="2" valign="top">Crude Odd ratio</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">95% CI</th>
                            <th align="left" colspan="1" rowspan="2" valign="top">
                                <italic toggle="yes">P</italic> value</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Lower</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Upper</th>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">
                                <bold>Mortality</bold>
                            </td>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                            <td colspan="1" rowspan="1"/>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet-to-lymphocyte ratio</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.002</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.002</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophil-to-lymphocyte ratio</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.078</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.049</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.109</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="5" rowspan="1" valign="top">
                                <bold>Delayed recovery (HLOS &gt;7 days)</bold>
                            </td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet-to-lymphocyte ratio</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.002</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.004</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.021</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophil-to-lymphocyte ratio</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.034</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.996</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.072</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.078</td>
                        </tr>
                    </tbody>
                </table>
                <table-wrap-foot>
                    <p>HLOS: Hospital Length of Stay.</p>
                </table-wrap-foot>
            </table-wrap>
            <p>
                <xref ref-type="table" rid="T5">Table 5</xref> depicts the results of multivariate regression analysis to determine independent predictors of mortality. After adjusting for the relevant covariates, being older than 55 years (OR 1.068; 95% CI 1.045 to 1.091; 
                <italic toggle="yes">P</italic>=0.001), hypertension (OR 0.437; 95% CI 0.255 to 0.751; 
                <italic toggle="yes">P</italic>=0.003), diabetes mellitus, (OR 1.730; 95% CI 1.050 to 2.851; 
                <italic toggle="yes">P</italic>=0.032), CRP (OR 1.004; 95% CI 1.002 to 1.007; 
                <italic toggle="yes">P</italic>=0.001), and renal failure (OR 6.620; 95% CI 3.989 to 10.989; 
                <italic toggle="yes">P</italic>=0.001) were found to be independent predictors of mortality. However, NLR (OR 1.039; 95% CI 0.998 to 1.082; 
                <italic toggle="yes">P</italic>=0.051) and PLR (OR 1.000; 95% CI 0.998 to 1.001; 
                <italic toggle="yes">P</italic>=0.581) were not independently associated with in-hospital mortality.</p>
            <table-wrap id="T5" orientation="portrait" position="float">
                <label>Table 5. </label>
                <caption>
                    <title>Multivariate regression analysis for predictors of mortality.</title>
                </caption>
                <table content-type="article-table" frame="hsides">
                    <thead>
                        <tr>
                            <th align="left" colspan="1" rowspan="2" valign="top">Variables</th>
                            <th align="left" colspan="1" rowspan="2" valign="top">Odd Ratio</th>
                            <th align="left" colspan="2" rowspan="1" valign="top">95% CI</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">
                                <italic toggle="yes">P</italic> value</th>
                        </tr>
                        <tr>
                            <th align="left" colspan="1" rowspan="1" valign="top">Lower</th>
                            <th align="left" colspan="1" rowspan="1" valign="top">Upper</th>
                            <th align="left" colspan="1" rowspan="1" valign="top"/>
                        </tr>
                    </thead>
                    <tbody>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Age</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.068</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.045</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.091</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Males</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.125</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.595</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.129</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.717</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Hypertension</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.437</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.255</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.751</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.003</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Diabetes Mellitus</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.730</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.050</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">2.851</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.032</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">C- reactive protein (CRP)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.004</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.002</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.007</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Platelet to lymphocyte ratio (PLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.000</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.998</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.001</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.581</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Neutrophil to lymphocyte ratio (NLR)</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.039</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.998</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">1.082</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.051</td>
                        </tr>
                        <tr>
                            <td align="left" colspan="1" rowspan="1" valign="top">Renal Failure</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">6.620</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">3.989</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">10.989</td>
                            <td align="left" colspan="1" rowspan="1" valign="top">0.001</td>
                        </tr>
                    </tbody>
                </table>
            </table-wrap>
        </sec>
        <sec id="sec12" sec-type="discussion">
            <title>Discussion</title>
            <p>Ever since the first cases of the COVID-19 pandemic were reported, healthcare institutions have worked to develop diagnostic tools and prognostic indications. The current study investigates associations between NLR, PLR, age, duration of hospital length of stay, and mortality in COVID-19 patients. This study demonstrates that patients with higher admission NLR and PLR levels were associated with delayed recovery, more intubation, and ICU admissions. In contrast, an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients than PLR.</p>
            <p>Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world.
                <sup>
                    <xref ref-type="bibr" rid="ref28">28</xref>
                </sup> In our analysis of 1016 COVID-19 patients, the overall in-hospital mortality was 11.9 %. The mortality in our study cohort was less than in previous reports from other countries.
                <sup>
                    <xref ref-type="bibr" rid="ref29">29</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref30">30</xref>
                </sup> Lower mortality was also observed in the elderly population (aged &gt;55). Several studies and meta-analyses have concluded that the predictive value of NLR and PLR could be used to stratify COVID-19 patients, and especially high NLR at admission has been associated with poor outcomes.
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref30">30</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref32">32</xref>
                </sup> Previous publications included older adults but did not perform a subgroup analysis to assess the estimated mortality risk for this age group. Ciccullo et al. demonstrated that younger age and NLR below three were associated with clinical improvement, while NLR over 4 predicted the transfer to ICU.
                <sup>
                    <xref ref-type="bibr" rid="ref31">31</xref>
                </sup> Based upon the ROC analysis, the cut-off value for NLR was 5.03, and PLR was 150.16 to predict mortality. This result is in concordance with previous studies, in which the proposed optimum cut-off values for NLR ranged from 3 to 6,
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref32">32</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref33">33</xref>
                </sup> and cut-off PLR values were between 140-160.
                <sup>
                    <xref ref-type="bibr" rid="ref32">32</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref34">34</xref>
                </sup> Liu et al. showed that older patients (&gt;50 years old) with NLR &#x2265;3.13 are more likely to develop a critical illness.
                <sup>
                    <xref ref-type="bibr" rid="ref35">35</xref>
                </sup> Yang et al. showed that elevated NLR and advanced age were associated with severe COVID-19 illness and independently predicted the worse clinical outcomes.
                <sup>
                    <xref ref-type="bibr" rid="ref36">36</xref>
                </sup>
            </p>
            <p>The NLR has emerged as a potent inflammatory marker with diagnostic and prognostic utility in various clinical conditions.
                <sup>
                    <xref ref-type="bibr" rid="ref11">11</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref13">13</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref15">15</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref16">16</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref21">21</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref22">22</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref35">35</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref37">37</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref39">39</xref>
                </sup> NLR represents the equilibrium of innate and adaptive immune responses.
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>
                </sup> A High NLR implies an aberrant immune response, with increased neutrophils and decreased lymphocytes. Also, neutrophil production can be augmented by virus-induced inflammatory factors such as IL-6, Interleukin-8 (IL-8), and tumor necrosis factor &#x03b1; (TNF- &#x03b1;).
                <sup>
                    <xref ref-type="bibr" rid="ref4">4</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref37">37</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref40">40</xref>
                </sup> Furthermore, it appears to be a more reliable technique than PLR, absolute neutrophils, and lymphocyte counts since confounders influence it less. The current study&#x2019;s asynchronous pattern of NLR and PLR highlights that NLR and PLR are both elevated during the onset of the COVID-19 disease. Still, NLR increases afterward, especially in older individuals. This would imply that NLR offers extra information regarding the ongoing inflammatory state in COVID-19 patients, especially those with poor prognoses. Our results suggest that NLR can be a more valuable predictor of poor prognosis in the different sub-categories of patients studied in the current study.</p>
            <p>However, neither NLR nor PLR were shown to be independent predictors of mortality on multivariate analysis, which contrasts with previous reports.
                <sup>
                    <xref ref-type="bibr" rid="ref1">1</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref24">24</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref31">31</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref38">38</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref41">41</xref>
                </sup>
                <sup>,</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref42">42</xref>
                </sup> This discrepancy could be explained by one of two factors. First, the pathogenesis of SARS-CoV-2 infection is complicated. Secondly, this could be attributed to the small sample size reported in the previous studies.</p>
            <p>The severity of infections, hematological derangements (NLR, PLR), and mortality rose sharply with age. This was especially true for infection criticality, in-hospital complications, and mortality, restricted for those under 50 but rapidly increased for those over 50. It has been established that patients of advanced age are more susceptible to COVID-19 mortality.
                <sup>
                    <xref ref-type="bibr" rid="ref43">43</xref>
                </sup>
                <sup>&#x2013;</sup>
                <sup>
                    <xref ref-type="bibr" rid="ref46">46</xref>
                </sup>
            </p>
            <p>Regarding the clinical outcome of patients concerning early and late recovery, we found that delayed recovery (HLOS&gt; seven days) was associated with advanced age, prolonged ICU stay and mechanical ventilation, and higher mortality. Also, a significantly higher proportion of the older population with prolonged HLOS had comorbidities, suggesting that advanced age and associated comorbidities require more extended hospitalization and have a greater risk of mortality.
                <sup>
                    <xref ref-type="bibr" rid="ref47">47</xref>
                </sup> Moreover, our cohort&#x2019;s higher mean PLR demonstrated a significant association with delayed recovery. While NLR showed a modest and suggestive increase in the odds of delayed recovery, the association was non-significant. Studies have shown that HLOS is age-dependent.
                <sup>
                    <xref ref-type="bibr" rid="ref48">48</xref>
                </sup> We could not find studies evaluating the impact of NLR and PLR as prognostic markers of early vs. delayed recovery.</p>
            <sec id="sec13">
                <title>Limitations</title>
                <p>Some limitations may have affected the study and warrant consideration
                    <bold>.</bold> This retrospective analysis did not document the patient&#x2019;s follow-up. Secondly, available input data were most complete at the national level, but the results&#x2019; generalizability could have been constrained by variations within Qatar&#x2019;s very diversified population. Also, the selection bias and power of the study cannot be ignored; the study would have benefitted from a larger sample size to reflect better the importance of NLR/PLR in the prognosis of patients with COVID-19. The cycle threshold (cT) value has been proposed as a potential prognostic indicator in patients with COVID-19. While this information was not available in the current study, it may be more valuable in refining the prognostic evaluation of COVID-19 patients if researchers compare and combine the NLR/PLR with cT findings. Lastly, a prospective study should ideally test the predictive value of NLR and PLR longitudinally. Despite these limitations, the study, tailored to the complexity of the epidemic, reproduces the observed biochemical trends and provides profound insights into the utility of NLR and PLR as prognostic markers in COVID-19 patients.</p>
            </sec>
        </sec>
        <sec id="sec14" sec-type="conclusion">
            <title>Conclusion</title>
            <p>Patients with higher NLR and PLR levels were associated with delayed recovery, ICU admissions, and intubation, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. However, none of these two parameters was found to be an independent predictor for death.</p>
        </sec>
        <sec id="sec15">
            <title>Ethics and consent</title>
            <p>This study was approved by the Research Ethics Committee of the Medical Research Center, Hamad Medical Corporation (HMC), Doha, Qatar (MRC-01-20-672 &amp; MRC-05-213) on 29 Sep 2020. A waiver of consent was granted for this retrospective study as there was no direct contact was made with the participants, and the data were collected anonymously.</p>
        </sec>
        <sec id="sec16">
            <title>Authors&#x2019; contributions</title>
            <p>All authors have substantially contributed to the acquisition, analysis, and interpretation of data for the work, drafting the work or revising it critically for important intellectual content, and final approval of the version to be published.</p>
        </sec>
    </body>
    <back>
        <sec id="sec19" sec-type="data-availability">
            <title>Data availability</title>
            <p>As the covid-19 data are owned by the medical research center and CDC department, I provided the email and condition by which the reader can access the de-identified data. All data are presented in the manuscript including tables and de-identified data can be accessed after a permission from the medical research center of HMC, Qatar (
                <email xlink:href="mailto:mrcinfo@hamad.qa">mrcinfo@hamad.qa</email>), on a reasonable research request</p>
        </sec>
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                        <name name-style="western">
                            <surname>Tan</surname>
                            <given-names>J</given-names>
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                        <name name-style="western">
                            <surname>Zhang</surname>
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                        </name>

                        <etal/>
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                    <article-title>Impact of sex and age on respiratory support and length of hospital stay among 1792 patients with COVID-19 in Wuhan, China.</article-title>
                    <source>

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                </mixed-citation>
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        </ref-list>
    </back>
    <sub-article article-type="reviewer-report" id="report385519">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.160935.r385519</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Paganelli</surname>
                        <given-names>Roberto</given-names>
                    </name>
                    <xref ref-type="aff" rid="r385519a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r385519a1">
                    <label>1</label>International Medical University in Rome, UniCamillus, Rome, Italy</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>4</day>
                <month>6</month>
                <year>2025</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2025 Paganelli R</copyright-statement>
                <copyright-year>2025</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport385519" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.146814.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>This is a retrospective study of COVID-19 patients' outcome at a group of Hospitals in Qatar in 2020, at the beginning of the pandemic. The authors seeked to evaluate the association of NLR and PLR at admission with mortality and severity of patients. The results show that NLR predicts mortality in all cases, and PLR is better for length of hospital stay. These parameters however are non independent predictors, and older subjects had a worse outcome.</p>
            <p> Most of this was known since the beginning of the pandemic, so it adds very little to existing literature. Moreover, the study does not mention the ethnic origin of the patients, who were mostly males.</p>
            <p> It may deserve publication as a record of the pandemic viewed from a small country with large immigration.</p>
            <p> The English language needs improvement.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>immunology</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report282772">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.160935.r282772</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Malatino</surname>
                        <given-names>Lorenzo</given-names>
                    </name>
                    <xref ref-type="aff" rid="r282772a1">1</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0003-2944-2729</uri>
                </contrib>
                <contrib contrib-type="author">
                    <name>
                        <surname>Isaia</surname>
                        <given-names>Ivan</given-names>
                    </name>
                    <xref ref-type="aff" rid="r282772a2">2</xref>
                    <xref ref-type="aff" rid="r282772a3">3</xref>
                    <role>Co-referee</role>
                </contrib>
                <aff id="r282772a1">
                    <label>1</label>Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy</aff>
                <aff id="r282772a2">
                    <label>2</label>Clinical and experimental medicine, University of Catania, Catania, Catania, Italy</aff>
                <aff id="r282772a3">
                    <label>3</label>Department of Clinical and Experimental Medicine, University of Catania, Clinica Medica-Ospedale Cannizzaro, Catania, Italy</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>6</day>
                <month>6</month>
                <year>2024</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2024 Malatino L and Isaia I</copyright-statement>
                <copyright-year>2024</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport282772" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.146814.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients. Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic.</p>
            <p> </p>
            <p> Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome. These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age.</p>
            <p> </p>
            <p> The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3])&#x00a0;in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F. Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome.</p>
            <p> </p>
            <p> In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so making difficult understanding the pathogenetic chain linking the pattern of &#x00a0;humoral factors to the prognosis of patients with Covid-19.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Yes</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Partly</p>
            <p>Reviewer Expertise:</p>
            <p>Internal and Clinical Medicine, Cardiology,&#x00a0;Neutrophil-to-Lymphocyte Ratio</p>
            <p>We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.</p>
        </body>
        <back>
            <ref-list>
                <title>References</title>
                <ref id="rep-ref-282772-1">
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                        <article-title>Neutrophil-to-Lymphocyte Ratio (NLR) Is a Promising Predictor of Mortality and Admission to Intensive Care Unit of COVID-19 Patients.</article-title>
                        <source>
                            <italic>J Clin Med</italic>
                        </source>.<year>2022</year>;<volume>11</volume>(<issue>8</issue>) :
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                        </source>.<year>2017</year>;<volume>65</volume>(<issue>8</issue>) :
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                <ref id="rep-ref-282772-4">
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                        <person-group person-group-type="author"/>:
                        <article-title>Assessing Humoral Immuno-Inflammatory Pathways Associated with Respiratory Failure in COVID-19 Patients.</article-title>
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                            <italic>J Clin Med</italic>
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                        <elocation-id>10.3390/jcm12124057</elocation-id>
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        </back>
        <sub-article article-type="response" id="comment11747-282772">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>El-Menyar</surname>
                            <given-names>Ayman</given-names>
                        </name>
                        <aff>Surgery, Hamad Medical Corporation, Doha, Doha, Qatar</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>10</day>
                    <month>6</month>
                    <year>2024</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Reviewer </bold>1 
                    <bold>Query: </bold>This paper by El Menyar et al. presents a retrospective multicenter survey conducted in a large cohort of young patients with Covid-19 disease. The key messages are: 1) elevated NLR and PLR levels are associated with longer hospitalization, increased ICU admission, and intubation rate; and 2) higher NLR values are associated with increased mortality in older COVID-19 patients.</p>
                <p> Taken together, these data suggest that NLR and PLR levels could serve as useful bedside laboratory tests for patient stratification during the Covid-19 pandemic. Unlike previous data by (Regolo M, et al., 2022 [Ref 1]) on NLR, the authors conclude that neither of these two parameters alone is an independent predictor of death. This finding appears to contradict the aforementioned key messages, as it raises the question of how diagnostic tools identifying both length of stay and ICU admission could fail to predict mortality. Additionally, previous data by (Regolo M, et al., 2022 [Ref 1]) , derived from a retrospective survey of older patients with Covid-19 disease, showed that NLR had better performance as compared to PLR and CRP in stratifying disease severity and outcome.</p>
                <p> These data were age- and sex-adjusted, so the differences with El Menyar et al. data cannot be attributed to older age. The better prognostic performance of NLR in (Regolo M, et al., 2022 [Ref 1]) may partially depend on NLR ability to predict survival in patients with pulmonary infections, as shown by (Cataudella E, et al., 2017 [Ref 2]) in community-acquired pneumonia (Cataudella E, et al., 2017 [Ref 2]), despite the different pattern of pulmonary involvement in CAP as compared to Covid-19. It may well be that the derangement between innate and adaptive immunity (Buonacera A, et al., 2022 [Ref 3]) in Covid-19 could be similar to that involved in the pathogenesis of CAP. In this respect, the complex humoral immuno-inflammatory pathway in Covid-19 patients was elegantly dissected by mediation analysis (Regolo M, et al., 2023 [Ref 4]), demonstrating that age, neutrophils, CRP, and lymphocytes are significantly and directly linked to PaO2/FiO2 (P/F), a marker of respiratory failure. The influence of inflammation on P/F, as reflected by CRP, was also mediated by neutrophils, indicating that neutrophils play a dual role. This is an important finding, given that NLR, but not PLR and CRP, was inversely and significantly related to P/F.</p>
                <p> &#x00a0;Unfortunately, data on P/F, the type of ventilation support, and the mean duration of hospital stay for patients are missing in El Menyar et al. paper. Moreover, it should be emphasized that the article by El Menyar et al. regards the initial period of the COVID-19 pandemic, which was characterized by the highest mortality and the most severe clinical outcome.</p>
                <p> In conclusion, the study by El Menyar et al. is generally confirmatory and is based on a larger survey than previous studies, which is notable. However, its limitations include the lack of data on respiratory function, ventilation support, and the mean duration of hospitalization. Additionally, objectives of this study were not clearly focused, so it makes it difficult understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with Covid19.</p>
                <p> </p>
                <p> 
                    <bold>
                        <underline>Reply to reviewer-1&#x00a0;</underline>
                    </bold>
                </p>
                <p> I would like to thank the reviewer for their thoughtful comments and for engaging with our research. We appreciate the opportunity to address your queries and provide further clarification on our findings.</p>
                <p> </p>
                <p> Regarding the discrepancy between our study and the previous findings by Regolo et al. on the predictive value of NLR for mortality in COVID-19 patients, we acknowledge the importance of understanding the nuances in different patient cohorts and disease presentations. While our study highlights the association of NLR and PLR with hospitalization duration and recovery based on the patient's age, the findings interestingly showed that higher admission NLR and PLR levels were associated with delayed recovery, whereas an enhanced NLR was associated with considerably higher mortality in older COVID-19 patients. In our study, univariate analysis showed that admission NLR levels significantly differed between survivors and non-survivors [5.8 (0.9-53.0) vs. 3.2 (0.27-72.0); p=0.001]. Consistent with Regolo et al., our data showed an association of NLR with predicting mortality and delayed recovery in COVID-19 patients. The crude odds ratio for NLR was 1.078 (95% CI 1.049-1.109; p=0.001) for mortality and 1.034 (95% CI 0.996-1.072; p=0.078) for delayed recovery. However, the independent association of NLR (OR 1.039; 95% CI 0.998-1.082; p=0.051) with mortality relatively disappeared in our multivariate model after adjusting for age and other relevant covariates. Though our data suggest a clinically relevant OR of 1.039, it failed to reach statistical significance (p=0.051).</p>
                <p> </p>
                <p> In the context of multivariate analysis, collinearity with other variables can diminish the independent predictive power of NLR. This may be due to the fact that NLR, PLR, and CRP, to some extent, are associated with the same pathophysiological chain, i.e., inflammation. Their interaction means that CRP might account for most of the variance related to inflammation, reducing the unique contribution of NLR. Similarly, PLR is another marker of inflammation and immune response, and its inclusion alongside NLR can lead to redundancy, with one ratio (PLR or NLR) not adding significant predictive value over the other. Additionally, larger sample sizes in multivariate analysis can increase the model's complexity, requiring careful consideration of potential confounders and interactions between variables.</p>
                <p> The specific observed differences between our study and Regolo et al.'s findings could be attributed to various factors, including differences in patient demographics, such as age, disease severity, methodology, and sample size. Our study focuses on a specific cohort of young patients during the initial phase of the pandemic, which may have unique characteristics compared to the older patient populations studied by Regolo et al. (the median age of the patient population was 72). Moreover, our cohort sample size was 1016 compared to 411 in Regolo et al.'s study. As sample sizes increase, the effect sizes of variables may become smaller, and statistically significant associations may be observed with smaller magnitudes of effect. This phenomenon can sometimes lead to a perception that the associations are less clinically meaningful.</p>
                <p> </p>
                <p> Despite a high per capita SARS-CoV-2 infection rate in the early phase of the COVID-19 pandemic, the case fatality rate in Qatar was among the lowest in the world. The overall in-hospital mortality was 11.9%, lower than the 19.5% reported by Regolo et al. Lower mortality was also observed in the elderly population (aged &gt;55). Lastly, we acknowledge the lack of data on respiratory function in our study. We believe that the objectives of our study were clearly defined and focused. Understanding the pathogenetic chain linking the pattern of humoral factors to the prognosis of patients with COVID-19 was outside the scope of our work.</p>
            </body>
        </sub-article>
    </sub-article>
</article>
