Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score
Identifieur interne : 000812 ( Pmc/Corpus ); précédent : 000811; suivant : 000813Mortality risk prediction in lupus patients complicated with invasive infection in the emergency department: LUPHAS score
Auteurs : Wanlong Wu ; Jun Ma ; Yuhong Zhou ; Chao Tang ; Feng Zhao ; Fangfang Sun ; Wenwen Xu ; Jie Chen ; Shuang Ye ; Yi ChenSource :
- Therapeutic Advances in Musculoskeletal Disease [ 1759-720X ] ; 2019.
Abstract
Infection remains a major cause of morbidity and mortality in patients with systemic lupus erythematosus (SLE). This study aimed to establish a clinical prediction model for the 3-month all-cause mortality of invasive infection events in patients with SLE in the emergency department.
SLE patients complicated with invasive infection admitted into the emergency department were included in this study. Patient’s demographic, clinical, and laboratory characteristics on admission were retrospectively collected as baseline data and compared between the deceased and the survivors. Independent predictors were identified by multivariable logistic regression analysis. A prediction model for all-cause mortality was established and evaluated by receiver operating characteristic (ROC) curve analysis.
A total of 130 eligible patients were collected with a cumulative 38.5%
3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum
prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure
Assessment (q
Based on a large emergency cohort of lupus patients complicated with invasive infection, the LUPHAS score was established to predict the short-term all-cause mortality, which could be a promising applicable tool for risk stratification in clinical practice.
Url:
DOI: 10.1177/1759720X19885559
PubMed: 31723357
PubMed Central: 6831971
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PMC:6831971Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Mortality risk prediction in lupus patients complicated with invasive
infection in the emergency department: LUPHAS score</title>
<author><name sortKey="Wu, Wanlong" sort="Wu, Wanlong" uniqKey="Wu W" first="Wanlong" last="Wu">Wanlong Wu</name>
</author>
<author><name sortKey="Ma, Jun" sort="Ma, Jun" uniqKey="Ma J" first="Jun" last="Ma">Jun Ma</name>
</author>
<author><name sortKey="Zhou, Yuhong" sort="Zhou, Yuhong" uniqKey="Zhou Y" first="Yuhong" last="Zhou">Yuhong Zhou</name>
</author>
<author><name sortKey="Tang, Chao" sort="Tang, Chao" uniqKey="Tang C" first="Chao" last="Tang">Chao Tang</name>
</author>
<author><name sortKey="Zhao, Feng" sort="Zhao, Feng" uniqKey="Zhao F" first="Feng" last="Zhao">Feng Zhao</name>
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<author><name sortKey="Sun, Fangfang" sort="Sun, Fangfang" uniqKey="Sun F" first="Fangfang" last="Sun">Fangfang Sun</name>
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<author><name sortKey="Xu, Wenwen" sort="Xu, Wenwen" uniqKey="Xu W" first="Wenwen" last="Xu">Wenwen Xu</name>
</author>
<author><name sortKey="Chen, Jie" sort="Chen, Jie" uniqKey="Chen J" first="Jie" last="Chen">Jie Chen</name>
</author>
<author><name sortKey="Ye, Shuang" sort="Ye, Shuang" uniqKey="Ye S" first="Shuang" last="Ye">Shuang Ye</name>
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<author><name sortKey="Chen, Yi" sort="Chen, Yi" uniqKey="Chen Y" first="Yi" last="Chen">Yi Chen</name>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Mortality risk prediction in lupus patients complicated with invasive
infection in the emergency department: LUPHAS score</title>
<author><name sortKey="Wu, Wanlong" sort="Wu, Wanlong" uniqKey="Wu W" first="Wanlong" last="Wu">Wanlong Wu</name>
</author>
<author><name sortKey="Ma, Jun" sort="Ma, Jun" uniqKey="Ma J" first="Jun" last="Ma">Jun Ma</name>
</author>
<author><name sortKey="Zhou, Yuhong" sort="Zhou, Yuhong" uniqKey="Zhou Y" first="Yuhong" last="Zhou">Yuhong Zhou</name>
</author>
<author><name sortKey="Tang, Chao" sort="Tang, Chao" uniqKey="Tang C" first="Chao" last="Tang">Chao Tang</name>
</author>
<author><name sortKey="Zhao, Feng" sort="Zhao, Feng" uniqKey="Zhao F" first="Feng" last="Zhao">Feng Zhao</name>
</author>
<author><name sortKey="Sun, Fangfang" sort="Sun, Fangfang" uniqKey="Sun F" first="Fangfang" last="Sun">Fangfang Sun</name>
</author>
<author><name sortKey="Xu, Wenwen" sort="Xu, Wenwen" uniqKey="Xu W" first="Wenwen" last="Xu">Wenwen Xu</name>
</author>
<author><name sortKey="Chen, Jie" sort="Chen, Jie" uniqKey="Chen J" first="Jie" last="Chen">Jie Chen</name>
</author>
<author><name sortKey="Ye, Shuang" sort="Ye, Shuang" uniqKey="Ye S" first="Shuang" last="Ye">Shuang Ye</name>
</author>
<author><name sortKey="Chen, Yi" sort="Chen, Yi" uniqKey="Chen Y" first="Yi" last="Chen">Yi Chen</name>
</author>
</analytic>
<series><title level="j">Therapeutic Advances in Musculoskeletal Disease</title>
<idno type="ISSN">1759-720X</idno>
<idno type="eISSN">1759-7218</idno>
<imprint><date when="2019">2019</date>
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<front><div type="abstract" xml:lang="en"><sec id="section1-1759720X19885559"><title>Background:</title>
<p>Infection remains a major cause of morbidity and mortality in patients with
systemic lupus erythematosus (SLE). This study aimed to establish a clinical
prediction model for the 3-month all-cause mortality of invasive infection
events in patients with SLE in the emergency department.</p>
</sec>
<sec id="section2-1759720X19885559"><title>Methods:</title>
<p>SLE patients complicated with invasive infection admitted into the emergency
department were included in this study. Patient’s demographic, clinical, and
laboratory characteristics on admission were retrospectively collected as
baseline data and compared between the deceased and the survivors.
Independent predictors were identified by multivariable logistic regression
analysis. A prediction model for all-cause mortality was established and
evaluated by receiver operating characteristic (ROC) curve analysis.</p>
</sec>
<sec id="section3-1759720X19885559"><title>Results:</title>
<p>A total of 130 eligible patients were collected with a cumulative 38.5%
3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum
prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure
Assessment (q<bold>S</bold>
OFA) score, and age at baseline were independent
predictors for all-cause mortality (LUPHAS). In contrast, a history of
hydroxychloroquine use was protective. In a combined, odds ratio-weighted
LUPHAS scoring system (score 3–22), patients were categorized to three
groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk
(score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3%
(18/23) respectively. ROC curve analysis indicated that a LUPHAS score could
effectively predict all-cause mortality [area under the curve (AUC) = 0.86,
CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA
score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI
95% 0.59–0.80) in the subgroup of lung infections
(<italic>n</italic>
= 108).</p>
</sec>
<sec id="section4-1759720X19885559"><title>Conclusions:</title>
<p>Based on a large emergency cohort of lupus patients complicated with invasive
infection, the LUPHAS score was established to predict the short-term
all-cause mortality, which could be a promising applicable tool for risk
stratification in clinical practice.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Ther Adv Musculoskelet Dis</journal-id>
<journal-id journal-id-type="iso-abbrev">Ther Adv Musculoskelet Dis</journal-id>
<journal-id journal-id-type="publisher-id">TAB</journal-id>
<journal-id journal-id-type="hwp">sptab</journal-id>
<journal-title-group><journal-title>Therapeutic Advances in Musculoskeletal Disease</journal-title>
</journal-title-group>
<issn pub-type="ppub">1759-720X</issn>
<issn pub-type="epub">1759-7218</issn>
<publisher><publisher-name>SAGE Publications</publisher-name>
<publisher-loc>Sage UK: London, England</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">31723357</article-id>
<article-id pub-id-type="pmc">6831971</article-id>
<article-id pub-id-type="doi">10.1177/1759720X19885559</article-id>
<article-id pub-id-type="publisher-id">10.1177_1759720X19885559</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>When Rheumatology and Infectious Disease Come Together</subject>
<subj-group subj-group-type="heading"><subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group><article-title>Mortality risk prediction in lupus patients complicated with invasive
infection in the emergency department: LUPHAS score</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0003-2727-7950</contrib-id>
<name><surname>Wu</surname>
<given-names>Wanlong</given-names>
</name>
<xref ref-type="author-notes" rid="fn1-1759720X19885559">*</xref>
<aff id="aff1-1759720X19885559">Department of Rheumatology, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Ma</surname>
<given-names>Jun</given-names>
</name>
<xref ref-type="author-notes" rid="fn1-1759720X19885559">*</xref>
<aff id="aff2-1759720X19885559">Department of Emergency Medicine, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Zhou</surname>
<given-names>Yuhong</given-names>
</name>
<aff id="aff3-1759720X19885559">Department of Emergency Medicine, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Tang</surname>
<given-names>Chao</given-names>
</name>
<aff id="aff4-1759720X19885559">Department of Emergency Medicine, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Zhao</surname>
<given-names>Feng</given-names>
</name>
<aff id="aff5-1759720X19885559">Department of Emergency Medicine, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Sun</surname>
<given-names>Fangfang</given-names>
</name>
<aff id="aff6-1759720X19885559">Department of Rheumatology, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Xu</surname>
<given-names>Wenwen</given-names>
</name>
<aff id="aff7-1759720X19885559">Department of Rheumatology, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Chen</surname>
<given-names>Jie</given-names>
</name>
<aff id="aff8-1759720X19885559">Department of Rheumatology, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Ye</surname>
<given-names>Shuang</given-names>
</name>
<aff id="aff9-1759720X19885559">Department of Rheumatology, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China</aff>
</contrib>
<contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0001-6642-7435</contrib-id>
<name><surname>Chen</surname>
<given-names>Yi</given-names>
</name>
<xref ref-type="corresp" rid="corresp1-1759720X19885559"></xref>
<aff id="aff10-1759720X19885559">Department of Emergency Medicine, South Campus, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, No. 2000, Jiangyue Road, Minhang District, Shanghai 201112, China</aff>
</contrib>
</contrib-group>
<author-notes><corresp id="corresp1-1759720X19885559"><email>chenyirenji@126.com</email>
</corresp>
<fn fn-type="equal" id="fn1-1759720X19885559"><label>*</label>
<p>Wanlong Wu and Jun Ma contributed equally to this article as co-first
authors</p>
</fn>
</author-notes>
<pub-date pub-type="epub"><day>05</day>
<month>11</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection"><year>2019</year>
</pub-date>
<volume>11</volume>
<elocation-id>1759720X19885559</elocation-id>
<history><date date-type="received"><day>17</day>
<month>7</month>
<year>2019</year>
</date>
<date date-type="accepted"><day>29</day>
<month>9</month>
<year>2019</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s), 2019</copyright-statement>
<copyright-year>2019</copyright-year>
<copyright-holder content-type="sage">SAGE Publications Ltd unless otherwise noted.
Manuscript content on this site is licensed under Creative Commons
Licenses</copyright-holder>
<license license-type="creative-commons" xlink:href="http://www.creativecommons.org/licenses/by-nc/4.0/"><license-p>This article is distributed under the terms of the Creative Commons
Attribution-NonCommercial 4.0 License (<ext-link ext-link-type="uri" xlink:href="http://www.creativecommons.org/licenses/by-nc/4.0/">http://www.creativecommons.org/licenses/by-nc/4.0/</ext-link>
) which
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).</license-p>
</license>
</permissions>
<abstract><sec id="section1-1759720X19885559"><title>Background:</title>
<p>Infection remains a major cause of morbidity and mortality in patients with
systemic lupus erythematosus (SLE). This study aimed to establish a clinical
prediction model for the 3-month all-cause mortality of invasive infection
events in patients with SLE in the emergency department.</p>
</sec>
<sec id="section2-1759720X19885559"><title>Methods:</title>
<p>SLE patients complicated with invasive infection admitted into the emergency
department were included in this study. Patient’s demographic, clinical, and
laboratory characteristics on admission were retrospectively collected as
baseline data and compared between the deceased and the survivors.
Independent predictors were identified by multivariable logistic regression
analysis. A prediction model for all-cause mortality was established and
evaluated by receiver operating characteristic (ROC) curve analysis.</p>
</sec>
<sec id="section3-1759720X19885559"><title>Results:</title>
<p>A total of 130 eligible patients were collected with a cumulative 38.5%
3-month mortality. Lymphocyte count <800/ul, urea >7.6mmol/l, maximum
prednisone dose in the past ⩾60 mg/d, quick Sequential Organ Failure
Assessment (q<bold>S</bold>
OFA) score, and age at baseline were independent
predictors for all-cause mortality (LUPHAS). In contrast, a history of
hydroxychloroquine use was protective. In a combined, odds ratio-weighted
LUPHAS scoring system (score 3–22), patients were categorized to three
groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk
(score 16–22), with mortalities of 4.9% (2/41), 45.9% (28/61), and 78.3%
(18/23) respectively. ROC curve analysis indicated that a LUPHAS score could
effectively predict all-cause mortality [area under the curve (AUC) = 0.86,
CI 95% 0.79–0.92]. In addition, LUPHAS score performed better than the qSOFA
score alone (AUC = 0.69, CI 95% 0.59–0.78), or CURB-65 score (AUC = 0.69, CI
95% 0.59–0.80) in the subgroup of lung infections
(<italic>n</italic>
= 108).</p>
</sec>
<sec id="section4-1759720X19885559"><title>Conclusions:</title>
<p>Based on a large emergency cohort of lupus patients complicated with invasive
infection, the LUPHAS score was established to predict the short-term
all-cause mortality, which could be a promising applicable tool for risk
stratification in clinical practice.</p>
</sec>
</abstract>
<kwd-group><kwd>emergency department</kwd>
<kwd>infection</kwd>
<kwd>mortality</kwd>
<kwd>prediction</kwd>
<kwd>systemic lupus erythematosus</kwd>
</kwd-group>
<funding-group><award-group id="award1-1759720X19885559"><funding-source id="funding1-1759720X19885559"><institution-wrap><institution>Shanghai Shenkang promoting project</institution>
<institution-id></institution-id>
</institution-wrap>
</funding-source>
<award-id rid="funding1-1759720X19885559">16CR1013A</award-id>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>cover-date</meta-name>
<meta-value>January-December 2019</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body><sec sec-type="intro" id="section5-1759720X19885559"><title>Introduction</title>
<p>Systemic lupus erythematosus (SLE) is a heterogeneous systemic autoimmune disease
with protean clinical manifestations.<sup><xref rid="bibr1-1759720X19885559" ref-type="bibr">1</xref>
</sup>
The overall survival of SLE has appreciably improved over the past decades
due to better disease control benefiting, at least partially, from immunosuppressive
agents. However, invasive infection remains an important and major cause of
morbidity and mortality in lupus patients in the context of the immune disturbed or
immunocompromised status.<sup><xref rid="bibr2-1759720X19885559" ref-type="bibr">2</xref>
<xref rid="bibr3-1759720X19885559" ref-type="bibr"></xref>
<xref rid="bibr4-1759720X19885559" ref-type="bibr"></xref>
–<xref rid="bibr5-1759720X19885559" ref-type="bibr">5</xref>
</sup>
</p>
<p>Several risk factors including age, active disease, renal involvement, previous
exposure of high-dose glucocorticoid, and immunosuppressants (e.g. cyclophosphamide,
rituximab) have been reported to be associated with incidence of infection in
SLE.<sup><xref rid="bibr6-1759720X19885559" ref-type="bibr">6</xref>
<xref rid="bibr7-1759720X19885559" ref-type="bibr"></xref>
<xref rid="bibr8-1759720X19885559" ref-type="bibr"></xref>
<xref rid="bibr9-1759720X19885559" ref-type="bibr"></xref>
<xref rid="bibr10-1759720X19885559" ref-type="bibr"></xref>
–<xref rid="bibr11-1759720X19885559" ref-type="bibr">11</xref>
</sup>
In contrast, it has been
accepted that antimalarial drugs have a protective role against infection.<sup><xref rid="bibr9-1759720X19885559" ref-type="bibr">9</xref>
,<xref rid="bibr12-1759720X19885559" ref-type="bibr">12</xref>
<xref rid="bibr13-1759720X19885559" ref-type="bibr"></xref>
–<xref rid="bibr14-1759720X19885559" ref-type="bibr">14</xref>
</sup>
</p>
<p>It is common that these acutely ill patients with severe infection presented to the
emergency room seeking medical attention. Although existing predictive tools
including Quick Sequential Organ Failure Assessment (qSOFA) and CURB-65 have been
widely used in the general population with infection for risk assessment,<sup><xref rid="bibr15-1759720X19885559" ref-type="bibr">15</xref>
,<xref rid="bibr16-1759720X19885559" ref-type="bibr">16</xref>
</sup>
there is a
lack of robust data on lupus patients who are admitted to the emergency department
with invasive infections. However, no applicable mortality prediction model, to the
best of our knowledge, is available for this specific subpopulation.</p>
<p>As a large tertiary referral center with a powerful rheumatology team, many severe
lupus patients are rereferred or transferred to the emergency department of our
hospital. We have built up a so called ‘Emergency-Rheum’ based on a
multidisciplinary approach with rheumatologists and emergency physicians working
closely together to manage these patients. In this unique setting, we have the
advantage of being able to investigate our cohort of SLE patients, complicated with
invasive infections, in the emergency department. In this study, we aimed to
identify the independent risk factors and to establish a clinical prediction model
for the 3-month all-cause mortality for patients with this life-threatening
condition.</p>
</sec>
<sec sec-type="methods" id="section6-1759720X19885559"><title>Methods</title>
<sec id="section7-1759720X19885559"><title>Study cohort</title>
<p>We conducted a retrospective observational study in a prospective
‘Emergency-Rheum’ cohort. The ‘Emergency-Rheum’ cohort was established in the
south campus of Renji Hospital in 2015. On the basis of multidisciplinary
collaboration, when patients with rheumatology diseases including lupus present
at the emergency department of our center, rheumatologists give consultation and
opinion upon disease evaluation and specific treatment as soon as possible. The
patients would also be followed up by the rheumatologist until they are admitted
to a ward if necessary.</p>
<p>Written informed consent was obtained from the patients included in our
‘Emergency-Rheum’ cohort. Then data including demographic, clinical, and
laboratory characteristics on admission covering the disease evaluation of both
critical condition and rheumatology diseases were collected. Once included in
the database, every patient was continuously followed up in the inpatient and
outpatient departments of our center using medical records or occasionally by
phone.</p>
<p>The current study was conducted by retrospectively analyzing the subcohort of
lupus patients with invasive infection. The study protocol was approved by the
ethics committees of Renji Hospital.</p>
<p>Eligible patients for this study fulfilled the following criteria: diagnosis of
SLE according to the 1997 American College of Rheumatology classification criteria,<sup><xref rid="bibr17-1759720X19885559" ref-type="bibr">17</xref>
</sup>
complications with an invasive infection when admitted into the emergency
department between May 2015 and June 2018.</p>
<p>Invasive infection was defined as a deep infection with definite microbiological
evidence, or was judged to be by the treating physician combining the symptoms,
laboratory, and imaging tests.<sup><xref rid="bibr8-1759720X19885559" ref-type="bibr">8</xref>
,<xref rid="bibr10-1759720X19885559" ref-type="bibr">10</xref>
,<xref rid="bibr18-1759720X19885559" ref-type="bibr">18</xref>
</sup>
For example, a patient was
diagnosed with pneumonia by combining respiratory symptoms and signs, positive
tests for sputum or bronchoalveolar lavage fluid culture, positive chest X-ray
or CT findings, elevated microorganism-associated serum markers including
procalcitonin, (1-3)-β-D-glucan or virus DNA. When it was difficult to
differentiate between infection and lupus activity in patients with negative
culture tests, treatment response to antimicrobial therapy was considered by the
treating physician to confirm the diagnosis of infection.<sup><xref rid="bibr16-1759720X19885559" ref-type="bibr">16</xref>
</sup>
</p>
<p>For every patient suspected of viral infection, possible organ-specific
manifestations were comprehensively evaluated, including pneumonia (interstitial
lung disease), hepatitis (elevated bilirubin, liver enzyme levels or both, and
absence of any other documented cause), retinitis (confirmed by an
ophthalmologist). In patients clinically suspected of viral infection,
cytomegalovirus (CMV) DNA and Epstein–Barr virus (EBV) DNA were then tested in
serum samples by quantitative PCR-based techniques. Positive CMV or EBV DNA
tests combined with objective findings of at least one infected organ were
required to establish the diagnosis of viral infection.<sup><xref rid="bibr19-1759720X19885559" ref-type="bibr">19</xref>
,<xref rid="bibr20-1759720X19885559" ref-type="bibr">20</xref>
</sup>
</p>
<p>All patients were followed up for at least 3 months or until death. We chose
3 months as the end-point for follow-up because most death events occur within a
short period due to uncontrolled, severe infection or accompanying lupus
activity in this subpopulation.</p>
</sec>
<sec id="section8-1759720X19885559"><title>Data collection</title>
<p>Patient’s demographic, clinical, and laboratory characteristics on admission and
medication history were retrospectively collected as baseline data. The detailed
information about invasive infection (sites and pathogens) were also collected.
The outcome was defined as all-cause death within 3 months since baseline.
According to the outcome data, patients were divided into two groups, deceased
and survivors.</p>
<p>The disease activity at baseline was evaluated by SLE Disease Activity Index 2000 (SLEDAI-2K).<sup><xref rid="bibr21-1759720X19885559" ref-type="bibr">21</xref>
</sup>
Existing tools that have been used to evaluate the severity of infection
including qSOFA and CURB-65 were also assessed as indicated at
baseline.<sup><xref rid="bibr15-1759720X19885559" ref-type="bibr">15</xref>
,<xref rid="bibr16-1759720X19885559" ref-type="bibr">16</xref>
</sup>
</p>
</sec>
<sec id="section9-1759720X19885559"><title>Statistical analysis</title>
<p>Baseline characteristics were described and compared between the deceased and
survivors by univariable analyses followed by Bonferroni correction. The
independent sample Student’s <italic>t</italic>
test, Mann–Whitney
<italic>U</italic>
test, Chi-square (χ<sup>2</sup>
) test, and Fisher’s exact
test were conducted, as appropriate.</p>
<p>The independent predictors for all-cause mortality within 3 months were
determined by multivariable logistic regression analysis. Candidate predictors
for the multivariable regression were selected by expert opinion based on
clinical significance, previous studies, and feasibility.</p>
<p>Independent predictors were then weighted by odds ratio (OR) values and combined
to establish a prediction model for all-cause mortality. Predictive and
discriminatory performance of the new prediction model was examined by applying
Kaplan–Meier survival plot and ROC curve analysis and then compared with qSOFA
and CURB-65.</p>
<p>All above-mentioned statistical analyses were performed using SPSS V.23 (Armonk,
NY, USA) or Graphpad 5.0 (San Diego, CA, USA) software. Significance was defined
as <italic>p</italic>
< 0.05.</p>
</sec>
</sec>
<sec sec-type="results" id="section10-1759720X19885559"><title>Results</title>
<sec id="section11-1759720X19885559"><title>Study population</title>
<p>A total of 130 SLE patients complicated with invasive infection who met the
inclusion criteria were analyzed in our cohort. Of those, a total of 50 (38.5%)
patients died within the 3-month follow-up. The mean follow-up period in the
deceased group was 3.34 ± 2.54 weeks. The numbers of deceased patients in the
1st, 2nd and 3rd months were 38, 9 and 3, respectively. Patients were
predominantly female (91%) with a mean age of 43.0 years on admission. The
median disease duration for SLE on admission was 4.0 years (IQR 0.5–10.0), and
the median disease duration for infection (time between initial symptom
attributed to infection and admission in our center) was 10.0 days (IQR
3.8–15.0). The detailed demographic, clinical, laboratory characteristics, and
medication history at baseline are provided in <xref rid="table1-1759720X19885559" ref-type="table">Table 1</xref>
.</p>
<table-wrap id="table1-1759720X19885559" orientation="portrait" position="float"><label>Table 1.</label>
<caption><p>Patient’s baseline characteristics and univariable comparisons in SLE
patients complicated with invasive infection in the emergency
department.</p>
</caption>
<alternatives><graphic xlink:href="10.1177_1759720X19885559-table1"></graphic>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead><tr><th align="left" rowspan="1" colspan="1">Characteristics</th>
<th align="left" rowspan="1" colspan="1">Whole cohort (<italic>n</italic>
= 130)</th>
<th align="left" rowspan="1" colspan="1">Survivors (<italic>n</italic>
= 80)</th>
<th align="left" rowspan="1" colspan="1">Deceased (<italic>n</italic>
= 50)</th>
<th align="left" rowspan="1" colspan="1"><italic>p</italic>
value</th>
</tr>
</thead>
<tbody><tr><td colspan="5" rowspan="1"><bold>Demographic</bold>
</td>
</tr>
<tr><td rowspan="1" colspan="1">Age on admission (year)</td>
<td rowspan="1" colspan="1">42.6 ± 14.2</td>
<td rowspan="1" colspan="1">40.9 ± 13.3</td>
<td rowspan="1" colspan="1">45.3 ± 15.3</td>
<td rowspan="1" colspan="1">0.099</td>
</tr>
<tr><td rowspan="1" colspan="1">Male sex</td>
<td rowspan="1" colspan="1">12 (9.2)</td>
<td rowspan="1" colspan="1">8 (10.0)</td>
<td rowspan="1" colspan="1">4 (8.0)</td>
<td rowspan="1" colspan="1">0.703</td>
</tr>
<tr><td rowspan="1" colspan="1">Disease duration of SLE (year)</td>
<td rowspan="1" colspan="1">6.6 ± 6.8</td>
<td rowspan="1" colspan="1">6.5 ± 7.2</td>
<td rowspan="1" colspan="1">6.7 ± 6.4</td>
<td rowspan="1" colspan="1">0.538</td>
</tr>
<tr><td rowspan="1" colspan="1">Disease duration of infection (day)</td>
<td rowspan="1" colspan="1">15.0 ± 21.7</td>
<td rowspan="1" colspan="1">14.8 ± 17.3</td>
<td rowspan="1" colspan="1">15.3 ± 27.5</td>
<td rowspan="1" colspan="1">0.284</td>
</tr>
<tr><td colspan="5" rowspan="1"><bold>SLE activity</bold>
</td>
</tr>
<tr><td rowspan="1" colspan="1"> SLEDAI score</td>
<td rowspan="1" colspan="1">9.0 ± 5.9</td>
<td rowspan="1" colspan="1">8.4 ± 5.5</td>
<td rowspan="1" colspan="1">9.9 ± 6.4</td>
<td rowspan="1" colspan="1">0.194</td>
</tr>
<tr><td rowspan="1" colspan="1"> Lupus nephritis</td>
<td rowspan="1" colspan="1">78 (60.0)</td>
<td rowspan="1" colspan="1">44 (55.0)</td>
<td rowspan="1" colspan="1">34 (68.0)</td>
<td rowspan="1" colspan="1">0.141</td>
</tr>
<tr><td rowspan="1" colspan="1"> Neuropsychiatric lupus</td>
<td rowspan="1" colspan="1">27 (20.8)</td>
<td rowspan="1" colspan="1">12 (15.0)</td>
<td rowspan="1" colspan="1">15 (30.0)</td>
<td rowspan="1" colspan="1">0.040</td>
</tr>
<tr><td rowspan="1" colspan="1"> Pulmonary hypertension<sup><xref ref-type="table-fn" rid="table-fn3-1759720X19885559">*</xref>
</sup>
</td>
<td rowspan="1" colspan="1">27 (20.8)</td>
<td rowspan="1" colspan="1">17 (21.3)</td>
<td rowspan="1" colspan="1">10 (20.0)</td>
<td rowspan="1" colspan="1">0.864</td>
</tr>
<tr><td colspan="5" rowspan="1"><bold>Infection site</bold>
</td>
</tr>
<tr><td rowspan="1" colspan="1"> Lung infection</td>
<td rowspan="1" colspan="1">108 (83.1)</td>
<td rowspan="1" colspan="1">65 (81.3)</td>
<td rowspan="1" colspan="1">43 (86.0)</td>
<td rowspan="1" colspan="1">0.482</td>
</tr>
<tr><td rowspan="1" colspan="1"> Blood stream infection</td>
<td rowspan="1" colspan="1">23 (17.7)</td>
<td rowspan="1" colspan="1">9 (11.3)</td>
<td rowspan="1" colspan="1">14 (28.0)</td>
<td rowspan="1" colspan="1">0.015</td>
</tr>
<tr><td colspan="5" rowspan="1"><bold>Laboratory tests</bold>
</td>
</tr>
<tr><td rowspan="1" colspan="1">ESR >20 mm/1 h</td>
<td rowspan="1" colspan="1">98/129 (76.0)</td>
<td rowspan="1" colspan="1">65 (81.3)</td>
<td rowspan="1" colspan="1">33/49 (67.3)</td>
<td rowspan="1" colspan="1">0.073</td>
</tr>
<tr><td rowspan="1" colspan="1">CRP elevation</td>
<td rowspan="1" colspan="1">103 (79.2)</td>
<td rowspan="1" colspan="1">58 (72.5)</td>
<td rowspan="1" colspan="1">45 (90.0)</td>
<td rowspan="1" colspan="1">0.017</td>
</tr>
<tr><td rowspan="1" colspan="1">Lymphocyte count <800/μl</td>
<td rowspan="1" colspan="1">92 (70.8)</td>
<td rowspan="1" colspan="1">50 (62.5)</td>
<td rowspan="1" colspan="1">42 (84.0)</td>
<td rowspan="1" colspan="1">0.009</td>
</tr>
<tr><td rowspan="1" colspan="1">Platelet count <10<sup>5</sup>
/μl</td>
<td rowspan="1" colspan="1">55 (42.3)</td>
<td rowspan="1" colspan="1">28 (35.0)</td>
<td rowspan="1" colspan="1">27 (54.0)</td>
<td rowspan="1" colspan="1">0.033</td>
</tr>
<tr><td rowspan="1" colspan="1">Hypoalbuminemia (<25 g/l)</td>
<td rowspan="1" colspan="1">56 (43.1)</td>
<td rowspan="1" colspan="1">25 (31.3)</td>
<td rowspan="1" colspan="1">31 (62.0)</td>
<td rowspan="1" colspan="1">0.001</td>
</tr>
<tr><td rowspan="1" colspan="1">Hypoglobulinemia (<20 g/l)</td>
<td rowspan="1" colspan="1">12/124 (9.7)</td>
<td rowspan="1" colspan="1">4/77 (5.2)</td>
<td rowspan="1" colspan="1">8/47 (17.0)</td>
<td rowspan="1" colspan="1">0.056</td>
</tr>
<tr><td rowspan="1" colspan="1">Urea >7.6 mmol/l</td>
<td rowspan="1" colspan="1">72/129 (55.8)</td>
<td rowspan="1" colspan="1">35 (43.8)</td>
<td rowspan="1" colspan="1">37/49 (75.5)</td>
<td rowspan="1" colspan="1"><0.001</td>
</tr>
<tr><td rowspan="1" colspan="1">Procalcitonin >0.5 μg/l</td>
<td rowspan="1" colspan="1">58/126 (46.0)</td>
<td rowspan="1" colspan="1">28/77 (36.4)</td>
<td rowspan="1" colspan="1">30/49 (61.2)</td>
<td rowspan="1" colspan="1">0.006</td>
</tr>
<tr><td rowspan="1" colspan="1">(1-3)-β-D-glucan >100 pg/ml</td>
<td rowspan="1" colspan="1">38/121 (31.4)</td>
<td rowspan="1" colspan="1">16/73 (21.9)</td>
<td rowspan="1" colspan="1">22/48 (45.8)</td>
<td rowspan="1" colspan="1">0.006</td>
</tr>
<tr><td colspan="5" rowspan="1"><bold>Medication history</bold>
</td>
</tr>
<tr><td rowspan="1" colspan="1">Maximum prednisone-equivalent dose in the past ⩾60 mg/d</td>
<td rowspan="1" colspan="1">86/126 (68.3)</td>
<td rowspan="1" colspan="1">45/77 (58.4)</td>
<td rowspan="1" colspan="1">41/49 (83.7)</td>
<td rowspan="1" colspan="1">0.003</td>
</tr>
<tr><td rowspan="1" colspan="1">History of immunosuppressant use in the past 6 months<sup><xref ref-type="table-fn" rid="table-fn4-1759720X19885559">$</xref>
</sup>
</td>
<td rowspan="1" colspan="1">78 (60.0)</td>
<td rowspan="1" colspan="1">43 (53.8)</td>
<td rowspan="1" colspan="1">35 (70.0)</td>
<td rowspan="1" colspan="1">0.066</td>
</tr>
<tr><td rowspan="1" colspan="1">History of hydroxychloroquine use</td>
<td rowspan="1" colspan="1">74 (56.9)</td>
<td rowspan="1" colspan="1">50 (62.5)</td>
<td rowspan="1" colspan="1">24 (48.0)</td>
<td rowspan="1" colspan="1">0.104</td>
</tr>
<tr><td colspan="5" rowspan="1"><bold>Comorbidity</bold>
</td>
</tr>
<tr><td rowspan="1" colspan="1">Diabetes</td>
<td rowspan="1" colspan="1">21 (16.2)</td>
<td rowspan="1" colspan="1">10 (12.5)</td>
<td rowspan="1" colspan="1">11 (22.0)</td>
<td rowspan="1" colspan="1">0.152</td>
</tr>
<tr><td rowspan="1" colspan="1">Chronic renal insufficiency</td>
<td rowspan="1" colspan="1">43 (33.1)</td>
<td rowspan="1" colspan="1">23 (28.7)</td>
<td rowspan="1" colspan="1">20 (40.0)</td>
<td rowspan="1" colspan="1">0.185</td>
</tr>
<tr><td rowspan="1" colspan="1">qSOFA score ⩾2<sup><xref ref-type="table-fn" rid="table-fn5-1759720X19885559">‡</xref>
</sup>
</td>
<td rowspan="1" colspan="1">9 (6.9)</td>
<td rowspan="1" colspan="1">0 (0.0)</td>
<td rowspan="1" colspan="1">9 (18.0)</td>
<td rowspan="1" colspan="1"><0.001</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot><fn id="table-fn1-1759720X19885559"><p>Data are presented as mean ± SD for continuous variables and number
(frequency) (%) for categorical variables.</p>
</fn>
<fn id="table-fn2-1759720X19885559"><p><italic>p</italic>
values of univariable comparisons of baseline
characteristics between survivors and deceased are shown
(Chi-squared tests or Fisher’s exact tests were used for categorical
variables and independent sample <italic>t</italic>
tests were used
for continuous variables, as appropriate).</p>
</fn>
<fn id="table-fn3-1759720X19885559"><label>*</label>
<p>Pulmonary hypertension was globally judged on echocardiography by the
treating physician.</p>
</fn>
<fn id="table-fn4-1759720X19885559"><label>$</label>
<p>Immunosuppressant use was defined as treatment with any of
methotrexate, azathioprine, cyclophosphamide, mycophenolate mofetil,
cyclosporine, and rituximab.</p>
</fn>
<fn id="table-fn5-1759720X19885559"><label>‡</label>
<p>The quick Sequential Organ Failure Assessment (qSOFA) score ranges
0–3 points, with 1 point each for systolic hypotension (⩽100 mm Hg),
tachypnea (⩾22/min), or altered mentation. Patients with a score ⩾2
are associated with a greater risk of death or prolonged intensive
care unit stay.</p>
</fn>
<fn id="table-fn6-1759720X19885559"><p>CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; qSOFA,
quick Sequential Organ Failure Assessment; SD, standard deviation;
SLE, Systemic Lupus Erythematosus; SLEDAI, Systemic Lupus
Erythematosus Disease Activity Index.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="section12-1759720X19885559"><title>Data on invasive infection</title>
<p>A total of 102 (78.5%) patients had evidence of pathogens. A total of 28 (21.5%)
patients were clinically diagnosed as having an invasive infection by the
treating physician, all of which were pneumonia.</p>
<p>Among the patients with positive microbiology, we found that 58 (56.9%) patients
had bacterial infections, 49 (48.0%) patients had fungal infections, 13 (12.7%)
patients had viral infections, 7 (6.9%) patients had mycobacterium infections,
and 25 (24.5%) patients had mixed infections (more than 1 species).</p>
<p>The most frequent bacteria species were <italic>Staphylococcus</italic>
(15.7%),
<italic>Klebsiella pneumoniae</italic>
(10.8%), <italic>Escherichia
coli</italic>
(9.8%) and <italic>Pseudomonas aeruginosa</italic>
(4.9%). The
dominant fungal species were <italic>Candida</italic>
(27.5%),
<italic>Aspergillus</italic>
(9.8%), <italic>Cryptococcus
neoformans</italic>
(4.9%), and <italic>Pneumocystis jeroveci</italic>
(2.9%). The remaining pathogens included CMV (10.8%), <italic>Mycobacterium
tuberculosis</italic>
(6.9%), EBV (3.9%), and <italic>Nocardia</italic>
(2.9%).</p>
<p>A total of 108 (83.1%) patients had lung infections, 23 (17.7%) patients had
blood stream infections, and 9 (6.9%) patients had a central nervous system
infection. These were the three most frequent infection sites in our cohort,
followed by gastrointestinal (4.6%), urinary tract (4.6%), and joint (3.8%)
infections, respectively.</p>
</sec>
<sec id="section13-1759720X19885559"><title>Univariable analysis</title>
<p>Results of univariable comparison of baseline parameters between survivors and
deceased are summarized in <xref rid="table1-1759720X19885559" ref-type="table">Table 1</xref>
. Deceased patients had a higher incidence of
neuropsychiatric lupus, blood stream infections, qSOFA score ⩾2, and maximum
prednisone dose in the past ⩾60 mg/d. C-reactive protein elevation, lymphocyte
count < 800/μl, platelet count <10<sup>5</sup>
/μl, hypoalbuminemia
(<25 g/l), urea >7.6 mmol/l, procalcitonin >0.5 μl/l, and
(1-3)-β-D-glucan >100 pg/ml were also observed more frequently in deceased
patients. By using the Bonferroni correction, the modified critical
<italic>p</italic>
value (α) was determined as 0.002. Significantly higher
incidence of qSOFA score ⩾2, hypoalbuminemia (<25 g/l), and urea
>7.6 mmol/l were found in the deceased than survivors
(<italic>p</italic>
⩽ 0.001).</p>
</sec>
<sec id="section14-1759720X19885559"><title>Multivariable analysis</title>
<p>In the final multivariable logistic regression model, which included 10
clinically meaningful candidate predictors, lymphocyte count <800/μl
(OR = 3.52, CI 95% 1.13–11.03), urea >7.6 mmol/l (OR = 3.75, CI 95%
1.25–11.22), maximum prednisone dose in the past ⩾60 mg/d (OR = 4.52, CI 95%
1.39–14.65), q<bold>S</bold>
OFA score (OR = 5.40, CI 95% 2.20–13.27), and age on
admission (OR = 1.05, CI 95% 1.01–1.09) were independently predictive for
3-month all-cause mortality in emergency lupus patients complicated with
invasive infection. However, the history of hydroxychloroquine use (OR = 0.30,
CI 95% 0.11–0.84) was protective (<xref rid="table2-1759720X19885559" ref-type="table">Table 2</xref>
).</p>
<table-wrap id="table2-1759720X19885559" orientation="portrait" position="float"><label>Table 2.</label>
<caption><p>Multivariable logistic regression model for 3-month all-cause death in
SLE patients complicated with invasive infection in the emergency
department.</p>
</caption>
<alternatives><graphic xlink:href="10.1177_1759720X19885559-table2"></graphic>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead><tr><th align="left" rowspan="1" colspan="1">Predictors</th>
<th align="left" rowspan="1" colspan="1"><italic>p</italic>
value</th>
<th align="left" rowspan="1" colspan="1">OR</th>
<th align="left" rowspan="1" colspan="1">CI 95%</th>
</tr>
</thead>
<tbody><tr><td rowspan="1" colspan="1"><bold>Age</bold>
</td>
<td rowspan="1" colspan="1">0.029</td>
<td rowspan="1" colspan="1">1.05</td>
<td rowspan="1" colspan="1">1.01–1.09</td>
</tr>
<tr><td rowspan="1" colspan="1">SLEDAI score</td>
<td rowspan="1" colspan="1">0.495</td>
<td rowspan="1" colspan="1">1.03</td>
<td rowspan="1" colspan="1">0.95–1.12</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>Lymphocyte count <800/</bold>
μl</td>
<td rowspan="1" colspan="1">0.031</td>
<td rowspan="1" colspan="1">3.52</td>
<td rowspan="1" colspan="1">1.13–11.03</td>
</tr>
<tr><td rowspan="1" colspan="1">Hypoalbuminemia (<25 g/l)</td>
<td rowspan="1" colspan="1">0.352</td>
<td rowspan="1" colspan="1">1.62</td>
<td rowspan="1" colspan="1">0.59–4.46</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>Urea >7.6</bold>
<bold>mmol/l</bold>
</td>
<td rowspan="1" colspan="1">0.018</td>
<td rowspan="1" colspan="1">3.75</td>
<td rowspan="1" colspan="1">1.25–11.22</td>
</tr>
<tr><td rowspan="1" colspan="1">Blood stream infection</td>
<td rowspan="1" colspan="1">0.112</td>
<td rowspan="1" colspan="1">3.01</td>
<td rowspan="1" colspan="1">0.77–11.67</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>Maximum prednisone dose in the past
⩾60</bold>
<bold>mg/d</bold>
</td>
<td rowspan="1" colspan="1">0.012</td>
<td rowspan="1" colspan="1">4.52</td>
<td rowspan="1" colspan="1">1.39–14.65</td>
</tr>
<tr><td rowspan="1" colspan="1">History of immunosuppressant use in the past 6 months</td>
<td rowspan="1" colspan="1">0.063</td>
<td rowspan="1" colspan="1">2.85</td>
<td rowspan="1" colspan="1">0.95–8.56</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>History of hydroxychloroquine use</bold>
</td>
<td rowspan="1" colspan="1">0.022</td>
<td rowspan="1" colspan="1">0.30</td>
<td rowspan="1" colspan="1">0.11–0.84</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>qSOFA score</bold>
</td>
<td rowspan="1" colspan="1"><0.001</td>
<td rowspan="1" colspan="1">5.40</td>
<td rowspan="1" colspan="1">2.20–13.27</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot><fn id="table-fn7-1759720X19885559"><p>Predictors highlighted in bold are significantly associated with
all-cause mortality.</p>
</fn>
<fn id="table-fn8-1759720X19885559"><p>OR, odds ratio; qSOFA, quick Sequential Organ Failure Assessment;
SLE, systemic Lupus Erythematosus; SLEDAI, Systemic Lupus
Erythematosus Disease Activity Index.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="section15-1759720X19885559"><title>Establishment of risk prediction model</title>
<p>We combined the six independent predictors to make a risk prediction model for
all-cause mortality and denominated this new scoring model as LUPHAS by
combining the initials of the predictors. All predictors were weighted by OR
values, giving a LUPHAS score ranging from 3 to 22 (<xref rid="table3-1759720X19885559" ref-type="table">Table 3</xref>
).</p>
<table-wrap id="table3-1759720X19885559" orientation="portrait" position="float"><label>Table 3.</label>
<caption><p>Establishment of the LUPHAS scoring system.</p>
</caption>
<alternatives><graphic xlink:href="10.1177_1759720X19885559-table3"></graphic>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead><tr><th rowspan="1" colspan="1"></th>
<th align="left" rowspan="1" colspan="1">Predictors</th>
<th align="left" rowspan="1" colspan="1">Points</th>
</tr>
</thead>
<tbody><tr><td rowspan="1" colspan="1"><bold>L</bold>
</td>
<td rowspan="1" colspan="1"><bold>L</bold>
ymphocyte count</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">⩾800/μl</td>
<td rowspan="1" colspan="1">1</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"><800/μl</td>
<td rowspan="1" colspan="1">4</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>U</bold>
</td>
<td rowspan="1" colspan="1"><bold>U</bold>
rea</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">⩽7.6 mmol/l</td>
<td rowspan="1" colspan="1">1</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">>7.6 mmol/l</td>
<td rowspan="1" colspan="1">4</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>P</bold>
</td>
<td rowspan="1" colspan="1">Maximum <bold>P</bold>
rednisone dose in the past</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1"><60 mg/d</td>
<td rowspan="1" colspan="1">1</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">⩾60 mg/d</td>
<td rowspan="1" colspan="1">5</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>H</bold>
</td>
<td rowspan="1" colspan="1">History of <bold>H</bold>
ydroxychloroquine use</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">Yes</td>
<td rowspan="1" colspan="1">−3</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">No</td>
<td rowspan="1" colspan="1">0</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>A</bold>
</td>
<td rowspan="1" colspan="1"><bold>A</bold>
ge, year</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">⩽20</td>
<td rowspan="1" colspan="1">1</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">21–40</td>
<td rowspan="1" colspan="1">2</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">41–60</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">>60</td>
<td rowspan="1" colspan="1">4</td>
</tr>
<tr><td rowspan="1" colspan="1"><bold>S</bold>
</td>
<td rowspan="1" colspan="1">q<bold>S</bold>
OFA score</td>
<td rowspan="1" colspan="1"></td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">0</td>
<td rowspan="1" colspan="1">0</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">1</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr><td rowspan="1" colspan="1"></td>
<td rowspan="1" colspan="1">⩾2</td>
<td rowspan="1" colspan="1">6</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot><fn id="table-fn9-1759720X19885559"><p>LUPHAS score was established by combining independent predictors,
weighted by odds ratio values.</p>
</fn>
<fn id="table-fn10-1759720X19885559"><p>qSOFA, quick Sequential Organ Failure Assessment.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>On the basis of the LUPHAS scoring system, apart from five patients without valid
data for dose of prednisone, all patients could be categorized into three
groups: low-risk (score 3–9), medium-risk (score 10–15), and high-risk (score
16–22). The mortalities were 4.9% (2/41), 45.9% (28/61), and 78.3% (18/23) in
low-risk, medium-risk and high-risk patients, respectively, compared with the
overall mortality of 38.4% (48/125).</p>
</sec>
<sec id="section16-1759720X19885559"><title>Evaluation of LUPHAS scoring system</title>
<p>The subsequent Kaplan–Meier survival analysis indicated that the probability of
all-cause death was significantly higher for medium-risk patients than low-risk
patients (<italic>p</italic>
< 0.0001).In addition, the high-risk patients
had an even higher probability of all-cause death than medium-risk patients
(<italic>p</italic>
= 0.005), according to the LUPHAS score (<xref ref-type="fig" rid="fig1-1759720X19885559">Figure 1</xref>
).</p>
<fig id="fig1-1759720X19885559" orientation="portrait" position="float"><label>Figure 1.</label>
<caption><p>Kaplan–Meier survival plot for time to all-cause death during follow-up
depending on the LUPHAS risk categories.</p>
</caption>
<graphic xlink:href="10.1177_1759720X19885559-fig1"></graphic>
</fig>
<p>ROC curve analysis indicated that the LUPHAS score could effectively predict
all-cause mortality in this population (area under the curve [AUC] = 0.86, CI
95% 0.79–0.92), with a sensitivity of 79.2% and a specificity of 80.5%.</p>
<p>In addition, the LUPHAS score performed better than the qSOFA score [area under
the curve (AUC) = 0.69, CI 95% 0.59–0.78] (sensitivity 64.0%, specificity 67.5%)
when predicting the short-term mortality of emergency lupus patients with
invasive infection in our cohort.</p>
<p>Similarly, in the subgroup of patients with lung infection
(<italic>n</italic>
= 108), the discriminatory performance of the LUPHAS score
was also superior than the CURB-65 score (AUC = 0.69, CI 95% 0.59–0.80)
(sensitivity 39.5%, specificity 87.7%) (<xref rid="table4-1759720X19885559" ref-type="table">Table 4</xref>
).</p>
<table-wrap id="table4-1759720X19885559" orientation="portrait" position="float"><label>Table 4.</label>
<caption><p>Discriminatory performance of LUPHAS score compared with qSOFA score and
CURB-65 score by receiver operating characteristic (ROC) curve
analysis.</p>
</caption>
<alternatives><graphic xlink:href="10.1177_1759720X19885559-table4"></graphic>
<table frame="hsides" rules="groups"><colgroup span="1"><col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead><tr><th align="left" rowspan="1" colspan="1">Model</th>
<th align="left" rowspan="1" colspan="1">AUROC (CI 95%)</th>
<th align="left" rowspan="1" colspan="1">Sensitivity</th>
<th align="left" rowspan="1" colspan="1">Specificity</th>
</tr>
</thead>
<tbody><tr><td colspan="4" rowspan="1">Total population (<italic>n</italic>
= 130)</td>
</tr>
<tr><td rowspan="1" colspan="1"> LUPHAS</td>
<td rowspan="1" colspan="1">0.86 (0.79–0.92)</td>
<td rowspan="1" colspan="1">79.2%</td>
<td rowspan="1" colspan="1">80.5%</td>
</tr>
<tr><td rowspan="1" colspan="1"> qSOFA</td>
<td rowspan="1" colspan="1">0.69 (0.59–0.78)</td>
<td rowspan="1" colspan="1">64.0%</td>
<td rowspan="1" colspan="1">67.5%</td>
</tr>
<tr><td colspan="4" rowspan="1">Subgroup of lung infection
(<italic>n</italic>
= 108)</td>
</tr>
<tr><td rowspan="1" colspan="1"> LUPHAS</td>
<td rowspan="1" colspan="1">0.84 (0.76–0.92)</td>
<td rowspan="1" colspan="1">78.0%</td>
<td rowspan="1" colspan="1">79.4%</td>
</tr>
<tr><td rowspan="1" colspan="1"> CURB-65</td>
<td rowspan="1" colspan="1">0.69 (0.59–0.80)</td>
<td rowspan="1" colspan="1">39.5%</td>
<td rowspan="1" colspan="1">87.7%</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot><fn id="table-fn11-1759720X19885559"><p>The quick Sequential Organ Failure Assessment (qSOFA) score ranges
0–3 points, with 1 point each for systolic hypotension (⩽100 mm Hg),
tachypnea (⩾22/min), or altered mentation. Patients with a score ⩾2
are associated with a greater risk of death or prolonged intensive
care unit stay.</p>
</fn>
<fn id="table-fn12-1759720X19885559"><p>CURB-65 is a validated clinical assessment tool for predicting
mortality in patients with community-acquired pneumonia, including
confusion, urea >7 mmol/l, respiratory rate >30/min, low blood
pressure (systolic <90 mm Hg, or diastolic <60 mm Hg, or both)
and age ⩾65. Patients with a score ⩾2 are associated with a higher
mortality and hospitalization needs to be considered.</p>
</fn>
<fn id="table-fn13-1759720X19885559"><p>AUROC, area under receiver operating characteristic curve; qSOFA, the
quick Sequential Organ Failure Assessment.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="section17-1759720X19885559"><title>Discussion</title>
<p>In this study, based on a large emergency cohort of SLE complicated with invasive
infection, we reported an extremely high short-term all-cause mortality rate
(38.5%), highlighting that special attention should be paid to these
immunocompromised patients. Several independent predictors were successfully
identified and a convenient mortality risk prediction model LUPHAS score was
established. To the best of our knowledge, the LUPHAS score is the first available
mortality risk prediction model in emergency patients with SLE and invasive
infection. The LUPHAS model integrates several clinical and laboratory parameters
including age, vital sign, medication history, and routine blood tests. All of these
can be easily obtained in an appropriate manner when a lupus patient presents in the
emergency department. Therefore, this applicable tool could assist the emergency
physicians and rheumatologists to identify those patients at higher risk and to
provide efficient triage for them, as well as appropriate management. We recommend
the patients at high-risk according to their LUPHAS score should be referred to an
intensive care unit as soon as possible due to the extremely high mortality (<xref ref-type="fig" rid="fig2-1759720X19885559">Figure 2</xref>
).</p>
<fig id="fig2-1759720X19885559" orientation="portrait" position="float"><label>Figure 2.</label>
<caption><p>Recommended triage flow chart for SLE patients complicated with invasive
infection admitted into the emergency department.</p>
</caption>
<graphic xlink:href="10.1177_1759720X19885559-fig2"></graphic>
</fig>
<p>Among the predictors reported in our cohort, older age and previous exposure of
high-dose glucocorticoids are the well-known risk factors for serious infection in
SLE. In addition, previous use of antimalarial drugs has proved to be protective for
infection in multiple studies. Of interest, the decreased peripheral lymphocyte
count and elevated urea concentration were first identified as predictive for
all-cause mortality in this population. The former parameters, easily accessed
<italic>via</italic>
complete blood count tests, could be a crude surrogate
marker for the cellular immune function. The latter could be a composite indicator
for renal function and catabolism, which is frequently elevated in critical
patients. As expected, the qSOFA score, a common evaluation tool widely used in the
field of emergency and critical care, was determined to be an independent predictor
for mortality in our cohort. To the best of our knowledge, such composite parameters
reflecting the vital signs have never been assessed in previous similar studies of
SLE patients. In addition, we weighted the hazards of various risk factors so that a
quantified scoring system could be established. This novel risk-staging system would
be an applicable tool for fast evaluation and stratification of patients.</p>
<p>Using ROC curve analysis, the LUPHAS score was shown to be capable of predicting
short-term all-cause mortality and performed better than the qSOFA score alone.
Because the lungs are the most frequent site of infection in lupus patients, the
LUPHAS score provided a superior evaluation tool to the CURB-65 score in this
specific subpopulation. Our data reinforced that invasive infection in SLE is a
complicated, heterogeneous clinical condition that requires a multidimensional
prediction tool.</p>
<p>Our ‘Emergency-Rheum’ multidisciplinary approach attempted to integrate the first-aid
skill of emergency physicians and the specialized knowledge of rheumatologists. This
system could help to identify severe clinical conditions in patients with rheumatic
diseases, therefore, the appropriate interventions could be implemented more
appropriately and efficiently, as we have shown previously when managing patients
with SLE-associated pulmonary hypertension in the emergency setting.<sup><xref rid="bibr22-1759720X19885559" ref-type="bibr">22</xref>
,<xref rid="bibr23-1759720X19885559" ref-type="bibr">23</xref>
</sup>
Further
prospective studies are required to address the question whether this approach,
combined with a LUPHAS-guided triage protocol could eventually improve the
short-term prognosis for patients with SLE invasive infections.</p>
<p>There are several limitations in our study. First, due to the retrospective study
design, there is missing data issues that frequently presented in the real-life
cohort. The parameters with >10% missing values (e.g. lymphocyte subset count by
flow cytometer, and levels) could rarely be considered for multivariable analysis.
Therefore, we could have missed some potential predictors. Second, as a result of
the observational design, we did not evaluate the effect of antimicrobial treatment
on the outcomes. However, antimicrobial therapy always depends on the individual
disease severity and specific microorganism and it is, therefore, difficult to
accurately exclude the influence of treatment in an unselected heterogeneous cohort.
In addition, there is a meaningful treatment-by-indication error in observational
studies, making interpretation of the results difficult. Third, we have already
verified the new prediction model in our prospective validation cohort. However,
external validation with qualified data from other centers is required to confirm
our findings. Finally, it is worth highlighting that the prediction model in our
study was derived from a relatively heterogeneous cohort in the emergency condition,
with mixed pathogen-based and clinical diagnoses, and a combination of various
infection types. The results should not be over-interpreted and extrapolated to the
overall lupus population.</p>
</sec>
<sec sec-type="conclusions" id="section18-1759720X19885559"><title>Conclusion</title>
<p>In this large emergency cohort of lupus patients complicated with invasive infection,
an impressively high short-term all-cause mortality was recorded, highlighting that
special attention should be paid to these patients. Several independent predictors
for all-cause mortality were successfully identified in our study. The real-world
evidence-based on the LUPHAS score could be a promising tool for the fast evaluation
and risk stratification of this population in clinical practice.</p>
</sec>
</body>
<back><fn-group><fn fn-type="financial-disclosure"><p><bold>Funding:</bold>
Shuang Ye has received funding from Shanghai Shenkang promoting project for
clinical skills of major diseases (16CR1013A). All other authors received no
financial support for the research, authorship, and/or publication of this
article.</p>
</fn>
<fn fn-type="COI-statement"><p><bold>Conflict of interest statement:</bold>
The authors declare that they have no conflicts of interest.</p>
</fn>
<fn fn-type="other"><p><bold>Ethical approval:</bold>
The retrospective study protocol was approved by the ethics committees of Renji
Hospital.</p>
</fn>
<fn fn-type="other"><p><bold>ORCID iDs:</bold>
Wanlong Wu <inline-graphic xlink:href="10.1177_1759720X19885559-img1.jpg"></inline-graphic>
<ext-link ext-link-type="uri" xlink:href="https://orcid.org/0000-0003-2727-7950">https://orcid.org/0000-0003-2727-7950</ext-link>
</p>
<p>Yi Chen <inline-graphic xlink:href="10.1177_1759720X19885559-img1.jpg"></inline-graphic>
<ext-link ext-link-type="uri" xlink:href="https://orcid.org/0000-0001-6642-7435">https://orcid.org/0000-0001-6642-7435</ext-link>
</p>
</fn>
</fn-group>
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