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\begin{document}

\title{Effect of anti-rheumatic drugs on the clinical outcome of autoimmune
rheumatic diseases patients infected with COVID-19: A systemic review
and meta-analysis}



\author[1]{Qin-Yi Su}%
\author[1]{Sheng-Xiao Zhang}%
\author[1]{Jing Luo}%
\author[1]{Ting Cheng}%
\author[2]{He-Yi Zhang}%
\author[2]{Yan Zhang}%
\author[2]{Bing-Ru Zhao}%
\author[3]{Chong Gao}%
\author[1]{Cai-Hong Wang}%
\affil[1]{Second Hospital of Shanxi Medical University}%
\affil[2]{Shanxi Medical University Jinci College}%
\affil[3]{Brigham and Women's Hospital Department of Pathology}%


\vspace{-1em}



  
  \date{May 18, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
The immune deregulation and disease-modifying anti-rheumatic drugs
(DMARDs) make patients with autoimmune rheumatic diseases (ARD) more
susceptible to infection. We aimed to investigate the prevalence and
clinical outcome of COVID-19 in ARD patients with different treatments.
PubMed, Embase, Medline, Cochrane Library, Web of Science were searched
to identify the relevant evidence up to March 20, 2023. The overall
prevalence of COVID-19 in ARD patients was 0.061. The ARD patients with
glucocorticoids (GC) treatment had the highest
prevalence{[}0.088(95\%CI:0.065-0.110){]} compared to biological (b)
DMARDs{[}0.041(95\%CI:0.031-0.051){]}, conventional synthetic (cs)
DMARDs{[}0.055(95\%CI:0.043-0.067){]}, and anti-TNF
therapy{[}0.029(95\%CI:0.003-0.056){]}. In contrast, those receiving
anti-TNF therapy had the lowest prevalence. The overall hospitalization
rate, ICU admission rate, and mortality of ARD patients due to COVID-19
were 0.402(95\%CI:0.330-0.476), 0.077(95\%CI:0.051-0.107), and
0.073(95\%CI:0.046-0.104). Using bDMARDs had lower hospitalization
rates{[}0.216(95\%CI:0.147-0.286){]}, ICU admission
rates{[}0.010(95\%CI:0.000-0.037){]}, and
mortality{[}0.039(95\%CI:0.007-0.087){]}. Regression analysis showed a
significant negative relationship between bDMARDs monotherapy and ICU
admission rates(regression coefficient:-0.156, 95\%CI:-0.260 - -0.051,
P=0.006). Patients using csDMARDs had higher hospitalization
rates{[}0.607(95\%CI:0.450-0.755){]}, ICU admission
rates{[}0.055(95\%CI:0.037-0.075){]}, and
mortality{[}0.074(95\%CI:0.041-0.114){]}. Patients using GC had a higher
hospitalization rate{[}0.703(95\%CI:0.449-0.910){]}, higher ICU
admission rate{[}0.094(95\%CI:0.046-0.152){]}, and lower
mortality{[}0.070(95\%CI:0.033-0.114){]}. Regression analysis showed a
significant positive correlation between GC monotherapy and
hospitalization(regression coefficient:0.484, 95\%CI:0.146-0.822,
P=0.006). For ARD patients, csDMARDs were associated with disease
severity in COVID-19, bDMARDs were associated with a reduced risk of
severe disease, and GC was effective in patients with severe
COVID-19-related respiratory failure.%
\end{abstract}%



\sloppy


\textbf{Effect of anti-rheumatic drugs on the clinical outcome of
autoimmune rheumatic diseases patients infected with COVID-19: A
systemic review and meta-analysis}

Qin-Yi Su\textsuperscript{1,2,3\#}, Sheng-Xiao
Zhang\textsuperscript{1,2,3\#}, Jing Luo\textsuperscript{1,2,3}, Ting
Cheng\textsuperscript{1,2,3}, He-Yi Zhang\textsuperscript{2,3}, Yan
Zhang\textsuperscript{2,3}, Bing-Ru Zhao\textsuperscript{2,3}, Chong
Gao\textsuperscript{4}, Cai-Hong Wang\textsuperscript{1,2,3*}

\textbf{1} Department of Rheumatology, The Second Hospital of Shanxi
Medical University, Taiyuan, Shanxi, China. \textbf{2} Shanxi Provincial
Key Laboratory of Rheumatism Immune Microecology, Taiyuan, Shanxi,
China. \textbf{3} Key Laboratory of Cellular Physiology at Shanxi
Medical University, Ministry of Education, Taiyuan, China.
\textbf{4}Brigham and Women's Hospital, Harvard Medical School,
Department of Pathology, Boston, United States of America

\#Qin-Yi Su and Sheng-Xiao Zhang contributed to the work equally and
should be regarded as co-first authors.

* Cai-Hong Wang is corresponding~authors.

*Cai-Hong Wang: Phone number: +8613603515399; E-mail address:
snwch@sina.com;Present address: 382, Wuyi Road, Taiyuan, Shanxi

\textbf{Data availability statement}

All data relevant to the study are included in the article or uploaded
as Supporting Information. The data are available by accessing the
published studies listed in supplementary material S2.

\textbf{Funding statement}

This work was supported by the National Science Foundation of China
(No.82001740), the National Social Science Fund of China (21BTQ050), the
National Natural Science Foundation of China (No. 81971543), Natural
Science Foundation of China (No. 81471618), Key Research and Development
(R\&D) Projects of Shanxi Province (201803D31119), and Four ``Batches''
Innovation Project of Invigorating Medical through Science and
Technology of Shanxi Province(NO 2022XM05).

\textbf{Conflict of interest disclosure}

The authors declare they have no competing interests.

\textbf{Ethics approval statement}

Not Applicable.

\textbf{Patient consent statement}

Not Applicable.

\textbf{Abstract:}

The immune deregulation and disease-modifying anti-rheumatic drugs
(DMARDs) make patients with autoimmune rheumatic diseases~(ARD) more
susceptible to infection. We aimed to investigate the prevalence and
clinical outcome of COVID-19 in ARD patients with different treatments.
PubMed, Embase, Medline, Cochrane Library, Web of Science were searched
to identify the relevant evidence up to March 20, 2023. The overall
prevalence of COVID-19 in ARD patients was 0.061. The ARD patients with
glucocorticoids (GC) treatment had the highest
prevalence{[}0.088(95\%CI:0.065-0.110){]} compared to biological (b)
DMARDs{[}0.041(95\%CI:0.031-0.051){]}, conventional synthetic (cs)
DMARDs{[}0.055(95\%CI:0.043-0.067){]}, and anti-TNF
therapy{[}0.029(95\%CI:0.003-0.056){]}. In contrast, those receiving
anti-TNF therapy had the lowest prevalence. The overall hospitalization
rate, ICU admission rate, and mortality of ARD patients due to COVID-19
were 0.402(95\%CI:0.330-0.476), 0.077(95\%CI:0.051-0.107), and
0.073(95\%CI:0.046-0.104). Using bDMARDs had lower hospitalization
rates{[}0.216(95\%CI:0.147-0.286){]}, ICU admission
rates{[}0.010(95\%CI:0.000-0.037){]}, and
mortality{[}0.039(95\%CI:0.007-0.087){]}. Regression analysis
showed~a~significant negative relationship between bDMARDs monotherapy
and ICU admission rates(regression coefficient:-0.156, 95\%CI:-0.260 -
-0.051, P=0.006). Patients using csDMARDs had higher hospitalization
rates{[}0.607(95\%CI:0.450-0.755){]}, ICU admission
rates{[}0.055(95\%CI:0.037-0.075){]}, and
mortality{[}0.074(95\%CI:0.041-0.114){]}. Patients using GC had a higher
hospitalization rate{[}0.703(95\%CI:0.449-0.910){]}, higher ICU
admission rate{[}0.094(95\%CI:0.046-0.152){]}, and lower
mortality{[}0.070(95\%CI:0.033-0.114){]}. Regression analysis
showed~a~significant positive correlation between GC monotherapy and
hospitalization(regression coefficient:0.484, 95\%CI:0.146-0.822,
P=0.006). For ARD patients, csDMARDs were associated with disease
severity in COVID-19, bDMARDs were associated with a reduced risk of
severe disease, and GC was effective in patients with severe
COVID-19-related respiratory failure.

\textbf{Keywords:} Coronavirus; Epidemiology; Immunity/Immunization



\textbf{1 Introduction}

The pandemic of novel coronavirus disease 2019(COVID-19) caused by the
emerging severe acute respiratory syndrome coronavirus 2(sars-cov-2) has
become a global health crisis(WHO, 2020b), leading to a large number of
infections and deaths\textsuperscript{1}. By March 20, 2023, over 608
million confirmed cases, including 6.50 million deaths, were
reported\textsuperscript{2}. Patients with autoimmune rheumatic
diseases(ARD) are frequently treated with immunosuppressive drugs, which
raises concern for infectious complications, placing patients and
physicians at a crossroads concerning the continuation or cessation of
these disease-modifying therapies.

Nowadays,~more~attention~has been paid to the impact of
disease-modifying antirheumatic drugs(DMARDs) on the prevalence of
COVID-19 and clinical outcomes in ARD patients. Some~studies~reported
fewer patients on low-or-medium-dose glucocorticoid(GC) therapy had
severe COVID-19 conditions compared to those without GC before the
COVID-19 diagnosis\textsuperscript{3}. Other studies found the use of
systemic glucocorticoids(sGC)(\textgreater{} 5 mg/day of prednisone) was
significantly associated with hospital admission caused by sars-cov-2
infection\textsuperscript{4}. In some registry studies, patients with
baseline TNF inhibitor therapy had lower odds of severe COVID-19
outcomes\textsuperscript{5-7}. Still, the risk of infection observed in
rheumatoid arthritis(RA) patients treated with biologic DMARDs(bDMARDs),
including TNF inhibitor therapy, was generally considered slightly
higher(from 1.5- up to 2-fold) compared with conventional synthetic
DMARDs(csDMARDs)\textsuperscript{8}. Each study or registry has a
limited sample size, and the analysis is not comprehensive. Therefore,
there is a need to integrate findings across studies to better
understand the risk of COVID-19 in patients with ARD.

This study aimed to determine the prevalence of COVID-19 in ARD patients
and investigate the effect of anti-rheumatic therapy on the clinical
outcome of ARD patients with COVID-19 to guide clinical treatment
better.

\textbf{2 Materials and Methods}

This systematic meta-analysis was~conducted by the Preferred Reporting
Items for Systematic Reviews and~Meta-Analyses
(PRISMA)~reporting~guidelines and registered on the International
Prospective Register of Systematic Reviews (PROSPERO) trial registry
(CRD42021292364)\textsuperscript{9}.

\textbf{2.1 Data sources and search strategy}

We searched for relevant studies published from inception to March 20,
2023, on CBM, CNKI, China Science and Technology Journal Database,
WanFang Data, PubMed, Embase, Web of Science, Cochrane Library, and
Medline, with no restrictions on publication language or primary
outcome. The articles were retrieved using a combination of subject
terms~and free words. The relevant papers were identified using Medical
Subject Headings (MeSH) terms: ``COVID-19'' and related free words. To
conduct subgroup analyses with each diagnosis, we also used the MeSH
terms for each specific disease, including ``ARDs'', ``Arthritis,
Rheumatoid'', ``Lupus Erythematosus, Systemic'', ``Sjogren's Syndrome'',
``Antiphospholipid Syndrome'', ``Spondylitis, Ankylosing'', ``Arthritis,
Psoriatic'', ``Vasculitis'', ``Gout'', ``Connective Tissue Diseases'',
``Scleroderma, Systemic''. In addition, we manually searched the
reference lists of published systematic reviews and original articles.
The search strategy were described in~Supplementary Material p1.

\textbf{2.2 Study selection and data extraction}

The meta-analysis of the results in original studies conducted on humans
that had the following MeSH terms and free words in the title or
abstract: `COVID-19' AND `ARDs' or `Arthritis, Rheumatoid' or `Lupus
Erythematosus, Systemic' or `Sjogren's Syndrome' or `Antiphospholipid
Syndrome' or `Spondylitis, Ankylosing' or `Arthritis, Psoriatic' or
`Vasculitis' or `Gout' or `Connective Tissue Diseases' or `Scleroderma,
Systemic'.

The meta-analysis excluded studies that were non-original, multiple
reports of the same or overlapping data, or conducted without a
control~group. Single case reports were excluded. As for the clinical
outcomes of COVID-19, studies including only hospitalized or dead
patients were excluded. In addition, we excluded studies with missing
data that could~not~be~obtained~even after~contacting~the~authors.

Two investigators independently assessed the studies based on the
eligibility criteria; a third investigator resolved disagreements. A
data collection sheet was used to record information related to the
first author's name, publication year, study design, study location,
numbers of patients diagnosed with COVID-19, type of medications, age,
gender, prevalence, and clinical outcomes of COVID-19 were collected. We
rated the quality of evidence according to the Grades of Recommendation,
Assessment, Development, and Evaluation (GRADE) approach to assess the
certainty of evidence obtained from the present meta-analysis. Evidence
was graded according to the Newcastle--Ottawa Quality Assessment Scale
(NOS). The quality of studies was assessed using~Stata 12.0.

\textbf{2.3 Statistical analysis}

We used Stata 12.0 software (Stata Corp, College Station, TX, USA) for
the meta-analysis. We undertook a meta-analysis of the prevalence and
clinical outcomes of COVID-19 among individuals with ARDs from
observational by using a random effects model. We evaluated the presence
of heterogeneity across studies by using
the~I\textsuperscript{2}~statistic. An~I\textsuperscript{2}~value of
\textless{}25\% indicates low heterogeneity, 25\%--75\% as moderate
heterogeneity, and \textgreater{}75\% as considerable heterogeneity. A
random effects regression model was used to assess the contributions of
each potential risk factor and medication class to the prevalence and
adverse clinical outcomes. Since the medication regimen of each patient
in the included studies could not be obtained, we regarded the drugs
used by the patients as the drugs used by all patients in the studies,
regardless of the proportion of the number of people who have used the
drugs in the studies. Besides, we regarded the drugs used by more than
50\% of the patients in this study as the primary drugs used by all
patients.

\textbf{3 Results}

\textbf{3.1 Study characteristics}

We identified 14169 citations through the literature search, excluded
1706 titles and abstracts after initial screening. The final 146
full-text articles met all eligibility criteria(Figure 1). To analyze
COVID-19 prevalence, we included 68 observational studies totaling
384582 patients with ARD. For clinical outcomes, we included 85 studies
with 167554 patients with ARD diagnosed with COVID-19. Among these
studies, 56 were included with data from 127984 ARD patients diagnosed
with COVID-19 who have used anti-rheumatic drugs to explore the effect
of anti-rheumatic drugs on clinical outcomes of COVID-19 in autoimmune
diseases. We divided the anti-rheumatic medications used by patients
into three classes:GC, csDMARDs, and bDMARDs, and we also analyzed the
anti-TNF therapy separately. The characteristics and outcomes of the
included studies are summarized in Supplementary Material p2.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} :Flow chart of the assessment of the studies
identified in the meta-analysis.

\textbf{3.2 Prevalence of COVID-19 in autoimmune rheumatic diseases}

A meta-analysis of 68 observational studies, including 384582 patients
with ARDs, showed that the prevalence of COVID-19 was
0.061(95\%CI:0.056-0.066)(Figure 2). In the subgroup analyses, the
prevalences of COVID-19 in RA, systemic lupus erythematosus(SLE),
ankylosing spondylitis(AS), connective tissue disease(CTD) and psoriatic
arthritis(PsA) were 0.028(95\%CI:0.019-0.037),
0.047(95\%CI:0.033-0.061), 0.074(95\%CI:0.039-0.108),
0.058(95\%CI:0.046-0.070) and 0.194(95\%CI:0.090-0.297), respectively RA
patients had the lowest prevalence, and PsA patients had the highest.
The prevalence of COVID-19 in vasculitis and systemic sclerosis(SSc) was
not statistically significant(Figure p3). Heterogeneity was considerable
overall(I\textsuperscript{2}=99.5\%), and in most subgroup analyses,
primarily due to the use of drugs for different diseases and differences
in study sizes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2} :The prevalence of COVID-19 in autoimmune rheumatic
diseases.

\textbf{3.3 Impact of drugs on the prevalence of COVID-19}

Drugs used by patients with ARDs impact the infection of COVID-19(Figure
p4). The regression analysis showed that anti-rheumatic medication did
not contribute to the risk of COVID-19(Figure p5 and p6). From the
perspective of the primary drugs, the prevalence of ARD patients mainly
using GC, bDMARDs, csDMARDs, and anti-TNF therapy was
0.088(95\%CI:0.065-0.110), 0.041(95\%CI:0.031-0.051),
0.055(95\%CI:0.043-0.067) and 0.029(95\%CI:0.003-0.056),
respectively(Figure 3). Patients mainly using GC had the highest
prevalence, and those using anti-TNF therapy had the lowest prevalence.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} :The prevalence of ARD patients after using
anti-rheumatic drugs from the perspective of the main drugs. (A) GC, (B)
bDMARDs, (C) csDMARDs, (D) anti-TNF therapy.

From the perspective of the primary drugs, the prevalences of SLE
patients mainly using GC and csDMARDs were 0.101(95\%CI:0.035-0.166) and
0.095(95\%CI:0.041-0.149), respectively, using GC had a higher
prevalence and using csDMARDs had a lower prevalence. The prevalence of
vasculitis patients using GC was 0.081(95\%CI:0.005-0.157), and using
bDMARDs and csDMARDs were both 0.047(95\%CI:-0.021 to 0.114),
respectively, using GC have the highest prevalence. The prevalence of AS
patients using bDMARDs was 0.074(95\%CI:0.039-0.108). Due to the small
number of studies and the limitations of statistical methods, the
prevalences of RA patients using bDMARDs and csDMARDs were both
0.028(95\%CI:0.019-0.037). Besides, The prevalence of PsA patients using
bDMARDs was 0.289(95\%CI:-0.099 to 0.677), suggesting the prevalence in
PsA was not statistically significant(Figure p7).

\textbf{3.4 Clinical outcomes of COVID-19 in autoimmune rheumatic
diseases}

A meta-analysis of 85 observational studies, including 167554 patients
with ARDs diagnosed with COVID-19, showed that the hospitalization rate
due to COVID-19 was 0.402(95\%CI:0.330-0.476)(Figure 4). Hospitalization
rates of AS, PsA, CTD, SLE, RA, SSc and vasculitis were
0.095(95\%CI:0.042-0.160), 0.148(95\%CI:0.021-0.331),
0.364(95\%CI:0.155-0.603), 0.325(95\%CI:0.125-0.566),
0.321(95\%CI:0.154-0.487), 0.535(95\%CI:0.171-0.884) and
0.627(95\%CI:0.576-0.678)(Figure p8). Notably, patients with vasculitis
had the highest hospitalization rate, and those with AS had the lowest
hospitalization rate. Heterogeneity was considerable
overall(I\textsuperscript{2}=99.8\%) and subgroup
analyses(I\textsuperscript{2}=42.3\%--99.9\%).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} :Clinical outcomes of COVID-19 in autoimmune rheumatic
diseases. (A) Hospitalization rates. (B) Mortality. (C) ICU admission
rate.

The ICU admission rate of COVID-19 patients with ARDs was
0.077(95\%CI:0.051-0.107)(Figure 4). ICU admission rates of RA, SLE and
CTD, PsA were 0.117(95\%CI:0.066-0.168), 0.218(95\%CI:0.099-0.361) and
0.054(95\%CI:0.028-0.081), 0.046(95\%CI:0.007-0.084)(Figure p9). The
mortality due to COVID-19 in ARD patients was
0.073(95\%CI:0.046-0.104)(Figure 4). Mortality rates of RA, SLE, CTD and
vasculitis were 0.049(95\%CI:0.025-0.079), 0.146(95\%CI:0.051-0.241),
0.058(95\%CI:0.016-0.120), and 0.224(95\%CI:0.179-0.269). The mortality
rates in AS, SSC, and gout were 0.000(95\%CI:0.000-0.018),
0.007(95\%CI:0.000-0.241), and 0.099(95\%CI:-0.138 to 0.336), which were
all not statistically significant(Figure p10). SLE patients had the
highest ICU admission rate, and vasculitis patients had the highest
mortality. In contrast, PsA patients had the lowest ICU admission rate,
and RA patients had the lowest mortality.

\textbf{3.5 Impact of drugs on clinical outcomes}

Considering the impact of treatment after infection with sars-cov-2 in
patients with ARDs on clinical outcomes, we conducted a subgroup
analysis. Without considering the proportion of patients using drugs,
regression analysis showed that ARD patients using GC had a higher
hospitalization rate of COVID-19(regression coefficient:0.484,
95\%confidence interval:0.146-0.822, P=0.006), and using bDMARDs had a
lower ICU admission rate of COVID-19(regression coefficient:-0.156,
95\%confidence interval:-0.260 - -0.051, P=0.006). The clinical outcomes
of ARD patients infected with COVID-19 who have used anti-rheumatic
drugs are shown in Supplementary Material Figure p11 and Figure p12.

From the perspective of major drugs, the hospitalization rates of ARD
patients using GC, bDMARDs, csDMARDs, and anti-TNF therapy were
0.703(95\%CI:0.449-0.910), 0.216(95\%CI:0.147-0.286),
0.607(95\%CI:0.450-0.755) and 0.271(95\%CI:0.132-0.409)(Figure 5).
Interestingly, the hospitalization rates of GC and csDMARD patients were
higher than the total rate, and patients with bDMARDs and anti-TNF
therapy were lower hospitalization than total cases. However, regression
analysis showed that regardless of whether the patients mainly used GC
or bDMARDs or csDMARDs or anti-TNF treatment, these treatment regimens
had no effect on the hospitalization rate of patients with
COVID-19(Figure p13).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5} :The effect of anti-rheumatic drugs on the clinical
outcomes of autoimmune rheumatic diseases from the perspective of the
main drugs. (1)The hospitalization rates of ARD patients after using
anti-rheumatic drugs. (A) GC, (B) bDMARDs, (C) csDMARDs, (D) anti-TNF
therapy. (2)The ICU admission rates of ARD patients after using
anti-rheumatic drugs. (E) GC, (F) bDMARDs, (G) csDMARDs, (H) anti-TNF
therapy. (3)The mortality of ARD patients after using anti-rheumatic
drugs. (I) GC, (J) bDMARDs, (K) csDMARDs, (L) anti-TNF therapy.

From the perspective of major drugs, the ICU admission rates of ARD
patients using GC, bDMARDs, csDMARDs, and anti-TNF therapy were
0.094(95\%CI:0.046-0.152), 0.010(95\%CI:0.000-0.037),
0.055(95\%CI:0.037-0.075) and 0.022(95\%CI:0.000-0.081)(Figure 5). The
ICU admission rates of patients using bDMARDs, csDMARDs and anti-TNF
therapy was lower than the total ICU rate. The mortalities in ARD
patients mainly using GC, bDMARDs, csDMARDs, and anti-TNF therapy were
0.070(95\%CI:0.033-0.114), 0.039(95\%CI:0.007-0.087),
0.074(95\%CI:0.041-0.114) and 0.068(95\%CI:0.034-0.111)(Figure 5).
Regression analysis showed that the~main use~of major drugs was not
associated with the ICU admission and mortality(Figure p14 and p15).

Since the effect of drugs on clinical outcomes is different in different
diseases, we performed a more detailed stratification. We only selected
the studies in which patients using a certain drug exceeded 50\% of the
total cases. In SLE, the hospitalization rates of patients mainly using
GC and csDMARDs were 0.670(95\%CI:0.406-0.933) and
0.575(95\%CI:0.434-0.716). The mortality due to COVID-19 in these
patients mainly using GC was 0.055(95\%CI:0.045-0.065). The
hospitalization rate of SLE patients using GC was higher than those
using DMARDs. The hospitalization rates of RA patients mainly using GC
and csDMARDs were both 0.563(95\%CI:0.192-0.934). The mortality of these
patients mainly using GC and csDMARDs were 0.077(95\%CI:0.035-0.120) and
0.059(95\%CI:0.022-0.109), respectively, the mortality of RA patients
using GC was higher than those of csDMARDs. The hospitalization rates of
CTD patients mainly using csDMARDs was 0.466(95\%CI:0.007-0.974)(Figure
p16).

\textbf{4 Discussion}

COVID-19, a pandemic respiratory infectious disease caused by severe
acute respiratory syndrome coronavirus 2(SARS-CoV-2), could lead to
autoimmune and autoinflammatory diseases\textsuperscript{10}. Patients
with ARD are associated with immune dysfunction, and often treated with
immunosuppressive drugs, making them more susceptible to infection.
However, our meta-analysis showed relatively little difference between
the prevalence of SARS-CoV-2 infections in ARD patients(0.061) and the
global prevalence of~SARS-CoV-2 infections(0.077) and the prevalence of
ARD patients even lower.

Among ARDs, the prevalence of PsA was the highest. The mucosal immune
system is the largest constituent of the immune system, protecting
against infectious threats at the primary internal
surface\textsuperscript{11,12}. SARS-CoV-2 infection occurs
predominantly at the respiratory mucosal surfaces, and decreased mucosal
immunity must be beneficial for virus infection\textsuperscript{13}. The
highest prevalence of PsA with red patches of skin topped with silvery
scales suggests that PsA patients may have some defects in mucosal
immunity, which is more prone to COVID-19
infections\textsuperscript{14}.

Vasculitis patients infected with COVID-19 had the highest mortality and
hospitalization rates. COVID-19 made a life-threatening escalation from
Th2 immune response to type 3 hypersensitivity with the subsequent
deposition of antigen-antibody complexes, particularly inside the walls
of blood vessels, to such an extent as to generate a systemic vasculitis
in the context of complex immune disease\textsuperscript{15,16}.
According to Gell and Coombs classification, type 3 hypersensitivity
occurs when excess or slight excess of soluble antigen leads to the
accumulation of immune complexes, and the innate immune system cannot
fully clear from the circulation. These antigen-antibody complexes
precipitate in tissues, especially in blood vessels, and induce a severe
inflammatory state through the action of complement anaphylaxis
toxins(C3a and C5a), which in turn stimulates the release of histamine
by mast cells and the recruitment of phagocytes\textsuperscript{17}.
Histopathologically, the result of this process in the vascular wall is
acute necrotizing vasculitis, accompanied by neutrophil infiltration,
karyorrhexis, and fibrinoid necrosis\textsuperscript{18}. Platelet
aggregation is associated with hypercoagulability and thrombosis
described during COVID-19\textsuperscript{19}. To prevent C3 activation,
anaphylatoxin in this process through specific inhibitors, like
compstatin-based AMY-101, provides effective therapeutic
results\textsuperscript{15,20}.

Among~the~ARD patients with COVID-19 infection,~SLE patients had the
highest ICU admission rate. They had higher rates of hypertension,
diabetes mellitus, obesity, and sedentary lifestyle, which were
significant risk factors for hospitalization and mortality in
COVID-19\textsuperscript{21}. Besides, data from the C19-GRA registry
suggested that long-term use of GC was associated with poorer
outcomes\textsuperscript{5,22}. GC is frequently used to treat SLE,
potentially making these patients vulnerable to a more severe COVID-19
disease course.

The influence of drugs on the COVID-19 infection rate of patients with
ARDs is one of the biggest concerns of medical care. We were surprised
that the prevalence of SARS-CoV-2 infection in patients with ARDs using
GC or csDMARDs or bDMARDs was lower than the global prevalence
of~COVID-19 infection, indicating that these treatments did not
necessarily increase this infection rate. Among all ARD patients
infected with COVID-19, patients mainly treated with GC had the highest
prevalence. GCs have strong anti-inflammatory effects and induce the
apoptosis of lymphocytes. These immune inhibitory effects prevent
lethality by excessive inflammation, but simultaneously increase the
susceptibility to infection\textsuperscript{23,24}.

Compared with the clinical outcomes of all COVID-19 patients with ARDs
treated with several therapies, we found patients using bDMARDs or
anti-TNF therapy had lower hospitalization rates, ICU admission rates,
and mortality. Besides, bDMARD monotherapy was associated with a lower
odds of hospitalization regression coefficient, and largely driven by
anti-TNF therapies. Mechanistic studies have suggested that upregulated
TNF-\selectlanguage{greek}α \selectlanguage{english}may lead to inflammatory cell death, aberrant germinal center
formation, and less robust humoral immune responses in fatal COVID-19,
providing a potential biologic rationale for the protective effect of
TNF inhibitors\textsuperscript{5,25-28}. In vitro studies have
demonstrated that IL-6 and TNF-\selectlanguage{greek}α \selectlanguage{english}have been regulated by the recombinant
protein S of SARS-CoV 2002, indicating that TNF-\selectlanguage{greek}α \selectlanguage{english}inhibitors or the IL-6
inhibitors could decrease the cytokine storm in COVID-19
patients\textsuperscript{29,30}. Anti-TNF therapy has been proposed as a
potential treatment for the hyperinflammatory phase of severe
COVID-19\textsuperscript{31-33}.

We found that GC monotherapy was associated with higher odds of the
hospitalization regression coefficient. Patients using csDMARDs had
higher hospitalization rates, ICU admission rates, and mortality, but
patients using GC had a higher hospitalization rate, higher ICU
admission rate, and lower mortality. In the included studies, the
csDMARDs drug used by most patients was hydroxychloroquine(HCQ). The
World Health Organization SOLIDARITY trial reported no clinical benefit
from HCQ for hospitalized patients with COVID-19\textsuperscript{34}.
Abundant evidence from numerous RCTs revealed HCQ does not prevent
SARS-CoV-2 infection or improve outcomes in mild, moderate, or severe
COVID-19\textsuperscript{35}. HCQ use may even be associated with lower
survival\textsuperscript{36}. Therefore, csDMARDs, especially HCQ, are
not suggested in the treatment of COVID-19\textsuperscript{25}.
Corticosteroids downregulate the release of cytokines, reducing the
damage caused by cytokine storms\textsuperscript{25}. GC is effective in
patients with severe COVID-19-related respiratory failure, but may not
have a benefit in patients with early or mild disease. The Infectious
Diseases Society of America(IDSA) guidelines recommend dexamethasone in
patients with COVID-19, hypoxemia, and/or critical
illness\textsuperscript{37}.

The effects of drugs used on clinical outcomes varied across ARDs.
SARS-CoV-2-infected SLE patients with GC treatment had the highest
hospitalization rates. sGC use(prednisone \textgreater{} 5 mg/day) in
SLE is a risk factor for severe COVID-19 requiring
hospitalization\textsuperscript{38}. SARS-CoV-2-infected RA patients
with csDMARDs or GC treatment had higher hospitalization rates, and
those using GC had the highest mortality. Continuous GC users, on the
one hand, inhibit the immune response and delay the clearance of the
pathogen. They suppress the host inflammatory response, which in the
case of viral infections of the respiratory tract is the primary
responsible for lung damage and ARDs\textsuperscript{39}. Clinic
evidence point to a predominantly negative effect of continuous
corticosteroids in managing this type of infection\textsuperscript{39}.

Our study had several limitations. Meta-analyses of studies regarding
the prevalence and clinical outcomes of COVID-19 had considerable
heterogeneities. The cause of this heterogeneity could be explained by
the differences in study size, including different diseases and study
locations. Thus, we performed a subgroup analysis to assess the
prevalence and clinical outcome of COVID-19 for each disease and
performed a regression. Second, we could not obtain the specific drug
use of each patient, and most patients used multi-drug combination
therapy, which led to biases in our analysis of the impact of drugs on
the prevalence of COVID-19 and clinical outcomes. Third, the sensitivity
of RT-PCR for SARS-CoV-2 from the nasopharyngeal swab is roughly
70\%\textsuperscript{40,41}. Fourth, only published studies were
included in the meta-analysis, and unpublished articles were excluded;
publication and study selection~bias~may have affected our results.
Therefore, further~studies~are needed to verify our results.

\textbf{5 Conclusion}

In conclusion, although our meta-analysis showed little difference
between the prevalence of SARS-CoV-2 infections in patients with ARDs
and the global prevalence of~SARS-CoV-2 infections, the prevalence of
COVID-19 in PsA was significantly higher than the global prevalence. The
use of DMARDs could reduce the risk of being infected with COVID-19, but
the use of GC increased this risk. csDMARDs were associated with disease
severity in COVID-19. bDMARDs, especially anti-TNF, were associated with
a reduced risk of severe disease. GC is effective in patients with
severe COVID-19-related respiratory failure but may not have a benefit
in patients with early or mild disease.

\textbf{6 Supplementary Information}

The data underlying this article are available in the article and in its
Supplementary material: \emph{https://kdocs.cn/l/crOQDUk0Owig.}

\textbf{7 Acknowledgements}

We would like to thank all the participants involved in this study.
Thanks to the researchers for sharing the original clinical cohort data,
which makes this meta-analysis possible. Special thanks to the careful
efforts made by reviewers and editors to improve articles.

\textbf{8 Abbreviations}

DMARDs Disease-modifying anti-rheumatic drugs

ARD Atoimmune rheumatic diseases

COVID-19 Coronavirus Disease 2019

GC Glucocorticoids

BDMARDs Biological Disease-modifying anti-rheumatic drugs

CSDMARDs conventional synthetic Disease-modifying anti-rheumatic drugs

Anti-TNF Anti-Tumor Necrosis Factor

ICU Intensive Care Units

Sars-cov-2 Syndrome coronavirus 2

WHO World Health Organisation

PRISMA Preferred Reporting Items for Systematic Reviews

PROSPERO Prospective Register of Systematic Reviews

MeSH Medical Subject Headings

GRADE Grades of Recommendation, Assessment, Development, and Evaluation

NOS Newcastle--Ottawa Quality Assessment Scale

RA Rheumatoid Arthritis

SLE Systemic Lupus Erythematosus

AS Ankylosing Spondylitis

CTD Connective Tissue Disease

PsA Psoriatic Arthritis

SSc Systemic Sclerosis

IL-6 Interleukin-6

TNF-\selectlanguage{greek}α \selectlanguage{english}Tumor Necrosis Factor-alpha

HCQ Hydroxychloroquine

IDSA The Infectious Diseases Society of America

\textbf{9 Author contributions}

QYS and SXZ contributed to the work equally and should be regarded as
co-first authors. QYS and SXZ designed the study. SXZ, TC and JL
searched the literature. QYS, HYZ and JL selected the data. QYS, SXZ,
BRZ and YZ analyzed the data. QYS and SXZ wrote the manuscript. CG and
CHW* contributed to manuscript revision, read, and approved the
submitted version.

\textbf{10 Funding Source}

This work was supported by the National Science Foundation of China
(No.82001740), the National Social Science Fund of China (21BTQ050), the
National Natural Science Foundation of China (No. 81971543), Natural
Science Foundation of China (No. 81471618), Key Research and Development
(R\&D) Projects of Shanxi Province (201803D31119), and Four ``Batches''
Innovation Project of Invigorating Medical through Science and
Technology of Shanxi Province(NO 2022XM05).

Data of global prevalence of~SARS-CoV-2 infections were obtained from
WHO Coronavirus Disease(COVID-19)Dashboard(Data last
updated:2023/3/20,9:40am CET).

\textbf{11 Availability of data and materials}

The datasets supporting the conclusions of this article are included
within the article and Supplementary material:
https://kdocs.cn/l/crOQDUk0Owig.

\textbf{12 Declarations}

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not Applicable.

Competing interests

The authors declare they have no competing interests.

\textbf{13 Reference}

1. Rezagholizadeh A, Khiali S, Sarbakhsh P, Entezari-Maleki T.
Remdesivir for treatment of COVID-19; an updated systematic review and
meta-analysis. \emph{Eur J Pharmacol.} 2021;897:173926.

2. World Health Organization. WHO Coronavirus (COVID-19)
Dashboard.\emph{https://covid19.who.int/} .

3. Gunduz Gurkan C, Karadogan D, Ufuk F, Cure O, Altinisik G. Management
of Patients with Connective Tissue Disease-associated Interstitial Lung
Diseases During the COVID-19 Pandemic. \emph{Turk Thorac
J.}2021;22(4):346-352.

4. Fernandez-Ruiz R, Paredes JL, Niewold TB. COVID-19 in patients with
systemic lupus erythematosus: lessons learned from the inflammatory
disease. \emph{Transl Res.} 2021;232:13-36.

5. Gianfrancesco M, Hyrich KL, Al-Adely S, et al. Characteristics
associated with hospitalisation for COVID-19 in people with rheumatic
disease: data from the COVID-19 Global Rheumatology Alliance
physician-reported registry. \emph{Ann Rheum Dis.} 2020;79(7):859-866.

6. Brenner EJ, Ungaro RC, Gearry RB, et al. Corticosteroids, But Not TNF
Antagonists, Are Associated With Adverse COVID-19 Outcomes in Patients
With Inflammatory Bowel Diseases: Results From an International
Registry. \emph{Gastroenterology.} 2020;159(2):481-491 e483.

7. Mahil SK, Dand N, Mason KJ, et al. Factors associated with adverse
COVID-19 outcomes in patients with psoriasis-insights from a global
registry-based study. \emph{J Allergy Clin Immunol.} 2021;147(1):60-71.

8. Listing J, Gerhold K, Zink A. The risk of infections associated with
rheumatoid arthritis, with its comorbidity and
treatment.\emph{Rheumatology (Oxford).} 2013;52(1):53-61.

9. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement:
an updated guideline for reporting systematic reviews. \emph{Rev Esp
Cardiol (Engl Ed).} 2021;74(9):790-799.

10. Robinson PC, Senior staff s, Bursle EC, Infectious diseases p,
Clinical m. Management of autoimmune disease during the COVID-19
pandemic. \emph{Aust Prescr.} 2020;43(5):146-147.

11. Chimenti MS, Perricone C, Novelli L, et al. Interaction between
microbiome and host genetics in psoriatic arthritis. \emph{Autoimmun
Rev.} 2018;17(3):276-283.

12. Raychaudhuri SK, Abria C, Mitra A, Raychaudhuri SP. Functional
significance of MAIT cells in psoriatic arthritis.
\emph{Cytokine.}2020;125:154855.

13. Lin L, Lu L, Cao W, Li T. Hypothesis for potential pathogenesis of
SARS-CoV-2 infection-a review of immune changes in patients with viral
pneumonia. \emph{Emerg Microbes Infect.} 2020;9(1):727-732.

14. Elmas OF, Demirbas A, Kutlu O, et al. Psoriasis and COVID-19: A
narrative review with treatment considerations. \emph{Dermatol
Ther.}2020;33(6):e13858.

15. Roncati L, Ligabue G, Fabbiani L, et al. Type 3 hypersensitivity in
COVID-19 vasculitis. \emph{Clin Immunol.} 2020;217:108487.

16. Song Y, Shen H, Schenten D, Shan P, Lee PJ, Goldstein DR. Aging
enhances the basal production of IL-6 and CCL2 in vascular smooth muscle
cells. \emph{Arterioscler Thromb Vasc Biol.} 2012;32(1):103-109.

17. Norman PS. Immunobiology: The immune system in health and
disease.\emph{Journal of Allergy and Clinical Immunology.}
1995;96(2):274.

18. McIlwain L, Carter JD, Bin-Sagheer S, Vasey FB, Nord J.
Hypersensitivity vasculitis with leukocytoclastic vasculitis secondary
to infliximab. \emph{Journal of clinical
gastroenterology.}2003;36(5):411-413.

19. Kollias A, Kyriakoulis KG, Dimakakos E, Poulakou G, Stergiou GS,
Syrigos K. Thromboembolic risk and anticoagulant therapy in COVID-19
patients: emerging evidence and call for action. \emph{Br J
Haematol.}2020;189(5):846-847.

20. Mastaglio S, Ruggeri A, Risitano AM, et al. The first case of
COVID-19 treated with the complement C3 inhibitor AMY-101. \emph{Clin
Immunol.} 2020;215:108450.

21. Goodman KE, Magder LS, Baghdadi JD, et al. Impact of Sex and
Metabolic Comorbidities on Coronavirus Disease 2019 (COVID-19) Mortality
Risk Across Age Groups: 66 646 Inpatients Across 613 U.S.
Hospitals.\emph{Clin Infect Dis.} 2021;73(11):e4113-e4123.

22. Strangfeld A, Schafer M, Gianfrancesco MA, et al. Factors associated
with COVID-19-related death in people with rheumatic diseases: results
from the COVID-19 Global Rheumatology Alliance physician-reported
registry. \emph{Ann Rheum Dis.} 2021;80(7):930-942.

23. Kan M, Himes BE. Insights into glucocorticoid responses derived from
omics studies. \emph{Pharmacol Ther.} 2021;218:107674.

24. Vandewalle J, Luypaert A, De Bosscher K, Libert C. Therapeutic
Mechanisms of Glucocorticoids. \emph{Trends Endocrinol
Metab.}2018;29(1):42-54.

25. D'Silva KM, Wallace ZS. COVID-19 and Disease-Modifying
Anti-rheumatic Drugs. \emph{Curr Rheumatol Rep.} 2021;23(5):28.

26. Wang L, He W, Yu X, et al. Coronavirus disease 2019 in elderly
patients: Characteristics and prognostic factors based on 4-week
follow-up. \emph{J Infect.} 2020;80(6):639-645.

27. Monti S, Balduzzi S, Delvino P, Bellis E, Quadrelli VS, Montecucco
C. Clinical course of COVID-19 in a series of patients with chronic
arthritis treated with immunosuppressive targeted therapies. \emph{Ann
Rheum Dis.} 2020;79(5):667-668.

28. Haberman RH, Castillo R, Chen A, et al. COVID-19 in Patients With
Inflammatory Arthritis: A Prospective Study on the Effects of
Comorbidities and Disease-Modifying Antirheumatic Drugs on Clinical
Outcomes. \emph{Arthritis Rheumatol.} 2020;72(12):1981-1989.

29. Migkos MP, Kaltsonoudis E, Pelechas E, et al. Use of conventional
synthetic and biologic disease-modifying anti-rheumatic drugs in
patients with rheumatic diseases contracting COVID-19: a single-center
experience. \emph{Rheumatol Int.} 2021;41(5):903-909.

30. Wang W, Ye L, Ye L, et al. Up-regulation of IL-6 and TNF-alpha
induced by SARS-coronavirus spike protein in murine macrophages via
NF-kappaB pathway. \emph{Virus Res.} 2007;128(1-2):1-8.

31. Kaneko N, Kuo HH, Boucau J, et al. Loss of Bcl-6-Expressing T
Follicular Helper Cells and Germinal Centers in COVID-19.
\emph{Cell.}2020;183(1):143-157 e113.

32. Karki R, Sharma BR, Tuladhar S, et al. Synergism of TNF-alpha and
IFN-gamma Triggers Inflammatory Cell Death, Tissue Damage, and Mortality
in SARS-CoV-2 Infection and Cytokine Shock Syndromes.
\emph{Cell.}2021;184(1):149-168 e117.

33. Robinson PC, Liew DFL, Liew JW, et al. The Potential for Repurposing
Anti-TNF as a Therapy for the Treatment of COVID-19. \emph{Med (N
Y).}2020;1(1):90-102.

34. Consortium WHOST, Pan H, Peto R, et al. Repurposed Antiviral Drugs
for Covid-19 - Interim WHO Solidarity Trial Results. \emph{N Engl J
Med.} 2021;384(6):497-511.

35. Group RC, Horby P, Mafham M, et al. Effect of Hydroxychloroquine in
Hospitalized Patients with Covid-19. \emph{N Engl J
Med.}2020;383(21):2030-2040.

36. Horby P, Mafham M, Linsell L, et al. Effect of Hydroxychloroquine in
Hospitalized Patients with Covid-19. \emph{N Engl J
Med.}2020;383(21):2030-2040.

37. Bhimraj A, Morgan RL, Shumaker AH, et al. Infectious Diseases
Society of America Guidelines on the Treatment and Management of
Patients with COVID-19. \emph{Clin Infect Dis.} 2020.

38. Chen TH, Cheng HT, Yeh CT. Epidemiology changes in peptic ulcer
diseases 18 years apart explored from the genetic aspects of
Helicobacter pylori. \emph{Transl Res.} 2021;232:115-120.

39. Favalli EG, Ingegnoli F, De Lucia O, Cincinelli G, Cimaz R, Caporali
R. COVID-19 infection and rheumatoid arthritis: Faraway, so
close!\emph{Autoimmun Rev.} 2020;19(5):102523.

40. Fang Y, Zhang H, Xie J, et al. Sensitivity of Chest CT for COVID-19:
Comparison to RT-PCR. \emph{Radiology.} 2020;296(2):E115-e117.

41. Ai T, Yang Z, Hou H, et al. Correlation of Chest CT and RT-PCR
Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of
1014 Cases. \emph{Radiology.} 2020;296(2):E32-e40.\selectlanguage{english}
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