Evidence map›Paper›PMID 38188040›Full record

ArticleJournal of translational autoimmunity2024

Effect of traditional therapeutics on prevalence and clinical outcomes of coronavirus disease 2019 in Chinese patients with autoimmune diseases.

Saisai Huang, Xiaolei Ma, Juan Cao, Mengru Du, Zhiling Zhao, Dandan Wang, Xue Xu, Jun Liang, Lingyun Sun

Open access · goldAbstract read
In one paragraph

Article in Journal of translational autoimmunity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 45% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Saisai HuangDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Xiaolei MaDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Juan CaoDepartment of Geriatrics, Nanjing Drum Hospital, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Mengru DuDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Zhiling ZhaoDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Dandan WangDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Xue XuDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Jun LiangDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Lingyun SunDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University , 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Nanjing Drum Tower Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The impact of the Coronavirus disease 2019 (COVID-19) pandemic on autoimmune diseases (AID) patients has been an important focus. This study was undertaken to characterize the incidence, clinical manifestations and hospitalization among AID affected by COVID-19 and to analyze the association between immunomodulatory medication and these outcomes. Clinical, demographic, maintenance treatment, symptoms and disease course data and outcomes of AID patients with COVID-19 infection were assessed via an online survey tool and printed copy from 1 January till February 28, 2023. A total of 432 patients with AID were enrolled in the study. The results showed the most common conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) was hydroxychloroquine (HCQ). The usage of csDMARDs didn't increase the risk of COVID-19 infection. Patients who warranted hospitalization were significantly older. ILD was associated with higher hospitalization rate. No csDMARDs other than calcineurin inhibitor (CNI) was associated with increased risk of hospitalization. HCQ intake was associated with cough. Compared with no glucocorticoids (GCs) group, high doses of GCs were accompanied with higher proportion of gastrointestinal symptoms and tachycardia, lower proportion of sore throat and ageusia. GCs didn't provoke the COVID-19 infection in patients with AID, but chronic use of oral GCs was significantly more common in those requiring hospitalization, and higher dose of GCs were correlated with higher risk of hospitalization. 97 patients discontinued csDMARDs after infection, which resulted in an elevated risk of hospitalization. Meanwhile, withdrawal of csDMARDs was associated with higher odds of disease flare and lower proportion of remission than maintenance groups. Collectively, our analysis provides the evidence that maintenance treatment of csDMARDs may be more prudent for AID patients during COVID-19 pandemic.

Indexed as

Autoimmune diseasesCOVID-19csDMARDsGCs

Identifiers

PMID38188040
PMCPMC10764252
OpenAlexW4389621994

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.