Evidence map›Paper›PMID 42180205›Full record

ArticleNursing research and practice2026

Latent Profile Analysis of Symptom Severity in Systemic Lupus Erythematosus (SLE) Patients: A Cross-Sectional Study.

Xingyu Yang, Shiyu Pu, Yuguang Shang, Xue Leng, Xiaomeng Wang, Shuyu Han, Chuangyi Zhang

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Article in Nursing research and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Xingyu YangSchool of Nursing, Peking University, Beijing, 100191, China, pku.edu.cn.ORCID https://orcid.org/0009-0007-9368-2432
Shiyu PuSchool of Nursing, Peking University, Beijing, 100191, China, pku.edu.cn.ORCID https://orcid.org/0009-0002-9534-8335
Yuguang ShangDepartment of Nursing, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China, cmu.edu.cn.ORCID https://orcid.org/0009-0002-8658-8337
Xue LengDepartment of Nursing, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China, cmu.edu.cn.ORCID https://orcid.org/0009-0001-6742-5826
Xiaomeng WangSchool of Nursing, Peking University, Beijing, 100191, China, pku.edu.cn.ORCID https://orcid.org/0009-0008-8077-1986
Shuyu HanSchool of Nursing, Peking University, Beijing, 100191, China, pku.edu.cn.ORCID https://orcid.org/0000-0002-2506-6888
Chuangyi ZhangSchool of Nursing, Peking University, Beijing, 100191, China, pku.edu.cn.ORCID https://orcid.org/0009-0000-8777-9835

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To explore the latent profiles of symptom severity among systemic lupus erythematosus (SLE) patients and analyze the influencing factors associated with these different latent profiles, providing clinical evidence for the management of SLE symptoms. Methods: Using convenience sampling, a questionnaire survey was conducted among SLE patients at Shengjing Hospital of China Medical University employing the SLE Symptom Checklist (SSC) between November 2022 and July 2023. Latent profile analysis (LPA) was performed to assess the severity of patients' symptoms, and univariate analysis along with multinomial logistic regression was utilized to analyze the factors influencing the latent profiles. Results: A total of 420 patients were included in the study. The results of LPA revealed that the severity of symptoms among SLE patients could be categorized into three latent profiles: mild (75.4%), moderate (17.6%), and severe (6.9%). The multinomial logistic regression analysis showed that male participants were more likely to belong to the "Severe Symptom Type" ( Conclusion: Our study enhanced understanding of SLE's complexity, identified gender, employment, and marital status as key factors, and advised targeted nursing for high-risk patients. Clinicians should consider SLE's system involvement and strengthen social support.

Indexed as

individualized interventionsinfluencing factorslatent profile analysissymptom managementsymptom severitysystemic lupus erythematosus

Identifiers

PMID42180205
PMCPMC13197648

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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.