Analysis of characteristics related to the disease activity of systemic lupus erythematosus and construction of an evaluation model.
- Author:
Hongyan WANG
1
;
Xinming LI
2
;
Kechi FANG
2
;
Huaqun ZHU
1
;
Rulin JIA
1
;
Jing WANG
2
Author Information
1. Department of Rheumatology and Immunology, Peking University People's Hospital, Beijing 100044, China.
2. Institute of Psychology, Chinese Academy of Sciences; Key Laboratory of Mental Health, Chinese Academy of Sciences, Beijing 100101, China.
- Publication Type:Journal Article
- Keywords:
Clinical indicators;
Clinical stratification;
Disease activity;
Systemic lupus erythematosus
- MeSH:
Humans;
Lupus Erythematosus, Systemic/diagnosis*;
Retrospective Studies;
Antibodies, Antinuclear/blood*;
Complement C3/metabolism*;
Complement C4/metabolism*;
Logistic Models;
Severity of Illness Index;
Leukocyte Count;
Female;
Male;
Serum Albumin/analysis*
- From:
Journal of Peking University(Health Sciences)
2024;56(6):1017-1022
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To stratify systemic lupus erythematosus (SLE) patients clinically, to analyze the clinical characteristics of patients with and without disease activity, and to explore the application va-lue of key clinical indicators in assessing disease activity, as well as to construct an evaluation model.
METHODS:A retrospective analysis was conducted on clinical data of the SLE patients diagnosed at Peking University People' s Hospital from May 1995 to April 2014. Demographic information, clinical manifestations, laboratory tests, and antibody detection results were collected. The patients were divided into active and inactive groups based on systemic lupus erythematosus disease activity index 2000(SLEDAI-2000)scores. t-tests, Mann-Whitney U tests, and χ2 tests were used to compare the differences between the groups. Spearman correlation analysis was used to evaluate the relevant clinical indicators associated with SLE activity in the active disease group. Based on the results of statistical analysis, a Logistic regression model was constructed, and the performance of the model was evaluated.
RESULTS:No significant differences were found in demographic characteristics between the two groups. In the active disease group, positive rates of antinuclear antibodies (ANA) and anti-double-stranded DNA antibodies (anti-dsDNA) were increased; white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HGB), lymphocytes (LY), total protein (TP), albumin (ALB), and complement 3(C3) levels were significantly decreased; while immunoglobulin A and G levels were markedly elevated. The correlation analysis results showed that hemoglobin, albumin, C3, and complement 4(C4) had higher correlation indices compared with other clinical indicators. Among these, C3 exhibited a certain negative correlation with disease activity. The Logistic regression model based on 12 significantly different indicators (P < 0.05) achieved an accuracy of 91.4%, sensitivity of 94.4%, specificity of 81.0%, and the area under curve (AUC) of the receiver operating characteristic (ROC) was 0.944.
CONCLUSION:This study comprehensively evaluated a range of clinical indicators related to SLE disease activity, providing a thorough understanding of both laboratory and clinical markers. The Logistic regression model, which was primarily constructed using laboratory test indicators, such as inflammatory markers, immune response parameters, and organ involvement metrics, demonstrated a high degree of accuracy in assessing the disease activity in SLE patients. Consequently, this model might provide a new basis for the diagnosis and treatment of SLE patients, offering significant clinical diagnostic value.
