Frontiers in immunology · 2026
Developing a new assessment model for systemic lupus erythematosus disease activity based on a case cohort.
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Liu Dan, Ma Ke, Liu Jing-Xuan, Wen Da-Wei, Han Meng, Xu Yi-Heng, Zhang Meng-Rui, Liang Hong-da, Zhang Yi-Jing, Li Ming
Аннотация
OBJECTIVE: To develop and validate a novel SLE disease activity scoring model designed to enhance diagnostic capability for moderate-to-severe disease activity in SLE patients compared to the SLE Disease Activity Index 2000 (SLEDAI-2K).
METHODS: All 1163 SLE patients from Shandong Provincial Hospital and the Affiliated Hospital of Qingdao University constituted the derivation cohort, while another 323 patients from Shandong Provincial Hospital served as the validation cohort. Disease activity was assessed for each patient using the Physician Global Assessment (PGA) and SLEDAI-2K. With PGA-defined moderate-to-severe SLE activity as the dependent variable, binary logistic regression analysis identified factors influencing disease activity and constructed a regression model. Receiver operating characteristic (ROC) curve analysis evaluated the model's discriminative ability. Correlations between the new scoring model, PGA, and SLEDAI-2K were examined.
RESULTS: Binary logistic regression identified 25 clinical manifestations as independent risk factors for higher SLE activity (all P<0.05): neuropsychiatric symptoms, visual impairment, vasculitis, arthritis, myositis, hematuria, proteinuria, pyuria, alopecia, rash, mucosal ulcers, pleurisy, pericarditis, hypocomplementemia, elevated anti-dsDNA, fever, thrombocytopenia, leukopenia, pulmonary hypertension, hypothyroidism, hypocalcemia, lymphadenopathy, abnormal liver function, high-titer ANUA. Using the 25 variables listed above, we constructed a new scoring model. ROC analysis for distinguishing moderate-to-severe activity showed areas under the curve of 0.972 (95% CI: 0.963-0.980, P<0.001) in the derivation cohort and 0.971 (95% CI: 0.958-0.985, P<0.001) in the validation cohort.
CONCLUSION: Twenty-five clinical manifestations were identified as independent risk factors for assessing moderate-to-severe SLE activity. The resulting model demonstrated enhanced accuracy in identifying moderate-to-severe disease activity states.
METHODS: All 1163 SLE patients from Shandong Provincial Hospital and the Affiliated Hospital of Qingdao University constituted the derivation cohort, while another 323 patients from Shandong Provincial Hospital served as the validation cohort. Disease activity was assessed for each patient using the Physician Global Assessment (PGA) and SLEDAI-2K. With PGA-defined moderate-to-severe SLE activity as the dependent variable, binary logistic regression analysis identified factors influencing disease activity and constructed a regression model. Receiver operating characteristic (ROC) curve analysis evaluated the model's discriminative ability. Correlations between the new scoring model, PGA, and SLEDAI-2K were examined.
RESULTS: Binary logistic regression identified 25 clinical manifestations as independent risk factors for higher SLE activity (all P<0.05): neuropsychiatric symptoms, visual impairment, vasculitis, arthritis, myositis, hematuria, proteinuria, pyuria, alopecia, rash, mucosal ulcers, pleurisy, pericarditis, hypocomplementemia, elevated anti-dsDNA, fever, thrombocytopenia, leukopenia, pulmonary hypertension, hypothyroidism, hypocalcemia, lymphadenopathy, abnormal liver function, high-titer ANUA. Using the 25 variables listed above, we constructed a new scoring model. ROC analysis for distinguishing moderate-to-severe activity showed areas under the curve of 0.972 (95% CI: 0.963-0.980, P<0.001) in the derivation cohort and 0.971 (95% CI: 0.958-0.985, P<0.001) in the validation cohort.
CONCLUSION: Twenty-five clinical manifestations were identified as independent risk factors for assessing moderate-to-severe SLE activity. The resulting model demonstrated enhanced accuracy in identifying moderate-to-severe disease activity states.