Validation and optimization of classification criteria for childhood-onset systemic lupus erythematosus in a multi-center Chinese cohort.
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: This study evaluated the diagnostic accuracy of the 2012 SLICC and 2019 EULAR/ACR criteria in Chinese cSLE patients and aimed to develop an optimized classification schema based on the 2019 EULAR/ACR criteria, specifically tailored for cSLE. Methods: Data from cSLE and control cases were extracted from the CAPRID database. Gold-standard diagnosis were established by consensus among 43 rheumatologists (≥80% agreement). From 1,390 consensus cases, a random selection of 1,045 cases (512 cSLE/533 non-cSLE) were allocated to derivation (n=522) and validation (n=523) cohorts. The 2012 SLICC and 2019 EULAR/ACR criteria were evaluated in the total cohort. Multiple optimization schemes were then developed through LASSO regression with expert consultation in the derivation cohort. All potential optimization schemes underwent validation in the validation cohort, from which the optimal scheme was selected and further evaluated in an ANA-positive subgroup. Results: = 0.138). Conclusion: Both criteria performed well in Chinese cSLE patients. Optimizing the 2019 EULAR/ACR criteria by removing alopecia and arthritis criteria and modifying the urinary protein criterion enhanced specificity without compromising sensitivity.
Source: PubMed
Institutions: Central South University, Second Xiangya Hospital of Central South University, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Children's Hospital of Chongqing Medical University, Chongqing Medical University, Shenzhen Children's Hospital, Nanjing Children's Hospital, Second Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Capital Medical University, Beijing Children’s Hospital
Language: English
Subfield: Rheumatology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4412855953