Scholarlink

<p>This systematic review evaluated the performance and risk of bias in Systemic Lupus Erythematosus (SLE) disease manifestations and case fatality prediction models, based on a search of PubMed, Embase, and Cochrane Library up to October 17, 2025. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects meta-analysis pooled the Area Under the Curve (AUC) values with 95% Confidence Intervals (CIs), with sensitivity and subgroup analyses. The study included 35 studies comprising 89 prediction models, primarily from China (92.3%). Designs were mainly cross-sectional (46.2%) or retrospective cohort (42.3%). Common predictor categories included immunologic/autoantibody profile s (<i>n</i> = 148) and biochemical parameters (<i>n</i> = 126). During development, pulmonary (AUC = 0.92, 95% CI: 0.78–0.87), perinatal (0.92, 0.83–1.03), and case fatality models (0.91, 0.89–0.95) performed highly, while cardiovascular models scored lower (0.78, 0.74–0.81). Upon validation, pulmonary models remained superior (0.86, 0.81–0.91); perinatal (0.82, 0.77–0.87) and cardiovascular models (0.80, 0.76–0.83) remained robust, whereas case fatality models declined markedly. Machine learning models showed greater potential for pulmonary outcomes (0.83, 0.78–0.89). Predictor number improved renal model performance but reduced accuracy for case fatality. All 89 models were rated high risk of bias and mostly low applicability per PROBAST.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月23日

查看全文
  • Xuanlin Li,
  • Yuejie Lu,
  • Sai Jiang,
  • Yanan Wang,
  • Hejing Pan,
  • Zhijun Xie,
  • Chengping Wen,
  • Lin Huang

摘要

This systematic review evaluated the performance and risk of bias in Systemic Lupus Erythematosus (SLE) disease manifestations and case fatality prediction models, based on a search of PubMed, Embase, and Cochrane Library up to October 17, 2025. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects meta-analysis pooled the Area Under the Curve (AUC) values with 95% Confidence Intervals (CIs), with sensitivity and subgroup analyses. The study included 35 studies comprising 89 prediction models, primarily from China (92.3%). Designs were mainly cross-sectional (46.2%) or retrospective cohort (42.3%). Common predictor categories included immunologic/autoantibody profile s (n = 148) and biochemical parameters (n = 126). During development, pulmonary (AUC = 0.92, 95% CI: 0.78–0.87), perinatal (0.92, 0.83–1.03), and case fatality models (0.91, 0.89–0.95) performed highly, while cardiovascular models scored lower (0.78, 0.74–0.81). Upon validation, pulmonary models remained superior (0.86, 0.81–0.91); perinatal (0.82, 0.77–0.87) and cardiovascular models (0.80, 0.76–0.83) remained robust, whereas case fatality models declined markedly. Machine learning models showed greater potential for pulmonary outcomes (0.83, 0.78–0.89). Predictor number improved renal model performance but reduced accuracy for case fatality. All 89 models were rated high risk of bias and mostly low applicability per PROBAST.