ArticleJournal of clinical medicine2023
Development of Clinical Decision Models for the Prediction of Systemic Lupus Erythematosus and Sjogren's Syndrome Overlap.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Comprehensive analysis of predictive models for disease manifestations and case fatality in systemic lupus erythematosus.NPJ digital medicine · 2026Review
- Analytical and clinical comparison of four SLE-associated autoantibodies in different autoimmune diseases.Scientific reports · 2025Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Systemic Lupus Erythematosus Risk Probability Index: ready for routine use? Results from a Chinese cohort.Lupus science & medicine · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo explore the clinical features of patients with systemic lupus erythematosus and Sjögren's syndrome overlap (SLE-SS) compared to concurrent SLE or primary SS (pSS) patients, we utilized a predictive machine learning-based tool to study SLE-SS.
methodsThis study included SLE, pSS, and SLE-SS patients hospitalized at Nanjing Drum Hospital from December 2018 to December 2020. To compare SLE versus SLE-SS patients, the patients were randomly assigned to discovery cohorts or validation cohorts by a computer program at a ratio of 7:3. To compare SS versus SLE-SS patients, computer programs were used to randomly assign patients to the discovery cohort or the validation cohort at a ratio of 7:3. In the discovery cohort, the best predictive features were determined using a least absolute shrinkage and selection operator (LASSO) logistic regression model among the candidate clinical and laboratory parameters. Based on these factors, the SLE-SS prediction tools were constructed and visualized as a nomogram. The results were validated in a validation cohort, and AUC, calibration plots, and decision curve analysis were used to assess the discrimination, calibration, and clinical utility of the predictive models.
resultsThis study of SLE versus SLE-SS included 290 patients, divided into a discovery cohort (
conclusionThe models are simple and accessible predictors, with good discrimination and calibration, and can be used as a routine tool to screen for SLE-SS.
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