Development of Clinical Decision Models for the Prediction of Systemic Lupus Erythematosus and Sjogren’s Syndrome Overlap

Abstract

Objective: To 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. Methods: This 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. Results: This study of SLE versus SLE-SS included 290 patients, divided into a discovery cohort (n = 203) and a validation cohort (n = 87). The five best characteristics were selected by LASSO logistic regression in the discovery cohort of SLE versus SLE-SS and were used to construct the predictive tool, including dry mouth, dry eye, anti-Ro52 positive, anti-SSB positive, and RF positive. This study of SS versus SLE-SS included 266 patients, divided into a discovery cohort (n = 187) and a validation cohort (n = 79). In the discovery cohort of SS versus SLE-SS, by using LASSO logistic regression, the eleven best features were selected to build the predictive tool, which included age at diagnosis (years), fever, dry mouth, photosensitivity, skin lesions, arthritis, proteinuria, hematuria, hypoalbuminemia, anti-dsDNA positive, and anti-Sm positive. The prediction model showed good discrimination, good calibration, and fair clinical usefulness in the discovery cohort. The results were validated in a validation cohort of patients. Conclusion: The models are simple and accessible predictors, with good discrimination and calibration, and can be used as a routine tool to screen for SLE-SS.