Development and internal validation of a prediction model for rheumatoid arthritis: a case-control study

Abstract

Abstract This study measured sociodemographic characteristics, dietary habits, lifestyle habits, genetics, and other factors that may contribute to the development of Rheumatoid Arthritis (RA). Independent risk factors for RA were identified by logistic regression analysis, and a prediction model was constructed. The area under the receiver operating characteristic curve (AUC) was used to evaluate the prediction accuracy of the model, and the calibration of the model was evaluated by the Hosmer–Lemeshow test. A total of 432 participants, comprising 216 healthy individuals and 216 patients diagnosed with RA at two hospitals in Sichuan, China, from March 2022 to January 2023 were included in this study. Logistic regression analysis revealed that occupation type, place of residence, history of mumps, dietary combination, sweet, damp dwelling, fish, vaccine history, and rs805297 were significantly associated with the pathogenesis of RA. The model constructed in this study showed good prediction, with an AUC of 0.912. The Youden index was 0.699, the sensitivity was 0.847 and the specificity was 0.852. The Hosmer–Lemeshow test results (χ2 = 8.441, P = 0.392) indicated that the model had good diagnostic value. The internal validation AUC was 0.942. We propose a new promising model for identifying individuals at risk of developing RA.

Keywords

• Rheumatoid arthritis
• Risk prediction model
• Risk factors
• Risk assessment
• Case-control study