Abstract
Progressive bone erosion (BE) is a prominent manifestation of structural damage in rheumatoid arthritis (RA). This study aimed to construct and validate a BE prediction model (BEPM) for RA based on musculoskeletal ultrasound and clinical risk factors. A total of 312 RA patients without BE were consecutively collected and followed up for 2years, who were divided into BE group and non-bone erosion group, as confirmed by two radiology experts based on at least two imaging techniques. Relevant clinical information, such as anti-cyclic citrullinated peptide (ACCP) antibody and disease duration, was collected. Musculoskeletal ultrasound examinations were performed on all patients upon admission. Univariate and binary logistic regression analyses were conducted to identify risk factors for BE and to establish the prediction model. The calibration, diagnostic efficacy, and clinical effectiveness of BEPM were evaluated by calibration curve plots, receiver-operating characteristic curve, and decision curve analysis (DCA). Binary logistic regression analysis screened four variables, including synovial hyperplasia, synovial blood flow, ACCP + , and disease duration, into the prediction model. BEPM exhibited good diagnostic performance in both the training and validation groups, with area under the curve values of 0.949 (95% CI 0.924-0.973) and 0.965 (95% CI 0.935-0.996), respectively. Using 0.376 as the optical cutoff value for BE, the sensitivity, specificity, and Youden index of the model in the training group were 86.0%, 90.4%, and 76.4%, respectively. In the DCA curves, BEPM indicated overall good population benefit. BEPM demonstrates high diagnostic performance and clinical utility, and may be a useful clinical model for predicting BE in patients with RA. Key Points • Synovial hyperplasia, synovial blood flow, ACCP+ , and disease duration are independent risk factors for bone erosion in RA patients. • BEPM exhibits good diagnostic performance in both the training and validation groups. • Combining musculoskeletal ultrasound parameters with key clinical risk factors to construct a bone erosion model is feasible.
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