1352: Prediction of 5-Year Fracture Risk in Rheumatoid and Psoriatic Arthritis Using REMS Technology

Background/Purpose: Autoimmune arthritis, including Rheumatoid Arthritis (RA) and Psoriatic Arthritis (PA), are among the most common forms of inflammatory joint diseases. These conditions affect joints and surrounding structures, often leading to systemic skeletal complications and an increased risk of fragility fractures. Treatment typically involves steroids-based therapy, mostly glucocorticoids, which are known to enhance bone resorption and reduce bone strength. Radiofrequency Echographic Multi-Spectrometry (REMS) is a non-ionizing technology that represents the alternative to the current standard Dual X-Ray Absorptiometry (DXA) and allows to predict the imminent (within 5 years) fracture risk (FR). Therefore, the aim of the present study is to assess the REMS-based 5-years FR in a cohort of patients with RA and PA.

Methods: REMS automatically analyses the raw, unprocessed backscattered radiofrequency signals by comparing them to a proprietary reference database, extracting both quantitative (BMD, T- and Z-score) and qualitative (bone microarchitecture via Fragility Score, FS) data. Furthermore, the REMS-measured parameters are integrated into a progressive fracture risk classification system consisting of 7 risk levels, R1 to R7, each corresponding to a defined probability of imminent FR. To assess the potential impact of steroid therapy on risk classification, a logistic regression analysis was carried out.

Results: A cohort of RA and PA patients (n = 56, Caucasian, both genders), who either received (n = 28, median age: 65.5 years old, 50% women under 65) or did not receive (n = 28, median age: 45 years old, 92.8% women under 65) steroids-based therapy underwent REMS scans at femoral neck. The FR analysis on hips revealed that 5.4% of the patients were classified in R1, 44.6% in R3, 48.2% in R5 and 1.8% in R7. Notably, 78.6% of the patients classified in the high-risk classes (R5-R7) were undergoing a steroid-based therapy, whereas 78.6% of those in the low-risk classes (R1-R4) were not receiving steroid treatment. The results from logistic regression showed an OR = 12,83 (95% CI = 3.57-46.15; p < 0.0001), indicating a strong and statistically significant association between steroid use and high fracture risk. Since the steroids-based patients showed a high FS (36.25 ± 8.44), it means that information of bone quality plays a greater impact on the FR probability, aligning with current literature stating that steroids-induced bone loss is more trabecular than cortical.

Conclusion: The analysis confirmed the detrimental impact that a steroid-based therapy can exert on the skeletal system, especially on the trabecular architecture, thus highlighting the potentiality of REMS to directly measure FR through the integration of data from both bone quality and quantity analysis. Furthermore, considering that the annual cost of providing care for osteoporotic fractures in USA is estimated to increase to $95 Bln in 2040, the presented approach will allow potential cost savings through effective prevention strategies, such as reducing the incidence of subsequent fractures in patients facing challenges to access bone test like women < 65.