POS1111 DIAGNOSIS OF OSTEOPOROSIS USING RADIOFREQUENCY ECHOGRAPHIC MULTI SPECTROMETRY (REMS) AT THE LUMBAR SPINE IN PATIENTS WITH DIFFERENT BODY MASS INDEX
Authors: B. Cortet, E. Dennison, A. Diez-Perez, M. Locquet, M. Muratore, D. Ovejero Crespo, X. Nogués, M. L. Brandi
Journal: Annals of the Rheumatic Diseases (2021)

Background: In recent years, the technology based on the analysis of raw ultrasound signals, Radiofrequency Echographic Multi Spectrometry (REMS), has been validated against Dual-energy X-ray Absorptiometry (DXA) for the diagnosis of osteoporosis and risk fracture prediction. Objectives: The aim of this multicenter observational study was to evaluate the diagnostic performance of REMS with respect to DXA in patients with different body mass index (BMI) categories. Methods: The inclusion criteria were: Caucasian women; age between 30 and 90 years; referral by their clinician for spinal DXA assessment; absence of significant walking impairment; signed informed consent. Patients underwent DXA and REMS scans at the lumbar spine, according to the procedures described in Di Paola et al. [1], including an a-posteriori quality check of the examinations in order to guarantee the maximum reliability of the diagnostic outputs. Three groups of patients were considered according to BMI:: underweight (BMI&lt;18.5 kg/m 2 ), normal weight (BMI≥18.5 kg/m 2 and &lt;25 kg/m 2 ), and overweight/obese (BMI≥25 kg/m 2 ). DXA and REMS BMD values were also stratified in 3 diagnostic categories: osteoporotic, osteopenic, or healthy. The degree of correlation between DXA and REMS BMD values was quantified by calculating Pearson’s correlation coefficient ( r ). The diagnostic concordance between REMS and DXA was assessed by Cohen’s K considering the 3 diagnostic categories. REMS´s sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for discriminating patients with and without osteoporosis were also calculated using DXA outcomes as reference. The same statistics were calculated accepting a 0.3 T-score tolerance on T-score values of borderline cases [1]. Results: Overall, 4282 patients were enrolled and, after the quality check, 3501 couples of scans were included in the analyses. Of these patients, 122 (3.5%) were underweight, 1964 (56.1%) were normal weight, and 1415 (40.4%) were overweight. The results of the diagnostic performance in patients after BMI stratification are reported in Table 1. Conclusion: We observed an excellent correlation between REMS-based diagnosis with respect to the osteoporosis diagnosis performed by DXA in patients from each BMI category. These results indicate that lumbar spine REMS analysis is a suitable and accurate diagnostic tool for patients with different BMI. References: [1]Di Paola M et al. Osteoporos Int. 2019 Feb;30(2):391-402. Table 1. Diagnostic performance of REMS with respect to DXA considered as reference for patients of different body size. BMI categories r No tolerance 0.3 T-score tolerance Sens. Spec. PPV NPV K Sens. Spec. PPV NPV K Underweight 0.946 97.5 91.5 86.8 98.4 0.89 98.2 93.2 89.2 99.3 0.91 Normal weight 0.946 92.6 94.4 87.0 96.9 0.85 97.5 96.7 92.3 99.0 0.93 Overweight 0.930 86.7 95.8 84.8 96.4 0.82 96.3 97.8 92.1 99.0 0.93 Abbreviations : BMI = body mass index; r = Pearson correlation coefficient; Sens. = sensitivity; Spec. = specificity; PPV = positive predictive value; NPV = negative predictive value; K = Cohen’s K. The authors BC, ED, ADP, ML, MM, XN, DOC are equal contributors listed in alphabetical order. Disclosure of Interests: None declared.