The utility of clinical decision tools for diagnosing osteoporosis in postmenopausal women with rheumatoid arthritis

BACKGROUND Patients with rheumatoid arthritis have a higher risk of low bone mineral density than normal age matched populations. There is limited evidence to support cost effectiveness of population screening in rheumatoid arthritis and case finding strategies have been proposed as a means to increase cost effectiveness of diagnostic screening for osteoporosis. This study aimed to assess the performance attributes of generic and rheumatoid arthritis specific clinical decision tools for diagnosing osteoporosis in a postmenopausal population with rheumatoid arthritis who attend ambulatory specialist rheumatology clinics. METHODS A cross-sectional study of 127 ambulatory post-menopausal women with rheumatoid arthritis was performed. Patients currently receiving or who had previously received bone active therapy were excluded. Eligible women underwent clinical assessment and dual-energy-xray absorptiometry (DXA) bone mineral density assessment. Clinical decision tools, including those specific for rheumatoid arthritis, were compared to seven generic post-menopausal tools to predict osteoporosis (defined as T score < -2.5). Sensitivity, specificity, positive predictive and negative predictive values and area under the curve were assessed. The diagnostic attributes of the clinical decision tools were compared by examination of the area under the receiver-operator-curve. RESULTS One hundred and twenty seven women participated. The median age was 62 (IQR 56-71) years. Median disease duration was 108 (60-168) months. Seventy two (57%) women had no record of a previous DXA examination. Eighty (63%) women had T scores at femoral neck or lumbar spine less than -1. The area under the ROC curve for clinical decision tool prediction of T score <-2.5 varied between 0.63 and 0.76. The rheumatoid arthritis specific decision tools did not perform better than generic tools, however, the National Osteoporosis Foundation score could potentially reduce the number of unnecessary DXA tests by approximately 45% in this population. CONCLUSION There was limited utility of clinical decision tools for predicting osteoporosis in this patient population. Fracture prediction tools that include risk factors independent of BMD are needed.