ABSTRACT
BACKGROUND: The FRAX® algorithm is a tool used to calculate the 10-year probability of fracture in patients with osteoporosis and is based the assessment of several risk factors. We assessed the performance and accuracy of the completion of the FRAX® anamnestic questionnaire by the radiographer without impact on the clinical workflow. METHODOLOGY: We evaluated the accuracy of fracture risk calculation by the radiographer using the FRAX® algorithm before and after specific training. A total of 100 women were enrolled in the study. The radiographer preliminarily administered the FRAX® questionnaire to all subjects before the execution of the DXA examination. After the end of the examination, a radiologist administered the questionnaire to the patient. Women were divided into two groups: group A (pre-training) and group B (post-training). The radiographer in group A completed the FRAX® questionnaire for the patients before training. For group B, the same radiographer completed the FRAX® questionnaire after training. The results of the FRAX® questionnaire completed by radiographer were compared with that completed by the referring physician. RESULTS: Before training, radiographer's accuracy ranged from 92% (question 7, alcohol consumption) to 36% (question 6, secondary osteoporosis). After training, accuracy values improved substantially, ranging from 100% to 92%. Analysis of the absolute values of FRAX® showed that in the pre-training group data tended to be overestimated by the radiographer, with both major and fractures probabilities being significantly higher when assessed by the radiographer (12% and 5.8%, respectively). After the training, there was a marked decrease in the variation between the FRAX® data calculated by the radiographer and the radiologist. CONCLUSIONS: The accuracy of fracture risk calculation by the radiographer using the FRAX® algorithm is significantly improved after a specific training period. This study demonstrates the importance of dedicated training radiographers on the FRAX® algorithm.