Due to concerns about cumulative radiation exposure in the pediatric population, it is not standard practice to perform spine radiographs in most conditions that predispose to vertebral fracture (VF). In this study we examined the accuracy of two clinical predictors, back pain and lumbar spine bone mineral density (LS BMD), to derive four case‐finding paradigms for detection of prevalent VF (PVF). Subjects were 400 children at risk for PVF (leukemia 186, rheumatic disorders 135, nephrotic syndrome 79). Back pain was assessed by patient report, LS BMD was measured by dual‐energy X‐ray absorptiometry, and PVF were quantified on spine radiographs using the modified Genant semiquantitative method. Forty‐four patients (11.0%) had PVF. Logistic regression analysis between LS BMD and PVF produced an odds ratio (OR) of 1.9 (95% confidence interval [CI], 1.5 to 2.5) per reduction in Z ‐score unit, an area under the receiver operating characteristic curve of 0.70 (95% CI, 0.60 to 0.79), and an optimal BMD Z ‐score cutoff of −1.6. Case identification using either low BMD alone (Z ‐score < −1.6) or back pain alone gave similar results for sensitivity (55%, 52%, respectively), specificity (78%, 81%, respectively), positive predictive value (PPV; 24%, 25%, respectively), and negative predictive value (NPV; 93%, 93%, respectively). The paradigm using low BMD plus back pain produced lower sensitivity (32%), higher specificity (96%), higher PPV (47%), and similar NPV (92%). The approach using low BMD or back pain had the highest sensitivity (75%), lowest specificity (64%), lowest PPV (20%), and highest NPV (95%). All paradigms had increased sensitivities for higher fracture grades. Our results show that BMD and back pain history can be used to identify children with the highest risk of PVF so that radiography can be used judiciously. The specific paradigm to be applied will depend on the expected PVF rate and the clinical approach to the use of radiography. © 2019 American Society for Bone and Mineral Research.
Senior Scientist, CHEO Research Institute