Prediction models of prevalent radiographic vertebral fractures among older women

Abstract

It is unknown how well prediction models incorporating multiple risk factors identify women with radiographic prevalent vertebral fracture (PVFx) compared with simpler models and what their value might be in clinical practice to select older women for lateral spine imaging. We compared 4 regression models for predicting PVFx in women aged 68 y and older enrolled in the Study of Osteoporotic Fractures with a femoral neck T-score ≤ -1.0, using area under receiving operator characteristic curves (AUROC) and a net reclassification index. The AUROC for a model with age, femoral neck bone mineral density, historical height loss (HHL), prior nonspine fracture, body mass index, back pain, and grip strength was only minimally better than that of a more parsimonious model with age, femoral neck bone mineral density, and historical height loss (AUROC 0.689 vs 0.679, p values for difference in 5 bootstrapped samples <0.001-0.35). The prevalence of PVFx among this older population of Caucasian women remained more than 20% even when women with low probability of PVFx, as estimated by the prediction models, were included in the screened population. These results suggest that lateral spine imaging is appropriate to consider for all Caucasian women aged 70 y and older with low bone mass to identify those with PVFx.

Keywords: Bone densitometry; model discrimination; prediction models; prevalent vertebral fracture; vertebral fracture assessment.