Assessing fracture risk using gradient boosting machine (GBM) models

Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. We studied two cohorts of postmenopausal women: 105 with and 99 without distal forearm fractures (Distal Forearm Cohort) and 40 with at least one grade 2 or 3 vertebral deformity and 78 with no vertebral fracture (Vertebral Cohort). Within each cohort, individual bone density, structure, or strength variables had areas under receiver operating characteristic curves (AUCs) ranging from 0.50 to 0.84 (median 0.61) for discriminating women with and without fracture. Using all possible variables in the GBM model, the AUCs were close to 1.0. Fracture predictions in the Vertebral Cohort using the GBM models built with the Distal Forearm Cohort had AUCs of 0.82-0.95, whereas predictions in the Distal Forearm Cohort using models built with the Vertebral Cohort had AUCs of 0.80-0.83. Attempts at capturing a comparable parametric model using the top variables from the Distal Forearm Cohort resulted in resulted in an AUC of 0.81. Relatively high AUCs for differing fracture types suggest that an underlying fracture propensity is being captured by this modeling approach. More complex modeling, such as with GBM, creates stronger fracture predictions and may allow deeper insights into information provided by advanced bone imaging techniques.