Abstract for treece_tr695

Cambridge University Engineering Department Technical Report CUED/F-INFENG/TR695

PREDICTING HIP FRACTURE TYPE WITH CORTICAL BONE MAPPING (CBM) IN THE OSTEOPOROTIC FRACTURES IN MEN (MROS) STUDY

G.M. Treece, A.H. Gee, C. Tonkin, S.K. Ewing, P.M. Cawthon, D. Black and K.E.S. Poole.

January 2015

Hip fracture risk is known to be related to material properties of the proximal femur, but prospec- tive studies investigating fracture prediction which add richer quantitative computed tomography (QCT) measures to more traditional dual energy X-ray (DXA)-based methods have shown only limited improve- ment. Though it is understood that fracture types have distinct relationships to predictors, few studies have sub-divided fracture into types, since this necessitates regional measurements and an increased num- ber of fracture cases. We here report on a study which makes use of cortical bone mapping (CBM) to accurately assess, with no prior anatomical presumptions, the distribution of femoral properties related to fracture type. CBM makes use of QCT data to measure the cortical and trabecular properties, and is accurate even for very thin cortices below the imaging resolution. We use a prospective case-cohort of older men: the osteoporotic fractures in men (MrOS) study, in which we analyse 99 fracture cases (44 trochanteric and 55 femoral neck, by centralised review of radiology reports) compared to a cohort of 308, randomly selected from 5,994 men over 65. To our knowledge, this is the largest QCT-based prospective hip fracture study to date, as well as the first to incorporate CBM analysis into fracture prediction. We demonstrate that both cortical mass surface density, and endocortical trabecular BMD, show significant difference in fracture cases vs. cohort, in regions which are appropriate to fracture type. We incorporate these regions into predictive models using Cox proportional hazards regression (with accommodations for the case-cohort sampling) to estimate hazard ratios, and logistic regression to estimate the area under the receiver operating characteristic curve (AUC). Adding CBM to DXA-based BMD leads to a small but significant (p < 0.005) improvement in model prediction for any fracture, with AUC increasing from 0.78 to 0.79, when assessed using leave-one-out cross-validation. For specific fracture types, the improvement is more significant (p < 0.0001) and more marked, with AUC improving from 0.71 to 0.77 (trochanteric fractures) and 0.76 to 0.82 (femoral neck fractures). In contrast, adding DXA-based BMD to a predictive model already containing CBM regions does not result in any significant improvement.

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