ORCID Profile
0000-0003-3602-551X
Current Organisation
University of Calgary
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Publisher: Elsevier BV
Date: 02-2021
Publisher: Wiley
Date: 14-01-2022
DOI: 10.1002/JBMR.4494
Abstract: Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most in iduals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one‐size‐fits‐all” approach to diagnosis and management of osteoporosis may not be suitable. High‐resolution peripheral quantitative computed tomography (HR‐pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR‐pQCT data from a multinational cohort ( n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c‐means clustering, an unsupervised machine‐learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR‐pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture ( p 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).
Publisher: Wiley
Date: 03-05-2023
DOI: 10.1002/JBMR.4808
Abstract: Most fracture risk assessment tools use clinical risk factors combined with bone mineral density (BMD) to improve assessment of osteoporosis however, stratifying fracture risk remains challenging. This study developed a fracture risk assessment tool that uses information about volumetric bone density and three‐dimensional structure, obtained using high‐resolution peripheral quantitative compute tomography (HR‐pQCT), to provide an alternative approach for patient‐specific assessment of fracture risk. Using an international prospective cohort of older adults ( n = 6802) we developed a tool to predict osteoporotic fracture risk, called μFRAC. The model was constructed using random survival forests, and input predictors included HR‐pQCT parameters summarizing BMD and microarchitecture alongside clinical risk factors (sex, age, height, weight, and prior adulthood fracture) and femoral neck areal BMD (FN aBMD). The performance of μFRAC was compared to the Fracture Risk Assessment Tool (FRAX) and a reference model built using FN aBMD and clinical covariates. μFRAC was predictive of osteoporotic fracture (c‐index = 0.673, p 0.001), modestly outperforming FRAX and FN aBMD models (c‐index = 0.617 and 0.636, respectively). Removal of FN aBMD and all clinical risk factors, except age, from μFRAC did not significantly impact its performance when estimating 5‐year and 10‐year fracture risk. The performance of μFRAC improved when only major osteoporotic fractures were considered (c‐index = 0.733, p 0.001). We developed a personalized fracture risk assessment tool based on HR‐pQCT that may provide an alternative approach to current clinical methods by leveraging direct measures of bone density and structure. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
Publisher: Wiley
Date: 22-08-2022
DOI: 10.1002/JBMR.4663
Abstract: Femoral neck areal bone mineral density (FN aBMD) is a key determinant of fracture risk in older adults however, the majority of in iduals who have a hip fracture are not considered osteoporotic according to their FN aBMD. This study uses novel tools to investigate the characteristics of bone microarchitecture that underpin bone fragility. Recent hip fracture patients ( n = 108, 77% female) were compared with sex‐ and age‐matched controls ( n = 216) using high‐resolution peripheral quantitative computed tomography (HR‐pQCT) imaging of the distal radius and tibia. Standard morphological analysis of bone microarchitecture, micro‐finite element analysis, and recently developed techniques to identify void spaces in bone microarchitecture were performed to evaluate differences between hip fracture patients and controls. In addition, a new approach for phenotyping bone microarchitecture was implemented to evaluate whether hip fractures in males and females occur more often in certain bone phenotypes. Overall, hip fracture patients had notable deterioration of bone microarchitecture and reduced bone mineral density compared with controls, especially at weight‐bearing sites (tibia and femoral neck). Hip fracture patients were more likely to have void spaces present at either site and had void spaces that were two to four times larger on average when compared with non‐fractured controls ( p 0.01). Finally, bone phenotyping revealed that hip fractures were significantly associated with the low density phenotype ( p 0.01), with the majority of patients classified in this phenotype (69%). However, female and male hip fracture populations were distributed differently across the bone phenotype continuum. These findings highlight how HR‐pQCT can provide insight into the underlying mechanisms of bone fragility by using information about bone phenotypes and identification of microarchitectural defects (void spaces). The added information suggests that HR‐pQCT can have a beneficial role in assessing the severity of structural deterioration in bone that is associated with osteoporotic hip fractures. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
Publisher: Wiley
Date: 20-07-2020
DOI: 10.1002/JBMR.4114
Publisher: Springer Science and Business Media LLC
Date: 29-11-2019
DOI: 10.1007/S00198-019-05214-0
Abstract: Manual correction of automatically generated contours for high-resolution peripheral quantitative computed tomography can be time consuming and introduces precision error. However, bias related to the automated protocol is unknown. This study provides insight into error bias that is present when using uncorrected contours and inter-operator precision error based on operator training. High-resolution peripheral quantitative computed tomography workflow includes manually correcting contours generated by the manufacturer's automated protocol. There is interest in minimizing corrections to save time and reduce precision error however, bias related to the automated protocol is unknown. This study quantifies error bias when contours are uncorrected and identifies the impact of operator training on bias and precision error. Forty-five radii and tibiae scans across a representative range of bone density were analyzed using the automated and manually corrected contours of three operators, with training ranging from beginner to expert, and compared with a "ground truth" to estimate bias. Inter-operator precision was measured across operators. The tibia had greater error bias than the radius when contours were uncorrected, with compartmental bone mineral densities and cortical microarchitecture having greatest biases, which could have significant implications for interpretation of studies using this skeletal site. Bias and precision error were greatest when contours were corrected by the beginner operator however, when this operator was removed, bias was no longer present and inter-operator precision was between 0.01 and 3.74% for all parameters except cortical porosity. These findings establish the need for manual correction and provide guidance on operator training needed to maximize workflow efficiency.
No related grants have been discovered for Danielle Whittier.