Purpose To describe a nonlinear finite element analysis method by using magnetic resonance (MR) images for the assessment of the mechanical competence of the hip and to demonstrate the reproducibility of the tool. Materials and Methods This prospective study received institutional review board approval and fully complied with HIPAA regulations for patient data. Written informed consent was obtained from all subjects. A nonlinear finite element analysis method was developed to estimate mechanical parameters that relate to hip fracture resistance by using MR images. Twenty-three women (mean age ± standard deviation, 61.7 years ± 13.8) were recruited from a single osteoporosis center. To thoroughly assess the reproducibility of the finite element method, three separate analyses were performed: a test-retest reproducibility analysis, where each of the first 13 subjects underwent MR imaging on three separate occasions to determine longitudinal variability, and an intra- and interoperator reproducibility analysis, where a single examination was performed in each of the next 10 subjects and four operators independently performed the analysis two times in each of the subjects. Reproducibility of parameters that reflect fracture resistance was assessed by using the intraclass correlation coefficient and the coefficient of variation. Results For test-retest reproducibility analysis and inter- and intraoperator analyses for proximal femur stiffness, yield strain, yield load, ultimate strain, ultimate load, resilience, and toughness in both stance and sideways-fall loading configurations each had an individual median coefficient of variation of less than 10%. Additionally, all measures had an intraclass correlation coefficient higher than 0.99. Conclusion This experiment demonstrates that the finite element analysis model can consistently and reliably provide fracture risk information on correctly segmented bone images. RSNA, 2016 Online supplemental material is available for this article.

High-resolution MRI-derived finite element analysis (FEA) has been used in translational research to estimate the mechanical competence of human bone. However, this method has yet to be validated adequately under in vivo imaging spatial resolution or signal-to-noise conditions. We therefore compared MRI-based metrics of bone strength to those obtained from direct, mechanical testing. The study was conducted on tibiae from 17 human donors (12 males and five females, aged 33 to 88years) with no medical history of conditions affecting bone mineral homeostasis. A 25mm segment from each distal tibia underwent MR imaging in a clinical 3-Tesla scanner using a fast large-angle spin-echo (FLASE) sequence at 0.137mm×0.137mm×0.410mm voxel size, in accordance with in vivo scanning protocol. The resulting high-resolution MR images were processed and used to generate bone volume fraction maps, which served as input for the micro-level FEA model. Simulated compression was applied to compute stiffness, yield strength, ultimate strength, modulus of resilience, and toughness, which were then compared to metrics obtained from mechanical testing. Moderate to strong positive correlations were found between computationally and experimentally derived values of stiffness (R=0.77, p<0.0001), yield strength (R=0.38, p=0.0082), ultimate strength (R=0.40, p=0.0067), and resilience (R=0.46, p=0.0026), but only a weak, albeit significant, correlation was found for toughness (R=0.26, p=0.036). Furthermore, experimentally derived yield strength and ultimate strength were moderately correlated with MRI-derived stiffness (R=0.48, p=0.0022 and R=0.58, p=0.0004, respectively). These results suggest that high-resolution MRI-based finite element (FE) models are effective in assessing mechanical parameters of distal skeletal extremities.

Background Rural patients with type 2 diabetes (T2D) may experience poor glycemic control due to limited access to T2D specialty care and self-management support. Telehealth can facilitate delivery of comprehensive T2D care to rural patients, but implementation in clinical practice is challenging. Objective To examine the implementation of Advanced Comprehensive Diabetes Care (ACDC), an evidence-based, comprehensive telehealth intervention for clinic-refractory, uncontrolled T2D. ACDC leverages existing Veterans Health Administration (VHA) Home Telehealth (HT) infrastructure, making delivery practical in rural areas. Design Mixed-methods implementation study. Participants 230 patients with clinic-refractory, uncontrolled T2D. Intervention ACDC bundles telemonitoring, self-management support, and specialist-guided medication management, and is delivered over 6 months using existing VHA HT clinical staffing/equipment. Patients may continue in a maintenance protocol after the initial 6-month intervention period. Main Measures Implementation was evaluated using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. The primary effectiveness outcome was hemoglobin A1c (HbA1c). Key Results From 2017 to 2020, ACDC was delivered to 230 patients across seven geographically diverse VHA sites; on average, patients were 59 years of age, 95% male, 80% white, and 14% Hispanic/Latinx. Patients completed an average of 10.1 of 12 scheduled encounters during the 6-month intervention period. Model-estimated mean baseline HbA1c was 9.56% and improved to 8.14% at 6 months (− 1.43%, 95% CI: − 1.64, − 1.21; P < .001). Benefits persisted at 12 (− 1.26%, 95% CI: − 1.48, − 1.05; P < .001) and 18 months (− 1.08%, 95% CI − 1.35, − 0.81; P < .001). Patients reported increased engagement in self-management and awareness of glycemic control, while clinicians and HT nurses reported a moderate workload increase. As of this submission, some sites have maintained delivery of ACDC for up to 4 years. Conclusions When strategically designed to leverage existing infrastructure, comprehensive telehealth interventions can be implemented successfully, even in rural areas. ACDC produced sustained improvements in glycemic control in a previously refractory population. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07281-8.

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