Evaluating Male Patients' Understanding of Osteoporosis Evaluation and Treatment Following a Distal Radius Fracture.

PURPOSE: Current estimates suggest that 1-2 million men in the United States have osteoporosis, yet the majority of osteoporosis literature focuses on postmenopausal women. Our aim was to understand men's awareness and knowledge of osteoporosis and its treatment. METHODS: Semistructured interviews were conducted with 20 male patients >50 years old who sustained a low-energy distal radius fracture. The goal was to ascertain patients' knowledge of osteoporosis, its management, and experience discussing osteoporosis with their primary care physicians (PCP). RESULTS: Participants had little knowledge of osteoporosis or its treatment. Many participants regarded osteoporosis as a women's disease. Most participants expressed concern regarding receiving a diagnosis of osteoporosis. Several patients stated that they believe osteoporosis may have contributed to their fracture. Families, friends, or mass media served as the primary information source for participants, but few had good self-reported understanding of the disease itself. The majority of participants reported never having discussed osteoporosis with their PCPs although almost half had received a dual x-ray absorptiometry scan. Participants expressed general interest in being tested/screened and generally were willing to undergo treatment despite the perception that medication has serious side effects. One patient expressed concern that treatment side effects could be worse than having osteoporosis. CONCLUSION: Critical knowledge gaps exist regarding osteoporosis diagnosis and treatment in at-risk male patients. Specifically, most patients were unaware they could be osteoporotic because of the perception of osteoporosis as a women's disease. Most patients had never discussed osteoporosis with their PCP. CLINICAL RELEVANCE: Male patients remain relatively unaware of osteoporosis as a disease entity. Opportunity exists for prevention of future fragility fractures by improving communication between patients and physicians regarding osteoporosis screening in men following low-energy distal radius fractures.

Community Walks: a cluster randomized controlled trial of a multilevel physical activity intervention for low income public housing residents.

BACKGROUND: Physical activity behavioral interventions to change individual-level drivers of activity, like motivation, attitudes, and self-efficacy, are often not sustained beyond the intervention period. Interventions at both environmental and individual levels might facilitate durable change. This community-based study seeks to test a multilevel, multicomponent intervention to increase moderate intensity physical activity among people with low incomes living in U.S. public housing developments, over a 2 year period. METHODS: The study design is a prospective, cluster randomized controlled trial, with housing developments (n=12) as the units of randomization. In a four-group, factorial trial, we will compare an environmental intervention (E) alone (3 developments), an individual intervention (I) alone (3 developments), an environmental plus individual (E+I) intervention (3 developments), against an assessment only control group (3 developments). The environmental only intervention consists of community health workers leading walking groups and indoor activities, a walking advocacy program for residents, and provision of walking maps/signage. The individual only intervention consists of a 12-week automated telephone program to increase physical activity motivation and self-efficacy. All residents are invited to participate in the intervention activities being delivered at their development. The primary outcome is change in moderate intensity physical activity measured via an accelerometer-based device among an evaluation cohort (n=50 individuals at each of the 12 developments) from baseline to 24-month follow up. Mediation (e.g., neighborhood walkability, motivation) and moderation (e.g., neighborhood stress) of our interventions will be assessed. Lastly, we will interview key informants to assess factors from the Consolidated Framework for Implementation Research domains to inform future implementation. DISCUSSION: We hypothesize participants living in developments in any of the three intervention groups (E only, I only, and E+I combined) will increase minutes of moderate intensity physical activity more than participants in control group developments. We expect delivery of an intervention package targeting environmental and social factors to become active, combined with the individual level intervention, will improve overall physical activity levels to recommended guidelines at the development level. If effective, this trial has the potential for implementation through other federal and state housing authorities. TRIAL REGISTRATION: Clinical Trails.gov PRS Protocol Registration and Results System, NCT05147298 . Registered 28 November 2021.

Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging.

The purpose of this study was to pair computed tomography (CT) imaging and machine learning for automated bone tumor segmentation and classification to aid clinicians in determining the need for biopsy. In this retrospective study (March 2005-October 2020), a dataset of 84 femur CT scans (50 females and 34 males, 20 years and older) with definitive histologic confirmation of bone lesion (71% malignant) were leveraged to perform automated tumor segmentation and classification. Our method involves a deep learning architecture that receives a DICOM slice and predicts (i) a segmentation mask over the estimated tumor region, and (ii) a corresponding class as benign or malignant. Class prediction for each case is then determined via majority voting. Statistical analysis was conducted via fivefold cross validation, with results reported as averages along with 95% confidence intervals. Despite the imbalance between benign and malignant cases in our dataset, our approach attains similar classification performances in specificity (75%) and sensitivity (79%). Average segmentation performance attains 56% Dice score and reaches up to 80% for an image slice in each scan. The proposed approach establishes the first steps in developing an automated deep learning method on bone tumor segmentation and classification from CT imaging. Our approach attains comparable quantitative performance to existing deep learning models using other imaging modalities, including X-ray. Moreover, visual analysis of bone tumor segmentation indicates that our model is capable of learning typical tumor characteristics and provides a promising direction in aiding the clinical decision process for biopsy.

Microvascular disease and early diabetes onset are associated with deficits in femoral neck bone density and structure among older adults with longstanding type 1 diabetes.

Adults with type 1 diabetes (T1D) have increased hip fracture risk, yet no studies have assessed volumetric bone density or structure at the hip in older adults with T1D. Here, we used previously collected 3D CT scans of the proximal femur from older adults with longstanding T1D and non-diabetic controls to identify bone deficits that may contribute to hip fracture in T1D. In this retrospective cohort study, we identified 101 adults with T1D and 181 age-, sex-, and race-matched non-diabetic controls (CON) who received abdominal or pelvis CT exams from 2010 to 2020. Among adults with T1D, 33 (33%) had mild-to-moderate nephropathy, 61 (60%) had neuropathy, and 71 (70%) had retinopathy. Within the whole cohort, adults with T1D tended to have lower FN density, though differences did not reach statistical significance. The subset of the T1D group who were diagnosed before age 15 had lower total BMC (-14%, TtBMC), cortical BMC (-19.5%, CtBMC), and smaller Ct cross-sectional area (-12.6, CtCSA) than their matched controls (p<.05 for all). Individuals with T1D who were diagnosed at a later age did not differ from controls in any bone outcome (p>.21). Furthermore, adults with T1D and nephropathy had lower FN aBMD (-10.6%), TtBMC (-17%), CtBMC (-24%), and smaller CtCSA (-15.4%) compared to matched controls (p<.05 for all). Adults with T1D and neuropathy had cortical bone deficits (8.4%-12%, p<.04). In summary, among older adults with T1D, those who were diagnosed before the age of 15 yr, as well as those with nephropathy and neuropathy had unfavorable bone outcomes at the FN, which may contribute to the high risk of hip fractures among patients with T1D. These novel observations highlight the longstanding detrimental impact of T1D when present during bone accrual and skeletal fragility as an additional complication of microvascular disease in individuals with T1D.

Older adults with type 1 diabetes (T1D) are at higher risk for hip fractures, but the reasons for this are unclear. In this study, we analyzed existing clinical CT scans of the hip from older adults with longstanding T1D and those without diabetes. Although overall bone density differences were not significant, older adults with T1D who were diagnosed before age 15 or had complications like nephropathy or neuropathy showed worse bone outcomes at the FN. These findings suggest that early onset T1D and related complications contribute to increased hip fracture risk.

Proximal femur fracture detection on plain radiography via feature pyramid networks.

Hip fractures exceed 250,000 cases annually in the United States, with the worldwide incidence projected to increase by 240-310% by 2050. Hip fractures are predominantly diagnosed by radiologist review of radiographs. In this study, we developed a deep learning model by extending the VarifocalNet Feature Pyramid Network (FPN) for detection and localization of proximal femur fractures from plain radiography with clinically relevant metrics. We used a dataset of 823 hip radiographs of 150 subjects with proximal femur fractures and 362 controls to develop and evaluate the deep learning model. Our model attained 0.94 specificity and 0.95 sensitivity in fracture detection over the diverse imaging dataset. We compared the performance of our model against five benchmark FPN models, demonstrating 6-14% sensitivity and 1-9% accuracy improvement. In addition, we demonstrated that our model outperforms a state-of-the-art transformer model based on DINO network by 17% sensitivity and 5% accuracy, while taking half the time on average to process a radiograph. The developed model can aid radiologists and support on-premise integration with hospital cloud services to enable automatic, opportunistic screening for hip fractures.