Osteoporosis International最新文献

This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation with quantitative computed tomography (QCT) results. The proposed models offer potential for screening patients at a high risk of osteoporosis and reducing unnecessary radiation and costs.

Purpose: To explore the feasibility of using a hybrid deep learning framework (HDLF) to establish a model for BMD prediction and classification based on BPX images. This study aimed to establish an automated tool for screening patients at a high risk of osteoporosis.

Methods: A total of 906 BPX scans from 453 subjects were included in this study, with QCT results serving as the reference standard. The training-validation set:independent test set ratio was 4:1. The L1-L3 vertebral bodies were manually annotated by experienced radiologists, and the HDLF was established to predict BMD and diagnose abnormality based on BPX images and clinical information. The performance metrics of the models were calculated and evaluated.

Results: The $R^2$ values of the BMD prediction regression model in the independent test set based on BPX images and multimodal data (BPX images and clinical information) were 0.77 and 0.79, respectively. The Pearson correlation coefficients were 0.88 and 0.89, respectively, with P-values < 0.001. Bland-Altman analysis revealed no significant difference between the predictions of the models and QCT results. The classification model achieved the highest AUC of 0.97 based on multimodal data in the independent test set, with an accuracy of 0.93, sensitivity of 0.84, specificity of 0.96, and F1 score of 0.93.

Conclusion: This study demonstrates that deep learning neural networks applied to BPX images can accurately predict BMD and perform classification diagnoses, which can reduce the radiation risk, economic consumption, and time consumption associated with specialized BMD measurement.

Osteoporosis is often underrecognized and undertreated following periprosthetic fractures (PPF). Our study found that between 2010 and 2020, there has been no significant change in the rates of osteoporosis screening or treatment within 1 year following PPF. Orthopedic surgeons can play an integral role in helping to curtail the osteoporosis epidemic.

Purpose: Periprosthetic fractures (PPF) typically occur from low-energy mechanisms and are pathognomonic for osteoporosis. However, osteoporosis is often underrecognized and undertreated. The aim of this study was to examine trends in dual energy X-ray absorptiometry (DXA) scans and treatment of osteoporosis after PPF between 2010 and 2020.

Methods: Patients older than 40 who experienced a lower extremity PPF between 2010 and 2020 and had no prior history of osteoporosis screening or treatment were identified utilizing a large national administrative database. Rates of bone mineral density (BMD) measurement using DXA and anti-osteoporotic treatment with pharmacotherapy, or either intervention within 1 year following experiencing a PPF were determined. The rate of change for these interventions was calculated using the compounded annual growth rate (CAGR), with linear regression used to determine whether trends were statistically significant.

Results: In total, 5.7% and 3.6% of patients were screened and treated for osteoporosis, respectively. Between 2010 and 2020, there was no significant change in rates of osteoporosis screening (CAGR + 0.1%; p = 0.13), treatment (CAGR - 2.4%; p = 0.29), or either intervention (CAGR - 1.1%; p = 0.77) within 1 year following PPF. Factors associated with intervention included older age, female sex, and increased comorbidities.

Conclusion: Our study found that there has been no significant change in the rates of osteoporosis screening or treatment within 1 year following PPF. Orthopedic surgeons and allied healthcare workers can play an integral role in helping to curtail the osteoporosis epidemic.

We investigated the efficacy of romosozumab in premenopausal women with low bone mass. Romosozumab substantially increased bone mineral density and trabecular bone score in these women, aligning with its proven therapeutic benefits for postmenopausal osteoporosis.

Purpose: Romosozumab, an anti-sclerostin antibody, is a promising anabolic agent that increases bone formation and decreases bone resorption. However, its efficacy in premenopausal women with low bone mass remains understudied.

Methods: We retrospectively reviewed premenopausal women with low bone mass treated with romosozumab (ROMO group) or drug-naïve patients (control group). Patients in the ROMO group were classified into the glucocorticoid-induced osteoporosis (GIOP), idiopathic osteoporosis (IOP), and pregnancy and lactation-induced osteoporosis (PLO) subgroups. Bone mineral density (BMD) and trabecular bone score (TBS) were measured before and after one year of romosozumab treatment.

Results: Twenty-five patients in the ROMO group and five in the control group were included in the study. Among patients in the ROMO group, 12 were in the GIOP, 9 in the IOP, and 4 in the PLO subgroups. The mean age was 37.0 years [32.0-42.0], and the median body mass index was 18.8 kg/m² [17.5-21.3]. After romosozumab treatment, lumbar spine (LS), femur neck (FN) BMD, and TBS increased from baseline (LSBMD, 12.8% [8.2-19.3], p < 0.001; FNBMD, 4.6% [- 0.6-10.7], p = 0.016; TBS, 4.1% ± 3.8, p < 0.001) in the ROMO group. Patients in both the GIOP and IOP subgroups showed a significant increase in LSBMD, while those in the IOP subgroup demonstrated significant increases in FNBMD.

Conclusion: We demonstrated romosozumab's efficacy in BMD increment in premenopausal women. Romosozumab may be a potential treatment option for premenopausal women with low bone mass, regardless of etiologies, although further research on fracture risk reduction is warranted.

Subsequent fracture rates and associated mortality were compared before and after the introduction of fracture liaison service (FLS). In 100,198 women and men, FLS was associated with 13% and 10% lower risk of subsequent fragility fractures and 18% and 15% lower mortality. The study suggests that FLS may prevent fractures.

Purpose: Efficient fracture prevention strategies are warranted to control the global fracture burden. We investigated the effect of a standardized fracture liaison service (FLS) intervention on subsequent fracture risk and mortality.

Methods: The NoFRACT study was designed as a multicenter, pragmatic, register-supported, stepped-wedge cluster-randomized trial. The FLS intervention was introduced in three clusters with 4-month intervals starting May 2015 through December 2018 and included evaluation of osteoporosis and treatment in patients over 50 years with a low-energy fracture. Based on data from the Norwegian Patient Registry, patients with index fractures were assigned to the control period (2011-2015) or intervention period (2015-2018) depending on the time of fracture. Rates of subsequent fragility fractures (distal forearm, proximal humerus, or hip) and all-cause mortality were calculated.

Results: A total of 100,198 patients (mean age 69.6 years) suffered an index fracture of any type. During a maximum follow-up of 4.7 years, 11% (6948) of the women and 6% (2014) of the men experienced a subsequent fragility fracture, and 20% (14,324) of the women and 22% (8,326) of the men died. FLS was associated with 13% lower subsequent fragility fracture risk in women (hazard ratio (HR) 0.87, 95% confidence intervals (CI) 0.83-0.92) and 10% in men (HR 0.90, 95% CI 0.81-0.99) and 18% lower mortality in women (HR 0.82, 95% CI 0.79-0.86) and 15% in men (HR 0.85, 95% CI 0.81-0.89).

Conclusion: A standardized FLS intervention was associated with a lower risk of subsequent fragility fractures and mortality and may contribute to reduce the global fracture burden.

Most subjects in osteoporosis clinical trials were women with postmenopausal osteoporosis and while bridging studies (BMD endpoint) provide an expectation that osteoporosis medications will reduce fracture risk in men. This real-world study shows direct evidence of fracture risk reduction among men with osteoporosis (36% of hip fracture reduction with denosumab).

Purpose: Direct evidence for fracture risk reduction of medications used among men with osteoporosis is very limited. This study aims to evaluate the real-world effectiveness of denosumab in reducing fracture risk.

Methods: This study included 13,797 men aged ≥ 50 years with osteoporosis who had initiated denosumab in Taiwan. Taiwan's National Health Insurance Research Database includes all Taiwan residents' complete health claim data. We compared incidence rates of clinical fractures between patients on denosumab 60 mg subcutaneously every 6 months (on-treatment) and patients ending therapy after one administration (off-treatment). Propensity score (PS) analysis, adjusting for measured differences at baseline covariates, was used to estimate the adjusted hazard ratio using a Cox proportion hazards model.

Results: During follow-up, 248 hip fracture events occurred. The crude incidence rates of hip fracture were 1.13 events and 1.73 events per 100 person-years in on-treatment and off-treatment cohorts, respectively. After PS inverse probability of treatment weighting, the cohorts achieved balance in all 59 covariates. The hip fracture event rate was lower in on-treatment cohort versus off-treatment cohort by 36% (hazard ratio, 0.64 [95% CI 0.50-0.83]). A similar magnitude of risk reduction was observed in clinical vertebral and nonvertebral fractures. A series of sensitivity analysis, including a validation analysis using a-million individual health records, demonstrated that unmeasured confounders were not suggested to impact study result interpretation.

Conclusion: In this large, real-world study evaluating denosumab treatment among men with osteoporosis, the observed fracture risk reductions were consistent with the available risk reductions demonstrated in clinical trials among women with postmenopausal osteoporosis.

Understanding how the questions used when interacting with chatbots impact the readability of the generated text is essential for effective health communication. Using descriptive queries instead of just keywords during interaction with ChatGPT results in more readable and understandable answers about fragility fractures.

Purpose: Large language models like ChatGPT can enhance patients' understanding of medical information, making health decisions more accessible. Complex terms, such as "fragility fracture," can confuse patients, so presenting its medical content in plain language is crucial. This study explored whether conversational prompts improve readability and understanding compared to keyword-based prompts when generating patient-centered health information on fragility fractures.

Methods: The 32 most frequently searched keywords related to "fragility fracture" and "osteoporotic fracture" were identified using Google Trends. From this set, 24 keywords were selected based on relevance and entered sequentially into ChatGPT. Each keyword was tested with two prompt types: (1) plain language with keywords embedded and (2) keywords alone. The readability and comprehensibility of the AI-generated responses were assessed using the Flesch-Kincaid reading ease (FKRE) and Flesch-Kincaid grade level (FKGL), respectively. The scores of the responses were compared using the Mann-Whitney U test.

Results: The FKRE scores indicated significantly higher readability with plain language prompts (median 34.35) compared to keyword-only prompts (median 23.60). Similarly, the FKGL indicated a lower grade level for plain language prompts (median 12.05) versus keyword-only (median 14.50), with both differences achieving statistical significance.

Conclusion: Our findings suggest that using conversational prompts can enhance the readability of AI-generated medical information on fragility fractures. Clinicians and content creators should consider this approach when using AI for patient education to optimize comprehension.