Recommendations on the post-acute management of the osteoporotic fracture - Patients with "very-high" Re-fracture risk

Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients

OBJECTIVE

This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.

METHODS

A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).

RESULTS

BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.

CONCLUSION

This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

Barriers to osteoporosis management and adherence to Clinical Practice Guideline: a comparative study between tertiary East Coast hospitals and a Fracture Liaison Services (FLS)-accredited hospital in Malaysia

This study compares osteoporosis management between tertiary East Coast hospitals and a FLS-accredited hospital in Malaysia. It identifies significant barriers and highlights the superior performance of FLS in areas like timely treatment initiation and treatment monitoring. The insights are crucial for improving osteoporosis management strategies.

INTRODUCTION

Osteoporosis management poses a substantial healthcare challenge, necessitating effective strategies and Clinical Practice Guidelines (CPG) adherence.

METHODS

The study employed a self-administered online questionnaire via Google Forms. Orthopedic clinicians from all study sites were invited to participate via messaging platforms. A total of 135 participants completed the questionnaire and the data was proceeded to statistical analyses.

RESULTS

The study identified significant barriers, including inadequate knowledge of current osteoporosis guidelines and medications (p = 0.014), limited choice of anti-osteoporosis medication (p < 0.001), insufficient post-fracture care staff (p < 0.001), patients' financial constraints due to socioeconomic status (p = 0.027), and lack of doctor-patient time (p = 0.042). FLS demonstrated superior performance in CPG adherence in areas such as clinical diagnosis of osteoporosis without BMD assessment (p = 0.046), timely treatment initiation (p < 0.001), treatment monitoring using BMD (p = 0.004), reassessment treatment after 3-5 years of bisphosphonate therapy (p = 0.034) and considering anabolic agents in very high-risk patients (p = 0.018).

CONCLUSION

The findings highlight an essential opportunity for improvement and emphasize the necessity for robust strategies and strict adherence to Clinical Practice Guidelines (CPG), especially within tertiary East Coast hospitals. The exemplary efficacy demonstrated by the FLS model strongly advocates for its broader integration across multiple hospitals, promising substantial advancements in osteoporotic patient care outcomes throughout Malaysia.

Risk factors for subsequent fractures in hip fracture patients: a nested case-control study

BACKGROUND

The risk factors for subsequent fractures following an initial hip fracture are not entirely understood. This study examined the clinical characteristics of hip fracture patients to identify potential risk factors associated with a higher risk of experiencing subsequent fractures.

METHODS

We conducted a nested case-control study using data from the Chinese PLA General Hospital Hip Fracture Cohort between January 2008 and March 2022. The cases were individuals who experienced subsequent fractures following an initial hip fracture. Each case was matched with up to 2 controls who did not develop subsequent fractures. Important clinical factors were compared across groups, including traditional fracture risk factors and potential risk factors (e.g., comorbidities, falls risk, physical impairment, calcium or vitamin D use, and anti-osteoporosis medications). Conditional logistic regression analyses were used to evaluate the impact of these clinical features as potential risk factors for subsequent fractures.

RESULTS

A total of 96 individuals who suffered from subsequent fractures were matched with 176 controls. The median time between the initial hip fracture and the subsequent fracture was 2.1 years. The overall proportion of patients receiving anti-osteoporosis treatment after initial hip fracture was 25.7%. In the multivariable regression analysis, living in a care facility (OR = 3.78, 95%CI: 1.53-9.34), longer hospital stays (OR = 1.05, 95%CI: 1.00-1.11), and falls after discharge (OR = 7.58, 95%CI: 3.37-17.04) were associated with higher odds of subsequent fractures.

CONCLUSIONS

This study showed that living in a care facility, longer hospital stays, and falls after discharge may be independent risk factors for repeat fractures following an initial hip fracture. These findings could be used to identify and manage patients at high risk of subsequent fractures.

An Outline on the Advancements in Surgical Management of Osteoporosis-Associated Fractures

Osteoporosis significantly impairs bone density and increases fracture risk, representing a substantial global health challenge. The effectiveness of traditional treatments such as calcium supplementation and exercise in completely preventing fractures is limited. This review explores recent advancements in surgical techniques and treatment modalities to manage osteoporotic fractures better and improve patient outcomes. Osteoporotic fractures demand specialized surgical techniques due to compromised bone quality. Vertebroplasty and kyphoplasty are minimally invasive procedures that provide rapid pain relief and structural support using bone cement. While vertebroplasty is effective, it carries risks of cement leakage and new fractures. Kyphoplasty, with added balloon inflation, reduces leakage risk and improves vertebral height restoration but is costlier. Cement-augmented screws enhance fixation but can increase adjacent fracture risk and pose long-term complications. Surgical advancements encompass robotic-assisted surgery, offering precision and accelerated recovery, alongside biologic agents like bone morphogenetic proteins (BMPs), which enhance bone healing while reducing secondary interventions and eliminating donor site morbidity. Bone graft substitutes such as calcium phosphate cements enhance biomechanical compatibility, decrease morbidity, and reduce fracture loss and pain. Balloon kyphoplasty aids in height restoration and pain relief and diminishes the risk of subsequent vertebral fractures. Bioglass scaffolds promote bone regeneration by improving bone mineral density and lowering the incidence of new fractures. Optimal perioperative care, including patient selection, nutritional management, and early mobilization strategies, is crucial for mitigating risks in vulnerable populations. While current surgical interventions provide significant pain relief and functional benefits, ongoing research and multidisciplinary collaboration are crucial to prospectively refine these techniques and reduce the burden of osteoporosis. New technologies, such as tissue engineering and gene editing, hold potential for future treatment paradigms.

A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture

Background Patients have the highest risk of subsequent fractures in the first few years after an initial fracture, yet models to predict short-term subsequent risk have not been developed. Purpose To develop and validate a deep learning prediction model for subsequent fracture risk using digitally reconstructed radiographs from hip CT in patients with recent hip fractures. Materials and Methods This retrospective study included adult patients who underwent three-dimensional hip CT due to a fracture from January 2004 to December 2020. Two-dimensional frontal, lateral, and axial digitally reconstructed radiographs were generated and assembled to construct an ensemble model. DenseNet modules were used to calculate risk probability based on extracted image features and fracture-free probability plots were output. Model performance was assessed using the C index and area under the receiver operating characteristic curve (AUC) and compared with other models using the paired t test. Results The training and validation set included 1012 patients (mean age, 74.5 years ± 13.3 [SD]; 706 female, 113 subsequent fracture) and the test set included 468 patients (mean age, 75.9 years ± 14.0; 335 female, 22 subsequent fractures). In the test set, the ensemble model had a higher C index (0.73) for predicting subsequent fractures than that of other image-based models (C index range, 0.59-0.70 for five of six models; P value range, < .001 to < .05). The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the 2-, 3-, and 5-year follow-ups, respectively; higher than that of most other image-based models at 2 years (AUC range, 0.57-0.71 for five of six models; P value range, < .001 to < .05) and 3 years (AUC range, 0.55-0.72 for four of six models; P value range, < .001 to < .05). Moreover, the AUCs achieved by the ensemble model were higher than that of a clinical model that included known risk factors (2-, 3-, and 5-year AUCs of 0.58, 0.64, and 0.70, respectively; P < .001 for all). Conclusion In patients with recent hip fractures, the ensemble deep learning model using digital reconstructed radiographs from hip CT showed good performance for predicting subsequent fractures in the short term. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Li and Jaremko in this issue.

Cumulative exposure to remnant cholesterol and the risk of fragility fractures: a longitudinal cohort study

Objective

To investigate the association between cumulative remnant cholesterol (cumRC) and the risk of new-onset fragility fractures.

Methods

This study included individuals who participated in the 2006, 2008, and 2010 Kailuan health examinations. Baseline characteristics were compared between groups according to cumRC quartiles. The incidence density was calculated, and the log-rank test was used to compare the cumulative incidence. Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI), and restricted cubic spline was used to examine the possibly non-linear relation between cumRC and the risk of fragility fractures. Additional analyses were performed with stratification by age (≥ or <65 years).

Results

A total of 43,839 individuals were included in this study. During the median follow-up period of 10.97 years, a total of 489 fragility fractures occurred. Multivariable Cox proportional hazards regression model 3 showed that the Q1 and Q4 groups versus the Q2 group were associated with a higher HR of fragility fracture (HR 1.61, 95% CI: 1.23-2.11; HR 1.38, 95% CI: 1.06-1.81), and restricted cubic spline regression analysis showed a non-linear relationship between cumRC level and the risk of fragility fractures (P Overall association < 0.001, P Non-linear association = 0.001). The association was significant in the age group <65 years but not in the age group ≥65 years. The sensitivity analyses were consistent with the main results.

Conclusions

Both too high and too low cumRC levels were associated with a greater risk of fragility fractures, and this association was more significant in young and middle-aged people.

Potential of Fatty Acids in Treating Sarcopenia: A Systematic Review

This paper presents a systematic review of studies investigating the effects of fatty acid supplementation in potentially preventing and treating sarcopenia. PubMed, Embase, and Web of Science databases were searched using the keywords 'fatty acid' and 'sarcopenia'. Results: A total of 14 clinical and 11 pre-clinical (including cell and animal studies) studies were included. Of the 14 clinical studies, 12 used omega-3 polyunsaturated fatty acids (PUFAs) as supplements, 1 study used ALA and 1 study used CLA. Seven studies combined the use of fatty acid with resistant exercises. Fatty acids were found to have a positive effect in eight studies and they had no significant outcome in six studies. The seven studies that incorporated exercise found that fatty acids had a better impact on elderlies. Four animal studies used novel fatty acids including eicosapentaenoic acid, trans-fatty acid, and olive leaf extraction as interventions. Three animal and four cell experiment studies revealed the possible mechanisms of how fatty acids affect muscles by improving regenerative capacity, reducing oxidative stress, mitochondrial and peroxisomal dysfunctions, and attenuating cell death. Conclusion: Fatty acids have proven their value in improving sarcopenia in pre-clinical experiments. However, current clinical studies show controversial results for its role on muscle, and thus the mechanisms need to be studied further. In the future, more well-designed randomized controlled trials are required to assess the effectiveness of using fatty acids in humans.

Anti-resorptive therapy in the osteometabolic patient affected by periodontitis. A joint position paper of the Italian Society of Orthopaedics and Traumatology (SIOT) and the Italian Society of Periodontology and Implantology (SIdP)

This joint report from the Italian Society of Orthopaedics and Traumatology (SIOT) and the Italian Society of Periodontology and Implantology (SIdP) aims for a consensus around the scientific rationale and clinical strategy for the management of osteoporotic patients affected by periodontitis who are undergoing anti-resorptive (AR) therapy to manage the risk of the occurrence of a medication-related osteonecrosis of the jaws (MRONJ). Osteoporosis and periodontitis are chronic diseases with a high prevalence in aging patients, and they share some of the same pathogenetic mechanisms based upon inflammation. Available evidence shows the relationship among osteoporosis, AR agents, periodontitis and implant therapy in relation to the incidence of MRONJ. Uncontrolled periodontitis may lead to tooth loss and to the need to replace teeth with dental implants. Tooth extraction and surgical dental procedures are recognized as the main risk factors for developing MRONJ in individuals taking AR therapy for osteometabolic conditions. Although the incidence of MRONJ in osteometabolic patients taking AR therapy may be as low as 0.9%, the increasing prevalence of osteoporosis and the high prevalence of periodontitis suggest that this potential complication should not be overlooked. Good clinical practice (GCP) guidelines are proposed that aim at a more integrated approach (prescriber, dentist, periodontist and dental hygienist) in the management of periodontitis patients undergoing AR therapy for osteometabolic disorders to reduce the risk of MRONJ. Dental professional and prescribers should educate patients regarding the potential risk associated with the long-term use of AR therapy and oral health behavior.