Assessment of Skeletal Strength: Bone Density Testing and Beyond

Utility of Trabecular Bone Score in the Management of Patients with Osteoporosis

Trabecular bone score (TBS) enhances assessment of fracture risk in older women and men across many race/ethnicities and with a broad range of comorbidities. The best validated clinical utility of TBS is for input in the FRAX algorithm to modify assessment of fracture risk in patients who are close to FRAX-based intervention thresholds, thereby possibly influencing treatment decisions. TBS has been shown to increase with anabolic therapy and to a lesser degree with denosumab. TBS-adjusted T-scores may be useful with treatment guidelines that do not include FRAX.

Predicting Osteoporotic Fracture in Patients With Early-Stage Diabetic Kidney Disease Using a Radiomic Model: A Longitudinal Cohort Study

OBJECTIVE

There is an urgent need for effective predictive strategies to accurately evaluate the risk of fragility fractures in elderly patients in the early stages of diabetic kidney disease (DKD).

METHODS

This longitudinal cohort study included 715 older patients in the early stages of DKD diagnosed between January 2015 and August 2019. Patients were randomly allocated to a training cohort (n = 499) and a validation cohort (n = 216). The least absolute shrinkage and selection operator method was used to select key features for dual-energy x-ray absorptiometry-based radiomic analysis. A radiomic model was constructed using Cox proportional hazards regression. The performance of the radiomic model was compared with that of traditional fracture assessment tools through a receiver operating characteristic curve, calibration curve, and decision curve analysis.

RESULTS

Over a mean follow-up period of 4.72 ± 1.60 years, 65 participants (9.09%) experienced incident fragility fractures. Seventeen features were ultimately selected to create the radiomic model. The calibration plots of this model demonstrated satisfactory agreement between the observed and predicted outcomes. Moreover, the radiomic model outperformed traditional fracture assessment tools in both the training and validation cohorts according to the area under the receiver operating characteristic curve and decision curve analysis.

CONCLUSIONS

The novel radiomic model has demonstrated a more effective prediction of fragility fracture in elderly patients in the early stages of DKDcompared to traditional fracture assessment tools.

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.