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

A predictive nomogram for selective screening of asymptomatic vertebral fractures: The Vietnam Osteoporosis Study

Objectives

Vertebral fractures are associated with disability and mortality, but most vertebral fractures are asymptomatic. The present study aimed to determine the incidence of and develop a predictive nomogram for asymptomatic vertebral fractures in Vietnamese adults.

Methods

This cohort study as a part of the Vietnam Osteoporosis Study involved 168 men and 287 women aged 50 years and older without a clinically diagnosed vertebral fracture. Their spine x-rays were taken at the recruitment and subsequent 2-year visit. Vertebral fractures were ascertained using the Genant's semi-quantitative method. We employed the Bayesian Model Averaging method to search for the optimal model for predicting asymptomatic vertebral fractures. A predictive nomogram was also developed to facilitate risk prediction.

Results

During a median of 2.38 years of follow-up, 13 men and 16 women developed an asymptomatic vertebral fracture, yielding the overall incidence rate of 28 fractures per 1000 person-years, or 33 fractures/1000 person-years in men and 24 fractures/1000 person-years in women, respectively. Most asymptomatic vertebral fractures were moderate, almost 1.5 times more common than mild fractures. The optimal model for predicting incident asymptomatic vertebral fractures included age, male sex and lower femoral neck T-score. The area under the receiver's operating characteristic curve was 0.91, with 95% CI ranging from 0.86 to 0.96.

Conclusions

Asymptomatic vertebral fractures were relatively common among adults in Vietnam. A simple model with sex, age and femoral neck T-score is helpful for selective screening of asymptomatic vertebral fractures in Vietnamese individuals.

Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis

PURPOSE

To evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches.

METHOD

A systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis. Bias risk was assessed using QUADAS-2. A bivariate mixed-effects model was used for the meta-analysis. Meta-analyses were performed according to five task types (vertebral fractures, osteoporotic vertebral fractures, differentiation of benign and malignant vertebral fractures, differentiation of acute and chronic vertebral fractures, and prediction of vertebral fractures). Subgroup analyses were conducted by different ML models (including ML and DL) and modeling methods (including CT, X-ray, MRI, and clinical features).

RESULTS

Eighty-one studies were included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and CT (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a sensitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0.99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebral fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (SROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures, ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better.

CONCLUSIONS

ML, especially DL models applied to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailored prevention strategies. Further research and validation are required to confirm ML's clinical efficacy.

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.