A nomogram for predicting low bone mineral density in the elderly using chest CT

Osteoporosis leads to fragility fractures and imposes a severe health burden on aging populations. Early detection of bone loss is essential for effective clinical intervention. This study aimed to develop a predictive nomogram for low bone mineral density (BMD) by leveraging opportunistic chest CT scans and clinical data.

This study performed a retrospective analysis of 217 patients, consisting of 71 males and 146 females. All patients underwent chest CT and dual-energy X-ray absorptiometry (DXA) within 30 days. The lowest DXA T-score was used to stratify participants into Normal and Low BMD groups. Univariate and correlation analyses were conducted for all variables, followed by multicollinearity checks. Multivariate logistic regression analysis was then performed to identify key predictors. Model performance was evaluated using ROC curves and validated with bootstrap calibration and Decision Curve Analysis (DCA).

T11 CT attenuation, age, and BMI emerged as significant independent predictors of diminished BMD (all P < 0.05). The final predictive model demonstrated good discriminatory performance, with an AUC of 0.864 (95% CI: 0.813-0.914). Furthermore, the Hosmer-Lemeshow test resulted in a p-value of 0.197, indicating a favorable model fit. DCA and calibration plots confirmed high clinical net benefit and predictive accuracy.

This opportunistic CT-based nomogram can effectively identify individuals at risk for bone mass deterioration. It can be used to opportunistically predict the risk of bone loss in middle-aged and older adults, and may serve as a cost-effective screening approach for older populations.