Construction and validation of a nomogram for screening for sarcopenia in patients with osteoporotic vertebral compression fracture

To explore and develop a screening model for sarcopenia in patients with osteoporotic vertebral compression fracture (OVCF) to enable early identification and intervention in order to reduce the adverse risks associated with sarcopenia. We created and validated a nomogram using data from 301 patients with OVCF.Independent risk factors in the training set were screened by single and multifactorial logistic regression analyses as well as lasso regression analyses, and a nomogram was created to predict the risk of sarcopenia in patients with OVCF. The predictive ability and clinical applicability of the nomogram were evaluated using the area under the curve (AUC), calibration curve, decision curve, and internal and external validation. In this study, we identified the ratio of serum indirect bilirubin to total bilirubin(OR = 0.01, high-density lipoprotein cholesterol OR = 4.06, serum uric acid levels OR = 0.99, height OR = 1.12, and weight OR = 0.85)as significant influencing factors for the occurrence of sarcopenia in patients with osteoporotic vertebral compression fractures.Using the Bootstrap resampling method, the modeling population data were resampled 1000 times, yielding a concordance index (C-index) of 0.863 (95% CI: 0.8121 ~ 0.9120). In both the raining set and validation set, ROC curves were plotted, and the AUC was calculated as 0.859 (95% CI: 0.810 ~ 0.908) and 0.811 (95% CI: 0.709 ~ 0.913), respectively, indicating that the predictive model has good discriminative ability. The Hosmer-Lemeshow test was performed on both groups, and calibration curves were plotted, resulting in P-values of 0.303 and 0.199, both greater than 0.05, indicating good calibration of the model. Additionally, the decision curves for both groups showed high net benefits. IBTBR, HDL-C, UA, height, and body weight are significant factors influencing sarcopenia. The nomogram constructed from these factors can predict the occurrence of sarcopenia in patients with OVCF conveniently, accurately, and reliably.