Multivariate Risk Factor Analysis and Clinical Prediction Model Construction of Intercostal Neuralgia Following Single-Segment Osteoporotic Thoracic Vertebral Compression Fractures
Aug 2026· International Journal of General Medicine· Vol 19· 0 citations· 27 references
Medicine
TL;DR
Post-fracture intercostal neuralgia in osteoporotic thoracic vertebrae is multifactorial, and middle thoracic vertebra fractures, injury of the thoracolumbar fascia, decreased bone density, intravertebral vacuum fissure, and depressive state are independent risk factors.
Abstract
Objective To explore the multiple risk factors for intercostal neuralgia after osteoporotic thoracic vertebral compression fractures (OVCF), and to construct a clinical prediction model. Methods The clinical data of 280 patients with single-segment thoracic OVCF admitted to our orthopedic department from January 2022 to December 2025 were retrospectively collected. Patients were categorized into neuropathic pain (NP) and non-neuropathic pain (non-NP) groups based on the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) scale, with a score of ≥12 defining the primary outcome of intercostal neuralgia as neuropathic pain. Demographic data, fracture-related parameters, bone density, bone metabolism markers, and psychological status (Hospital Anxiety and Depression Scale, HADS) were collected. Univariate and multivariate Logistic regression analyses were used to screen independent risk factors, and the discrimination of the prediction model (area under the ROC curve, AUC) was evaluated. The model’s performance was internally validated using the Bootstrap method. Results Among the 280 patients, the incidence of neuropathic pain was 21.8% (61/280). Multivariate analysis identified six independent risk factors: middle thoracic fracture (T5-T8, OR=4.603), thoracolumbar fascia injury (TLFI, OR=4.883), injured vertebral width ratio (per 0.1 increase, OR=3.973), decreased bone mineral density T-score (per 1-unit decrease, OR=2.685), intravertebral vacuum cleft (IVC, OR=2.764), and depressive state (HADS≥8, OR=2.586). The prediction model showed good calibration (Hosmer-Lemeshow P=0.412) and discrimination (AUC=0.843, 95% CI: 0.789–0.897), with sensitivity 80.3%, specificity 76.7%, and negative predictive value 93.2%. Bootstrap internal validation yielded an optimism-corrected AUC of 0.831. Conclusion Post-fracture intercostal neuralgia in osteoporotic thoracic vertebrae is multifactorial. Middle thoracic vertebra fractures, injury of the thoracolumbar fascia, increased ratio of injured vertebra width, decreased bone density, intravertebral vacuum fissure, and depressive state are independent risk factors. However, external validation in prospective multicenter studies is required before routine clinical implementation.
The nomogram developed based on conventional clinical data in this study, has undergone internal validation and demonstrated efficacy in predicting NVCF following PVP, and serves as a valuable decision-making tool for clinicians.
Yi-Qi Wu, Qing Song, Canwei Hu et al.· Frontiers in Medicine· 0 citations
Age, number of operated vertebrae, cement leakage, PINP, 25(OH)D, and BMD are closely associated with and are independent risk factors for secondary fractures after PVP/PKP in OTF patients, and the nomogram model based on these factors has high value.
Jiawei Fu, Guan-Hua Xu, Jia-jia Chen et al.· Frontiers in Surgery· 0 citations
This study aimed to investigate the risk factors associated with 1-year postoperative mortality in patients with lumbar compression fractures and to construct and validate a predictive nomogram model. Clinical data of patients admitted between January 2021 and December 2024 were retrospectively analyzed. Independent predictors of 1-year mortality were identified using univariate and multivariate logistic regression analyses. A nomogram was constructed based on the final model. Model discrimination was evaluated using the receiver operating characteristic curve and the area under the curve. Calibration, Bootstrap resampling, and 10-fold cross-validation were used for internal validation. A total of 378 patients were included, of whom 21 (5.56%) died within 1 year postoperatively. Five independent predictors were identified: bone mineral density ≤ −2.5, multiple segmental fractures, age > 70 years, albumin ≤ 40 g/L, and neutrophil-to-lymphocyte ratio > 4. The nomogram showed good discriminative performance, with an area under the curve of 0.826 in the training cohort and 0.813 in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes. One-year postoperative mortality in lumbar compression fracture patients is influenced by multiple clinical and inflammatory factors. The proposed nomogram demonstrates good discriminative ability and may help clinicians identify high-risk patients for early intervention.
Thoracic and lumbar vertebroplasty yielded comparable 2-year PROMs with no statistically significant differences detected between cohorts, suggesting that the vertebral region may not be a major determinant of patient-reported recovery following vertebroplasty for OVCFs.
Yong Ng, Jeremy Tze En Lim, Lei Jiang et al.· Asian Spine Journal· 0 citations
Early identification of patients at risk of incident vertebral fracture remains challenging because routine clinical risk assessment does not fully capture local spinal fragility. This single-center retrospective cohort study evaluated whether deep learning (DL) features extracted from baseline thoracolumbar lateral radiographs improve the prediction of incident vertebral fracture within 2 years when combined with clinical risk factors. A total of 2,173 patients were included and chronologically divided into a derivation cohort (n = 1,449) and an internal validation cohort (n = 724). DL features were derived from baseline radiographs, and LASSO-Cox regression was used to select predictors and build a clinical model, a DL model, and a combined model. Performance was assessed by bootstrap optimism correction, temporal internal validation, calibration, decision curve analysis, time-dependent net reclassification improvement (NRI), integrated discrimination improvement (IDI), and sensitivity analyses. Of 2,048 candidate DL features, 5 were retained to generate a DL score, which remained an independent predictor in the combined model (HR 1.64, 95% CI 1.34-2.01; P < 0.001). In internal validation, the combined model achieved a C-index of 0.759, a 2-year AUC of 0.774, and a 2-year Brier score of 0.077, all superior to the clinical model, with good calibration (intercept 0.012; slope 0.972). Compared with the clinical model, the combined model also improved reclassification (2-year NRI 0.316 in derivation and 0.241 in validation) and discrimination (2-year IDI 0.047 and 0.033, respectively; all P < 0.01), and provided greater net benefit on decision curve analysis. Sensitivity analyses were consistent with the primary results. Combining DL features from thoracolumbar lateral radiographs with clinical risk factors may enable more accurate individualized prediction of incident vertebral fracture within 2 years.
Weijie Yang, Wen-Qin Gu, Wei Zhang et al.· Journal of Visualized Experi...· 0 citations
The random forest model demonstrated strong discriminatory ability and calibration in predicting fixation failure in this single-center retrospective cohort, and shows promise for perioperative risk stratification.
Hongfei Li, Hou-Li Zhao· Medicine· 0 citations
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