Risk Factors and a Prediction Nomogram for Vertebral Fracture Cascade in Patients with Sarcopenia

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A total of 889 patients with osteoporotic vertebral fractures were included, of whom 193 (21.7%) developed VFC. Univariate and multivariate logistic regression analyses identified advanced age, BMI ≥ 28 kg/m², history of steroid use, thoracolumbar fracture, shorter fracture-to-surgery interval, and sarcopenia as independent risk factors for VFC. A nomogram incorporating these variables was constructed and demonstrated good predictive performance, with area under the curve values of 0.778 in the training set and 0.710 in the validation set. Calibration and decision curve analyses confirmed the model’s accuracy and clinical utility. This nomogram provides a practical tool for early identification of high-risk patients, facilitating targeted interventions to prevent VFC in clinical practice. Sarcopenia Vertebral Fracture Cascade Nomogram Osteoporotic Vertebral Fracture Prediction Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sarcopenia is a systemic musculoskeletal syndrome characterized by the progressive loss of muscle mass and strength, which leads to physical disability, reduced quality of life, and increased risk of adverse events including mortality. It is marked by high prevalence, an insidious onset, and a progressively worsening course 1 . The pathogenesis of this disease is associated with genetic and external environmental factors. Beyond age-related factors, physical activity, nutrition, and diseases (including chronic diseases or malignant tumors) can all lead to skeletal muscle loss, thereby contributing to the development of sarcopenia 2 . As the most common osteoporotic fracture in elderly patients, vertebral fractures have shown an increasing trend in both incidence and mortality rates. Moreover, the increase in mortality is associated with the number and severity of vertebral fractures 3 . Existing studies have demonstrated that in elderly patients, the risk of subsequent vertebral fractures increases exponentially after sustaining an initial vertebral fracture. Such vertebral fractures that occur two or more times are defined as "Vertebral Fracture Cascade (VFC)" 4,5 . Sarcopenia can significantly increase the risk of falls, fractures, and even death in older adults 6,7 . This study investigates the risk factors of VFC and develops a nomogram predictive model, providing a reference for early clinical identification of high-risk populations for VFC and implementation of intervention measures. Materials and methods Study Design This study selected a total of 889 patients diagnosed with osteoporotic vertebral fractures at our hospital between September 2019 and September 2023. Inclusion criteria: 1) Patients with a definitive diagnosis of vertebral fracture. 2) A confirmed diagnosis of osteoporosis. Exclusion criteria: 1) Pathological fractures, including myeloma and primary or secondary malignant tumors of the vertebra.2) Patients with mental disorders or severe cognitive impairment.3) Patients unable to live independently. Written informed consent was obtained from all participants. This study was conducted in compliance with the Declaration of Helsinki and relevant regulatory guidelines, following approval by the Medical Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Ethics Approval No.: IIT2024-127-002). Assessment of sarcopenia The diagnostic criteria for sarcopenia refer to the 2019 Asian Working Group for Sarcopenia (AWG 2019) , which mainly include assessments of muscle strength, muscle mass, and physical function: 1) Measurement of handgrip strength (HGS): HGS < 28 kg in males and < 18 kg in females; 2) Height-adjusted skeletal muscle index (SMI): measured by DXA, < 7.0 kg/m² in males or < 5.4 kg/m² in females; 3) 6-meter walking speed: < 1.0 m/s. Sarcopenia is diagnosed when criterion 2) is met along with either criterion 1) or 3); severe sarcopenia is diagnosed when all three criteria are met simultaneously. Vertebral Fracture Cascade (VFC): refers to the occurrence of two or more vertebral fractures. Data collection Patient demographic data collection included: gender, age, body mass index (BMI), Bone Mineral Density (BMD)，fracture-related clinical information, comorbidities (hypertension, diabetes, liver and kidney diseases), and personal history (including History of Alcohol and smoking Use). Statistical analyses and risk model construction Statistical analyses were performed using R software. Continuous variables were presented as mean ± standard deviation or median [interquartile range], and compared using t-tests or Mann-Whitney U tests as appropriate. Categorical variables were expressed as counts (percentages) and compared via Chi-square or Fisher's exact tests. Univariate logistic regression identified variables (p<0.05) for inclusion in multivariate analysis. A stepwise selection method was applied to build the final model, with results reported as odds ratios (ORs), 95% confidence intervals and p-values. Model fit was assessed using residual deviation, log-likelihood, AIC and BIC. A nomogram was constructed for individualized risk prediction. Discriminative ability was evaluated by ROC analysis with AUC calculated via bootstrapping (1000 replicates). Calibration was tested using bias-corrected calibration curves (500 bootstrap replicates), reporting mean absolute error. Clinical utility was assessed via decision curve analysis. All tests were two-sided with statistical significance set at p<0.05. Ethical Approval Ethics Committee of Affiliated Beijing Shijitan Hospital of Captial Medical University has approved our research. Results Comparison of Patient Baseline Characteristics A total of 889 patients (200 males and 689 females) were enrolled in this study. Cascading fractures were detected in 193 patients (21.7%), while the remaining 696 were classified as non-cascading fractures, (Table1). Table 1 Comparison of Baseline Characteristics Characteristic Non-VFC VFC statistical value P value N 696 193 Age（years） 77.50 (68.75-83.25) 80.00 (75.00-84.00) 10.423 0.001 Fracture to surgery interval（weeks） 7.00 (5.00-15.00) 7.00 (3.00-15.00) 6.270 0.012 Cement Volume (mL) 5.40 (4.30-5.60) 5.40 (4.50-5.60) 0.783 0.376 Gender 0.095 0.758 -Male 155 (22.27%) 45 (23.32%) -Female 541 (77.73%) 148 (76.68%) BMI≥28kg/m 2 28.408 <0.001 -No 520 (74.71%) 106 (54.92%) -Yes 176 (25.29%) 87 (45.08%) Diabetes Mellitus 4.119 0.042 -No 575 (82.61%) 147 (76.17%) -Yes 121 (17.39%) 46 (23.83%) History of Steroid Use 15.297 <0.001 -No 558 (80.17%) 129 (66.84%) -Yes 138 (19.83%) 64 (33.16%) Thoracolumbar Fracture 24.648 <0.001 -No 275 (39.51%) 39 (20.21%) -Yes 421 (60.49%) 154 (79.79%) BMD T<-2.5 2.851 0.091 -No 415 (59.63%) 102 (52.85%) -Yes 281 (40.37%) 91 (47.15%) Surgery Approach of PVP 0.008 0.928 -Unilateral 464 (66.67%) 128 (66.32%) -Bilateral 232 (33.33%) 65 (33.68%) Hypertension 8.051 0.005 -No 376 (54.02%) 82 (42.49%) -Yes 320 (45.98%) 111 (57.51%) Thyroid Disease 0.015 0.901 -No 626 (89.94%) 173 (89.64%) -Yes 70 (10.06%) 20 (10.36%) Liver Disease 0.256 0.613 -No 661 (94.97%) 185 (95.85%) -Yes 35 (5.03%) 8 (4.15%) Kidney Disease 0.023 0.880 -No 658 (94.54%) 183 (94.82%) -Yes 38 (5.46%) 10 (5.18%) History of Smoking Use 0.259 0.611 -No 591 (84.91%) 161 (83.42%) -Yes 105 (15.09%) 32 (16.58%) History of Alcohol Use 1.191 0.275 -No 612 (87.93%) 164 (84.97%) -Yes 84 (12.07%) 29 (15.03%) Cement Leakage 0.101 0.750 -No 526 (75.57%) 148 (76.68%) -Yes 170 (24.43%) 45 (23.32%) Sarcopenia 61.566 <0.001 -No 556 (79.89%) 100 (51.81%) -Yes 140 (20.11%) 93 (48.19%) VFC: Vertebral Fracture Cascade BMD: Bone Mineral Density PVP: Percutaneous Vertebroplasty Risk Factor Analysis of VFC Univariable Logistic Regression In the univariable logistic regression analysis, age (OR = 1.033, 95% CI: 1.012–1.057, P = 0.003), elevated BMI (OR = 2.502, 95% CI: 1.687–3.709, P < 0.001), History of steroid use (OR = 2.293, 95% CI: 1.500–3.484, P < 0.001), thoracolumbar fracture (OR = 2.776, 95% CI: 1.769–4.496, P < 0.001), hypertension (OR = 1.914, 95% CI: 1.302–2.832, P = 0.001), fracture to surgery interval (OR = 0.964, 95% CI: 0.939–0.988, P = 0.004), and Sarcopenia (OR = 3.523, 95% CI: 2.360–5.269, P < 0.001) were all significantly associated with the risk of VFC. In contrast, variables such as sex, diabetes, BMD, surgical approach, thyroid disease, liver disease, kidney disease, cement volume, smoking, History of alcohol use, and cement leakage showed no significant association with the occurrence of VFC (all P > 0.05),(Table 2). Table 2 Univariable Logistic Regression for VFC Variable OR CI_lower CI_upper p_value Age 1.033 1.012 1.057 0.003 Gender 0.748 0.483 1.175 0.198 BMI≥28kg/m 2 2.502 1.687 3.709 <0.001 Diabetes Mellitus 1.390 0.865 2.192 0.164 History of Steroid Use 2.293 1.500 3.484 <0.001 Thoracolumbar Fracture 2.776 1.769 4.496 <0.001 BMD T-score<-2.5 1.350 0.920 1.979 0.125 Surgical Approach (Uni/Bi) 1.216 0.810 1.810 0.339 Fracture to surgery interval（w） 0.964 0.939 0.988 0.004 Hypertension 1.914 1.302 2.832 0.001 Thyroid Disease 1.289 0.686 2.313 0.410 Liver Disease 1.512 0.574 3.587 0.369 Kidney Disease 0.927 0.389 1.973 0.852 Cement Volume (mL) 0.970 0.777 1.217 0.789 History of Smoking Use 1.291 0.784 2.077 0.302 History of Alcohol Use 1.287 0.719 2.218 0.378 Cement Leakage 0.774 0.480 1.217 0.280 Sarcopenia 3.523 2.360 5.269 <0.001 Multivariable Logistic Regression In multivariable logistic regression analysis, age (OR = 1.033, 95% CI: 1.009–1.058, P = 0.008), high BMI (OR = 2.817, 95% CI: 1.823–4.374, P < 0.001), History of steroid use (OR = 2.573, 95% CI: 1.609–4.112, P < 0.001), thoracolumbar fracture (OR = 3.421, 95% CI: 2.087–5.801, P < 0.001), fracture to surgery interval (OR = 0.962, 95% CI: 0.935–0.988, P = 0.006), and Sarcopenia (OR = 3.669, 95% CI: 2.372–5.706, P < 0.001) were identified as independent factors influencing the occurrence of VFC. The odds ratios and their corresponding 95%CI for these variables consistently indicated a statistically significant impact on the risk of VFC, (Table 3). Table 3 Multivariable Logistic Regression for VFC OR std.error statistic p.value conf.low conf.high (Intercept) 0.005 0.985 -5.397 <0.001 0.001 0.032 Age 1.033 0.012 2.671 0.008 1.009 1.058 BMI≥28 kg/m 2 2.817 0.223 4.647 <0.001 1.823 4.374 History of Steroid Use 2.573 0.239 3.958 <0.001 1.609 4.112 Thoracolumbar Fracture 3.421 0.260 4.731 <0.001 2.087 5.801 Fracture to surgery interval（w） 0.962 0.014 -2.757 0.006 0.935 0.988 Sarcopenia 3.669 0.223 5.817 <0.001 2.372 5.706 Development of a Nomogram for Predicting VFC with Sarcopenia Based on the multivariable logistic regression model, this study developed a nomogram for predicting the risk of VFC, which incorporates variables such as age, fracture to surgery interval, History of steroid use, BMI, sarcopenia, and thoracolumbar fracture ,(Figure 1). Figure 1 Nomogram for predicting VFC. Variables are scored based on their regression coefficients. Red dots represent an example patient's actual characteristics, with a total score of 364 points. This corresponds to a predicted probability of 4.78%, as shown on the probability axis below. Assessment of the VFC Risk Prediction Model Assessment of Model Performance using the ROC Curve The model's performance was evaluated using ROC-AUC analysis. The cohort was randomly split into a training set (70%, n=622; 135 cases) and a validation set (30%, n=267; 58 cases). The model showed good discrimination in the training set (AUC=0.778, 95% CI: 0.729–0.823) and maintained stable performance in the validation set (AUC=0.710, 95% CI: 0.637–0.787), confirming its robustness for predicting VFC risk,(Figure 2). Calibration Curve To evaluate the calibration of the multivariable logistic regression model for predicting the risk of VFC, calibration curves were plotted for the training set (n=622) and the validation set (n=267), respectively. The calibration curve for the training set demonstrated a high degree of agreement between the model-predicted probabilities and the actual observed probabilities. The bias-corrected line closely overlapped with the ideal line, with a mean absolute error (MAE) of 0.006, indicating excellent calibration performance within the training set. The validation set calibration curve also showed good agreement, with an MAE of 0.037, demonstrating that the model's predicted probabilities align well with the actual observed frequencies in the external dataset. These results provide a reliable statistical basis for the clinical application of the model,(Fig3,A,B). Decision Curve To further evaluate the clinical utility of the model, decision curve analysis (DCA) was employed to assess the net benefit of the VFC risk prediction model in both the training and validation sets. The results demonstrated that in the training set (n=622), across a high-risk threshold probability range of 0.05–0.60, the model yielded a standardized net benefit superior to both the "intervene-all" and "intervene-none" strategies. This indicates that the model can offer tangible clinical utility for decision-making within this threshold interval. In the validation set (n=267), the model also exhibited a higher net benefit within the threshold range of 0.05–0.45, further confirming its robustness and clinical applicability,(Fig 4). Discussion This study included a total of 889 eligible patients. The detection rates for sarcopenia and VFC were 26.2% and 21.7%, respectively. Univariate and multivariate regression analyses identified advanced age, BMI ≥28 kg/m², fracture to surgery interval, history of steroid use, thoracolumbar fracture, and sarcopenia as independent risk factors for cascading fractures. This study revealed a statistically significant older average age in patients with cascading fractures compared to those without. An osteoporotic vertebral fracture is a mechanical event whose occurrence depends not only on the mechanical load borne by the vertebra but more critically on its anatomical shape, bone microstructure, and tissue properties 8 .Vertebral strength is compromised by age-related declines in BMD and deterioration of bone microarchitecture. Research indicates that from the age of 40, bone density loss can reach up to 30% in men and 50% in women 9 .Meanwhile, the incidence of sarcopenia progressively increases with age. Sarcopenia is a progressive and generalized skeletal muscle disorder characterized by the progressive loss of muscle strength, mass, and physical performance. It can be classified as primary or secondary 10 .The primary form is often associated with aging and manifests as a degenerative decline in muscle mass and quality. Secondary sarcopenia, which may occur in younger populations, is linked to factors such as prolonged bed rest or immobilization (e.g., disuse atrophy), inadequate diet and nutrition (including insufficient energy/protein intake, malabsorption, gastrointestinal disorders, anorexia), and certain underlying wasting diseases, such as cancer 11,12 .Research indicates that with advancing age, total body muscle mass declines. Between the ages of 50 and 60, an individual's muscle mass can be reduced by up to 40%, which is equivalent to approximately 1% of total body weight 13 .The muscles attached to the vertebrae serve the dual function of generating movement and providing stability to protect the spinal structure. Studies indicate that without muscular support, the spine's compressive load threshold before buckling is a mere 2 kg 8 .With advancing age, not only does the peak force of muscles decline, but the rate of muscle force and power development also slows. These alterations in force generation may compromise the normal load-bearing capacity of the spine, as coordinated co-contraction of antagonistic muscles during flexion and extension is crucial for maintaining spinal stability 14 . The reduction in muscle mass diminishes its ability to generate sufficient load on the skeleton. This can readily trigger a "disuse paradigm," causing the internal architecture of bone tissue, including the trabeculae, to gradually lose its original biological function and efficacy. Consequently, bone brittleness increases, making it susceptible to fractures under gravitational stress 15 .Osteoporotic vertebral compression fractures (OVCF) are the most common type of fracture in the elderly, which is characterized by intensified back pain, limited mobility, and kyphotic deformity. In these patients, poor sagittal alignment is associated with both sarcopenia and persistent low back pain 16 .Kyphoplasty is currently the mainstream treatment for vertebral fractures, with reports indicating that balloon kyphoplasty can rapidly relieve pain, facilitate early ambulation, maintain activities of daily living, and aid in patient reintegration into society 17,18 .However, balloon kyphoplasty is merely a symptomatic treatment and does not address the underlying osteoporosis, which may lead to new vertebral fractures after surgery. Among these, adjacent vertebral fractures merit particular attention. Risk factors for adjacent fractures include advanced age, pre-existing vertebral fractures, increased local kyphosis due to vertebral collapse, and the presence of intravertebral clefts 19,20 .After an initial vertebral fracture occurs, the risk of subsequent fractures in other vertebrae increases by 4 to 7 times. Furthermore, this risk grows exponentially with the number of existing fractures. Therefore, following the initial treatment, proactive postoperative management, prediction of subsequent fractures, and preemptive interventions are critically important to prevent fractures in adjacent segments. Current research remains divided on whether obesity constitutes a risk factor for osteoporotic vertebral fractures. The effects of high-BMI obesity and fat distribution-based obesity (e.g., abdominal obesity) on subsequent fractures differ substantially. While low BMI is associated with increased fracture risk, high BMI may act as a protective factor against osteoporotic fractures. However, other studies indicate an elevated risk of osteoporotic fractures among women with high BMI. A recent meta-analysis demonstrated that after adjusting for BMD in women, high BMI actually increases the risk of osteoporotic fractures 21 .Ren et al. 22 reported that a higher body mass index increases the incidence of new symptomatic osteoporotic vertebral fractures following percutaneous vertebroplasty (PVP). Furthermore, they confirmed that a high BMI (OR = 1.268) is significantly associated with adjacent segment fractures after PVP. This finding is consistent with the results of our study. Furthermore, body fat distribution, particularly abdominal fat, may exert differential effects on bone health, potentially exhibiting a negative correlation with BMD and thereby increasing fracture risk 23,24 .Previous studies have reported that abdominal obesity, as measured by waist circumference (WC) and waist-to-hip ratio (WHR), is positively associated with the incidence of hip fractures and vertebral fractures 25,26 .However, there is currently limited research reporting the impact of BMI on VFC. Existing research provides compelling evidence that cement leakage, lower BMI, and BMD are risk factors for subsequent vertebral fractures (SVF) following PVP 27 . The reported incidence of adjacent vertebral fractures following kyphoplasty ranges from 6.5% to 33%, with higher risks observed in females, older adults, fractures at the thoracolumbar junction, and individuals with a history of vertebral fractures 20,28 . Research indicates that the mean load required to cause a fracture in the thoracolumbar spine decreases from approximately 8000 N at age 25 to about 2000 N by age 75. In some elderly cadaveric thoracic spines, this value can be as low as 500 N 9 . Osteoporotic vertebral fractures do not occur uniformly along the spine, with a predilection for the thoracic region (T7-T8) and the thoracolumbar junction (T11-L1). One possible explanation for this discrepancy lies in the biomechanical alterations resulting from changes in spinal curvature. Briggs et al. 29 utilized a biomechanical model of the spine to demonstrate that older subjects with severe thoracic kyphosis experience greater flexion moments and compressive forces on their vertebrae. Due to the kyphotic deformity, the patient's center of gravity shifts forward, leading to increased bending moments around the apex of the kyphosis. This not only triggers pain but also further elevates the risk of additional fractures 30 . This finding aligns with the results of the present study, which identified thoracolumbar fracture as an independent risk factor for cascading fractures. Furthermore, at the thoracolumbar junction where spinal curvature transitions from kyphosis to lordosis, the rigid thoracic cage gives way to the more mobile segments. The higher incidence of osteoporotic vertebral fractures at T11-L1 may be attributed to the lack of protective support from the rib cage in these segments immediately below the thoracic spine, leaving them vulnerable to additional loads 31 . Differences in bone mineral density (BMD) and bone strength across spinal segments may represent another factor contributing to the uneven distribution of osteoporotic vertebral fractures. The compressive loads borne by vertebral bodies typically gradually increase from the thoracic to the lumbar region. Burklein et al. 32 compared the compressive strength of T6, T10, and L3 vertebral bodies from 119 human cadavers and found a weak correlation between different spinal segments, indicating inherent heterogeneity in bone strength throughout the spine, which may partly explain the regional variations in fracture incidence. Glucocorticoid use can lead to glucocorticoid-induced osteoporosis (GIOP). Studies have demonstrated that fracture risk increases immediately after initiating glucocorticoid (GCs) therapy, with particularly pronounced effects observed in the spine and proximal femur 32 . One proposed mechanism is that corticosteroids can induce apoptosis in osteoblasts and osteocytes. Another explanation suggests that the rapid alteration in fracture risk is primarily driven by significant changes in bone turnover, mediated through induced microarchitectural deterioration of bone quality 33 . Glucocorticoids help maintain the dynamic equilibrium between osteoblasts and osteoclasts, thereby regulating the balance between bone formation and resorption. At physiological doses, these effects remain relatively limited. However, with exogenous administration, bone resorption predominates over bone formation 34 . Glucocorticoids bind to specific sequences in the promoter regions of target genes via intracellular glucocorticoid receptors (GR), suppressing the expression of key osteogenic genes such as Runx2 and Osterix. This process impedes osteoblast differentiation and bone matrix formation, while also inhibiting the expression of osteocalcin and alkaline phosphatase, ultimately reducing bone mineralization capacity 35 . Concurrently, GCs upregulate the RANKL/OPG ratio, promoting osteoclast activation and bone resorption. They also suppress the expression of genes associated with osteoclast apoptosis, thereby prolonging osteoclast lifespan, enhancing bone resorption, and ultimately increasing fracture risk 36 . This finding is consistent with the results of our study. Furthermore, this study identified that the timing of surgery for the initial fracture holds significant value in predicting the risk of subsequent fractures postoperatively. Yannick et al. 37 categorized 230 fracture patients into acute (6 weeks) groups. Their study demonstrated that patients in the acute and subacute groups achieved better restoration of vertebral height and kyphotic angle postoperatively, along with reduced postoperative analgesic use, whereas no significant improvements were observed in the chronic group. Two additional studies (involving 51 and 36 patients, respectively) concluded that early kyphoplasty performed within 4 weeks enables superior spinal alignment correction compared to delayed intervention 38,39 . During the acute phase of fresh fractures, spontaneous height restoration is more readily achievable, whereas in the late-stage fractures, fibrous tissue formation and bone healing impede positional correction. Bone cement alleviates pain by stabilizing micro-movements within the fractured vertebra and disrupting free nerve endings, thereby facilitating early patient recovery 40 . Early correction of spinal alignment and restoration of spinal stability can thereby effectively reduce the risk of adjacent vertebral fractures. Based on age, BMI ≥28 kg/m², history of steroid use, thoracolumbar fracture, fracture to surgery interval, and sarcopenia, this study developed a predictive model for the risk of VFC in patients with sarcopenia following surgery. Evaluation demonstrated that the model has satisfactory predictive performance. As the relevant risk factors can be readily assessed in clinical practice, the nomogram enables individualized calculation of the risk of subsequent vertebral fractures postoperatively, thereby providing a reference for formulating effective preventive measures. Therefore, the risk prediction model established in this study shows promising clinical application value. Limitation ： This single-center cross-sectional study has inherent limitations in sample representativeness, and caution should be exercised when generalizing the conclusions. Although sarcopenia was diagnosed according to the AWGS criteria, measurement variability in grip strength and skeletal muscle mass may persist. Potential confounding factors (e.g., nutrition, physical activity, vitamin D status) were not fully controlled. The cross-sectional design cannot establish causality, and systematic records of postoperative rehabilitation and medication were lacking. Future prospective, multicenter studies are warranted for further validation. Declarations Acknowledgements We thank all participating patients, the Medical Ethics Committee of Beijing Shijitan Hospital for their support, and our colleagues for assistance with data collection. Author contributions Ruizhao Zhao: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization. Xinyao Lv: Investigation, Data Curation, Resources, Writing – Review & Editing. Zijian Wang: Software, Validation, Formal Analysis, Data Curation, Visualization. Junjie Qiao: Investigation, Resources, Project Administration. Xiutong Fang: Conceptualization, Methodology, Resources, Writing – Review & Editing, Supervision, Project Administration, Funding Acquisition. Funding This study was supported by the Scientific and Technological Research and Development Program of China State Railway Group Co., Ltd. (J2023Z603). Competing interests The authors declare no competing interests. Consent for publication Not Applicable. Availability of Data and Materials The datasets used and analyzed during this study are available from the corresponding author on reasonable request. Ethics approval and consent Informed consent has been obtained from all relevant participants for this study. The authors confirm that this study strictly adheres to The Declaration of Helsinki.This study was conducted in compliance with the Declaration of Helsinki and relevant regulatory guidelines, following approval by the Medical Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Ethics Approval No.: IIT2024-127-002). References Tagliafico AS, Bignotti B, Torri L, Rossi F. Sarcopenia: how to measure, when and why. Radiol Med. 2022;127:228–37. 10.1007/s11547-022-01450-3 . Cho MR, Lee S, Song SK. A Review of Sarcopenia Pathophysiology, Diagnosis, Treatment and Future Direction. 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1","display":"","copyAsset":false,"role":"figure","size":104944,"visible":true,"origin":"","legend":"\u003cp\u003eA nomogram for predicting VFC\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7999138/v1/6cd56ba37f32a8701cf4c75b.png"},{"id":96049290,"identity":"6d06fb67-4c14-42f4-a8eb-9037510c9938","added_by":"auto","created_at":"2025-11-17 06:31:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83747,"visible":true,"origin":"","legend":"\u003cp\u003e(A), ROC Curve of the Training Set\u003c/p\u003e\n\u003cp\u003e(B), ROC Curve of the validationSet\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7999138/v1/bae3c776e0880fc00b57fdb6.png"},{"id":96049287,"identity":"05cae2f2-0875-4677-bd24-c4e25837a575","added_by":"auto","created_at":"2025-11-17 06:31:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57216,"visible":true,"origin":"","legend":"\u003cp\u003e(A)：Calibration Curve for the Training Set. B=1000 repetitions, boot; Mean absolute erro=0.006 N=622\u003c/p\u003e\n\u003cp\u003e(B) Calibration curve for the validation set (B = 1000 bootstrap repetitions; Mean absolute error = 0.037; N = 267）\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7999138/v1/615eef4cdefbae6b698b9b76.png"},{"id":96247071,"identity":"b1ef5059-a822-4e70-b1fe-01743e582d99","added_by":"auto","created_at":"2025-11-19 07:27:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80354,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Curve\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7999138/v1/1870667bbf7b8ff48a5ed94f.png"},{"id":97673841,"identity":"a2885fa6-d5ed-4dce-aa59-b927ba97485c","added_by":"auto","created_at":"2025-12-08 09:41:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1111643,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7999138/v1/a83d779a-1afc-4cb0-9e16-883186eaec9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors and a Prediction Nomogram for Vertebral Fracture Cascade in Patients with Sarcopenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is a systemic musculoskeletal syndrome characterized by the progressive loss of muscle mass and strength, which leads to physical disability, reduced quality of life, and increased risk of adverse events including mortality. It is marked by high prevalence, an insidious onset, and a progressively worsening course \u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;The pathogenesis of this disease is associated with genetic and external environmental factors. Beyond age-related factors, physical activity, nutrition, and diseases (including chronic diseases or malignant tumors) can all lead to skeletal muscle loss, thereby contributing to the development of sarcopenia \u003csup\u003e2\u003c/sup\u003e. As the most common osteoporotic fracture in elderly patients, vertebral fractures have shown an increasing trend in both incidence and mortality rates. Moreover, the increase in mortality is associated with the number and severity of vertebral fractures\u003csup\u003e3\u003c/sup\u003e. Existing studies have demonstrated that in elderly patients, the risk of subsequent vertebral fractures increases exponentially after sustaining an initial vertebral fracture. Such vertebral fractures that occur two or more times are defined as \u0026quot;Vertebral Fracture Cascade (VFC)\u0026quot;\u003csup\u003e4,5\u003c/sup\u003e. Sarcopenia can significantly increase the risk of falls, fractures, and even death in older adults\u003csup\u003e6,7\u003c/sup\u003e. This study investigates the risk factors of VFC and develops a nomogram predictive model, providing a reference for early clinical identification of high-risk populations for VFC and implementation of intervention measures.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study selected a total of 889 patients diagnosed with osteoporotic vertebral fractures at our hospital between September 2019 and September 2023. Inclusion criteria: 1) Patients with a definitive diagnosis of vertebral fracture. 2) A confirmed diagnosis of osteoporosis. Exclusion criteria: 1) Pathological fractures, including myeloma and primary or secondary malignant tumors of the vertebra.2) Patients with mental disorders or severe cognitive impairment.3) Patients unable to live independently. Written informed consent was obtained from all participants. This study was conducted in compliance with the Declaration of Helsinki and relevant regulatory guidelines, following approval by the Medical Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Ethics Approval No.: IIT2024-127-002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic criteria for sarcopenia refer to the 2019 Asian Working Group for Sarcopenia (AWG 2019) , which mainly include assessments of muscle strength, muscle mass, and physical function: 1) Measurement of handgrip strength (HGS): HGS \u0026lt; 28 kg in males and \u0026lt; 18 kg in females; 2) Height-adjusted skeletal muscle index (SMI): measured by DXA, \u0026lt; 7.0 kg/m\u0026sup2; in males or \u0026lt; 5.4 kg/m\u0026sup2; in females; 3) 6-meter walking speed: \u0026lt; 1.0 m/s. Sarcopenia is diagnosed when criterion 2) is met along with either criterion 1) or 3); severe sarcopenia is diagnosed when all three criteria are met simultaneously.\u003c/p\u003e\n\u003cp\u003eVertebral Fracture Cascade (VFC): refers to the occurrence of two or more vertebral fractures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient demographic data collection included: gender, age, body mass index (BMI), Bone Mineral Density (BMD)，fracture-related clinical information, comorbidities (hypertension, diabetes, liver and kidney diseases), and personal history (including History of Alcohol and smoking Use).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses and risk model construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R software. Continuous variables were presented as mean \u0026plusmn; standard deviation or median [interquartile range], and compared using t-tests or Mann-Whitney U tests as appropriate. Categorical variables were expressed as counts (percentages) and compared via Chi-square or Fisher\u0026apos;s exact tests. Univariate logistic regression identified variables (p\u0026lt;0.05) for inclusion in multivariate analysis. A stepwise selection method was applied to build the final model, with results reported as odds ratios (ORs), 95% confidence intervals and p-values. Model fit was assessed using residual deviation, log-likelihood, AIC and BIC. A nomogram was constructed for individualized risk prediction. Discriminative ability was evaluated by ROC analysis with AUC calculated via bootstrapping (1000 replicates). Calibration was tested using bias-corrected calibration curves (500 bootstrap replicates), reporting mean absolute error. Clinical utility was assessed via decision curve analysis. All tests were two-sided with statistical significance set at p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics Committee of Affiliated Beijing Shijitan Hospital of Captial Medical University has approved our research.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eComparison of Patient Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA\u0026nbsp;total of 889 patients (200 males and 689 females) were enrolled in this study. Cascading fractures were detected in 193 patients (21.7%), while the remaining 696 were classified as non-cascading fractures, (Table1).\u003c/p\u003e\n\u003cp\u003eTable 1 Comparison of Baseline Characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" title=\"表3 预分析组间比较结果-总体\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-VFC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003estatistical value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge（years）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.50 (68.75-83.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80.00 (75.00-84.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFracture to surgery interval（weeks）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00 (5.00-15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.00 (3.00-15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCement Volume (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.40 (4.30-5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.40 (4.50-5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e155 (22.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (23.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e541 (77.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e148 (76.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026ge;28kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e520 (74.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106 (54.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176 (25.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87 (45.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e575 (82.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147 (76.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e121 (17.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (23.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Steroid Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e558 (80.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e129 (66.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e138 (19.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64 (33.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThoracolumbar Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e275 (39.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39 (20.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e421 (60.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e154 (79.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMD T\u0026lt;-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e415 (59.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e102 (52.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e281 (40.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91 (47.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurgery Approach of PVP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Unilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e464 (66.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e128 (66.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Bilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e232 (33.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65 (33.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e376 (54.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (42.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e320 (45.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111 (57.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThyroid Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e626 (89.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e173 (89.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70 (10.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20 (10.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e661 (94.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e185 (95.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (5.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (4.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e658 (94.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e183 (94.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (5.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (5.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Smoking Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e591 (84.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e161 (83.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105 (15.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (16.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Alcohol Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e612 (87.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e164 (84.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e84 (12.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (15.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCement Leakage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e526 (75.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e148 (76.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e170 (24.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45 (23.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e556 (79.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100 (51.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e140 (20.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (48.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eVFC:\u0026nbsp;Vertebral Fracture Cascade\u003c/p\u003e\n\u003cp\u003eBMD: Bone Mineral Density\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePVP:\u0026nbsp;Percutaneous Vertebroplasty\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Factor Analysis of VFC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariable Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the univariable logistic regression analysis, age (OR = 1.033, 95% CI: 1.012\u0026ndash;1.057, \u003cem\u003eP\u003c/em\u003e= 0.003), elevated BMI (OR = 2.502, 95% CI: 1.687\u0026ndash;3.709, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), History of steroid use (OR = 2.293, 95% CI: 1.500\u0026ndash;3.484, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), thoracolumbar fracture (OR = 2.776, 95% CI: 1.769\u0026ndash;4.496, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), hypertension (OR = 1.914, 95% CI: 1.302\u0026ndash;2.832, \u003cem\u003eP\u003c/em\u003e = 0.001), fracture to surgery interval (OR = 0.964, 95% CI: 0.939\u0026ndash;0.988, \u003cem\u003eP\u003c/em\u003e = 0.004), and Sarcopenia (OR = 3.523, 95% CI: 2.360\u0026ndash;5.269, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were all significantly associated with the risk of VFC. In contrast, variables such as sex, diabetes, BMD, surgical approach, thyroid disease, liver disease, kidney disease, cement volume, smoking, History of alcohol use, and cement leakage showed no significant association with the occurrence of VFC (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05),(Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2 Univariable Logistic Regression for VFC\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" title=\"表4 单因素logistic回归结果\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI_lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI_upper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep_value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026ge;28kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Steroid Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThoracolumbar Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMD T-score\u0026lt;-2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurgical Approach (Uni/Bi)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFracture to surgery interval（w）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThyroid Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiver Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCement Volume (mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Smoking Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Alcohol Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCement Leakage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn multivariable logistic regression analysis, age (OR = 1.033, 95% CI: 1.009\u0026ndash;1.058, \u003cem\u003eP\u003c/em\u003e = 0.008), high BMI (OR = 2.817, 95% CI: 1.823\u0026ndash;4.374, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), History of steroid use (OR = 2.573, 95% CI: 1.609\u0026ndash;4.112, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), thoracolumbar fracture (OR = 3.421, 95% CI: 2.087\u0026ndash;5.801, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), fracture to surgery interval (OR = 0.962, 95% CI: 0.935\u0026ndash;0.988, \u003cem\u003eP\u003c/em\u003e = 0.006), and Sarcopenia (OR = 3.669, 95% CI: 2.372\u0026ndash;5.706, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were identified as independent factors influencing the occurrence of VFC. The odds ratios and their corresponding 95%CI\u003cem\u003e\u0026nbsp;\u003c/em\u003efor these variables consistently indicated a statistically significant impact on the risk of VFC, (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3 Multivariable Logistic Regression for VFC\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" title=\"表5 多因素logistic回归结果\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003estd.error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep.value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003econf.low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003econf.high\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026ge;28 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHistory of Steroid Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThoracolumbar Fracture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFracture to surgery interval（w）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSarcopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment of a Nomogram for Predicting VFC with Sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the multivariable logistic regression model, this study developed a nomogram for predicting the risk of VFC, which incorporates variables such as age, fracture to surgery interval, History of steroid use, BMI, sarcopenia, and thoracolumbar fracture ,(Figure 1).\u003c/p\u003e\n\u003cp\u003eFigure 1 Nomogram for predicting VFC. Variables are scored based on their regression coefficients. Red dots represent an example patient\u0026apos;s actual characteristics, with a total score of 364 points. This corresponds to a predicted probability of 4.78%, as shown on the probability axis below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of the VFC Risk Prediction Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Model Performance using the ROC Curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s performance was evaluated using ROC-AUC analysis. The cohort was randomly split into a training set (70%, n=622; 135 cases) and a validation set (30%, n=267; 58 cases). The model showed good discrimination in the training set (AUC=0.778, 95% CI: 0.729\u0026ndash;0.823) and maintained stable performance in the validation set (AUC=0.710, 95% CI: 0.637\u0026ndash;0.787), confirming its robustness for predicting VFC risk,(Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalibration Curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the calibration of the multivariable logistic regression model for predicting the risk of VFC, calibration curves were plotted for the training set (n=622) and the validation set (n=267), respectively. The calibration curve for the training set demonstrated a high degree of agreement between the model-predicted probabilities and the actual observed probabilities. The bias-corrected line closely overlapped with the ideal line, with a mean absolute error (MAE) of 0.006, indicating excellent calibration performance within the training set. The validation set calibration curve also showed good agreement, with an MAE of 0.037, demonstrating that the model\u0026apos;s predicted probabilities align well with the actual observed frequencies in the external dataset. These results provide a reliable statistical basis for the clinical application of the model,(Fig3,A,B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecision Curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further evaluate the clinical utility of the model, decision curve analysis (DCA) was employed to assess the net benefit of the VFC risk prediction model in both the training and validation sets. The results demonstrated that in the training set (n=622), across a high-risk threshold probability range of 0.05\u0026ndash;0.60, the model yielded a standardized net benefit superior to both the \u0026quot;intervene-all\u0026quot; and \u0026quot;intervene-none\u0026quot; strategies. This indicates that the model can offer tangible clinical utility for decision-making within this threshold interval. In the validation set (n=267), the model also exhibited a higher net benefit within the threshold range of 0.05\u0026ndash;0.45, further confirming its robustness and clinical applicability,(Fig 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study included a total of 889 eligible patients. The detection rates for sarcopenia and VFC were 26.2% and 21.7%, respectively. Univariate and multivariate regression analyses identified advanced age, BMI \u0026ge;28 kg/m\u0026sup2;, fracture to surgery interval, history of steroid use, thoracolumbar fracture, and sarcopenia as independent risk factors for cascading fractures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study revealed a statistically significant older average age in patients with cascading fractures compared to those without. An osteoporotic vertebral fracture is a mechanical event whose occurrence depends not only on the mechanical load borne by the vertebra but more critically on its anatomical shape, bone microstructure, and tissue properties\u003csup\u003e8\u003c/sup\u003e.Vertebral strength is compromised by age-related declines in BMD and deterioration of bone microarchitecture. Research indicates that from the age of 40, bone density loss can reach up to 30% in men and 50% in women\u003csup\u003e9\u003c/sup\u003e.Meanwhile, the incidence of sarcopenia progressively increases with age.\u003c/p\u003e\n\u003cp\u003eSarcopenia is a progressive and generalized skeletal muscle disorder characterized by the progressive loss of muscle strength, mass, and physical performance. It can be classified as primary or secondary\u003csup\u003e10\u003c/sup\u003e.The primary form is often associated with aging and manifests as a degenerative decline in muscle mass and quality. Secondary sarcopenia, which may occur in younger populations, is linked to factors such as prolonged bed rest or immobilization (e.g., disuse atrophy), inadequate diet and nutrition (including insufficient energy/protein intake, malabsorption, gastrointestinal disorders, anorexia), and certain underlying wasting diseases, such as cancer\u003csup\u003e11,12\u003c/sup\u003e.Research indicates that with advancing age, total body muscle mass declines. Between the ages of 50 and 60, an individual\u0026apos;s muscle mass can be reduced by up to 40%, which is equivalent to approximately 1% of total body weight\u003csup\u003e13\u003c/sup\u003e.The muscles attached to the vertebrae serve the dual function of generating movement and providing stability to protect the spinal structure. Studies indicate that without muscular support, the spine\u0026apos;s compressive load threshold before buckling is a mere 2 kg\u003csup\u003e8\u003c/sup\u003e.With advancing age, not only does the peak force of muscles decline, but the rate of muscle force and power development also slows. These alterations in force generation may compromise the normal load-bearing capacity of the spine, as coordinated co-contraction of antagonistic muscles during flexion and extension is crucial for maintaining spinal stability\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe reduction in muscle mass diminishes its ability to generate sufficient load on the skeleton. This can readily trigger a \u0026quot;disuse paradigm,\u0026quot; causing the internal architecture of bone tissue, including the trabeculae, to gradually lose its original biological function and efficacy. Consequently, bone brittleness increases, making it susceptible to fractures under gravitational stress\u003csup\u003e15\u003c/sup\u003e.Osteoporotic vertebral compression fractures (OVCF) are the most common type of fracture in the elderly, which is characterized by intensified back pain, limited mobility, and kyphotic deformity. In these patients, poor sagittal alignment is associated with both sarcopenia and persistent low back pain\u003csup\u003e16\u003c/sup\u003e.Kyphoplasty is currently the mainstream treatment for vertebral fractures, with reports indicating that balloon kyphoplasty can rapidly relieve pain, facilitate early ambulation, maintain activities of daily living, and aid in patient reintegration into society\u003csup\u003e17,18\u003c/sup\u003e.However, balloon kyphoplasty is merely a symptomatic treatment and does not address the underlying osteoporosis, which may lead to new vertebral fractures after surgery. Among these, adjacent vertebral fractures merit particular attention. Risk factors for adjacent fractures include advanced age, pre-existing vertebral fractures, increased local kyphosis due to vertebral collapse, and the presence of intravertebral clefts\u003csup\u003e19,20\u003c/sup\u003e.After an initial vertebral fracture occurs, the risk of subsequent fractures in other vertebrae increases by 4 to 7 times. Furthermore, this risk grows exponentially with the number of existing fractures. Therefore, following the initial treatment, proactive postoperative management, prediction of subsequent fractures, and preemptive interventions are critically important to prevent fractures in adjacent segments.\u003c/p\u003e\n\u003cp\u003eCurrent research remains divided on whether obesity constitutes a risk factor for osteoporotic vertebral fractures. The effects of high-BMI obesity and fat distribution-based obesity (e.g., abdominal obesity) on subsequent fractures differ substantially. While low BMI is associated with increased fracture risk, high BMI may act as a protective factor against osteoporotic fractures. However, other studies indicate an elevated risk of osteoporotic fractures among women with high BMI. A recent meta-analysis demonstrated that after adjusting for BMD in women, high BMI actually increases the risk of osteoporotic fractures\u003csup\u003e21\u003c/sup\u003e.Ren et al.\u003csup\u003e22\u003c/sup\u003e reported that a higher body mass index increases the incidence of new symptomatic osteoporotic vertebral fractures following percutaneous vertebroplasty (PVP). Furthermore, they confirmed that a high BMI (OR = 1.268) is significantly associated with adjacent segment fractures after PVP. This finding is consistent with the results of our study. Furthermore, body fat distribution, particularly abdominal fat, may exert differential effects on bone health, potentially exhibiting a negative correlation with BMD and thereby increasing fracture risk\u003csup\u003e23,24\u003c/sup\u003e.Previous studies have reported that abdominal obesity, as measured by waist circumference (WC) and waist-to-hip ratio (WHR), is positively associated with the incidence of hip fractures and vertebral fractures\u003csup\u003e25,26\u003c/sup\u003e.However, there is currently limited research reporting the impact of BMI on VFC. Existing research provides compelling evidence that cement leakage, lower BMI, and BMD are risk factors for subsequent vertebral fractures (SVF) following PVP\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe reported incidence of adjacent vertebral fractures following kyphoplasty ranges from 6.5% to 33%, with higher risks observed in females, older adults, fractures at the thoracolumbar junction, and individuals with a history of vertebral fractures\u003csup\u003e20,28\u003c/sup\u003e.\u0026nbsp;Research indicates that the mean load required to cause a fracture in the thoracolumbar spine decreases from approximately 8000 N at age 25 to about 2000 N by age 75. In some elderly cadaveric thoracic spines, this value can be as low as 500 N \u003csup\u003e9\u003c/sup\u003e. Osteoporotic vertebral fractures do not occur uniformly along the spine, with a predilection for the thoracic region (T7-T8) and the thoracolumbar junction (T11-L1).\u0026nbsp;One possible explanation for this discrepancy lies in the biomechanical alterations resulting from changes in spinal curvature. Briggs et al.\u003csup\u003e29\u003c/sup\u003e utilized a biomechanical model of the spine to demonstrate that older subjects with severe thoracic kyphosis experience greater flexion moments and compressive forces on their vertebrae. Due to the kyphotic deformity, the patient\u0026apos;s center of gravity shifts forward, leading to increased bending moments around the apex of the kyphosis. This not only triggers pain but also further elevates the risk of additional fractures\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e. This finding aligns with the results of the present study, which identified thoracolumbar fracture as an independent risk factor for cascading fractures. Furthermore, at the thoracolumbar junction where spinal curvature transitions from kyphosis to lordosis, the rigid thoracic cage gives way to the more mobile segments. The higher incidence of osteoporotic vertebral fractures at T11-L1 may be attributed to the lack of protective support from the rib cage in these segments immediately below the thoracic spine, leaving them vulnerable to additional loads\u003csup\u003e31\u003c/sup\u003e. Differences in bone mineral density (BMD) and bone strength across spinal segments may represent another factor contributing to the uneven distribution of osteoporotic vertebral fractures. The compressive loads borne by vertebral bodies typically gradually increase from the thoracic to the lumbar region.\u0026nbsp;Burklein et al.\u003csup\u003e32\u003c/sup\u003e compared the compressive strength of T6, T10, and L3 vertebral bodies from 119 human cadavers and found a weak correlation between different spinal segments, indicating inherent heterogeneity in bone strength throughout the spine, which may partly explain the regional variations in fracture incidence.\u003c/p\u003e\n\u003cp\u003eGlucocorticoid use can lead to glucocorticoid-induced osteoporosis (GIOP). Studies have demonstrated that fracture risk increases immediately after initiating glucocorticoid (GCs) therapy, with particularly pronounced effects observed in the spine and proximal femur\u003csup\u003e32\u003c/sup\u003e.\u0026nbsp;One proposed mechanism is that corticosteroids can induce apoptosis in osteoblasts and osteocytes. Another explanation suggests that the rapid alteration in fracture risk is primarily driven by significant changes in bone turnover, mediated through induced microarchitectural deterioration of bone quality \u003csup\u003e33\u003c/sup\u003e. Glucocorticoids help maintain the dynamic equilibrium between osteoblasts and osteoclasts, thereby regulating the balance between bone formation and resorption. At physiological doses, these effects remain relatively limited. However, with exogenous administration, bone resorption predominates over bone formation \u003csup\u003e34\u003c/sup\u003e.\u0026nbsp;Glucocorticoids bind to specific sequences in the promoter regions of target genes via intracellular glucocorticoid receptors (GR), suppressing the expression of key osteogenic genes such as Runx2 and Osterix. This process impedes osteoblast differentiation and bone matrix formation, while also inhibiting the expression of osteocalcin and alkaline phosphatase, ultimately reducing bone mineralization capacity \u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;Concurrently, GCs upregulate the RANKL/OPG ratio, promoting osteoclast activation and bone resorption. They also suppress the expression of genes associated with osteoclast apoptosis, thereby prolonging osteoclast lifespan, enhancing bone resorption, and ultimately increasing fracture risk \u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;This finding is consistent with the results of our study.\u003c/p\u003e\n\u003cp\u003eFurthermore, this study identified that the timing of surgery for the initial fracture holds significant value in predicting the risk of subsequent fractures postoperatively. Yannick et al.\u003csup\u003e37\u003c/sup\u003e categorized 230 fracture patients into acute (\u0026lt;2 weeks), subacute (2-6 weeks), and chronic (\u0026gt;6 weeks) groups. Their study demonstrated that patients in the acute and subacute groups achieved better restoration of vertebral height and kyphotic angle postoperatively, along with reduced postoperative analgesic use, whereas no significant improvements were observed in the chronic group. Two additional studies (involving 51 and 36 patients, respectively) concluded that early kyphoplasty performed within 4 weeks enables superior spinal alignment correction compared to delayed intervention\u0026nbsp;\u003csup\u003e38,39\u003c/sup\u003e.\u0026nbsp;During the acute phase of fresh fractures, spontaneous height restoration is more readily achievable, whereas in the late-stage fractures, fibrous tissue formation and bone healing impede positional correction. Bone cement alleviates pain by stabilizing micro-movements within the fractured vertebra and disrupting free nerve endings, thereby facilitating early patient recovery\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e.\u0026nbsp;Early correction of spinal alignment and restoration of spinal stability can thereby effectively reduce the risk of adjacent vertebral fractures.\u003c/p\u003e\n\u003cp\u003eBased on age, BMI\u0026nbsp;\u0026ge;28 kg/m\u0026sup2;, history of steroid use, thoracolumbar fracture, \u0026nbsp;fracture to surgery interval, and sarcopenia, this study developed a predictive model for the risk of VFC in patients with sarcopenia following surgery. Evaluation demonstrated that the model has satisfactory predictive performance. As the relevant risk factors can be readily assessed in clinical practice, the nomogram enables individualized calculation of the risk of subsequent vertebral fractures postoperatively, thereby providing a reference for formulating effective preventive measures. Therefore, the risk prediction model established in this study shows promising clinical application value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis single-center cross-sectional study has inherent limitations in sample representativeness, and caution should be exercised when generalizing the conclusions. Although sarcopenia was diagnosed according to the AWGS criteria, measurement variability in grip strength and skeletal muscle mass may persist. Potential confounding factors (e.g., nutrition, physical activity, vitamin D status) were not fully controlled. The cross-sectional design cannot establish causality, and systematic records of postoperative rehabilitation and medication were lacking. Future prospective, multicenter studies are warranted for further validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participating patients, the Medical Ethics Committee of Beijing Shijitan Hospital for their support, and our colleagues for assistance with data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuizhao Zhao: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization.\u003c/p\u003e\n\u003cp\u003eXinyao Lv: Investigation, Data Curation, Resources, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eZijian Wang: Software, Validation, Formal Analysis, Data Curation, Visualization.\u003c/p\u003e\n\u003cp\u003eJunjie Qiao: Investigation, Resources, Project Administration.\u003c/p\u003e\n\u003cp\u003eXiutong Fang: Conceptualization, Methodology, Resources, Writing \u0026ndash; Review \u0026amp; Editing, Supervision, Project Administration, Funding Acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Scientific and Technological Research and Development Program of China State Railway Group Co., Ltd. (J2023Z603).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent has been obtained from all relevant participants for this study. The authors confirm that this study strictly adheres to The Declaration of Helsinki.This study was conducted in compliance with the Declaration of Helsinki and relevant regulatory guidelines, following approval by the Medical Ethics Committee of Beijing Shijitan Hospital, Capital Medical University (Ethics Approval No.: IIT2024-127-002).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTagliafico AS, Bignotti B, Torri L, Rossi F. Sarcopenia: how to measure, when and why. Radiol Med. 2022;127:228\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11547-022-01450-3\u003c/span\u003e\u003cspan address=\"10.1007/s11547-022-01450-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho MR, Lee S, Song SK. A Review of Sarcopenia Pathophysiology, Diagnosis, Treatment and Future Direction. 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Balloon kyphoplasty versus percutaneous vertebroplasty for treatment of osteoporotic vertebral compression fractures (OVCFs). Osteoporos Int. 2016;27:2823\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00198-016-3610-y\u003c/span\u003e\u003cspan address=\"10.1007/s00198-016-3610-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Vertebral Fracture Cascade, Nomogram, Osteoporotic Vertebral Fracture, Prediction Model","lastPublishedDoi":"10.21203/rs.3.rs-7999138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7999138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to identify risk factors and develop a predictive nomogram for vertebral fracture cascade (VFC) in patients with sarcopenia. A total of 889 patients with osteoporotic vertebral fractures were included, of whom 193 (21.7%) developed VFC. Univariate and multivariate logistic regression analyses identified advanced age, BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;, history of steroid use, thoracolumbar fracture, shorter fracture-to-surgery interval, and sarcopenia as independent risk factors for VFC. A nomogram incorporating these variables was constructed and demonstrated good predictive performance, with area under the curve values of 0.778 in the training set and 0.710 in the validation set. Calibration and decision curve analyses confirmed the model\u0026rsquo;s accuracy and clinical utility. This nomogram provides a practical tool for early identification of high-risk patients, facilitating targeted interventions to prevent VFC in clinical practice.\u003c/p\u003e","manuscriptTitle":"Risk Factors and a Prediction Nomogram for Vertebral Fracture Cascade in Patients with Sarcopenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-17 06:31:31","doi":"10.21203/rs.3.rs-7999138/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d98b24d6-0bed-426d-9bdb-373c352a890b","owner":[],"postedDate":"November 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T11:09:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-17 06:31:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7999138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7999138","identity":"rs-7999138","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}