Development and Validation of a Clinically Useful Nomogram for Predicting Sarcopenia in Bone Tumor Patients

Development and Validation of a Clinically Useful Nomogram for Predicting Sarcopenia in Bone Tumor Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Clinically Useful Nomogram for Predicting Sarcopenia in Bone Tumor Patients Jun Yu, Jie Huang, Qinghua Ma, Yanyu Chen, Xinyue Huang, Jian Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9305260/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: Sarcopenia represents a highly prevalent and prognostically detrimental complication in patients with bone tumors. Its progressive and dynamic nature hinders early intervention and amplifies clinical care burdens, highlighting an unmet need for tailored risk stratification tools. Objective: To determine the prevalence and independent risk factors of sarcopenia in patients with bone tumors, and to develop, internally validate, and comparatively evaluate a clinically practical nomogram for individualized sarcopenia risk prediction. Design: A prospective, single-center study was conducted. A total of 400 inpatients with bone tumors were consecutively enrolled between August 2023 and February 2025 for model development. An independent cohort of 140 patients admitted from March to June 2025 was used for head-to-head validation of the nomogram against five established sarcopenia screening tools. Setting and Participants: This study was performed at a tertiary cancer hospital, enrolling 540 patients with bone tumors using convenience sampling. Methods: (1) Model development and internal validation: Eligible patients were randomly assigned at a 7:3 ratio to a training cohort (n=280) and an internal validation cohort (n=120). Data encompassed demographics, Tampa Scale of Kinesiophobia-11 (TSK-11), Fried Frailty Phenotype, Athens Insomnia Scale (AIS), and routine laboratory parameters. Sarcopenia was defined per the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. Predictors were selected using Lasso regression, and the nomogram was constructed using multivariable logistic regression. Model performance was assessed via discrimination (ROC–AUC), calibration (calibration curves and Hosmer–Lemeshow test), and clinical utility (decision curve analysis, DCA). (2) Comparative validation: The nomogram was directly compared with five brief screening tools: SARC-F, SARC-Calf, SARC-F+EBM, MSRA-7, and MSRA-5 in 140 patients. Results: Six independent predictors were integrated into the nomogram: regular exercise, body mass index (BMI), serum phosphorus, frailty, kinesiophobia, and sleep disturbance (all P<0.05). The nomogram yielded excellent discrimination, with an AUC of 0.935 (95% CI: 0.836–0.950) in the training cohort and 0.902 (95% CI: 0.808–0.910) in the internal validation cohort. The Hosmer–Lemeshow test confirmed good calibration (χ²=3.684, P=0.884). In head-to-head testing, the nomogram achieved the highest AUC (0.926), sensitivity (93.55%), negative predictive value (91.49%), Youden index (0.816), and Kappa coefficient (0.724) among all tools. Conclusions: This novel nomogram demonstrates robust predictive performance and clinical reliability for identifying sarcopenia in patients with bone tumors. It enables early, individualized risk assessment and supports timely targeted interventions to optimize clinical outcomes. Bone Neoplasms Sarcopenia Risk Assessment Predictive Model Nomogram Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Summary This study identifies key determinants of sarcopenia in patients with bone tumors and establishes a user‑friendly nomogram for risk prediction. Direct comparison with five conventional screening instruments confirms that the nomogram provides superior accuracy for early sarcopenia detection and risk stratification. This clinically applicable tool supports timely, personalized interventions and may improve prognosis and health‑related quality of life in this high‑risk population. 1. Introduction Bone tumors encompass both primary and secondary tumors that originate in the bones and associated tissues [1] . More than 70–80% of cancer patients develop bone metastases, with breast, lung, and prostate cancers being the most common primary sites of origin for bone tumor metastases. As the incidence of cancers continues to rise, the prevalence of bone tumors is also increasing [2–4] . While advancements in surgical treatments, radiotherapy, chemotherapy, and immunotherapy have improved survival rates and reduced disability, bone tumors still pose a significant threat to patients’ health, life expectancy, and quality of life [5] . Factors such as surgical trauma, chemotherapy toxicity, pathological fractures, paraplegia, and kinesiophobia contribute to reduced physical activity and prolonged immobility in these patients. This, in turn, decreases muscle protein synthesis and elevates the risk of sarcopenia. Sarcopenia is a progressive systemic skeletal muscle disorder characterized by reduced muscle strength, muscle mass, and/or physical performance [6] . In patients with bone tumors, sarcopenia exacerbates the risks of falls, fractures, disability, complications, and mortality [7] . Additionally, it prolongs hospital stays, increases healthcare costs, and imposes substantial burdens on families, healthcare facilities, and society. Despite its significance, the diagnosis and measurement of sarcopenia lack standardization. Reported incidence rates of sarcopenia vary widely, ranging from 5.5% to 25.7%, due to differences in population characteristics and diagnostic criteria [8] . Muscle mass assessment is crucial for diagnosing sarcopenia, with common methods including computed tomography (CT), magnetic resonance imaging (MRI), and dual-energy X-ray absorptiometry (DXA). However, these modalities are expensive, involve radiation exposure, and require specialized equipment, making them less feasible for use in primary care settings. While bioelectrical impedance analysis (BIA) is portable and user-friendly, its accuracy depends on device-specific algorithms, limiting its reliability [8] . Similarly, questionnaires like the Sarcopenia Five-item Questionnaire (SARC-F) and its combination with calf circumference (SARC-CalF) lack sensitivity and are influenced by factors such as age, gender, and cognitive function [9, 10] . Thus, there is a pressing need for a simple, non-invasive, practical, and reliable assessment tool tailored for sarcopenia screening in patients with bone tumors. Nomogram-based risk prediction models are visual, intuitive tools that have been successfully applied in various populations, including the elderly and patients with kidney diseases, diabetes, and gastrointestinal tumors [11–13] . However, the development of sarcopenia-specific risk prediction models for patients with bone tumors remains limited. These patients face unique challenges, such as pain from tumor invasion, amputation-related complications, paraplegia due to spinal cord compression, and cancer-related fatigue. As a result, existing diagnostic instruments and prediction models are often unsuitable for this population. This study aims to identify the key factors influencing sarcopenia in patients with bone tumors and to develop and validate a nomogram-based risk prediction model. The goal is to provide a practical and reliable tool for early detection and intervention, ultimately aiding healthcare professionals in improving patient outcomes and quality of life. 2. Methods 2.1Study Design This was a cross-sectional study. This study received ethical approval from the Ethics Committee of Yunnan Cancer Hospital (KYLX2023-171). All participants provided written informed consent before participating in the study. 2.2Study Participants This study used a convenience sampling method. Participants were patients with bone tumors treated at Yunnan Cancer Hospital between August 2023 and June 2025.Inclusion criteria were meeting the diagnostic criteria outlined in the NCCN "Clinical Practice Guidelines for Bone Tumors," being 18 years or older, having a disease duration exceeding three months, and demonstrating clear consciousness and communication abilities. Patients were excluded if they had lower limb edema, mental disorders, severe hearing, visual, or language impairments, inability to stand, metal implants, limb deformities, or if their limbs were covered by instruments that prevented measurement. 2.3Sample Size Development of a Risk Prediction Model: The sample size was determined based on logistic regression analysis, which requires at least 10 cases per variable. With an estimated 5–6 variables in the final model, the minimum sample size was calculated to be 50–60 cases. Considering the approximate sarcopenia incidence of 30% in bone tumor patients and accounting for a 10% invalid data rate, at least 220 cases were needed in the modeling group. Adopting a 70:30 ratio for modeling and validation, 280 patients were allocated to the modeling group and 120 to the validation group. Comparative validation of the risk prediction model and five common sarcopenia assessment scales: Inpatients admitted to Yunnan Cancer Hospital between March 2025 and June 2025 were enrolled as the study population for the applied validation phase. The sample size was calculated in accordance with Kendall’s sample size estimation principle, which recommends a sample-to-item ratio ranging from 5:1 to 10:1. Given that a total of 20 items were included across the screening scales and the risk prediction model, and accounting for a 20% invalid questionnaire rate, the required sample size was estimated to be 125 to 250 cases. Ultimately, 140 patients with bone tumors were included in the analysis. 2.4Measurements 2.4.1Assessment Tools Used During Risk Prediction Model Development (1) General information : The research team designed the study independently through expert consultation, incorporating a range of variables including age, gender, ethnicity, tumor location, educational background, monthly income, and pain. The electronic medical record system was consulted to collect biochemical indicators such as BMI, red blood cells, white blood cells, platelets, serum calcium, serum phosphorus, procalcitonin, and C-reactive protein. (2)The diagnostic criteria for sarcopenia proposed by AWGS 2019 are adopted [14] , including the following: Firstly, the subject's muscle strength, or grip strength, will be assessed. The initial objective is to ascertain the optimal weight parameters for men and women, respectively, as follows: a minimum of 28 kilograms for men and a minimum of 18 kilograms for women. The secondary objective is to determine the physical activity capacity, specifically the walking speed. The subject demonstrated a walking speed of less than 1 meter per second, indicating a lower than average ambulatory speed. Additionally, the subject exhibited a skeletal muscle index indicative of reduced muscle mass. The body mass index (BMI) is calculated as the weight in kilograms divided by the square of the height in meters, and values of less than 7.0 kg/m² are considered underweight for men and less than 5.7 kg/m² are considered underweight for women. Sarcopenia is characterized by the fulfillment of criterion 3 and either criterion 1 or 2. (4) Fried Frailty Phenotype : The Fried Frailty Phenotype Scale is a tool used to assess the presence of signs associated with the frailty syndrome in older adults [15] . This approach is currently the most widely used method for assessing frailty. The Chinese version of the scale utilized in this study encompasses five components: weight loss over the past year; fatigue and activity duration over the past week; current grip strength; and physical activity capacity. Each item is assigned a point value of 1, with a total possible score of 5 points. A score of 0 indicates no frailty, 1–2 indicates pre-frailty, and 3–5 indicates frailty. In this study, the Cronbach's α coefficient for the scale in question was 0.826, indicating good reliability [16] . (5)The Tampa Scale for Kinesiophobia-11 (TSK-11) is a measurement tool designed to assess the severity of kinesiophobia, also known as fear of movement, in individuals suffering from anxiety disorders [17] . The scale under consideration is comprised of 11 items, which are divided into four dimensions: perception of danger, avoidance of exercise, fear of exercise, and functional impairment. The scale utilizes a 4-point Likert scale, ranging from strongly disagree to strongly agree, with scores from 1 to 4. The total score ranges from 11 to 44 points, with a score > 26 indicating kinesiophobia. In this study, the Cronbach's α coefficient for the scale in question was 0.88, and the test-retest reliability was 0.80, thereby demonstrating good construct validity and convergent validity. (6)The Athens Insomnia Scale (AIS) is a tool designed to assess insomnia symptoms. This scale is a Chinese version of a self-assessment questionnaire for sleep disorders, comprising eight items: difficulty falling asleep, nighttime sleep interruptions, early awakening, satisfaction with sleep duration and quality, the impact of sleep on daytime mood and social functioning, and daytime sleepiness. The scale utilizes a four-point rating scale ranging from 0 to 3, where 0–3 indicates the absence of sleep disorder, 4–6 signifies the potential presence of insomnia, and ≥ 7 corresponds to a diagnosis of insomnia [18] . The Cronbach's α coefficient for this scale was 0.825, indicating good reliability. 2.4.2 Study Instruments for Comparative Validation of the Risk Prediction Model and Five Sarcopenia Screening Tools (1) Sarcopenia Five-Item Questionnaire (SARC-F) The SARC-F was originally developed by Malmstrom et al [19] .and consists of five items assessing muscle strength, walking ability, chair rise capacity, stair-climbing performance, and fall history. Each item is scored on a scale of 0 to 2 points, yielding a total score ranging from 0 to 10 points. A total score of ≥ 4 points indicates an elevated risk of sarcopenia. This questionnaire demonstrates favorable reliability and validity, with a Cronbach’s α coefficient of 0.849. (2)Modified Sarcopenia Five-Item Questionnaire (SARC-Calf) : The SARC-Calf was developed by Barbosa-Silva et al [20] .s a novel screening tool that integrates calf circumference measurement with the original SARC-F questionnaire. It retains the original scoring criteria of the SARC-F and adds an additional item for calf circumference: 10 points are assigned if calf circumference is ≤ 34 cm in men or ≤ 33 cm in women, and 0 points otherwise. The total score ranges from 0 to 20 points, and a total score of ≥ 11 points suggests the presence of sarcopenia risk. This questionnaire exhibits good reliability and validity, with a Cronbach’s α coefficient of 0.812. (3)Sarcopenia Five-Item Questionnaire Combined with Age and BMI (SARC-F + EBM) : The SARC-F + EBM was proposed by Kurita et al [21] .as an updated screening tool that combines the original SARC-F questionnaire with age and body mass index (BMI). It maintains the original scoring algorithm of the SARC-F and incorporates two additional categorical scoring items: 10 points are allocated for age ≥ 75 years (0 points otherwise), and 10 points for BMI ≤ 21 kg/m (0 points otherwise). The total score ranges from 0 to 30 points, and a cutoff score of ≥ 12 points is indicative of sarcopenia risk. In the present study, this questionnaire showed satisfactory reliability and validity, with a Cronbach’s α coefficient of 0.834. (4)Mini Sarcopenia Risk Assessment Questionnaire (MSRA) : The MSRA questionnaire was originally designed by Rossi et al [22] , and translated into Chinese by Yang et al [23] .in 2018. It comprises two versions: the 7-item Mini Sarcopenia Risk Assessment questionnaire (MSRA-7) and the 5-item Mini Sarcopenia Risk Assessment questionnaire (MSRA-5).Specifically, the MSRA-7 assesses seven domains: age, number of hospital admissions in the past year, walking ability, number of daily meals, weight loss in the past year, and intake of protein and dairy products. Each item is scored 0–5 or 0–10 points, with a total score ranging from 0 to 40 points; a total score of ≤ 30 points denotes sarcopenia risk. The MSRA-5 is a shortened version of the MSRA-7, excluding the items assessing protein and dairy intake. Each item is scored 0–5, 0–10, or 0–15 points, with a total score ranging from 0 to 60 points; a total score of ≤ 45 points indicates sarcopenia risk. In this study, both versions of the MSRA presented acceptable reliability and validity, with Cronbach’s α coefficients of 0.792 (MSRA-7) and 0.801 (MSRA-5), respectively. 2.5Data Collection Prior to the implementation of the study, a research team was established and underwent unified training. The training covered various aspects, including methods for collecting indicators, how to use measurement tools, how to evaluate questionnaire items, and important considerations regarding techniques used during the survey. Participants were required to pass an assessment before proceeding with the research. After data collection is completed, research team members will review each questionnaire individually. If any important items are missing from the questionnaire, the data will be deemed invalid and excluded. In cases of incomplete data, a re-evaluation and re-entry should be conducted promptly. Data entry will be performed using a parallel double-entry method, with a third party conducting a review to ensure the accuracy and reliability of the data. Grip strength measurement: Muscle strength was assessed at admission using an electronic grip strength meter (Shangshan EH-101). Patients stood upright with their legs naturally apart and their arms hanging naturally at their sides. Measurements were taken twice with the dominant hand, with an interval of more than 5 seconds between each measurement, and the maximum value was recorded. Walking speed measurement: A 6-meter walking test is used to evaluate the patient's physical condition. Upon admission, the patient walks 6 meters at a steady pace without any assistance. The time required for two walks is measured, and the average is recorded. Muscle mass measurement: At admission, the Inbody270 body composition analyzer produced by Basbeth Co., Ltd. of South Korea was used. This instrument measures muscle mass through bioelectrical impedance analysis. The limb skeletal muscle index (kg/m²) = limb skeletal muscle mass (kg) / height² (m²). The following conditions were excluded during measurement: (1) No physical activity within 10 minutes prior to measurement; (2) At least 2 hours after a meal; (3) No metal objects or electronic devices were carried during measurement; (4) No medications that could affect measurement accuracy were used (e.g., furosemide, mannitol, etc.). After entering basic information, the patient stands barefoot on the body composition analyzer, with the soles of both feet aligned with the electrodes at the bottom of the instrument. Both hands naturally grip the electrodes, and both arms hang naturally downward without touching the sides of the body. Regular exercise: Regular exercise refers to exercising more than three times a week for more than 30 minutes each time for at least six months. Assessment of limb edema: It is imperative to select common areas that exhibit thin subcutaneous tissue. Such areas include the inner side of the ankle, the front of the shin, the back of the hand, and the fingers. The application of vertical pressure to the selected area should be executed with a moderate degree of force, using the thumb or index finger. This pressure should be sustained for a duration of 5–10 seconds, followed by a release. The skin at the pressure point will exhibit a slight blanching reaction, indicative of the application of pressure. The presence of an indentation that persists and requires more than 3–5 seconds to return to its normal state is indicative of edema. 2.6Statistical Analysis Data analysis was conducted using SPSS 29.0. Continuous variables were presented as mean ± standard deviation (SD) or median with quartiles, depending on distribution normality, and compared using t-tests or U-tests. Categorical variables were analyzed using chi-square or Fisher's exact tests. Variables with P < 0.05 in univariate analysis were included in multivariate logistic regression. Nomogram construction and validation were performed using R software (version 4.4.1), and the model’s performance was assessed for discrimination (ROC curve and AUC), calibration (calibration curve and Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis).Patients were stratified into sarcopenia and non-sarcopenia groups based on the screening results of the risk prediction model and the five sarcopenia questionnaires. The grouping frequencies were then compared against the diagnostic results obtained using the AWGS-2019 sarcopenia screening criteria. The area under the receiver operating characteristic (ROC) curve (AUC) with corresponding 95% confidence interval (CI), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Kappa coefficient were calculated to evaluate the screening performance of each tool. 3. Results 3.1Demographic Characteristics A total of 400 patients with bone tumors were included in the study, comprising 211 males (52.75%) and 189 females (47.25%). Among them, 105 patients (26.25%) were diagnosed with sarcopenia, while 295 patients did not have sarcopenia. The age range for the non-sarcopenia group was 18–80 years (mean 50.68 ± 14.28), and for the sarcopenia group, 18–87 years (mean 58.18 ± 15.46). Univariate analysis revealed statistically significant differences between the two groups for factors such as age, economic status, pain duration, regular exercise, osteoporosis, presence of chronic diseases, tumor type, BMI, hemoglobin, platelets, albumin, indirect bilirubin, serum calcium, serum phosphorus, C-reactive protein, fibrin degradation products, fibrinogen, and calcitonin (P < 0.05). Detailed results are provided in Table 1 . Table 1 Demographic characteristics (n = 400) Variables Sarcopenia(n = 105) Non Sarcopenia (n = 295) χ 2 /t/z P Age(years) 18.809 <0.001 18 ~ 49 24 115 50 ~ 59 28 91 60 ~ 69 32 66 ≥ 70 21 23 Gender 0.676 0.411 Male 59 152 Female 46 143 Ethnicity 0.040 0.841 Han nationality 86 239 Ethnic minorities 19 56 Occupation 2.458 0.652 Farming 44 121 Self-employment 4 17 Technical Personnel 8 30 Enterprises and Institutions 3 14 Other 46 113 Educational degree 7.321 0.065 Elementary school and below 61 127 Junior high school 25 90 High school/vocational school 11 48 College and above 8 30 Monthly income 18.044 <0.001 7000 4 30 Pain duration 10.547 0.032 January to March 62 198 April to June 26 47 July to September 6 5 October to December 2 4 After December 9 41 Regular exercise 38.831 <0.001 Yes 30 183 No 75 112 Osteoporosis 21.879 <0.001 Yes 68 113 No 37 182 Chronic diseases 25.766 <0.001 Yes 61 89 No 44 206 Tumor nature 21.561 <0.001 Benign 10 97 Malignant 95 198 BMI 20.01 ± 2.52 23.47 ± 3.05 10.428 <0.001 Hemoglobin(g/L) 114.00 ± 17.53 125.49 ± 20.07 5.546 <0.001 Red blood cell(10 12 /L) 3.87(3.32,4.35) 4.23(3.76,4.63) -4.294 <0.001 White blood cell(10 9 /L) 7.73(5.56,10.04) 7.87(5.69,10.52) -0.573 0.567 Platelet(10 9 /L) 267.53 ± 112.23 238.63 ± 83.910 -2.760 0.006 Albumin(g/L) 36.99 ± 5.41 39.02 ± 4.68 3.660 <0.001 Indirect bilirubin(µmol/L) 4.90(3.30,7.25) 5.30(3.78,8.03) -1.979 0.048 Calcium(mmol/L) 2.15 ± 0.18 2.20 ± 0.15 2.607 0.009 Phosphorus(mmol/L) 0.97 ± 0.29 1.13 ± 0.27 4.827 <0.001 Urea(mmol/L) 227.0(148.0,300.0) 263.0(204.0,334.0) -2.864 0.004 C-reactive protein(mg/L) 15.91(6.01,36.29) 6.62(2.02,18.31) -4.302 <0.001 FDP(µg/mL) 5.08(2.44,9.67) 2.49(2.08,4.04) -5.150 <0.001 Fib(g/L) 4.30(3.37,5.45) 3.59(3.01,4.57) -3.723 <0.001 PCT(ng/mL) 0.07(0.03,0.15) 0.03(0.01,0.07) -5.378 <0.001 FFP 73.342 <0.001 No frailty 27 187 Pre-frality 49 99 Frality 29 9 TSK.11 60.880 <0.001 Yes 72 76 No 33 219 AIS 63.592 <0.001 No sleep problems 37 223 Suspected insomnia 32 49 Insomnia 36 23 Abbreviations: FFP: Fried Frailty Phenotype; TSK.11:Tampa Scale for Kinesiophobia-11; AIS: Athens Insomnia Scale. 3.2Construction of the Nomogram Risk Prediction Model The 400 patients were randomly divided into a development group (n = 280) and a validation group (n = 120) in a 7:3 ratio using R software version 4.4.1. Lasso regression analysis was used to screen variables, with the regression coefficients and cross-validation path diagram shown in Figs. 1 and 2 , respectively. Logistic regression was performed with sarcopenia occurrence (No = 0, Yes = 1) as the dependent variable and significant factors from the univariate analysis as independent variables (Table 2 ). The final analysis identified six significant predictors of sarcopenia: regular exercise, BMI, serum phosphorus, frailty status, kinesiophobia, and sleep status (P < 0.05, Supplementary Table 3). Table 2 Assignment table for independent variables Variables Assignment method Regular exercise Yes = 1, No = 0 BMI Original value Phosphorus Original value FFP No frailty = 1, Pre-frailty = 2, Frailty = 3 TSK.11 Yes = 1, No = 0 AIS No sleep problems = 1, Suspected insomnia = 2, Insomnia = 3 Table 3 Results of Logistic regression analysis for sarcopenia in patients with bone tumors (N = 400) Variables β SE Waldχ 2 P OR 95% CI Regular exercise -1.316 0.354 13.789 <0.001 0.268 0.134 ~ 0.537 BMI -0.483 0.073 44.401 <0.001 0.617 0.535 ~ 0.711 Phosphorus -1.945 0.646 9.770 0.003 0.143 0.040 ~ 0.507 FFP 18.407 <0.001 Pre-frailty 0.710 0.360 3.879 <0.001 0.049 1.003 ~ 4.120 Frailty 2.590 0.611 17.996 <0.001 13.333 4.029 ~ 44.124 TSK.11 1.064 0.340 9.770 0.002 2.897 1.487 ~ 5.643 AIS 23.601 <0.001 Suspected insomnia 1.369 0.399 11.753 <0.001 3.931 1.797 ~ 8.597 Insomnia 2.018 0.460 19.582 <0.001 7.524 3.057 ~ 18.520 Using these six factors, a nomogram risk prediction model was constructed to estimate the probability of sarcopenia in patients with bone tumors. The nomogram assigns scores for each risk factor, which are summed to produce a total score. A total score of 119 corresponds to a 50% probability of sarcopenia, while a score of 140 indicates a 90% probability (Supplementary Fig. 3). 3.3Evaluation of the Nomogram Prediction Model The nomogram's performance was assessed using receiver operating characteristic (ROC) curves for the development and validation groups (Figs. 4 and 5 ). The area under the curve (AUC) for the development group was 0.935 (95% CI: 0.836 ~ 0.950), with a sensitivity of 0.903 and specificity of 0.865. For the validation group, the AUC was 0.902 (95% CI:0.808 ~ 0.910), with sensitivity and specificity values of 0.944 and 0.742, respectively, demonstrating strong discriminatory ability. Calibration curves for both groups showed high agreement between predicted and actual probabilities of sarcopenia (Figs. 6 and 7 ). The Hosmer-Lemeshow goodness-of-fit test indicated no significant difference between predicted and observed values (χ² = 3.684, P = 0.884), confirming that the model is well-calibrated. Decision curve analysis (DCA) further evaluated the model's clinical utility (Figs. 8 and 9 ). In the development group, the model provided clinical benefit within a threshold probability range of 4%–100%, while for the validation group, benefit was observed within the 4%–83% range. These results highlight the nomogram's practicality and utility in clinical decision-making. 3.4 Comparative Results of the Risk Prediction Model and Five Sarcopenia Assessment Tools 3.4.1Screening Efficacy Analysis of the Risk Prediction Model versus Five Sarcopenia Questionnaires Using the AWGS-2019 diagnostic criteria for sarcopenia as the reference standard, the risk prediction model achieved the largest area under the ROC curve (AUC = 0.926), as well as the highest sensitivity (93.55%), negative predictive value (NPV = 91.49%) and Youden index (0.816). At its optimal cutoff value, this model exhibited high sensitivity (93.5%) and specificity (88.1%)(Table 4 , Table 5 and Fig. 10 .). Table 4 Diagnostic Accuracy Analysis of the Risk Prediction Model and Five Sarcopenia Questionnaires (n = 140) Screening Tool Sensitivity (%) Specificity (%) PPV(%) NPV(%) AUC(95%CI) SARC-F 32.05% 93.69% 64.10% 79.69% 0.760(0.670 ~ 0.851) SARC-Calf 80.77% 81.08% 60.00% 92.31% 0.823(0.747 ~ 0.899) SARC-F + EBM 44.87% 95.50% 77.78% 83.14% 0.846(0.763 ~ 0.928) MSRA-7 74.36% 65.32% 42.03% 87.88% 0.780(0.716 ~ 0.844) MSRA-5 74.36% 62.16% 40.28% 87.34% 0.798(0.735 ~ 0.861) Risk Prediction Model 93.55% 88.07% 69.05% 97.96% 0.926(0.868 ~ 0.985) All values were less than 0.05. 3.4.2 Comparison of Screening Results Between the Risk Prediction Model and Five Sarcopenia Questionnaires Based on the AWGS-2019 Sarcopenia Screening Criteria Patients were classified into sarcopenia and non-sarcopenia groups according to the screening results of the risk prediction model and the five sarcopenia questionnaires. The grouping frequencies were compared with the diagnostic outcomes based on the AWGS-2019 sarcopenia screening criteria. The results revealed that the risk prediction model yielded a Kappa coefficient of 0.724, indicating good consistency; the Kappa values of SARC-Calf and SARC-F + EBM were 0.523 and 0.471, respectively, suggesting moderate consistency; while the Kappa coefficients of SARC-F, MSRA-7 and MSRA-5 were 0.308, 0.292 and 0.265, respectively, demonstrating weak consistency (Table 6 ). Table 6 Comparison of Screening Results Between the Risk Prediction Model and Five Sarcopenia Questionnaires Based on the AWGS-2019 Sarcopenia Screening Criteria (n = 140) Screening Tool AWGS-2019 Sarcopenia Screening Criteria(n) Total(n) Kappa Value P Value Yes No SARC-F (+) 10 7 17 0.308 <0.001 (-) 21 102 123 Total 31 109 140 SARC-Calf (+) 25 21 46 0.523 <0.001 (-) 6 88 94 Total 31 109 140 SARC-F + EBM (+) 14 5 19 0.471 <0.001 (-) 17 104 121 Total 31 109 140 MSRA-7 (+) 23 38 61 0.292 <0.001 (-) 8 71 79 Total 31 109 140 MSRA-5 (+) 23 41 64 0.265 <0.001 (-) 8 68 76 Total 31 109 140 Risk Prediction Model (+) 29 13 42 0.724 <0.001 (-) 2 96 98 Total 31 109 140 4. Discussion In recent years, risk prediction models for sarcopenia in patients with malignant tumors have become a research hotspot. Most studies have focused on gastric cancer, colorectal cancer, liver cancer, nasopharyngeal carcinoma, and other malignancies, and the prevalence and influencing factors of sarcopenia vary considerably across these populations, mainly due to differences in study cohorts and inclusion/exclusion criteria [24] , The present study found that the prevalence of sarcopenia in patients with bone tumors was 26.25%, which was higher than the overall prevalence of sarcopenia in patients with cancer [25] 。Therefore, the development of a nomogram risk prediction model for sarcopenia in patients with bone tumors can to some extent fill the research gaps in the field of sarcopenia and provide important value for the identification and assessment of sarcopenia in this population.A visual nomogram prediction model was constructed based on the following predictors: regular exercise, BMI, serum phosphorus, frailty, kinesiophobia, and sleep status. The model showed favorable predictive performance, with an AUC of 0.935 (95% CI: 0.836–0.950) in the training set and an AUC of 0.902 (95% CI: 0.808–0.910) in the test set. In addition, the calibration curve of the nomogram demonstrated high predictive accuracy.In the applied analysis, based on the AWGS 2019 sarcopenia screening criteria, the sarcopenia risk prediction model for patients with bone tumors achieved a larger AUC (0.926), higher sensitivity (93.55%), negative predictive value (NPV, 91.49%), Youden index (0.816), and better consistency (Kappa = 0.724) compared with the five sarcopenia questionnaires. At the optimal cutoff value, the model also exhibited high sensitivity (93.5%) and specificity (88.1%). This risk prediction model was superior to the five commonly used sarcopenia questionnaires in screening for sarcopenia in patients with bone tumors. Meanwhile, the model included six influencing factors covering both subjective and objective indicators, making the assessment concise, convenient, and reliable.Thus, the establishment of this nomogram risk prediction model for sarcopenia in patients with bone tumors can help medical staff pay early attention to independent risk factors including regular exercise, BMI, serum phosphorus, frailty, kinesiophobia, and sleep status, and provide evidence and directions for personalized therapeutic and nursing interventions for sarcopenia in patients with bone tumors. Regular exercise, BMI, serum phosphorus, frailty, kinesiophobia, and sleep status emerged as independent predictors of sarcopenia in this study. Using these factors, a visual nomogram was constructed, which demonstrated excellent predictive performance with an AUC of 0.935 in the development group and 0.902 in the validation group. The calibration curve indicated high agreement between predicted and observed outcomes, highlighting the model's reliability. This nomogram equips medical staff to identify at-risk patients early, enabling personalized interventions to prevent or mitigate sarcopenia. The present study found that regular exercise is a protective factor against sarcopenia in bone tumor patients, which is consistent with the study by Yang et al [26] . Regular exercise effectively reduces the risk of sarcopenia, mainly because it promotes the release of anabolic hormones, activates molecular pathways that inhibit protein breakdown and enhance muscle mass. This not only improves muscle contractile capacity but also better maintains muscle function [26] . However, bone tumor patients experience decreased physical activity due to factors such as severe surgical trauma, implant placement, prolonged bed rest, movement disorders, and adverse reactions caused by chemoradiotherapy. This further leads to a decrease in muscle strength and mass, which in turn reduces physical activity, ultimately forming a vicious cycle. Decreased physical activity results in mitochondrial dysfunction, accelerates skeletal muscle cell apoptosis, and thus increases the risk of osteoporosis [27] . Meanwhile, bone can also affect myofiber synthesis through transforming growth factor-β (TGF-β) and osteocalcin [28] . Studies have shown that the duration of bed rest is positively correlated with the decrease in muscle mass and content: 10 days of bed rest leads to a 30% reduction in muscle protein synthesis, a 6% decrease in leg muscle mass, and a 16% decline in muscle strength [29, 30] . Medical staff should carry out health education on sarcopenia for bone tumor patients, elaborating on its definition, hazards, and risk factors. Functional exercises should be guided according to different surgical sites: for the upper extremities, grip strength training with a dynamometer and pulley-assisted shoulder and elbow activities are recommended; for the lower extremities, straight leg raising, resistance band resistance training, and gait training should be performed; after spinal surgery, focus should be placed on diaphragmatic breathing and core muscle group training. Combined with phases such as the perioperative period and the early to middle rehabilitation stage, resistance training, aerobic exercise, balance training, and flexibility training should be gradually incorporated to prevent the occurrence of sarcopenia. Studies have shown that BMI has become a predictor of chronic diseases, and low BMI also increases the risk of decreased muscle mass and strength [31] . Although sarcopenic obesity is also a current research hotspot, the present study found that low BMI is more likely to lead to sarcopenia in bone tumor patients, mainly related to nutrition, chronic diseases, and lack of exercise. The main reasons are as follows: (1) During the illness, patients and their families have insufficient understanding of nutrition-related knowledge, leading to an unreasonable diet structure [32] ; bone tumor patients have poor sleep and appetite due to pain and psychological factors, gastrointestinal reactions during chemotherapy, and consumption of body nutrients by tumor cells. These factors all affect nutrient intake, fail to effectively promote the anabolic metabolism of muscle protein, result in the body being in a negative nitrogen balance, and lead to a decrease in myofibrillar protein content. (2) Most bone tumor patients have metastatic tumors; when complicated with other tumors and chronic diseases, metabolic disorders and increased levels of inflammatory factor C-reactive protein (CRP) occur, which reduce the levels of growth hormone (GH) and insulin-like growth factor−1 (IGF−1), and activate a variety of signaling pathways. This accelerates protein catabolism and inhibits its synthesis, causing skeletal muscle atrophy and thus sarcopenia [33] . It is recommended to conduct early screening of physiological indicators such as BMI and muscle mass, as well as inflammatory factors in bone tumor patients. For populations with low BMI, personalized diets containing high-quality proteins and branched-chain amino acids (BCAAs) should be formulated, and simultaneous nutrition knowledge education should be carried out to optimize the diet structure. Patients are encouraged to perform regular exercise within the tolerable range, improve sleep and appetite through pain management and psychological intervention, and prevent the occurrence of sarcopenia by monitoring changes in body weight and muscle strength. The present study found that hypophosphatemic patients with bone tumors are more likely to experience muscle loss, which is consistent with the study by Cai et al. [34] . Maintaining a higher serum phosphorus level reduces the occurrence of sarcopenia, thereby improving and prolonging patients' survival time. The main reasons are as follows: A high-protein diet is the main source of phosphorus [35] . However, tumor cells continuously absorb nutrients from the human body; poor appetite caused by pain and movement disorders, as well as physiological and psychological factors such as gastrointestinal reactions, fatigue, myelosuppression, anxiety, and depression induced by chemotherapy, lead to high protein consumption and low intake, which exacerbates the occurrence of hypophosphatemia [36] . Phosphorus is an important component of adenosine triphosphate (ATP) synthesis. Hypophosphatemia leads to decreased ATP production, affecting muscle energy supply and impairing mitochondrial function [37] . Secondly, hypophosphatemia alters muscle protein metabolism, inhibiting protein synthesis and activating the protein degradation system [38] . Furthermore, intracellular signal transduction is abnormal, such as impaired IGF−1 signaling pathway and disrupted calcium signaling, which affect muscle growth and contraction [39] . Finally, hypophosphatemia affects the bone-muscle axis, and abnormal mechanical signal transduction and imbalanced growth factors jointly promote muscle atrophy and increase the risk of sarcopenia [40] . Therefore, it is necessary to increase the intake of high-protein foods to supplement phosphorus, such as lean meat and beans. At the same time, it is crucial to closely monitor serum phosphorus levels and the occurrence of symptoms such as muscle weakness and anorexia, actively address pain, chemotherapy side effects, and psychological issues, reduce protein consumption, maintain normal body metabolism, and lower the risk of sarcopenia. The association between frailty and sarcopenia has been confirmed in relevant studies, all of which show a positive correlation between the occurrence of frailty and sarcopenia [33, 41] , which is consistent with the results of this study. This is mainly because bone tumor patients experience a decline in physiological reserve due to comprehensive treatments such as surgery, chemoradiotherapy, and targeted therapy, leading to gradual frailty of the body. During the frailty process, motor unit reorganization and loss, motor neuron decline, weakened peripheral nerve innervation, and decreased skeletal muscle remodeling capacity result in reduced muscle mass [42] . At the same time, frail patients are often in a state of chronic inflammation, producing inflammatory mediators such as interleukin−6 (IL−6) and tumor necrosis factor-α (TNF-α), which promote muscle protein breakdown and inhibit synthesis [43] . Immune dysfunction makes them more susceptible to infections, and immune cells release more inflammatory mediators to exacerbate inflammation [44] ; in addition, tumor progression secretes factors that interfere with muscle growth. Furthermore, frailty leads to decreased mobility, resulting in weakened mechanical stimulation of muscles. Normal activities can activate signaling pathways that promote muscle protein synthesis, while reduced activity prevents the normal activation of these pathways, and poor blood circulation affects muscle nutrient supply [45] . It is necessary to raise medical staff's attention to the frailty symptoms of bone tumor patients, conduct early screening of the frailty degree of bone tumor patients, conduct assessments from multiple aspects such as nutrition, exercise, and psychology, and formulate preventive measures and personalized intervention plans according to frailty characteristics. This is of great significance for reducing the occurrence of sarcopenia in bone tumor patients. This study showed that the incidence of kinesiophobia in bone tumor patients with sarcopenia (65.71%) was significantly higher than that in non-sarcopenia patients (25.76%). When bone tumor patients were complicated with kinesiophobia, the risk of sarcopenia increased by 2.897 times.On the one hand, bone tumor surgery is associated with severe trauma. Due to concerns that activities may exacerbate pain, displace implants, and affect wound healing, patients have low compliance with activities. This fear leads them to reduce activities, resulting in insufficient motor stimulation of muscles, subsequent disuse atrophy, and weakened neuromuscular signal transmission, which impairs muscle function and mass [46] . On the other hand, long-term kinesiophobia imposes a heavy psychological and physiological burden. Psychologically, patients are in a state of fear and anxiety, and the stress response leads to hormonal imbalance, which inhibits muscle protein synthesis, affects appetite and nutrient intake, and is not conducive to muscle growth and repair [47] . Physiologically, activity avoidance behavior reduces physical activity, increases sedentary time, causes poor muscle blood circulation, impairs metabolism, and decreases sleep quality. This affects the secretion of growth hormone and inhibits muscle anabolic metabolism [48] . Kinesiophobia not only delays postoperative functional recovery but also may lead to decreased limb function or disability, which further aggravates fear, forms a vicious cycle, and increases the risk of sarcopenia [48] . Therefore, it is necessary to assess the causes of patients' kinesiophobia, correct misconceptions in a timely manner, conduct active communication and guidance, perform psychological intervention and counseling when necessary, improve patients' awareness of the role of functional exercise in disease recovery and their self-confidence, thereby reducing their kinesiophobia level and further decreasing the occurrence of sarcopenia. Inadequate sleep leads to elevated cortisol levels and reduced secretion of testosterone and growth factors, thereby triggering muscle loss and myofiber atrophy [49] . This study also found that sleep status is an important influencing factor for sarcopenia in bone tumor patients. Bone tumor patients experience decreased comfort due to conditions such as cancer-related pain, limb swelling, and postoperative physical activity disorders. In addition, worries and fears about the disease, side effects of chemoradiotherapy, and environmental changes all affect the sleep status of bone tumor patients. The main reasons are as follows: (1) Inadequate sleep reduces the secretion of relevant hormones. Deficiency of growth hormone inhibits muscle anabolic metabolism, making it difficult to obtain sufficient nutrients to maintain muscle growth. Inadequate sleep also causes physical fatigue and mental stress. Bone tumor patients are inherently weak, and inadequate sleep further exacerbates their fatigue, reduces physical activity capacity, and thereby decreases normal stimulation to muscles [50] . (2) Meanwhile, mental stress activates the stress response system, leading to increased secretion of hormones such as cortisol and promoting muscle breakdown [49] . (3) Patients may experience decreased appetite due to poor sleep, resulting in insufficient intake of nutrients required for muscle growth such as protein, which further increases the risk of sarcopenia [51] . Therefore, it is necessary to actively create a good sleep environment for patients, and provide painkiller medication guidance and other methods to improve sleep quality, thereby reducing the adverse effects of inadequate sleep on muscle health. This study has several limitations. First, it was conducted at a single center with a relatively small sample size, which may limit the generalizability of the findings. Second, BMI cannot be applied to patients with limb amputations, restricting its utility in some cases. Finally, the nomogram model has not undergone external validation. Future multicenter studies with larger sample sizes and external validation are necessary to enhance the model's robustness and applicability. By addressing these limitations and expanding research efforts, this nomogram risk prediction model holds promise as a valuable tool for early identification and intervention in sarcopenia among patients with bone tumors, ultimately improving patient outcomes. 5. Conclusion In conclusion, this study developed and internally validated a clinically robust nomogram for predicting sarcopenia risk in patients with bone tumors. The model integrates objective laboratory, functional, and patient-reported measures to provide comprehensive, individualized risk stratification. This user-friendly instrument enables early identification of high-risk patients and supports timely, personalized interventions to mitigate sarcopenia and improve clinical outcomes and quality of life in this vulnerable population. Declarations Funding This study was supported by two projects: the Innovation Fund Project for Postgraduates of Kunming Medical University (2024S221) and the Science and Technology Plan Project of the Department of Science and Technology of Yunnan Province (202401AV070001-334). Data Availability Research materials and data will be made available upon request. Declaration of Competing Interest The authors declare no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Acknowledgments Not applicable. Human Ethics and Consent to Participate Declarations This study was approved by the Ethics Committee of Yunnan Cancer Hospital (Ethics Number: KYLX2023-171). All participants provided written informed consent prior to enrollment, with a clear understanding of the study purpose, procedures, potential risks, and rights, and voluntarily agreed to participate. Consent to Participate Declaration All human participants have signed written informed consent forms, agreeing to participate in the study and the collection and analysis of relevant data. Name of Approval Committee/IRB in Ethics Approval Declaration Ethics Committee of Yunnan Cancer Hospital. Clinical Trial Number Clinical trial number: not applicable. References Xu D, Li B, Liu W, et al. Deep learning-based detection of primary bone tumors around the knee joint on radiographs: a multicenter study[J]. Quantitative imaging in medicine and surgery, 2024,14(8):5420-5433. Niu X H. Interpretation of 2020 NCCN Clinical Practice Guidelines in Oncology-Bone Cancer[J]. Zhonghua wai ke za zhi [Chinese journal of surgery], 2020,58(6):430-434. Knapp B J, Cittolin-Santos G F, Flanagan M E, et al. Incidence and risk factors for bone metastases at presentation in solid tumors[J]. Frontiers in oncology, 2024,14:1392667. Bingfeng H, Rongshou Z, Hongmei Z, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024,4(1):47-52. Xi Y, Qiao L, Na B, et al. Primary malignant bone tumors incidence, mortality, and trends in China from 2000 to 2015[J]. Chin Med J (Engl), 2023,136(17):2037-2043. Abe T, Yoshimura Y, Sato Y, et al. Sarcopenia as a Robust Predictor of Readmission within 6 Months among Individuals Experiencing Acute Stroke[J]. Annals of geriatric medicine and research, 2024,28(3):307-314. Su S, Wang R, Chen Z, et al. The causal effect of sarcopenia-associated traits on brain cortical structure: A Mendelian randomization study[J]. Archives of gerontology and geriatrics, 2024,118:105302. Cui H, Wang Z, Wu J, et al. Chinese expert consensus on prevention and intervention for elderly with sarcopenia (2023)[J]. Aging medicine (Milton (N.S.W)), 2023,6(2):104-115. Mo Y, Su Y, Dong X, et al. Development and Validation of a Nomogram for Predicting Sarcopenia in Community-Dwelling Older Adults[J]. Journal of the American Medical Directors Association, 2022,23(5):715-721. Yu J, Guan Q, Chen Y, et al. Comparative assessment of sarcopenia screening tools for patients with bone tumors: insights for enhanced clinical application[J]. Frontiers in nutrition, 2025,12:1584706. Li Q, Cheng H, Cen W, et al. Development and validation of a predictive model for the risk of sarcopenia in the older adults in China[J]. European journal of medical research, 2024,29(1):278. Zhang Y, Zhang L, Guan Y, et al. Establishment and validation of a risk prediction model for sarcopenia in gastrointestinal cancer patients: A systematic review and meta-analysis-based approach[J]. Clinical nutrition (Edinburgh, Scotland), 2024,43(11):91-98. Leng Y, Wang G, Xie R, et al. Risk Prediction Models for Sarcopenia in Dialysis Patients: A Systematic Review[J]. Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation, 2025,35(1):146-155. Chen L, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment[J]. J Am Med Dir Assoc, 2020,21(3):300-307. Varan H D, Deniz O, Çöteli S, et al. Validity and reliability of Fried frailty phenotype in Turkish population[J]. Turkish journal of medical sciences, 2022,52(2):524-527. Yang X L, Yuan Y M, Si H X. Association between frailty and the risk of chronic comorbidities among middle-aged and older Chinese adults[J]. Chinese Journal of Disease Control & Prevention, 2026, 30(02):177-182. Farzad M, MacDermid J, Packham T. Fear of Movement in People With CRPS: A Psychometric Evaluation of the Tampa Scale-11 for Kinesiophobia[J]. The Clinical journal of pain, 2024,40(1):26-34. Chung K, Kan K K, Yeung W. Assessing insomnia in adolescents: comparison of Insomnia Severity Index, Athens Insomnia Scale and Sleep Quality Index[J]. Sleep medicine, 2011,12(5):463-470. Malmstrom T K, Morley J E. SARC-F: a simple questionnaire to rapidly diagnose sarcopenia[J]. Journal of the American Medical Directors Association, 2013,14(8):531-532. Barbosa-Silva T G, Menezes A M B, Bielemann R M, et al. Enhancing SARC-F: Improving Sarcopenia Screening in the Clinical Practice[J]. Journal of the American Medical Directors Association, 2016,17(12):1136-1141. Kurita N, Wakita T, Kamitani T, et al. SARC-F Validation and SARC-F+EBM Derivation in Musculoskeletal Disease: The SPSS-OK Study[J]. The journal of nutrition, health & aging, 2019,23(8):732-738. Rossi A P, Micciolo R, Rubele S, et al. Assessing the Risk of Sarcopenia in the Elderly: The Mini Sarcopenia Risk Assessment (MSRA) Questionnaire[J]. The journal of nutrition, health & aging, 2017,21(6):743-749. Yang M, Hu X, Xie L, et al. Validation of the Chinese version of the Mini Sarcopenia Risk Assessment questionnaire in community-dwelling older adults[J]. Medicine, 2018,97(37):e12426. Zhang Y, Zhu Y. Development and validation of risk prediction model for sarcopenia in patients with colorectal cancer[J]. Frontiers in oncology, 2023,13:1172096. Wang M, Yang P, Zhou L, et al. Thoracic Sarcopenia was a Poor Prognostic Predictor in Patients Receiving Immunotherapy for Advanced Non-small-cell Lung Cancer[J]. Academic radiology, 2025,32(1):526-532. Yang Z, Li X, Song W, et al. Associations between meeting 24-h movement guidelines and sarcopenia risk among adults aged ≥ 55 years in five low- and middle-income countries[J]. Complementary therapies in clinical practice, 2024,57:101887. Nasso R, D'Errico A, Motti M L, et al. Dietary Protein and Physical Exercise for the Treatment of Sarcopenia[J]. Clinics and practice, 2024,14(4):1451-1467. Xu K, Feng X, Xu Z, et al. Association of sarcopenia with osteoporosis in Chinese patients with type 2 diabetes[J]. BMC musculoskeletal disorders, 2024,25(1):226. Marusic U, Narici M, Simunic B, et al. Nonuniform loss of muscle strength and atrophy during bed rest: a systematic review[J]. Journal of applied physiology (Bethesda, Md. : 1985), 2021,131(1):194-206. Lu L, Du G, Qi C, et al. %BF, Rather Than BMI, is Associated with an Increased Risk of Sarcopenia in Hospitalized Postmenopausal Chinese Women with Type 2 Diabetes Mellitus[J]. Diabetes, metabolic syndrome and obesity : targets and therapy, 2024,17:4223-4231. Wu W, Duan F, Liu Z, et al. BMI-stratified cutoff values for spinal sarcopenia in Chinese adults based on CT measures: a multicentre study.[J]. J Orthop Surg Res, 2025,20(1):365. Merchant R A, Kit M W W, Lim J Y, et al. Association of central obesity and high body mass index with function and cognition in older adults[J]. Endocrine connections, 2021,10(8):909-917. Luo S, Chen X, Hou L, et al. The accuracy of body mass index and calf circumference values when assessing sarcopenia in a multi-ethnic cohort of middle-aged and older adults: West China health and aging trend study results[J]. Heliyon, 2023,9(4):e15027. Cai G, Ying J, Pan M, et al. Development of a risk prediction nomogram for sarcopenia in hemodialysis patients[J]. BMC nephrology, 2022,23(1):319. Almeida N S, Rocha R, Souza C A, et al. Energy and nutrient intake by people with and without sarcopenia diagnosed by the European Working Group on Sarcopenia in Older People: a systematic review and meta-analysis.[J]. NUTR REV, 2024,82(12):1666-1677. van Atteveld J E, de Winter D T C, Pluimakers V G, et al. Frailty and sarcopenia within the earliest national Dutch childhood cancer survivor cohort (DCCSS-LATER): a cross-sectional study[J]. The lancet. Healthy longevity, 2023,4(4):e155-e165. Zhang S, Yang X, An N, et al. Risk Factors and Predictive Models for Sarcopenia in Older Adults[J]. Aging medicine (Milton (N.S.W)), 2025,8(3):192-199. Li Y, Gu W, Hao W, et al. Associations of four important dietary pattern scores, micronutrients with sarcopenia and osteopenia in adults: results from the National Health and Nutrition Examination Survey[J]. Frontiers in nutrition, 2025,12:1583795. Kawao N, Nishikawa A, Matsumura D, et al. Roles of Insulin-Like Growth Factor-1 in Muscle Wasting and Osteopenia in Mice with Hyponatremia[J]. Calcified tissue international, 2025,116(1):61. Yang Z, Hao L, Kuati A, et al. Triclosan exposure exacerbates muscle wasting via senescence and IGF-1 pathway suppression: A combined epidemiological and experimental study[J]. Ecotoxicology and environmental safety, 2025,304:119142. Welch C, Greig C, Majid Z, et al. Induced frailty and acute sarcopenia are overlapping consequences of hospitalisation in older adults[J]. Journal of frailty, sarcopenia and falls, 2022,7(3):103-116. Chen Y, Chen X, Zhong L, et al. Bibliometric analysis of research trends in the relationship between frailty and neoplasms over the past decade[J]. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024,32(8):536. Debruin D, McRae N L, Addinsall A B, et al. In dystrophic mdx hindlimb muscles where fibrosis is limited, versican haploinsufficiency transiently improves contractile function without reducing inflammation[J]. American journal of physiology. Cell physiology, 2024,327(4):C1035-C1050. Li Z, Li H, Wang P, et al. Relationship between aggregate index of systemic inflammation and mortality from CCD and malignant neoplasms in diabetic patients[J]. Scientific reports, 2025,15(1):26545. Jiang H, Inoue S, Hatakeyama J, et al. Effects of aging and resistance exercise on muscle strength, physiological properties, longevity proteins, and telomere length in SAMP8 mice[J]. Biogerontology, 2025,26(2):88. Sakuma S, Kimpara K, Kawai Y, et al. Integrating Physical Therapy and Virtual Reality to Manage Pain-Related Fear of Movement in Patients With Chronic Pain: A Randomized Controlled Trial[J]. Cureus, 2025,17(2):e79551. Engman V, Critchlow A, Laakkonen E, et al. The Role and Regulation of Intramuscular Sex Hormones in Skeletal Muscle: A Systematic Review.[J]. 2025,110(6):e1732-e1746. Tokgoz M A, Ergisi Y, Odluyurt M, et al. he effect of pain catastrophizing and kinesiophobia on the result of shoulder arthroscopy[J]. Agri, 2021,33(4):232-236. Han P, Hou L, Liang Z, et al. Both Short and Long Sleep Durations are Risk Factors for Sarcopenia in Suburban-Dwelling Older Chinese Individuals: A 3-Year Longitudinal Study[J]. Nat Sci Sleep, 2022(14):1089-1096. Yuan S, Larsson S C. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences[J]. Metabolism: clinical and experimental, 2023,144:155533. Liu K, Luo J, Chen Y, et al. Association between sarcopenia and sleep disorders: a cross-sectional population based study[J]. Frontiers in nutrition, 2024,11:1415743. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 07 Apr, 2026 Editor assigned by journal 04 Apr, 2026 Submission checks completed at journal 04 Apr, 2026 First submitted to journal 02 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9305260","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626492839,"identity":"d91522de-5541-4fcf-98b1-910375dcf278","order_by":0,"name":"Jun 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5","display":"","copyAsset":false,"role":"figure","size":46106,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the validation group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/d952935fd278e6df70be5035.jpg"},{"id":107489481,"identity":"9527f7cd-9f5f-4488-a95c-cf0d2fa36018","added_by":"auto","created_at":"2026-04-22 02:47:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":72819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the development group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/883fe42354f97eba197bc994.jpg"},{"id":107453777,"identity":"a3b0cce3-9320-40c9-be1c-2ebddf76f045","added_by":"auto","created_at":"2026-04-21 15:36:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":79584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the validation group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/a7ca40f890511ea8b3465d6f.jpg"},{"id":107489099,"identity":"361b0be8-0e2c-4fab-959a-d08a6399ed10","added_by":"auto","created_at":"2026-04-22 02:46:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":52911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical decision curve (DCA curve) of the modeling group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/3264e20205f00d5fb6328eb5.jpg"},{"id":107489095,"identity":"9665a0f7-8b11-4ff4-aa76-c2c3eadb028b","added_by":"auto","created_at":"2026-04-22 02:46:35","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":49423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical decision curve (DCA curve) of the validation group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/d308173cccd666694ca58334.jpg"},{"id":107488833,"identity":"ebbd56db-8527-4305-bd75-394202fc4b8c","added_by":"auto","created_at":"2026-04-22 02:45:56","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":71478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic Curves of the Risk Prediction Model and Five Sarcopenia Questionnaires\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/f6d70bb67dfa66f7f21e2c5f.jpg"},{"id":107490392,"identity":"11fca129-1af4-44db-a886-b83ff72d897a","added_by":"auto","created_at":"2026-04-22 02:52:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1736409,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9305260/v1/b1088bc6-7996-4a1e-b402-c264462b0044.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Clinically Useful Nomogram for Predicting Sarcopenia in Bone Tumor Patients","fulltext":[{"header":"Summary ","content":"\u003cp\u003eThis study identifies key determinants of sarcopenia in patients with bone tumors and establishes a user‑friendly nomogram for risk prediction. Direct comparison with five conventional screening instruments confirms that the nomogram provides superior accuracy for early sarcopenia detection and risk stratification. This clinically applicable tool supports timely, personalized interventions and may improve prognosis and health‑related quality of life in this high‑risk population.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eBone tumors encompass both primary and secondary tumors that originate in the bones and associated tissues\u003csup\u003e[1]\u003c/sup\u003e. More than 70\u0026ndash;80% of cancer patients develop bone metastases, with breast, lung, and prostate cancers being the most common primary sites of origin for bone tumor metastases. As the incidence of cancers continues to rise, the prevalence of bone tumors is also increasing \u003csup\u003e[2\u0026ndash;4]\u003c/sup\u003e. While advancements in surgical treatments, radiotherapy, chemotherapy, and immunotherapy have improved survival rates and reduced disability, bone tumors still pose a significant threat to patients\u0026rsquo; health, life expectancy, and quality of life \u003csup\u003e[5]\u003c/sup\u003e. Factors such as surgical trauma, chemotherapy toxicity, pathological fractures, paraplegia, and kinesiophobia contribute to reduced physical activity and prolonged immobility in these patients. This, in turn, decreases muscle protein synthesis and elevates the risk of sarcopenia.\u003c/p\u003e \u003cp\u003eSarcopenia is a progressive systemic skeletal muscle disorder characterized by reduced muscle strength, muscle mass, and/or physical performance \u003csup\u003e[6]\u003c/sup\u003e. In patients with bone tumors, sarcopenia exacerbates the risks of falls, fractures, disability, complications, and mortality \u003csup\u003e[7]\u003c/sup\u003e. Additionally, it prolongs hospital stays, increases healthcare costs, and imposes substantial burdens on families, healthcare facilities, and society. Despite its significance, the diagnosis and measurement of sarcopenia lack standardization. Reported incidence rates of sarcopenia vary widely, ranging from 5.5% to 25.7%, due to differences in population characteristics and diagnostic criteria \u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMuscle mass assessment is crucial for diagnosing sarcopenia, with common methods including computed tomography (CT), magnetic resonance imaging (MRI), and dual-energy X-ray absorptiometry (DXA). However, these modalities are expensive, involve radiation exposure, and require specialized equipment, making them less feasible for use in primary care settings. While bioelectrical impedance analysis (BIA) is portable and user-friendly, its accuracy depends on device-specific algorithms, limiting its reliability \u003csup\u003e[8]\u003c/sup\u003e. Similarly, questionnaires like the Sarcopenia Five-item Questionnaire (SARC-F) and its combination with calf circumference (SARC-CalF) lack sensitivity and are influenced by factors such as age, gender, and cognitive function \u003csup\u003e[9, 10]\u003c/sup\u003e. Thus, there is a pressing need for a simple, non-invasive, practical, and reliable assessment tool tailored for sarcopenia screening in patients with bone tumors.\u003c/p\u003e \u003cp\u003eNomogram-based risk prediction models are visual, intuitive tools that have been successfully applied in various populations, including the elderly and patients with kidney diseases, diabetes, and gastrointestinal tumors \u003csup\u003e[11\u0026ndash;13]\u003c/sup\u003e. However, the development of sarcopenia-specific risk prediction models for patients with bone tumors remains limited. These patients face unique challenges, such as pain from tumor invasion, amputation-related complications, paraplegia due to spinal cord compression, and cancer-related fatigue. As a result, existing diagnostic instruments and prediction models are often unsuitable for this population.\u003c/p\u003e \u003cp\u003eThis study aims to identify the key factors influencing sarcopenia in patients with bone tumors and to develop and validate a nomogram-based risk prediction model. The goal is to provide a practical and reliable tool for early detection and intervention, ultimately aiding healthcare professionals in improving patient outcomes and quality of life.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Study Design\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study. This study received ethical approval from the Ethics Committee of Yunnan Cancer Hospital (KYLX2023-171). All participants provided written informed consent before participating in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2Study Participants\u003c/h2\u003e \u003cp\u003eThis study used a convenience sampling method. Participants were patients with bone tumors treated at Yunnan Cancer Hospital between August 2023 and June 2025.Inclusion criteria were meeting the diagnostic criteria outlined in the NCCN \"Clinical Practice Guidelines for Bone Tumors,\" being 18 years or older, having a disease duration exceeding three months, and demonstrating clear consciousness and communication abilities. Patients were excluded if they had lower limb edema, mental disorders, severe hearing, visual, or language impairments, inability to stand, metal implants, limb deformities, or if their limbs were covered by instruments that prevented measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3Sample Size\u003c/h2\u003e \u003cp\u003eDevelopment of a Risk Prediction Model: The sample size was determined based on logistic regression analysis, which requires at least 10 cases per variable. With an estimated 5\u0026ndash;6 variables in the final model, the minimum sample size was calculated to be 50\u0026ndash;60 cases. Considering the approximate sarcopenia incidence of 30% in bone tumor patients and accounting for a 10% invalid data rate, at least 220 cases were needed in the modeling group. Adopting a 70:30 ratio for modeling and validation, 280 patients were allocated to the modeling group and 120 to the validation group.\u003c/p\u003e \u003cp\u003eComparative validation of the risk prediction model and five common sarcopenia assessment scales: Inpatients admitted to Yunnan Cancer Hospital between March 2025 and June 2025 were enrolled as the study population for the applied validation phase. The sample size was calculated in accordance with Kendall\u0026rsquo;s sample size estimation principle, which recommends a sample-to-item ratio ranging from 5:1 to 10:1. Given that a total of 20 items were included across the screening scales and the risk prediction model, and accounting for a 20% invalid questionnaire rate, the required sample size was estimated to be 125 to 250 cases. Ultimately, 140 patients with bone tumors were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4Measurements\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1Assessment Tools Used During Risk Prediction Model Development\u003c/h2\u003e \u003cp\u003e \u003cb\u003e(1) General information\u003c/b\u003e: The research team designed the study independently through expert consultation, incorporating a range of variables including age, gender, ethnicity, tumor location, educational background, monthly income, and pain. The electronic medical record system was consulted to collect biochemical indicators such as BMI, red blood cells, white blood cells, platelets, serum calcium, serum phosphorus, procalcitonin, and C-reactive protein.\u003c/p\u003e \u003cp\u003e(2)The diagnostic criteria for sarcopenia proposed by AWGS 2019 are adopted\u003csup\u003e[14]\u003c/sup\u003e, including the following: Firstly, the subject's muscle strength, or grip strength, will be assessed. The initial objective is to ascertain the optimal weight parameters for men and women, respectively, as follows: a minimum of 28 kilograms for men and a minimum of 18 kilograms for women. The secondary objective is to determine the physical activity capacity, specifically the walking speed. The subject demonstrated a walking speed of less than 1 meter per second, indicating a lower than average ambulatory speed. Additionally, the subject exhibited a skeletal muscle index indicative of reduced muscle mass. The body mass index (BMI) is calculated as the weight in kilograms divided by the square of the height in meters, and values of less than 7.0 kg/m\u0026sup2; are considered underweight for men and less than 5.7 kg/m\u0026sup2; are considered underweight for women. Sarcopenia is characterized by the fulfillment of criterion 3 and either criterion 1 or 2.\u003c/p\u003e \u003cp\u003e(4)\u003cb\u003eFried Frailty Phenotype\u003c/b\u003e: The Fried Frailty Phenotype Scale is a tool used to assess the presence of signs associated with the frailty syndrome in older adults\u003csup\u003e[15]\u003c/sup\u003e. This approach is currently the most widely used method for assessing frailty. The Chinese version of the scale utilized in this study encompasses five components: weight loss over the past year; fatigue and activity duration over the past week; current grip strength; and physical activity capacity. Each item is assigned a point value of 1, with a total possible score of 5 points. A score of 0 indicates no frailty, 1\u0026ndash;2 indicates pre-frailty, and 3\u0026ndash;5 indicates frailty. In this study, the Cronbach's α coefficient for the scale in question was 0.826, indicating good reliability\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e(5)The Tampa Scale for Kinesiophobia-11 (TSK-11) is a measurement tool designed to assess the severity of kinesiophobia, also known as fear of movement, in individuals suffering from anxiety disorders\u003csup\u003e[17]\u003c/sup\u003e. The scale under consideration is comprised of 11 items, which are divided into four dimensions: perception of danger, avoidance of exercise, fear of exercise, and functional impairment. The scale utilizes a 4-point Likert scale, ranging from strongly disagree to strongly agree, with scores from 1 to 4. The total score ranges from 11 to 44 points, with a score\u0026thinsp;\u0026gt;\u0026thinsp;26 indicating kinesiophobia. In this study, the Cronbach's α coefficient for the scale in question was 0.88, and the test-retest reliability was 0.80, thereby demonstrating good construct validity and convergent validity.\u003c/p\u003e \u003cp\u003e(6)The Athens Insomnia Scale (AIS) is a tool designed to assess insomnia symptoms. This scale is a Chinese version of a self-assessment questionnaire for sleep disorders, comprising eight items: difficulty falling asleep, nighttime sleep interruptions, early awakening, satisfaction with sleep duration and quality, the impact of sleep on daytime mood and social functioning, and daytime sleepiness. The scale utilizes a four-point rating scale ranging from 0 to 3, where 0\u0026ndash;3 indicates the absence of sleep disorder, 4\u0026ndash;6 signifies the potential presence of insomnia, and \u0026ge;\u0026thinsp;7 corresponds to a diagnosis of insomnia\u003csup\u003e[18]\u003c/sup\u003e. The Cronbach's α coefficient for this scale was 0.825, indicating good reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Study Instruments for Comparative Validation of the Risk Prediction Model and Five Sarcopenia Screening Tools\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003e(1) Sarcopenia Five-Item Questionnaire (SARC-F)\u003c/strong\u003e \u003cp\u003eThe SARC-F was originally developed by Malmstrom et al\u003csup\u003e[19]\u003c/sup\u003e.and consists of five items assessing muscle strength, walking ability, chair rise capacity, stair-climbing performance, and fall history. Each item is scored on a scale of 0 to 2 points, yielding a total score ranging from 0 to 10 points. A total score of \u0026ge;\u0026thinsp;4 points indicates an elevated risk of sarcopenia. This questionnaire demonstrates favorable reliability and validity, with a Cronbach\u0026rsquo;s α coefficient of 0.849.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(2)Modified Sarcopenia Five-Item Questionnaire (SARC-Calf)\u003c/b\u003e: The SARC-Calf was developed by Barbosa-Silva et al\u003csup\u003e[20]\u003c/sup\u003e.s a novel screening tool that integrates calf circumference measurement with the original SARC-F questionnaire. It retains the original scoring criteria of the SARC-F and adds an additional item for calf circumference: 10 points are assigned if calf circumference is \u0026le;\u0026thinsp;34 cm in men or \u0026le;\u0026thinsp;33 cm in women, and 0 points otherwise. The total score ranges from 0 to 20 points, and a total score of \u0026ge;\u0026thinsp;11 points suggests the presence of sarcopenia risk. This questionnaire exhibits good reliability and validity, with a Cronbach\u0026rsquo;s α coefficient of 0.812.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3)Sarcopenia Five-Item Questionnaire Combined with Age and BMI (SARC-F\u0026thinsp;+\u0026thinsp;EBM)\u003c/b\u003e: The SARC-F\u0026thinsp;+\u0026thinsp;EBM was proposed by Kurita et al\u003csup\u003e[21]\u003c/sup\u003e.as an updated screening tool that combines the original SARC-F questionnaire with age and body mass index (BMI). It maintains the original scoring algorithm of the SARC-F and incorporates two additional categorical scoring items: 10 points are allocated for age\u0026thinsp;\u0026ge;\u0026thinsp;75 years (0 points otherwise), and 10 points for BMI\u0026thinsp;\u0026le;\u0026thinsp;21 kg/m (0 points otherwise). The total score ranges from 0 to 30 points, and a cutoff score of \u0026ge;\u0026thinsp;12 points is indicative of sarcopenia risk. In the present study, this questionnaire showed satisfactory reliability and validity, with a Cronbach\u0026rsquo;s α coefficient of 0.834.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(4)Mini Sarcopenia Risk Assessment Questionnaire (MSRA)\u003c/b\u003e: The MSRA questionnaire was originally designed by Rossi et al\u003csup\u003e[22]\u003c/sup\u003e, and translated into Chinese by Yang et al\u003csup\u003e[23]\u003c/sup\u003e.in 2018. It comprises two versions: the 7-item Mini Sarcopenia Risk Assessment questionnaire (MSRA-7) and the 5-item Mini Sarcopenia Risk Assessment questionnaire (MSRA-5).Specifically, the MSRA-7 assesses seven domains: age, number of hospital admissions in the past year, walking ability, number of daily meals, weight loss in the past year, and intake of protein and dairy products. Each item is scored 0\u0026ndash;5 or 0\u0026ndash;10 points, with a total score ranging from 0 to 40 points; a total score of \u0026le;\u0026thinsp;30 points denotes sarcopenia risk. The MSRA-5 is a shortened version of the MSRA-7, excluding the items assessing protein and dairy intake. Each item is scored 0\u0026ndash;5, 0\u0026ndash;10, or 0\u0026ndash;15 points, with a total score ranging from 0 to 60 points; a total score of \u0026le;\u0026thinsp;45 points indicates sarcopenia risk. In this study, both versions of the MSRA presented acceptable reliability and validity, with Cronbach\u0026rsquo;s α coefficients of 0.792 (MSRA-7) and 0.801 (MSRA-5), respectively.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5Data Collection\u003c/h2\u003e \u003cp\u003ePrior to the implementation of the study, a research team was established and underwent unified training. The training covered various aspects, including methods for collecting indicators, how to use measurement tools, how to evaluate questionnaire items, and important considerations regarding techniques used during the survey. Participants were required to pass an assessment before proceeding with the research. After data collection is completed, research team members will review each questionnaire individually. If any important items are missing from the questionnaire, the data will be deemed invalid and excluded. In cases of incomplete data, a re-evaluation and re-entry should be conducted promptly. Data entry will be performed using a parallel double-entry method, with a third party conducting a review to ensure the accuracy and reliability of the data.\u003c/p\u003e \u003cp\u003eGrip strength measurement: Muscle strength was assessed at admission using an electronic grip strength meter (Shangshan EH-101). Patients stood upright with their legs naturally apart and their arms hanging naturally at their sides. Measurements were taken twice with the dominant hand, with an interval of more than 5 seconds between each measurement, and the maximum value was recorded.\u003c/p\u003e \u003cp\u003eWalking speed measurement: A 6-meter walking test is used to evaluate the patient's physical condition. Upon admission, the patient walks 6 meters at a steady pace without any assistance. The time required for two walks is measured, and the average is recorded.\u003c/p\u003e \u003cp\u003eMuscle mass measurement: At admission, the Inbody270 body composition analyzer produced by Basbeth Co., Ltd. of South Korea was used. This instrument measures muscle mass through bioelectrical impedance analysis. The limb skeletal muscle index (kg/m\u0026sup2;) = limb skeletal muscle mass (kg) / height\u0026sup2; (m\u0026sup2;). The following conditions were excluded during measurement: (1) No physical activity within 10 minutes prior to measurement; (2) At least 2 hours after a meal; (3) No metal objects or electronic devices were carried during measurement; (4) No medications that could affect measurement accuracy were used (e.g., furosemide, mannitol, etc.). After entering basic information, the patient stands barefoot on the body composition analyzer, with the soles of both feet aligned with the electrodes at the bottom of the instrument. Both hands naturally grip the electrodes, and both arms hang naturally downward without touching the sides of the body.\u003c/p\u003e \u003cp\u003eRegular exercise: Regular exercise refers to exercising more than three times a week for more than 30 minutes each time for at least six months.\u003c/p\u003e \u003cp\u003eAssessment of limb edema: It is imperative to select common areas that exhibit thin subcutaneous tissue. Such areas include the inner side of the ankle, the front of the shin, the back of the hand, and the fingers. The application of vertical pressure to the selected area should be executed with a moderate degree of force, using the thumb or index finger. This pressure should be sustained for a duration of 5\u0026ndash;10 seconds, followed by a release. The skin at the pressure point will exhibit a slight blanching reaction, indicative of the application of pressure. The presence of an indentation that persists and requires more than 3\u0026ndash;5 seconds to return to its normal state is indicative of edema.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6Statistical Analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted using SPSS 29.0. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with quartiles, depending on distribution normality, and compared using t-tests or U-tests. Categorical variables were analyzed using chi-square or Fisher's exact tests. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate logistic regression. Nomogram construction and validation were performed using R software (version 4.4.1), and the model\u0026rsquo;s performance was assessed for discrimination (ROC curve and AUC), calibration (calibration curve and Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis).Patients were stratified into sarcopenia and non-sarcopenia groups based on the screening results of the risk prediction model and the five sarcopenia questionnaires. The grouping frequencies were then compared against the diagnostic results obtained using the AWGS-2019 sarcopenia screening criteria. The area under the receiver operating characteristic (ROC) curve (AUC) with corresponding 95% confidence interval (CI), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Kappa coefficient were calculated to evaluate the screening performance of each tool.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1Demographic Characteristics\u003c/h2\u003e \u003cp\u003eA total of 400 patients with bone tumors were included in the study, comprising 211 males (52.75%) and 189 females (47.25%). Among them, 105 patients (26.25%) were diagnosed with sarcopenia, while 295 patients did not have sarcopenia. The age range for the non-sarcopenia group was 18\u0026ndash;80 years (mean 50.68\u0026thinsp;\u0026plusmn;\u0026thinsp;14.28), and for the sarcopenia group, 18\u0026ndash;87 years (mean 58.18\u0026thinsp;\u0026plusmn;\u0026thinsp;15.46). Univariate analysis revealed statistically significant differences between the two groups for factors such as age, economic status, pain duration, regular exercise, osteoporosis, presence of chronic diseases, tumor type, BMI, hemoglobin, platelets, albumin, indirect bilirubin, serum calcium, serum phosphorus, C-reactive protein, fibrin degradation products, fibrinogen, and calcitonin (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics (n\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarcopenia(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon Sarcopenia (n\u0026thinsp;=\u0026thinsp;295)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e /t/z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026thinsp;~\u0026thinsp;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026thinsp;~\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic minorities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical Personnel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnterprises and Institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school/vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026thinsp;~\u0026thinsp;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000\u0026thinsp;~\u0026thinsp;7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary to March\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril to June\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly to September\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober to December\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter December\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor nature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114.00\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125.49\u0026thinsp;\u0026plusmn;\u0026thinsp;20.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cell(10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.87(3.32,4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.23(3.76,4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.73(5.56,10.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.87(5.69,10.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267.53\u0026thinsp;\u0026plusmn;\u0026thinsp;112.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238.63\u0026thinsp;\u0026plusmn;\u0026thinsp;83.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.90(3.30,7.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.30(3.78,8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227.0(148.0,300.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263.0(204.0,334.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.91(6.01,36.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.62(2.02,18.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP(\u0026micro;g/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.08(2.44,9.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49(2.08,4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFib(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30(3.37,5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59(3.01,4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT(ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07(0.03,0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03(0.01,0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-frality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSK.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo sleep problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: FFP: Fried Frailty Phenotype; TSK.11:Tampa Scale for Kinesiophobia-11; AIS: Athens Insomnia Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2Construction of the Nomogram Risk Prediction Model\u003c/h2\u003e \u003cp\u003eThe 400 patients were randomly divided into a development group (n\u0026thinsp;=\u0026thinsp;280) and a validation group (n\u0026thinsp;=\u0026thinsp;120) in a 7:3 ratio using R software version 4.4.1. Lasso regression analysis was used to screen variables, with the regression coefficients and cross-validation path diagram shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively. Logistic regression was performed with sarcopenia occurrence (No\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1) as the dependent variable and significant factors from the univariate analysis as independent variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The final analysis identified six significant predictors of sarcopenia: regular exercise, BMI, serum phosphorus, frailty status, kinesiophobia, and sleep status (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssignment table for independent variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment method\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo frailty\u0026thinsp;=\u0026thinsp;1, Pre-frailty\u0026thinsp;=\u0026thinsp;2, Frailty\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSK.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo sleep problems\u0026thinsp;=\u0026thinsp;1, Suspected insomnia\u0026thinsp;=\u0026thinsp;2, Insomnia\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eResults of Logistic regression analysis for sarcopenia in patients with bone tumors\u003c/b\u003e (N\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u0026thinsp;~\u0026thinsp;0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.535\u0026thinsp;~\u0026thinsp;0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u0026thinsp;~\u0026thinsp;0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-frailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u0026thinsp;~\u0026thinsp;4.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.029\u0026thinsp;~\u0026thinsp;44.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSK.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.487\u0026thinsp;~\u0026thinsp;5.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected insomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.797\u0026thinsp;~\u0026thinsp;8.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsomnia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.057\u0026thinsp;~\u0026thinsp;18.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing these six factors, a nomogram risk prediction model was constructed to estimate the probability of sarcopenia in patients with bone tumors. The nomogram assigns scores for each risk factor, which are summed to produce a total score. A total score of 119 corresponds to a 50% probability of sarcopenia, while a score of 140 indicates a 90% probability (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3Evaluation of the Nomogram Prediction Model\u003c/h2\u003e \u003cp\u003eThe nomogram's performance was assessed using receiver operating characteristic (ROC) curves for the development and validation groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The area under the curve (AUC) for the development group was 0.935 (95% CI: 0.836\u0026thinsp;~\u0026thinsp;0.950), with a sensitivity of 0.903 and specificity of 0.865. For the validation group, the AUC was 0.902 (95% CI:0.808\u0026thinsp;~\u0026thinsp;0.910), with sensitivity and specificity values of 0.944 and 0.742, respectively, demonstrating strong discriminatory ability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCalibration curves for both groups showed high agreement between predicted and actual probabilities of sarcopenia (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The Hosmer-Lemeshow goodness-of-fit test indicated no significant difference between predicted and observed values (χ\u0026sup2; = 3.684, P\u0026thinsp;=\u0026thinsp;0.884), confirming that the model is well-calibrated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDecision curve analysis (DCA) further evaluated the model's clinical utility (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In the development group, the model provided clinical benefit within a threshold probability range of 4%\u0026ndash;100%, while for the validation group, benefit was observed within the 4%\u0026ndash;83% range. These results highlight the nomogram's practicality and utility in clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparative Results of the Risk Prediction Model and Five Sarcopenia Assessment Tools\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1Screening Efficacy Analysis of the Risk Prediction Model versus Five Sarcopenia Questionnaires\u003c/h2\u003e \u003cp\u003eUsing the AWGS-2019 diagnostic criteria for sarcopenia as the reference standard, the risk prediction model achieved the largest area under the ROC curve (AUC\u0026thinsp;=\u0026thinsp;0.926), as well as the highest sensitivity (93.55%), negative predictive value (NPV\u0026thinsp;=\u0026thinsp;91.49%) and Youden index (0.816). At its optimal cutoff value, this model exhibited high sensitivity (93.5%) and specificity (88.1%)(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;5 and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Accuracy Analysis of the Risk Prediction Model and Five Sarcopenia Questionnaires (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreening Tool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPV(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPV(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARC-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.760(0.670\u0026thinsp;~\u0026thinsp;0.851)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARC-Calf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.823(0.747\u0026thinsp;~\u0026thinsp;0.899)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARC-F\u0026thinsp;+\u0026thinsp;EBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.846(0.763\u0026thinsp;~\u0026thinsp;0.928)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSRA-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.780(0.716\u0026thinsp;~\u0026thinsp;0.844)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSRA-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.798(0.735\u0026thinsp;~\u0026thinsp;0.861)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Prediction Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.926(0.868\u0026thinsp;~\u0026thinsp;0.985)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAll values were less than 0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4.2 Comparison of Screening Results Between the Risk Prediction Model and Five Sarcopenia Questionnaires Based on the AWGS-2019 Sarcopenia Screening Criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatients were classified into sarcopenia and non-sarcopenia groups according to the screening results of the risk prediction model and the five sarcopenia questionnaires. The grouping frequencies were compared with the diagnostic outcomes based on the AWGS-2019 sarcopenia screening criteria. The results revealed that the risk prediction model yielded a Kappa coefficient of 0.724, indicating good consistency; the Kappa values of SARC-Calf and SARC-F\u0026thinsp;+\u0026thinsp;EBM were 0.523 and 0.471, respectively, suggesting moderate consistency; while the Kappa coefficients of SARC-F, MSRA-7 and MSRA-5 were 0.308, 0.292 and 0.265, respectively, demonstrating weak consistency (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Screening Results Between the Risk Prediction Model and Five Sarcopenia Questionnaires Based on the AWGS-2019 Sarcopenia Screening Criteria (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eScreening Tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAWGS-2019 Sarcopenia Screening Criteria(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKappa Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSARC-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSARC-Calf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSARC-F\u0026thinsp;+\u0026thinsp;EBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMSRA-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMSRA-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRisk Prediction Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn recent years, risk prediction models for sarcopenia in patients with malignant tumors have become a research hotspot. Most studies have focused on gastric cancer, colorectal cancer, liver cancer, nasopharyngeal carcinoma, and other malignancies, and the prevalence and influencing factors of sarcopenia vary considerably across these populations, mainly due to differences in study cohorts and inclusion/exclusion criteria\u003csup\u003e[24]\u003c/sup\u003e, The present study found that the prevalence of sarcopenia in patients with bone tumors was 26.25%, which was higher than the overall prevalence of sarcopenia in patients with cancer\u003csup\u003e[25]\u003c/sup\u003e。Therefore, the development of a nomogram risk prediction model for sarcopenia in patients with bone tumors can to some extent fill the research gaps in the field of sarcopenia and provide important value for the identification and assessment of sarcopenia in this population.A visual nomogram prediction model was constructed based on the following predictors: regular exercise, BMI, serum phosphorus, frailty, kinesiophobia, and sleep status. The model showed favorable predictive performance, with an AUC of 0.935 (95% CI: 0.836\u0026ndash;0.950) in the training set and an AUC of 0.902 (95% CI: 0.808\u0026ndash;0.910) in the test set. In addition, the calibration curve of the nomogram demonstrated high predictive accuracy.In the applied analysis, based on the AWGS 2019 sarcopenia screening criteria, the sarcopenia risk prediction model for patients with bone tumors achieved a larger AUC (0.926), higher sensitivity (93.55%), negative predictive value (NPV, 91.49%), Youden index (0.816), and better consistency (Kappa\u0026thinsp;=\u0026thinsp;0.724) compared with the five sarcopenia questionnaires. At the optimal cutoff value, the model also exhibited high sensitivity (93.5%) and specificity (88.1%). This risk prediction model was superior to the five commonly used sarcopenia questionnaires in screening for sarcopenia in patients with bone tumors. Meanwhile, the model included six influencing factors covering both subjective and objective indicators, making the assessment concise, convenient, and reliable.Thus, the establishment of this nomogram risk prediction model for sarcopenia in patients with bone tumors can help medical staff pay early attention to independent risk factors including regular exercise, BMI, serum phosphorus, frailty, kinesiophobia, and sleep status, and provide evidence and directions for personalized therapeutic and nursing interventions for sarcopenia in patients with bone tumors.\u003c/p\u003e \u003cp\u003eRegular exercise, BMI, serum phosphorus, frailty, kinesiophobia, and sleep status emerged as independent predictors of sarcopenia in this study. Using these factors, a visual nomogram was constructed, which demonstrated excellent predictive performance with an AUC of 0.935 in the development group and 0.902 in the validation group. The calibration curve indicated high agreement between predicted and observed outcomes, highlighting the model's reliability. This nomogram equips medical staff to identify at-risk patients early, enabling personalized interventions to prevent or mitigate sarcopenia.\u003c/p\u003e \u003cp\u003eThe present study found that regular exercise is a protective factor against sarcopenia in bone tumor patients, which is consistent with the study by Yang et al\u003csup\u003e[26]\u003c/sup\u003e. Regular exercise effectively reduces the risk of sarcopenia, mainly because it promotes the release of anabolic hormones, activates molecular pathways that inhibit protein breakdown and enhance muscle mass. This not only improves muscle contractile capacity but also better maintains muscle function \u003csup\u003e[26]\u003c/sup\u003e. However, bone tumor patients experience decreased physical activity due to factors such as severe surgical trauma, implant placement, prolonged bed rest, movement disorders, and adverse reactions caused by chemoradiotherapy. This further leads to a decrease in muscle strength and mass, which in turn reduces physical activity, ultimately forming a vicious cycle. Decreased physical activity results in mitochondrial dysfunction, accelerates skeletal muscle cell apoptosis, and thus increases the risk of osteoporosis \u003csup\u003e[27]\u003c/sup\u003e. Meanwhile, bone can also affect myofiber synthesis through transforming growth factor-β (TGF-β) and osteocalcin \u003csup\u003e[28]\u003c/sup\u003e. Studies have shown that the duration of bed rest is positively correlated with the decrease in muscle mass and content: 10 days of bed rest leads to a 30% reduction in muscle protein synthesis, a 6% decrease in leg muscle mass, and a 16% decline in muscle strength \u003csup\u003e[29, 30]\u003c/sup\u003e. Medical staff should carry out health education on sarcopenia for bone tumor patients, elaborating on its definition, hazards, and risk factors. Functional exercises should be guided according to different surgical sites: for the upper extremities, grip strength training with a dynamometer and pulley-assisted shoulder and elbow activities are recommended; for the lower extremities, straight leg raising, resistance band resistance training, and gait training should be performed; after spinal surgery, focus should be placed on diaphragmatic breathing and core muscle group training. Combined with phases such as the perioperative period and the early to middle rehabilitation stage, resistance training, aerobic exercise, balance training, and flexibility training should be gradually incorporated to prevent the occurrence of sarcopenia.\u003c/p\u003e \u003cp\u003eStudies have shown that BMI has become a predictor of chronic diseases, and low BMI also increases the risk of decreased muscle mass and strength \u003csup\u003e[31]\u003c/sup\u003e. Although sarcopenic obesity is also a current research hotspot, the present study found that low BMI is more likely to lead to sarcopenia in bone tumor patients, mainly related to nutrition, chronic diseases, and lack of exercise. The main reasons are as follows: (1) During the illness, patients and their families have insufficient understanding of nutrition-related knowledge, leading to an unreasonable diet structure \u003csup\u003e[32]\u003c/sup\u003e; bone tumor patients have poor sleep and appetite due to pain and psychological factors, gastrointestinal reactions during chemotherapy, and consumption of body nutrients by tumor cells. These factors all affect nutrient intake, fail to effectively promote the anabolic metabolism of muscle protein, result in the body being in a negative nitrogen balance, and lead to a decrease in myofibrillar protein content. (2) Most bone tumor patients have metastatic tumors; when complicated with other tumors and chronic diseases, metabolic disorders and increased levels of inflammatory factor C-reactive protein (CRP) occur, which reduce the levels of growth hormone (GH) and insulin-like growth factor\u0026minus;1 (IGF\u0026minus;1), and activate a variety of signaling pathways. This accelerates protein catabolism and inhibits its synthesis, causing skeletal muscle atrophy and thus sarcopenia \u003csup\u003e[33]\u003c/sup\u003e. It is recommended to conduct early screening of physiological indicators such as BMI and muscle mass, as well as inflammatory factors in bone tumor patients. For populations with low BMI, personalized diets containing high-quality proteins and branched-chain amino acids (BCAAs) should be formulated, and simultaneous nutrition knowledge education should be carried out to optimize the diet structure. Patients are encouraged to perform regular exercise within the tolerable range, improve sleep and appetite through pain management and psychological intervention, and prevent the occurrence of sarcopenia by monitoring changes in body weight and muscle strength.\u003c/p\u003e \u003cp\u003eThe present study found that hypophosphatemic patients with bone tumors are more likely to experience muscle loss, which is consistent with the study by Cai et al. \u003csup\u003e[34]\u003c/sup\u003e. Maintaining a higher serum phosphorus level reduces the occurrence of sarcopenia, thereby improving and prolonging patients' survival time. The main reasons are as follows: A high-protein diet is the main source of phosphorus \u003csup\u003e[35]\u003c/sup\u003e. However, tumor cells continuously absorb nutrients from the human body; poor appetite caused by pain and movement disorders, as well as physiological and psychological factors such as gastrointestinal reactions, fatigue, myelosuppression, anxiety, and depression induced by chemotherapy, lead to high protein consumption and low intake, which exacerbates the occurrence of hypophosphatemia \u003csup\u003e[36]\u003c/sup\u003e. Phosphorus is an important component of adenosine triphosphate (ATP) synthesis. Hypophosphatemia leads to decreased ATP production, affecting muscle energy supply and impairing mitochondrial function \u003csup\u003e[37]\u003c/sup\u003e. Secondly, hypophosphatemia alters muscle protein metabolism, inhibiting protein synthesis and activating the protein degradation system \u003csup\u003e[38]\u003c/sup\u003e. Furthermore, intracellular signal transduction is abnormal, such as impaired IGF\u0026minus;1 signaling pathway and disrupted calcium signaling, which affect muscle growth and contraction\u003csup\u003e[39]\u003c/sup\u003e. Finally, hypophosphatemia affects the bone-muscle axis, and abnormal mechanical signal transduction and imbalanced growth factors jointly promote muscle atrophy and increase the risk of sarcopenia \u003csup\u003e[40]\u003c/sup\u003e. Therefore, it is necessary to increase the intake of high-protein foods to supplement phosphorus, such as lean meat and beans. At the same time, it is crucial to closely monitor serum phosphorus levels and the occurrence of symptoms such as muscle weakness and anorexia, actively address pain, chemotherapy side effects, and psychological issues, reduce protein consumption, maintain normal body metabolism, and lower the risk of sarcopenia.\u003c/p\u003e \u003cp\u003eThe association between frailty and sarcopenia has been confirmed in relevant studies, all of which show a positive correlation between the occurrence of frailty and sarcopenia \u003csup\u003e[33, 41]\u003c/sup\u003e, which is consistent with the results of this study. This is mainly because bone tumor patients experience a decline in physiological reserve due to comprehensive treatments such as surgery, chemoradiotherapy, and targeted therapy, leading to gradual frailty of the body. During the frailty process, motor unit reorganization and loss, motor neuron decline, weakened peripheral nerve innervation, and decreased skeletal muscle remodeling capacity result in reduced muscle mass \u003csup\u003e[42]\u003c/sup\u003e. At the same time, frail patients are often in a state of chronic inflammation, producing inflammatory mediators such as interleukin\u0026minus;6 (IL\u0026minus;6) and tumor necrosis factor-α (TNF-α), which promote muscle protein breakdown and inhibit synthesis \u003csup\u003e[43]\u003c/sup\u003e. Immune dysfunction makes them more susceptible to infections, and immune cells release more inflammatory mediators to exacerbate inflammation \u003csup\u003e[44]\u003c/sup\u003e; in addition, tumor progression secretes factors that interfere with muscle growth. Furthermore, frailty leads to decreased mobility, resulting in weakened mechanical stimulation of muscles. Normal activities can activate signaling pathways that promote muscle protein synthesis, while reduced activity prevents the normal activation of these pathways, and poor blood circulation affects muscle nutrient supply \u003csup\u003e[45]\u003c/sup\u003e. It is necessary to raise medical staff's attention to the frailty symptoms of bone tumor patients, conduct early screening of the frailty degree of bone tumor patients, conduct assessments from multiple aspects such as nutrition, exercise, and psychology, and formulate preventive measures and personalized intervention plans according to frailty characteristics. This is of great significance for reducing the occurrence of sarcopenia in bone tumor patients.\u003c/p\u003e \u003cp\u003eThis study showed that the incidence of kinesiophobia in bone tumor patients with sarcopenia (65.71%) was significantly higher than that in non-sarcopenia patients (25.76%). When bone tumor patients were complicated with kinesiophobia, the risk of sarcopenia increased by 2.897 times.On the one hand, bone tumor surgery is associated with severe trauma. Due to concerns that activities may exacerbate pain, displace implants, and affect wound healing, patients have low compliance with activities. This fear leads them to reduce activities, resulting in insufficient motor stimulation of muscles, subsequent disuse atrophy, and weakened neuromuscular signal transmission, which impairs muscle function and mass \u003csup\u003e[46]\u003c/sup\u003e. On the other hand, long-term kinesiophobia imposes a heavy psychological and physiological burden. Psychologically, patients are in a state of fear and anxiety, and the stress response leads to hormonal imbalance, which inhibits muscle protein synthesis, affects appetite and nutrient intake, and is not conducive to muscle growth and repair \u003csup\u003e[47]\u003c/sup\u003e. Physiologically, activity avoidance behavior reduces physical activity, increases sedentary time, causes poor muscle blood circulation, impairs metabolism, and decreases sleep quality. This affects the secretion of growth hormone and inhibits muscle anabolic metabolism \u003csup\u003e[48]\u003c/sup\u003e. Kinesiophobia not only delays postoperative functional recovery but also may lead to decreased limb function or disability, which further aggravates fear, forms a vicious cycle, and increases the risk of sarcopenia \u003csup\u003e[48]\u003c/sup\u003e. Therefore, it is necessary to assess the causes of patients' kinesiophobia, correct misconceptions in a timely manner, conduct active communication and guidance, perform psychological intervention and counseling when necessary, improve patients' awareness of the role of functional exercise in disease recovery and their self-confidence, thereby reducing their kinesiophobia level and further decreasing the occurrence of sarcopenia.\u003c/p\u003e \u003cp\u003eInadequate sleep leads to elevated cortisol levels and reduced secretion of testosterone and growth factors, thereby triggering muscle loss and myofiber atrophy \u003csup\u003e[49]\u003c/sup\u003e. This study also found that sleep status is an important influencing factor for sarcopenia in bone tumor patients. Bone tumor patients experience decreased comfort due to conditions such as cancer-related pain, limb swelling, and postoperative physical activity disorders. In addition, worries and fears about the disease, side effects of chemoradiotherapy, and environmental changes all affect the sleep status of bone tumor patients. The main reasons are as follows: (1) Inadequate sleep reduces the secretion of relevant hormones. Deficiency of growth hormone inhibits muscle anabolic metabolism, making it difficult to obtain sufficient nutrients to maintain muscle growth. Inadequate sleep also causes physical fatigue and mental stress. Bone tumor patients are inherently weak, and inadequate sleep further exacerbates their fatigue, reduces physical activity capacity, and thereby decreases normal stimulation to muscles \u003csup\u003e[50]\u003c/sup\u003e. (2) Meanwhile, mental stress activates the stress response system, leading to increased secretion of hormones such as cortisol and promoting muscle breakdown \u003csup\u003e[49]\u003c/sup\u003e. (3) Patients may experience decreased appetite due to poor sleep, resulting in insufficient intake of nutrients required for muscle growth such as protein, which further increases the risk of sarcopenia \u003csup\u003e[51]\u003c/sup\u003e. Therefore, it is necessary to actively create a good sleep environment for patients, and provide painkiller medication guidance and other methods to improve sleep quality, thereby reducing the adverse effects of inadequate sleep on muscle health.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it was conducted at a single center with a relatively small sample size, which may limit the generalizability of the findings. Second, BMI cannot be applied to patients with limb amputations, restricting its utility in some cases. Finally, the nomogram model has not undergone external validation. Future multicenter studies with larger sample sizes and external validation are necessary to enhance the model's robustness and applicability. By addressing these limitations and expanding research efforts, this nomogram risk prediction model holds promise as a valuable tool for early identification and intervention in sarcopenia among patients with bone tumors, ultimately improving patient outcomes.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study developed and internally validated a clinically robust nomogram for predicting sarcopenia risk in patients with bone tumors. The model integrates objective laboratory, functional, and patient-reported measures to provide comprehensive, individualized risk stratification. This user-friendly instrument enables early identification of high-risk patients and supports timely, personalized interventions to mitigate sarcopenia and improve clinical outcomes and quality of life in this vulnerable population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by two projects: the Innovation Fund Project for Postgraduates of Kunming Medical University (2024S221) and the Science and Technology Plan Project of the Department of Science and Technology of Yunnan Province (202401AV070001-334).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch materials and data will be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Yunnan Cancer Hospital (Ethics Number: KYLX2023-171). All participants provided written informed consent prior to enrollment, with a clear understanding of the study purpose, procedures, potential risks, and rights, and voluntarily agreed to participate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll human participants have signed written informed consent forms, agreeing to participate in the study and the collection and analysis of relevant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eName of Approval Committee/IRB in Ethics Approval Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics Committee of Yunnan Cancer Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXu D, Li B, Liu W, et al. Deep learning-based detection of primary bone tumors around the knee joint on radiographs: a multicenter study[J]. Quantitative imaging in medicine and surgery, 2024,14(8):5420-5433.\u003c/li\u003e\n\u003cli\u003eNiu X H. Interpretation of 2020 NCCN Clinical Practice Guidelines in Oncology-Bone Cancer[J]. Zhonghua wai ke za zhi [Chinese journal of surgery], 2020,58(6):430-434.\u003c/li\u003e\n\u003cli\u003eKnapp B J, Cittolin-Santos G F, Flanagan M E, et al. Incidence and risk factors for bone metastases at presentation in solid tumors[J]. Frontiers in oncology, 2024,14:1392667.\u003c/li\u003e\n\u003cli\u003eBingfeng H, Rongshou Z, Hongmei Z, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024,4(1):47-52.\u003c/li\u003e\n\u003cli\u003eXi Y, Qiao L, Na B, et al. Primary malignant bone tumors incidence, mortality, and trends in China from 2000 to 2015[J]. Chin Med J (Engl), 2023,136(17):2037-2043.\u003c/li\u003e\n\u003cli\u003eAbe T, Yoshimura Y, Sato Y, et al. Sarcopenia as a Robust Predictor of Readmission within 6 Months among Individuals Experiencing Acute Stroke[J]. Annals of geriatric medicine and research, 2024,28(3):307-314.\u003c/li\u003e\n\u003cli\u003eSu S, Wang R, Chen Z, et al. The causal effect of sarcopenia-associated traits on brain cortical structure: A Mendelian randomization study[J]. Archives of gerontology and geriatrics, 2024,118:105302.\u003c/li\u003e\n\u003cli\u003eCui H, Wang Z, Wu J, et al. Chinese expert consensus on prevention and intervention for elderly with sarcopenia (2023)[J]. Aging medicine (Milton (N.S.W)), 2023,6(2):104-115.\u003c/li\u003e\n\u003cli\u003eMo Y, Su Y, Dong X, et al. Development and Validation of a Nomogram for Predicting Sarcopenia in Community-Dwelling Older Adults[J]. Journal of the American Medical Directors Association, 2022,23(5):715-721.\u003c/li\u003e\n\u003cli\u003eYu J, Guan Q, Chen Y, et al. Comparative assessment of sarcopenia screening tools for patients with bone tumors: insights for enhanced clinical application[J]. Frontiers in nutrition, 2025,12:1584706.\u003c/li\u003e\n\u003cli\u003eLi Q, Cheng H, Cen W, et al. Development and validation of a predictive model for the risk of sarcopenia in the older adults in China[J]. European journal of medical research, 2024,29(1):278.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhang L, Guan Y, et al. Establishment and validation of a risk prediction model for sarcopenia in gastrointestinal cancer patients: A systematic review and meta-analysis-based approach[J]. Clinical nutrition (Edinburgh, Scotland), 2024,43(11):91-98.\u003c/li\u003e\n\u003cli\u003eLeng Y, Wang G, Xie R, et al. Risk Prediction Models for Sarcopenia in Dialysis Patients: A Systematic Review[J]. Journal of renal nutrition : the official journal of the Council on Renal Nutrition of the National Kidney Foundation, 2025,35(1):146-155.\u003c/li\u003e\n\u003cli\u003eChen L, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment[J]. J Am Med Dir Assoc, 2020,21(3):300-307.\u003c/li\u003e\n\u003cli\u003eVaran H D, Deniz O, \u0026Ccedil;\u0026ouml;teli S, et al. Validity and reliability of Fried frailty phenotype in Turkish population[J]. Turkish journal of medical sciences, 2022,52(2):524-527.\u003c/li\u003e\n\u003cli\u003eYang X L, Yuan Y M, Si H X. Association between frailty and the risk of chronic comorbidities among middle-aged and older Chinese adults[J]. Chinese Journal of Disease Control \u0026amp; Prevention, 2026, 30(02):177-182.\u003c/li\u003e\n\u003cli\u003eFarzad M, MacDermid J, Packham T. Fear of Movement in People With CRPS: A Psychometric Evaluation of the Tampa Scale-11 for Kinesiophobia[J]. The Clinical journal of pain, 2024,40(1):26-34.\u003c/li\u003e\n\u003cli\u003eChung K, Kan K K, Yeung W. Assessing insomnia in adolescents: comparison of Insomnia Severity Index, Athens Insomnia Scale and Sleep Quality Index[J]. Sleep medicine, 2011,12(5):463-470.\u003c/li\u003e\n\u003cli\u003eMalmstrom T K, Morley J E. SARC-F: a simple questionnaire to rapidly diagnose sarcopenia[J]. Journal of the American Medical Directors Association, 2013,14(8):531-532.\u003c/li\u003e\n\u003cli\u003eBarbosa-Silva T G, Menezes A M B, Bielemann R M, et al. Enhancing SARC-F: Improving Sarcopenia Screening in the Clinical Practice[J]. Journal of the American Medical Directors Association, 2016,17(12):1136-1141.\u003c/li\u003e\n\u003cli\u003eKurita N, Wakita T, Kamitani T, et al. SARC-F Validation and SARC-F+EBM Derivation in Musculoskeletal Disease: The SPSS-OK Study[J]. The journal of nutrition, health \u0026amp; aging, 2019,23(8):732-738.\u003c/li\u003e\n\u003cli\u003eRossi A P, Micciolo R, Rubele S, et al. Assessing the Risk of Sarcopenia in the Elderly: The Mini Sarcopenia Risk Assessment (MSRA) Questionnaire[J]. The journal of nutrition, health \u0026amp; aging, 2017,21(6):743-749.\u003c/li\u003e\n\u003cli\u003eYang M, Hu X, Xie L, et al. Validation of the Chinese version of the Mini Sarcopenia Risk Assessment questionnaire in community-dwelling older adults[J]. Medicine, 2018,97(37):e12426.\u003c/li\u003e\n\u003cli\u003eZhang Y, Zhu Y. Development and validation of risk prediction model for sarcopenia in patients with colorectal cancer[J]. Frontiers in oncology, 2023,13:1172096.\u003c/li\u003e\n\u003cli\u003eWang M, Yang P, Zhou L, et al. Thoracic Sarcopenia was a Poor Prognostic Predictor in Patients Receiving Immunotherapy for Advanced Non-small-cell Lung Cancer[J]. Academic radiology, 2025,32(1):526-532.\u003c/li\u003e\n\u003cli\u003eYang Z, Li X, Song W, et al. Associations between meeting 24-h movement guidelines and sarcopenia risk among adults aged \u0026ge; 55 years in five low- and middle-income countries[J]. Complementary therapies in clinical practice, 2024,57:101887.\u003c/li\u003e\n\u003cli\u003eNasso R, D\u0026apos;Errico A, Motti M L, et al. Dietary Protein and Physical Exercise for the Treatment of Sarcopenia[J]. Clinics and practice, 2024,14(4):1451-1467.\u003c/li\u003e\n\u003cli\u003eXu K, Feng X, Xu Z, et al. Association of sarcopenia with osteoporosis in Chinese patients with type 2 diabetes[J]. BMC musculoskeletal disorders, 2024,25(1):226.\u003c/li\u003e\n\u003cli\u003eMarusic U, Narici M, Simunic B, et al. Nonuniform loss of muscle strength and atrophy during bed rest: a systematic review[J]. Journal of applied physiology (Bethesda, Md. : 1985), 2021,131(1):194-206.\u003c/li\u003e\n\u003cli\u003eLu L, Du G, Qi C, et al. %BF, Rather Than BMI, is Associated with an Increased Risk of Sarcopenia in Hospitalized Postmenopausal Chinese Women with Type 2 Diabetes Mellitus[J]. Diabetes, metabolic syndrome and obesity : targets and therapy, 2024,17:4223-4231.\u003c/li\u003e\n\u003cli\u003eWu W, Duan F, Liu Z, et al. BMI-stratified cutoff values for spinal sarcopenia in Chinese adults based on CT measures: a multicentre study.[J]. J Orthop Surg Res, 2025,20(1):365.\u003c/li\u003e\n\u003cli\u003eMerchant R A, Kit M W W, Lim J Y, et al. Association of central obesity and high body mass index with function and cognition in older adults[J]. Endocrine connections, 2021,10(8):909-917.\u003c/li\u003e\n\u003cli\u003eLuo S, Chen X, Hou L, et al. The accuracy of body mass index and calf circumference values when assessing sarcopenia in a multi-ethnic cohort of middle-aged and older adults: West China health and aging trend study results[J]. Heliyon, 2023,9(4):e15027.\u003c/li\u003e\n\u003cli\u003eCai G, Ying J, Pan M, et al. Development of a risk prediction nomogram for sarcopenia in hemodialysis patients[J]. BMC nephrology, 2022,23(1):319.\u003c/li\u003e\n\u003cli\u003eAlmeida N S, Rocha R, Souza C A, et al. Energy and nutrient intake by people with and without sarcopenia diagnosed by the European Working Group on Sarcopenia in Older People: a systematic review and meta-analysis.[J]. NUTR REV, 2024,82(12):1666-1677.\u003c/li\u003e\n\u003cli\u003evan Atteveld J E, de Winter D T C, Pluimakers V G, et al. Frailty and sarcopenia within the earliest national Dutch childhood cancer survivor cohort (DCCSS-LATER): a cross-sectional study[J]. The lancet. Healthy longevity, 2023,4(4):e155-e165.\u003c/li\u003e\n\u003cli\u003eZhang S, Yang X, An N, et al. Risk Factors and Predictive Models for Sarcopenia in Older Adults[J]. Aging medicine (Milton (N.S.W)), 2025,8(3):192-199.\u003c/li\u003e\n\u003cli\u003eLi Y, Gu W, Hao W, et al. Associations of four important dietary pattern scores, micronutrients with sarcopenia and osteopenia in adults: results from the National Health and Nutrition Examination Survey[J]. Frontiers in nutrition, 2025,12:1583795.\u003c/li\u003e\n\u003cli\u003eKawao N, Nishikawa A, Matsumura D, et al. Roles of Insulin-Like Growth Factor-1 in Muscle Wasting and Osteopenia in Mice with Hyponatremia[J]. Calcified tissue international, 2025,116(1):61.\u003c/li\u003e\n\u003cli\u003eYang Z, Hao L, Kuati A, et al. Triclosan exposure exacerbates muscle wasting via senescence and IGF-1 pathway suppression: A combined epidemiological and experimental study[J]. Ecotoxicology and environmental safety, 2025,304:119142.\u003c/li\u003e\n\u003cli\u003eWelch C, Greig C, Majid Z, et al. Induced frailty and acute sarcopenia are overlapping consequences of hospitalisation in older adults[J]. Journal of frailty, sarcopenia and falls, 2022,7(3):103-116.\u003c/li\u003e\n\u003cli\u003eChen Y, Chen X, Zhong L, et al. Bibliometric analysis of research trends in the relationship between frailty and neoplasms over the past decade[J]. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024,32(8):536.\u003c/li\u003e\n\u003cli\u003eDebruin D, McRae N L, Addinsall A B, et al. In dystrophic mdx hindlimb muscles where fibrosis is limited, versican haploinsufficiency transiently improves contractile function without reducing inflammation[J]. American journal of physiology. Cell physiology, 2024,327(4):C1035-C1050.\u003c/li\u003e\n\u003cli\u003eLi Z, Li H, Wang P, et al. Relationship between aggregate index of systemic inflammation and mortality from CCD and malignant neoplasms in diabetic patients[J]. Scientific reports, 2025,15(1):26545.\u003c/li\u003e\n\u003cli\u003eJiang H, Inoue S, Hatakeyama J, et al. Effects of aging and resistance exercise on muscle strength, physiological properties, longevity proteins, and telomere length in SAMP8 mice[J]. Biogerontology, 2025,26(2):88.\u003c/li\u003e\n\u003cli\u003eSakuma S, Kimpara K, Kawai Y, et al. Integrating Physical Therapy and Virtual Reality to Manage Pain-Related Fear of Movement in Patients With Chronic Pain: A Randomized Controlled Trial[J]. Cureus, 2025,17(2):e79551.\u003c/li\u003e\n\u003cli\u003eEngman V, Critchlow A, Laakkonen E, et al. The Role and Regulation of Intramuscular Sex Hormones in Skeletal Muscle: A Systematic Review.[J]. 2025,110(6):e1732-e1746.\u003c/li\u003e\n\u003cli\u003eTokgoz M A, Ergisi Y, Odluyurt M, et al. he effect of pain catastrophizing and kinesiophobia on the result of shoulder arthroscopy[J]. Agri, 2021,33(4):232-236.\u003c/li\u003e\n\u003cli\u003eHan P, Hou L, Liang Z, et al. Both Short and Long Sleep Durations are Risk Factors for Sarcopenia in Suburban-Dwelling Older Chinese Individuals: A 3-Year Longitudinal Study[J]. Nat Sci Sleep, 2022(14):1089-1096.\u003c/li\u003e\n\u003cli\u003eYuan S, Larsson S C. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences[J]. Metabolism: clinical and experimental, 2023,144:155533.\u003c/li\u003e\n\u003cli\u003eLiu K, Luo J, Chen Y, et al. Association between sarcopenia and sleep disorders: a cross-sectional population based study[J]. Frontiers in nutrition, 2024,11:1415743.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bone Neoplasms, Sarcopenia, Risk Assessment, Predictive Model, Nomogram, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9305260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9305260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Sarcopenia represents a highly prevalent and prognostically detrimental complication in patients with bone tumors. Its progressive and dynamic nature hinders early intervention and amplifies clinical care burdens, highlighting an unmet need for tailored risk stratification tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To determine the prevalence and independent risk factors of sarcopenia in patients with bone tumors, and to develop, internally validate, and comparatively evaluate a clinically practical nomogram for individualized sarcopenia risk prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign:\u003c/strong\u003e A prospective, single-center study was conducted. A total of 400 inpatients with bone tumors were consecutively enrolled between August 2023 and February 2025 for model development. An independent cohort of 140 patients admitted from March to June 2025 was used for head-to-head validation of the nomogram against five established sarcopenia screening tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting and Participants:\u003c/strong\u003e This study was performed at a tertiary cancer hospital, enrolling 540 patients with bone tumors using convenience sampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e \u003cstrong\u003e(1) Model development and internal validation:\u003c/strong\u003e Eligible patients were randomly assigned at a 7:3 ratio to a training cohort (n=280) and an internal validation cohort (n=120). Data encompassed demographics, Tampa Scale of Kinesiophobia-11 (TSK-11), Fried Frailty Phenotype, Athens Insomnia Scale (AIS), and routine laboratory parameters. Sarcopenia was defined per the 2019 Asian Working Group for Sarcopenia (AWGS) criteria. Predictors were selected using Lasso regression, and the nomogram was constructed using multivariable logistic regression. Model performance was assessed via discrimination (ROC–AUC), calibration (calibration curves and Hosmer–Lemeshow test), and clinical utility (decision curve analysis, DCA).\u003cstrong\u003e (2) Comparative validation: \u003c/strong\u003eThe nomogram was directly compared with five brief screening tools: SARC-F, SARC-Calf, SARC-F+EBM, MSRA-7, and MSRA-5 in 140 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Six independent predictors were integrated into the nomogram: regular exercise, body mass index (BMI), serum phosphorus, frailty, kinesiophobia, and sleep disturbance (all P\u0026lt;0.05). The nomogram yielded excellent discrimination, with an AUC of 0.935 (95% CI: 0.836–0.950) in the training cohort and 0.902 (95% CI: 0.808–0.910) in the internal validation cohort. The Hosmer–Lemeshow test confirmed good calibration (χ²=3.684, P=0.884). In head-to-head testing, the nomogram achieved the highest AUC (0.926), sensitivity (93.55%), negative predictive value (91.49%), Youden index (0.816), and Kappa coefficient (0.724) among all tools.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This novel nomogram demonstrates robust predictive performance and clinical reliability for identifying sarcopenia in patients with bone tumors. It enables early, individualized risk assessment and supports timely targeted interventions to optimize clinical outcomes.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Clinically Useful Nomogram for Predicting Sarcopenia in Bone Tumor Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 15:36:36","doi":"10.21203/rs.3.rs-9305260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-13T23:57:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T13:19:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-04T11:10:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-04T11:10:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2026-04-02T15:47:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f2561c9-523a-4753-a4cd-02f8beeb8957","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T15:36:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 15:36:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9305260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9305260","identity":"rs-9305260","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}