Development and Validation of a Nomogram Model for Predicting Postoperative Nonunion in Femoral Shaft Fractures

Development and Validation of a Nomogram Model for Predicting Postoperative Nonunion in Femoral Shaft Fractures | 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 Nomogram Model for Predicting Postoperative Nonunion in Femoral Shaft Fractures Zhilong Hao, yefan Zhang, Jiahao Zeng, Chao Yang, Haifeng Dang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6395838/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2025 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted 12 You are reading this latest preprint version Abstract Objective：To identify independent risk factors for nonunion following femoral shaft fracture surgery and develop a clinically applicable nomogram model for personalized risk prediction. Methods：A retrospective cohort study included 804 patients with femoral shaft fractures treated at Xijing Hospital (2014–2020). Patients were divided into development (n=561) and validation (n=243) cohorts. Variables were screened via LASSO regression, and a nomogram was constructed using multivariate logistic regression. Model performance was assessed using ROC curves, calibration plots, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Results：Five independent predictors of nonunion were identified: smoking (OR=3.094, 95% CI:1.790–5.350), high-energy injury (OR=2.454, 95% CI:1.167–5.159), multiple injuries (OR=2.897, 95% CI:1.580–5.312), internal fixation method (OR=3.437, 95% CI:1.519–7.778), and fixation failure (OR=3.437, 95% CI:1.519–7.778). The nomogram demonstrated excellent discrimination (AUC=0.828 in development, 0.835 in validation cohorts) and calibration (Hosmer-Lemeshow P=0.463 and P=0.858, respectively). DCA confirmed clinical utility at threshold probabilities >15%. Conclusion：This nomogram provides a practical tool for predicting nonunion risk in femoral shaft fractures, enabling early intervention for high-risk patients. Clinical trial number：Not applicable. femoral shaft fracture postoperative complications nonunion risk factors nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Femoral shaft fracture refers to the fracture [1,2] occurring 2~5cm under the microtrochanter to 5cm above the condyle of the femur. It is a common orthopedic disease with a high incidence rate, primarily resulting from high-energy injuries like industrial accidents and traffic collisions. The global annual incidence of femoral shaft fracture is about 0.01%~0.021%, of which 2% are open fracture [3,4]. Femoral shaft fractures typically require orthopedic surgical intervention for treatment, with a variety of surgical techniques available, such as internal fixation with plates and screws or intramedullary nailing. The goal of these surgeries is to correct the fracture displacement and ensure proper healing. Most patients can expect a good reduction and recovery, provided that the treatment is tailored to their specific condition and complications are managed effectively.tion and functional recovery, but no matter what kind of surgery (minimally invasive or open), Postoperative nonunion incidence rates for femoral shaft fractures range from 4.6% to 23% [5,6], with studies indicating that despite advancements in treatment and internal fixation materials, nonunion remains a significant clinical challenge . Nonunion is one of the serious complications after femoral shaft fracture, which can lead to long-term pain, physical disability, mental health problems and increased economic burden of patients, as well as a slow recovery of normal working efficiency [7], which is seriously life-threatening. In recent years, the prevention and treatment of nonunion after femoral shaft fracture has become a significant clinical focus in orthopedics [8,9]. Despite the progress in surgical techniques and fracture fixation methods, there remains a lack of comprehensive research on the risk factors and a standardized risk assessment model for nonunion following femoral shaft surgery. The risk assessment model is a diagnostic model that predicts the probability of a disease outcome through a combination of risk and protective factors. This model can be used for the clinical assessment of the disease risk.Numerous reports indicate a significant incidence of nonunion following femoral shaft fracture surgeries, with a notable percentage requiring revision surgeries. Given these challenges, it is imperative to develop a risk assessment model to predict the likelihood of nonunion post-surgery. In line with the retrospective study methodology, this research compiled clinical and follow-up data from patients with femoral shaft fractures, spanning from January 2014 to December 2020, sourced from the orthopedic follow-up database at Xijing Hospital. The objective of this study is to offer a theoretical basis for the prevention and treatment of nonunion after femoral shaft fracture. 1 Methods 1.1 Inclusion criteria for study subjects: ① age ≥18 years;② no infection before and during ; ③ follow-up for more than 1 year, complete cases and imaging data. Exclusion criteria: ① bilateral femoral shaft fractures; ②treated conservatively or without surgery; ③ with severe bone metabolic disease;④history of previous fractures on the side; ⑤ fractures from primary or metastatic bone tumor. This study was approved by the hospital ethics committee. Nonunion is defined as a condition that occurs at least 9 months after an injury or fracture, and there has been no tendency for further healing for 3 months. On X-ray examination, manifestations include a gap at the fracture site, hardening of the fracture ends, occlusion of the medullary cavity, and the absence of continuous callus formation [10].Follow-up was conducted through outpatient clinic, telephone, Wechat, QQ, and postoperative non-union was used as the endpoint event. The follow-up time was up to December 2020 for 12 months. A total of 804 patients with severe femoral shaft fractures, aged 18-81 years, mean age (41.29 ± 15.13), were enrolled by cluster random sampling by 7:3 ratio into the development cohort (n=561) and validation cohort (n=243). Among the 561 patients in the development cohort, 484 patients had healed normally, included in the fracture healing group (N=484), and 77 patients had postoperative nonunion and included in the nonunion group (N=77). 1.2 Data collection Collected data encompassed gender, age, BMI (physical fitness index), admission BMI, calculated as weight / height² (kg/m²), smoking history (defined as ≥1 dose/day for ≥6 consecutive/cumulative months), and alcohol consumption （Alcohol consumption is defined as averaging more than once a week）, hypertension, hypoproteinemia [11] (<35 g/L), diabetes mellitus, osteoporosis (T value measured by dual-energy X-ray absorptiometry ≤-2.5).Injury mechanism (high-energy injury and low-energy injury), fracture side (left side and right side), reduction mode (open and closed),AO classification of fractures (Type A, Type B and Type C), fracture site (proximal one-third, middle one-third and distal one-third of the femoral shaft), operation time, internal fixation method (intramedullary nail, nail-plate system and external fixator) , failure of internal fixation (plate fracture, screw loosening, internal fixation loosening, etc.) [12], postoperative infection, application of anticoagulants, NSAID drugs, quinolone antibiotics, delayed weight-bearing exercise (> 12 weeks). 1.3 Statistical analysis Statistical analyses were performed using SPSS 23.0 (IBM Corp) and R 4.0 (R Foundation). Continuous variables were expressed as mean ± standard deviation (X±S) and compared using Student's t-tests. Categorical variables were expressed as frequency counts (percentage) and analyzed with χ² tests. Variable screening was performed through LASSO regression, followed by predictive model development using multivariable logistic regression analysis and nomogram construction. The prediction model was validated through three domains: 1.Discrimination : Area under the ROC curve (AUC). 2.Calibration : Calibration curves with osmer-Lemeshow goodness-of-fit test. 3.Clinical utility : Decision curve analysis (DCA) Statistical significance threshold was set at P<0.05 for all analyses. 2 Results 2.1 Univariate analysis of relevant data of the postoperative fracture healing group and nonunion group of the femoral shaft fracture in the development cohort. The results of the two groups: gender, age, BMI, osteoporosis, hypoproteinemia, diabetes, hypertension, fracture site, fracture side, AO classification, reduction mode, operation time, premature exercise, the use of anticoagulants, quinolone antibiotics, no statistical significance (P> 0.05), Smoking, alcohol consumption, high-energy trauma, open fractures, multiple injuries, and the use of NSAIDs drugs are all factors that can contribute to the complexity of fracture treatment. These factors are statistically significant in the context of fracture management (P<0.05). (Table 1) Table 1 presents the univariate analysis of relevant data for fracture healing and nonunion groups, highlighting the importance of early diagnosis and intervention in managing nonunion. project Fracture healing group (N=484,%) Bone nonunion group (N=77,%) X2 or T values P price sex woman 125（25.8） 22（28.6） 0.259 0.611 man 359（74.2） 55（71.4） age 43.17±16.899 43.58±17.045 40.53±15.797 t=1.474 0.141 BMI ≥28 14（2.9） 6（7.8） 4.362 0.106 24≤BMI＜28 130（26.9） 18（23.4） ＜24 340（70.2） 53（68.8） smoke deny 364（75.2） 32（41.6） 36.228 <0.001 yes 120（24.8） 45（58.4） drink deny 372（76.9） 40（51.9） 21.136 <0.001 yes 112（23.1） 37（48.1） osteoporosis deny 380（78.5） 62（80.5） 0.160 0.689 yes 104（21.5） 15（19.5） Hypoproteinemia deny 434（89.7） 68（88.3） 0.130 0.718 yes 50（10.3） 9（11.7） diabetes mellitus deny 426（88.0） 67（87.0） 0.063 0.802 yes 58（12.0） 10（13.0） hypertension deny 409（84.5） 63（81.8） 0.359 0.549 yes 75（15.5） 14（18.2） fracture site Up to 1 / 3 53（11.0） 6（7.8） 5.811 0.055 In 1 / 3 289（59.7） 57（74.0） The next 1 / 3 142（29.3） 14（18.2） Fracture side farewell right 230（47.5） 32（41.6） 0.949 0.330 the left side 254（52.5） 45（58.4） High energy damage deny 208（43.0） 11（14.3） 22.976 ＜0.001 yes 276（57.0） 66（85.7） compound fracture deny 377（77.9） 42（54.5） 19.154 ＜0.001 yes 107（22.1） 35（45.5） AO somatotype A mould 69（14.3） 12（15.6） 4.415 0.110 B mould 107（22.1） 9（11.7） C mould 308（63.6） 56（72.7） initial condition mode closed reduction 95（19.6） 11（14.3） 1.237 0.266 Open reset 389（80.4） 66（85.7） multiple injury deny 337（69.6） 23（29.9） 45.672 <0.001 yes 147（30.4） 54（70.1） Operation time 230.10±77.603 233.03±74.544 224.81±94.818 t=0.726 0.470 Internal fixation mode bone nail 375（77.5） 46（59.7） 42.541 ＜0.001 Nail system 81（16.7） 9（11.7） External fixed frame 28（5.8） 22（28.6） Internal fixation failed deny 461（95.2） 63（81.8） 19.449 <0.001 yes 23（4.8） 14（18.2） POI deny 471（97.3） 71（92.2） 5.294 0.021 yes 13（2.7） 6（7.8） Early weight-bearing exercise Endovirus for 12 weeks 321（66.3） 58（75.3） 2.456 0.117 <12 Weeks 163（33.7） 19（24.7） The use of anticoagulants deny 77（15.9） 17（22.1） 1.812 0.178 yes 407（84.1） 60（77.9） NSAIDS Drug use deny 197（40.7） 20（26.0） 6.076 0.014 yes 287（59.3） 57（74） Use of quinolone antibiotics deny 326（67.4） 52（67.5） 0.001 0.975 yes 158（32.6） 25（32.5） Note: BMI: constitution index, BMI= weight / height 2 (kg/m2) 2.2 LASSO regression Based on the demographics, trauma-related conditions, and iatrogenic conditions of the patients in the development cohort, LASSO regression analysis was used to screen out 5 predictive variables with non-zero coefficients from 24 variables (Figure 1). The process involved drawing vertical lines at λ values corresponding to the minimum (λ = 0.008) and one standard error (λ = 0.035) as shown in Figure 2, to determine the optimal subset of predictors. When log (λ) = -3.347, the LASSO regression model was optimal. The predictor variables screened were smoking, high energy injury, multiple injuries, mode of internal fixation, failure of internal fixation. 2.3 A prediction model was developed with postoperative healing of femoral shaft fractures as the dependent variable (coded as: fracture healing = 0, bone union = 1). Using LASSO regression analysis (see Table 2 and Table 3), the results indicated that smoking, high-energy injury, multiple injuries, internal fixation mode, and internal fixation failure were significant predictors of femoral shaft fracture nonunion (P < 0.05). Based on these predictive variables, a nomogram was constructed (as shown in Figure 2) to visually depict the functional relationships among variables within the mathematical model. This tool facilitates rapid and accurate probability estimations, particularly in clinical and engineering contexts. Each variable can be assigned a score according to the nomogram, and the total score is calculated by summing the individual scores. The probability of nonunion can then be estimated by drawing a vertical line downward from the total score. Table 2: multivariate Logistic regression analysis factor regression coefficient standard error Wald price P price OR 95%CI lower limit superior limit smoke 1.130 0.279 16.346 ＜0.001 3.094 1.790 5.350 High energy damage 0.898 0.379 5.602 0.018 2.454 1.167 5.159 multiple injury 1.064 0.309 11.827 0.001 2.897 1.580 5.312 Intramedullary nail (for reference) 6.239 0.044 1 Nail system -0.921 0.376 5.999 0.014 0.398 0.191 0.832 External fixed frame -0.974 0.518 3.535 0.060 0.377 0.137 1.042 Internal fixation failed 1.235 0.417 8.778 0.003 3.437 1.519 7.778 constant -2.819 0.548 26.516 ＜0.001 0.060 Table 3: Multivariate Logistic Regression Analysis of Risk Factors for Nonunion after Femoral Shaft Fracture Surgery variable assignment History of smoking No =0, Yes =1 High energy damage No =0, Yes =1 multiple injury No =0, Yes =1 Internal fixation mode Nail =1, nail system =2, external holder =3 Internal fixation failed No =0, Yes =1 Fracture healing Nonunion =1, and fracture healing =0 Note: The 95%CI means the 95% confidence interval 2.4 Validation of the prediction model The validation of this predictive model was mainly based on the discrimination and calibration of the model. The discrimination of the model was evaluated by plotting the ROC curve of the predictive model for predicting the occurrence of nonunion in patients with femoral shaft fractures.The AUC values of the development and validation cohorts were 0.828 (95% CI: 0.783-0.872) and 0.835 (95% CI: 0.770-0.900), respectively, with corresponding cutoff values of 0.023 and 0.157. (fig.3).These results suggest a strong predictive performance of the model, as indicated by the high AUC values, which are indicative of the model's ability to distinguish between positive and negative outcomes effectively and good discrimination ability of the prediction model. Meanwhile, The Hosmer-Lemeshow goodness-of-fit test indicated a good model fit, with P-values of 0.463 in the development cohort and 0.858 in the validation cohort, both above the conventional threshold of 0.05, suggesting that the observed differences are likely due to random variation.The model exhibits a high level of calibration, as evidenced by its prediction probability being largely consistent with the actual probability.In addition, the calibration curves of the development cohort and the validation cohort showed moderate consistency, indicating that the predictive model possesses good calibration ability（fig.4).In conclusion, the Nomogram of the predictive model has moderate predictive ability. 2.5 Clinical application Using decision curve analysis (DCA) to evaluate the clinical validity of the prediction model, we conducted DCA for the nomogram predicting postoperative nonunion risk in patients with femoral shaft fractures (Figure 5). The results indicated that if the threshold probabilities for both patients and clinicians exceed 15%, the risk of postoperative nonunion following fracture in the current study is more favorable compared to a universal intervention strategy for all patients. Within this range, the net benefit of the prediction model was significantly higher than the two extreme scenarios: either all patients receiving clinical intervention or none receiving it. 3 Discussion At present, the Nomogram, as a disease prediction tool based on multivariate regression analysis[13], has gained widespread application in clinical research, particularly in the field of orthopedic disease risk prediction[14,15]. Its high reliability and practicality have been acknowledged by the medical community, leading to its extensive use in clinical practice. Nomogram-based prediction models are renowned for their simplicity, intuitiveness, and ease of comprehension[16]. These models incorporate clinical factors such as age, gender, and laboratory test results to predict the likelihood of disease occurrence, progression, and prognosis. They are especially valuable for visually illustrating the influence of various factors on prediction outcomes, thereby assisting clinicians in accurately assessing patient risks and devising personalized treatment strategies. In this study, 15.9% of patients with femoral shaft fracture in the development cohort experienced nonunion, aligning with the range of nonunion rates (8% to 23%) reported in recent literature [17], indicating a significant risk for nonunion in femoral shaft fractures. Using LASSO regression and multifactor Logistic regression analysis identified five independent risk factors for postoperative nonunion: smoking, high energy injury, multiple injuries, internal fixation, and internal fixation failure., a nomogram prediction model of femoral shaft fracture patients was constructed and verified according to these independent risk factors. The prediction model demonstrated robust predictive capabilities, with an AUC of 0.828 for the development cohort and an AUC of 0.835 for the external validation cohort, indicating a high level of prediction accuracy. Meanwhile, the calibration curves for both the development cohort and the validation cohort showed good agreement between the actual diagnosis and the predicted diagnosis. In addition, the DCA curve also suggested that the prediction model has a very good clinical validity. This prediction model shows that for patients with femoral shaft fracture, early targeted intervention measures for high-risk groups in clinical treatment can effectively reduce the risk of bone nonunion in patients with femoral shaft fracture. Studies and clinical observations have consistently shown that cigarette smoking is significantly associated with bone nonunion [18-20]. This study's findings, with an odds ratio (OR) of 3.094 for smoking and a 95% confidence interval (CI) ranging from 1.790 to 5.350, align with existing literature by confirming that smoking significantly increases the risk of nonunion. The results are consistent with the understanding that smoking impairs bone healing, as evidenced by the inhibition of collagen production and the disruption of oxygen supply to the fracture site. Boesmuller et al., in a retrospective study, analyzed the database [21] of ORIF patients receiving PHF at their institution, and found that 13% had nonunion and that smoking was the only risk factor associated with nonunion rate (p <0.002). Smolle et al [22] meta-analyzed the negative effects of smoking on orthopaedic and trauma patients, and found that the risk of postoperative nonunion was significantly higher than that of smokers, indicating that smoking had a harmful effect on the incidence of fractures and (subsequently) the development of nonunion. Smoking affects fracture healing because nicotine, the main component in tobacco, inhibits blood vessel growth, has adverse effects on local blood transport and osteoblast function, affects the fracture healing process, extends the fracture healing time, and leads to an increased risk of bone nonunion [23]. In addition, nicotine can also reduce the blood oxygen content, increase the risk of wound infection, necrosis, and further affect the fracture healing [24]. The relationship between high energy injury and bone nonunion Local blood supply is one of the most important factors affecting fracture healing. All the factors affecting local blood supply will cause the slowdown of fracture healing speed. High-energy damage not only easily causes serious comminuted fractures and periosteum damage, but also often leads to vascular and nerve damage, impairing local blood supply to the fracture site, blocking neurotrophic effects, and thus making fracture healing more difficult [25], at the same time, high Energy damage impairs the biological environment of fracture healing, causing larger hematoma and bleeding necrosis, which triggers a local traumatic inflammatory reaction of extended duration. The formation of larger hematoma disrupts local circulation, thereby affecting the repair of fracture tissue and the connection process of bone cells, have a negative impact on fracture healing. In a study by O'Halloran et al [26], which examined 382 cases of tibial fractures, it was found that high-energy injuries resulted in nonunion at a rate of 14.7%. This is consistent with findings from other studies, such as one that reported a 39.1% nonunion rate in high-energy injuries (Reference 0), and another that highlighted the challenges of nonunion, particularly in the middle and distal thirds of the tibia (Reference 3). and through multifactorial Logistic analysis, high energy injury was an independent risk factor for nonunion. The results of this study also demonstrated that high energy injury (OR = 2.454,95%CI: 1.167~5.159) significantly increased the risk of nonunion, consistent with related studies reported [27]. The relationship between multiple injuries and nonunion has shown that the fracture has the risk of delayed healing and nonunion after multiple trauma[28]. Basic research has shown that the physiological disturbances resulting from hemorrhagic shock post-trauma can adversely affect fracture healing. This is supported by clinical studies, such as one which found that early and appropriate fluid resuscitation is crucial for the successful treatment of patients with multiple fractures and hemorrhagic shock. Using an animal model, Litche et al. have demonstrated that mice subjected to a closed femoral fracture followed by hemorrhagic shock exhibit poor and delayed fracture healing, as compared to controled [29]. with mice receiving femoral fracture alone. Similarly, Bundkirchen et al[30] found that hemorrhagic shock delayed callus formation in femoral fracture mice, altered callus composition and reduced fracture healing strength compared to control mice that did not develop hemorrhagic shock. Base excess (BD) can be used to quantify the common values of hypoperfusion, as a surrogate marker of hemorrhagic shock, Sardesai et al. confirmed in a retrospective clinical study that BD≥6 mmol/L is a risk factor for nonunion within 24 hours in multiple injuries. The results showed that local tissue hypoxia and increased anaerobic metabolism after acute injury may initiate pathological bone healing, leading to non-union [31]. The study found that patients with multiple injuries had an odds ratio of 2.897 (95% CI: 1.580 to 5.312) for postoperative nonunion, indicating a significantly increased risk compared to those without multiple injuries, which aligns with previous research findings [26,27]. Association between internal fixation method and nonunion Intramedullary nail fixation is deemed a significant risk factor for postoperative nonunion following femoral shaft fractures. Currently, intramedullary nailing stands as the established gold standard for treating such fractures.It is one of the most successful techniques in orthopedics for treating diaphyseal and metaphyseal fractures[32]. Facilitating fracture healing by providing a relative stability, While a limited incision maintains the blood supply, In particular, the periosteal blood supplies [33] .However, relevant studies show that, The relatively high incidence of nonunion after intramedullary nailing for femoral shaft fractures [34] .As is reported in the literature, The incidence of nonunion varies with age, ranging from 2.0% to 9.2% across different age groups [35]. However, the incidence of cases with free bone fragments can increase to 12.5% to 34% [36, 37] which is significantly higher than that of simple fractures. Therefor, With the increase in the severity of fractures, the incidence of fracture nonunion significantly rises. Some patients even require open reduction. This may be attributed to insufficient medial cortical support in comminuted fractures, aligning with findings from relevant literature. The primary challenge with intramedullary nailing in femoral shaft fractures is the complexity of reducing the medial wall, which can lead to a lack of support against bending forces and varus torque, potentially increasing the risk of nonunion. Our results demonstrated a clear trend, with 77 cases of femoral nonunionons, 46 cases occurred after intramedullary nailing. The nail approach, as evidenced by multiple studies, has demonstrated a significant impact on postoperative outcomes, particularly in reducing the incidence of nonunion. It may mainly be related to the condition of the patients admitted to the hospital. The majority of the patients have complex conditions, including open injuries and comminuted fractures, which consequently increases the risk of bone nonunion. And intramedullary nail treatment of Postoperative bone nonunion following femoral fracture is a multifactorial issue, with operator-related factors playing a significant roleSurgeons with insufficient clinical experience may have inadequate assessment and insufficient emphasis on the fracture type and bone structure reconstruction. Improper selection of internal fixation devices, insufficient strength or length of the internal fixation may lead to the failure of internal fixation, ultimately resulting in the nonunion of the fracture . Early weight-bearing and functional exercises without proper healing can exacerbate these issues, leading to same question [38]. In medical activities, the patients’condition should be considered comprehensively, and the internal fixation should be reasonably selected, so as to reduce the risk of bone nonunion. The relationship between internal fixation failure and bone nonunion Internal fixation failure is a significant predictor of bone nonunion [14]. In this study, internal fixation failure (n = 14, 18.2%) represented a significant proportion of bone nonunion cases, suggesting a strong correlation between the two. Indeed, our results indicate that the failure of internal fixation (OR = 3.437, 95% CI: 1.519~7.778) significantly elevates the risk of bone nonunion.t with related studies reporting [39]. Johnson et al [40] conducted a Logistic regression analysis to identify risk factors for failure of proximal intramedullary nail fixation within 10 years. Their study concluded that subtrochanteric fracture was an independent risk factor for internal fixation failure after intramedullary nailing. The growth of a fracture requires local mechanical stability. Improper selection of internal fixation, failure of internal fixation, or insufficient strength of the internal fixation will all lead to an unstable environment for the fracture. Shear forces cause micro-movements at the fracture ends. Excessive micro-movements will increase exudation, resulting in the formation of fibrous cartilage at the fracture ends that is difficult to fully ossify. The widened distance between the fracture ends leads to the occurrence of nonunion [41]. However, there are still some shortcomings in this study: First, this study is a retrospective study, The prediction efficiency of the nomogram prediction model still needs to be verified by more external data, In particular, the utilization of multicenter, large-sample prospective cohort studies across various regions and ethnic groups; next, The risk factor analysis did not include all potential risk factors affecting the development of nonunion after femoral shaft fracture, Such as laboratory indicators (blood routine, blood biochemistry, coagulation function), psychological factors, anxiety factors and other factors; last, The patients had a relatively younger mean age, and the injury mechanism was predominantly high-energy damage. Therefore, the results of this study have limited predictive power for low-energy nonunion in elderly patients., Further studies are needed. 4 Conclusions This study developed a comprehensive personalized risk prediction model that integrates five key predictor variables: smoking, high-energy injury, multiple injuries, internal fixation mode, and internal fixation failure. The ROC curve, calibration curve, and decision curve analysis (DCA) of both the development cohort and the external validation cohort consistently demonstrated that the model exhibits excellent predictive performance, making it suitable for clinical application and capable of providing effective diagnostic and treatment guidance for clinicians. In clinical practice, patients can be actively encouraged to quit smoking through early-stage health education, while timely and precisely targeted interventions can be implemented for high-risk individuals to reduce the risk of non-union. These measures not only alleviate the economic burden associated with non-union but also enhance the quality of life for affected patients. Abbreviations ROC Receiver operating characteristic DCA Decision curve analysis BMI Body mess index AUC Area under the ROC curve OR Odds ratio CI Confidence interval ORIF Open Reduction and Internal Fixation PHF Proximal humeral fracture BD Base excess Declarations Acknowledgments None Author contributions All authors participated in the conception and design of this study.Yefan Zhang, Zhilong Hao, Jiahao Zeng and Chao Yang contributed to data collection and analysis. Yefan Zhang writed the first draft of the manuscript. Junjun Fan, Donglin Li and Haifeng Dang revised the manuscript.All authors read and agreed to the final manuscript. Funding Clinical Trial of 3D-Printed HA-TCP Bone Material for the Repair of Bone Defects and Strategic Research on the Treatment of Osteoarthritis with Adhesives (Project Number: 2023XJSM19) Data availability The datasets analyzed during this study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was approved by the Ethics Committee of Xijing Hospital of Military Medical University (approval No. QX20221008-1), and the need for informed consent was waived by the ethics committee. All procedures involving human participants in this study were conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable Competing interests The authors declare no competing interests Author details 1 Department of Orthopedics, Xijing Hospital of Military Medical University (Xi 'an, 710032),China . Email:fanjunjunys＠163.com References KA E, JH P, ZS R, et al. 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DOI: 10.5435/00124635-200702000-00004. E S, SD G, M G, et al. Femoral and tibial blood supply: A trigger for non-union?[J]. Injury, 2014,45(11):1665-1673. DOI: 10.1016/j.injury.2014.09.006. SH P, GM K, BH H, et al. Nonunion of subtrochanteric fractures: Comminution or Malreduction.[J]. Pakistan journal of medical sciences, 2016,32(3):591-594. DOI: 10.12669/pjms.323.9897. LA M, SA A, AHRW S. The risk of non-union per fracture: current myths and revised figures from a population of over 4 million adults.[J]. Acta orthopaedica, 2017,88(4):434-439. DOI: 10.1080/17453674.2017.1321351. JR L, HJ K, KB L. Effects of third fragment size and displacement on non-union of femoral shaft fractures after locking for intramedullary nailing.[J]. Orthopaedics & traumatology, surgery & research : OTSR, 2016,102(2):175-181. DOI: 10.1016/j.otsr.2015.11.014. E S, RM W, PV G. Leeds-Genoa Non-Union Index: a clinical tool for asessing the need for early intervention after long bone fracture fixation.[J]. International orthopaedics, 2020,44(1):161-172. DOI: 10.1007/s00264-019-04376-0. Ma Y, Hu G, Hu W, et al. Surgical factors contributing to nonunion in femoral shaft fracture following intramedullary nailing[J]. Chinese journal of traumatology = Zhonghua chuang shang za zhi, 2016,19(2):109-112. DOI: 10.1016/j.cjtee.2016.01.012. Quan K, Xu Q, Zhu M, et al. Analysis of Risk Factors for Non-union After Surgery for Limb Fractures: A Case-Control Study of 669 Subjects[J]. Front Surg, 2021,8:754150. DOI: 10.3389/fsurg.2021.754150. NA J, C U, M V, et al. Risk factors for intramedullary nail breakage in proximal femoral fractures: a 10-year retrospective review.[J]. Annals of the Royal College of Surgeons of England, 2017,99(2):145-150. DOI: 10.1308/rcsann.2016.0297. J X, YC J, QL K, et al. Management of hypertrophic nonunion with failure of internal fixation by distraction osteogenesis.[J]. Injury, 2015,46(10):2030-2035. DOI: 10.1016/j.injury.2015.06.020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2025 Read the published version in BMC Musculoskeletal Disorders → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 03 Jun, 2025 Reviewers agreed at journal 12 May, 2025 Reviews received at journal 07 May, 2025 Reviews received at journal 03 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 17 Apr, 2025 Editor invited by journal 14 Apr, 2025 Editor assigned by journal 11 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 07 Apr, 2025 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. 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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-6395838","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447104516,"identity":"a7f8da27-a172-46d2-b2a5-132f3083f74e","order_by":0,"name":"Zhilong Hao","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhilong","middleName":"","lastName":"Hao","suffix":""},{"id":447104517,"identity":"9d3b7922-b90a-4aa6-8594-ca100e39067a","order_by":1,"name":"yefan Zhang","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"yefan","middleName":"","lastName":"Zhang","suffix":""},{"id":447104518,"identity":"08a37ecb-a1f9-487f-9a61-5a70605d191b","order_by":2,"name":"Jiahao Zeng","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Zeng","suffix":""},{"id":447104519,"identity":"eae40817-085e-4711-af92-742b6ffaf1af","order_by":3,"name":"Chao Yang","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Yang","suffix":""},{"id":447104520,"identity":"727a80c3-94ca-4bbb-a684-026c58028ff0","order_by":4,"name":"Haifeng Dang","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Dang","suffix":""},{"id":447104521,"identity":"0ec0f397-dae9-4b61-861c-506fc52dcde4","order_by":5,"name":"Donglin Li","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Donglin","middleName":"","lastName":"Li","suffix":""},{"id":447104522,"identity":"0ef5e726-3e2e-4cef-8aec-88172ec9b8d9","order_by":6,"name":"Junjun Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3SoQ7CMBCA4SNLbqajyJIAz1CyZJZXadXMICSYCQSCDEHwGN5hDnBTw3R+chjUEDgkIwRwWyUJ/VRF/zbNFcAwfpC9AOAA+Fy3ChHOmxOSfBOLFyrVS96we15aGomdJTPn0JZ75l9CuUCgq7WoT8hEuI5CedwGXi4PPWAqi2uTEQTcdSKUcR5gLhUCZ+P6hNDynfiXqYwsjYR9bhEeaCYlH+4idGNVukyolDS+hdDA49co7ccn/3y7h/MBXW3qkwpWo6wm2BGvM5q2P1kFQPVPaKKz2TAM4x89AA/rSAN8DWuxAAAAAElFTkSuQmCC","orcid":"","institution":"Xijing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2025-04-07 15:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6395838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6395838/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12891-025-09064-2","type":"published","date":"2025-09-30T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82118532,"identity":"39953b6d-63c8-46e7-aa8a-28ee256027d7","added_by":"auto","created_at":"2025-05-07 03:07:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escreened the predictor variables using the LASSO\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395838/v1/e9a0bddc3445739f6913b8f8.jpg"},{"id":82118533,"identity":"a9ce5a51-2112-46a5-b8d2-047a2aea7344","added_by":"auto","created_at":"2025-05-07 03:07:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003enomogram of risk prediction model for nonunion after femoral shaft fracture\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395838/v1/f01c55eeca07eb1f78143460.jpg"},{"id":82118537,"identity":"5819badc-6158-4f8f-a3cb-0a2316e30f80","added_by":"auto","created_at":"2025-05-07 03:07:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27675,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prediction model assesses the ROC curve to forecast the likelihood of nonunion in femoral shaft fractures.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395838/v1/7c029d887e0110a3f3a85413.jpg"},{"id":82118536,"identity":"7c518a19-b3ee-4078-b802-cebbbdcd625b","added_by":"auto","created_at":"2025-05-07 03:07:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58448,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe calibration curve of the prediction model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6395838/v1/7d3a49e5767d9dad28bd071a.png"},{"id":82120393,"identity":"ab7ea335-4ab4-4712-8018-cad2e78f17d4","added_by":"auto","created_at":"2025-05-07 03:15:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve Analysis (DCA) of the prediction model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6395838/v1/4ed8a4ebd428f541a9fc1d44.jpg"},{"id":92883699,"identity":"d3df23ac-520f-4828-88b5-1987c1eefcfa","added_by":"auto","created_at":"2025-10-06 16:08:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6395838/v1/e780bfa6-5185-41c9-89e1-ec58f5823987.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram Model for Predicting Postoperative Nonunion in Femoral Shaft Fractures","fulltext":[{"header":"Background","content":"\u003cp\u003eFemoral shaft fracture refers to the fracture [1,2] occurring 2~5cm under the microtrochanter to 5cm above the condyle of the femur. It is a common orthopedic disease with a high incidence rate, primarily resulting from high-energy injuries like industrial accidents and traffic collisions. The global annual incidence of femoral shaft fracture is about 0.01%~0.021%, of which 2% are open fracture [3,4]. Femoral shaft fractures typically require orthopedic surgical intervention for treatment, with a variety of surgical techniques available, such as internal fixation with plates and screws or intramedullary nailing. The goal of these surgeries is to correct the fracture displacement and ensure proper healing. Most patients can expect a good reduction and recovery, provided that the treatment is tailored to their specific condition and complications are managed effectively.tion and functional recovery, but no matter what kind of surgery (minimally invasive or open), Postoperative nonunion incidence rates for femoral shaft fractures range from 4.6% to 23% [5,6], with studies indicating that despite advancements in treatment and internal fixation materials, nonunion remains a significant clinical challenge . Nonunion is one of the serious complications after femoral shaft fracture, which can lead to long-term pain, physical disability, mental health problems and increased economic burden of patients, as well as a slow recovery of normal working efficiency [7], which is seriously life-threatening.\u003c/p\u003e\n\u003cp\u003eIn recent years, the prevention and treatment of nonunion after femoral shaft fracture has become a significant clinical focus in orthopedics [8,9]. Despite the progress in surgical techniques and fracture fixation methods, there remains a lack of comprehensive research on the risk factors and a standardized risk assessment model for nonunion following femoral shaft surgery. The risk assessment model is a diagnostic model that predicts the probability of a disease outcome through a combination of risk and protective factors. This model can be used for the clinical assessment of the disease risk.Numerous reports indicate a significant incidence of nonunion following femoral shaft fracture surgeries, with a notable percentage requiring revision surgeries. Given these challenges, it is imperative to develop a risk assessment model to predict the likelihood of nonunion post-surgery. In line with the retrospective study methodology, this research compiled clinical and follow-up data from patients with femoral shaft fractures, spanning from January 2014 to December 2020, sourced from the orthopedic follow-up database at Xijing Hospital. The objective of this study is to offer a theoretical basis for the prevention and treatment of nonunion after femoral shaft fracture.\u003c/p\u003e"},{"header":"1 Methods","content":"\u003cp\u003e1.1 Inclusion criteria for study subjects:\u0026nbsp;①\u0026nbsp;age\u0026nbsp;\u0026ge;18 years;②\u0026nbsp;no infection before and during ;\u0026nbsp;③\u0026nbsp;follow-up for more than 1 year, complete cases and imaging data. Exclusion criteria:\u0026nbsp;①\u0026nbsp;bilateral femoral shaft fractures;\u0026nbsp;②treated conservatively or without surgery;\u0026nbsp;③\u0026nbsp;with severe bone metabolic disease;④history of previous fractures on the side;\u0026nbsp;⑤\u0026nbsp;fractures from primary or metastatic bone tumor. This study was approved by the hospital ethics committee.\u003c/p\u003e\n\u003cp\u003eNonunion is defined as a condition that occurs at least 9 months after an injury or fracture, and there has been no tendency for further healing for 3 months. On X-ray examination, manifestations include a gap at the fracture site, hardening of the fracture ends, occlusion of the medullary cavity, and the absence of continuous callus formation\u0026nbsp;[10].Follow-up was conducted through outpatient clinic, telephone, Wechat, QQ, and postoperative non-union was used as the endpoint event. The follow-up time was up to December 2020 for 12 months. A total of 804 patients with severe femoral shaft fractures, aged 18-81 years, mean age (41.29\u0026nbsp;\u0026plusmn;\u0026nbsp;15.13), were enrolled by cluster random sampling by 7:3 ratio into the development cohort (n=561) and validation cohort (n=243). Among the 561 patients in the development cohort, 484 patients had healed normally, included in the fracture healing group (N=484), and 77 patients had postoperative nonunion and included in the nonunion group (N=77).\u003c/p\u003e\n\u003cp\u003e1.2 Data collection\u003c/p\u003e\n\u003cp\u003eCollected data encompassed gender, age, BMI (physical fitness index), admission BMI, calculated as weight / height\u0026sup2;\u0026nbsp;(kg/m\u0026sup2;), smoking history (defined as\u0026nbsp;\u0026ge;1 dose/day for\u0026nbsp;\u0026ge;6 consecutive/cumulative months), and alcohol consumption\u003c/p\u003e\n\u003cp\u003e（Alcohol consumption is defined as averaging more than once a week）, hypertension, hypoproteinemia\u0026nbsp;[11]\u0026nbsp;(\u0026lt;35 g/L), diabetes mellitus, osteoporosis (T value measured by dual-energy X-ray absorptiometry\u0026nbsp;\u0026le;-2.5).Injury mechanism (high-energy injury and low-energy injury), fracture side (left side and right side), reduction mode (open and closed),AO classification of fractures (Type A, Type B and Type C), fracture site (proximal one-third, middle one-third and distal one-third of the femoral shaft), operation time, internal fixation method (intramedullary nail, nail-plate system and external fixator) , failure of internal fixation (plate fracture, screw loosening, internal fixation loosening, etc.)\u0026nbsp;[12], postoperative infection, application of anticoagulants, NSAID drugs, quinolone antibiotics, delayed weight-bearing exercise (\u0026gt; 12 weeks).\u003c/p\u003e\n\u003cp\u003e1.3 Statistical analysis\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS 23.0 (IBM Corp) and R 4.0 (R Foundation). Continuous variables were expressed as mean\u0026nbsp;\u0026plusmn;\u0026nbsp;standard deviation (X\u0026plusmn;S) and compared using Student\u0026apos;s t-tests. Categorical variables were expressed as frequency counts (percentage) and analyzed with\u0026nbsp;\u0026chi;\u0026sup2;\u0026nbsp;tests.\u0026nbsp;Variable screening was performed through LASSO regression, followed by predictive model development using multivariable logistic regression analysis and nomogram construction.\u003c/p\u003e\n\u003cp\u003eThe prediction model was validated through three domains:\u003c/p\u003e\n\u003cp\u003e1.Discrimination\u0026nbsp;: Area under the ROC curve (AUC).\u003c/p\u003e\n\u003cp\u003e2.Calibration\u0026nbsp;: Calibration curves with osmer-Lemeshow goodness-of-fit test.\u003c/p\u003e\n\u003cp\u003e3.Clinical utility\u0026nbsp;: Decision curve analysis (DCA)\u003c/p\u003e\n\u003cp\u003eStatistical significance threshold was set at P\u0026lt;0.05 for all analyses.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e2.1 Univariate analysis of relevant data of the postoperative fracture healing group and nonunion group of the femoral shaft fracture in the development cohort.\u003c/p\u003e\n\u003cp\u003eThe results of the two groups: gender, age, BMI, osteoporosis, hypoproteinemia, diabetes, hypertension, fracture site, fracture side, AO classification, reduction mode, operation time, premature exercise, the use of anticoagulants, quinolone antibiotics, no statistical significance (P\u0026gt; 0.05), Smoking, alcohol consumption, high-energy trauma, open fractures, multiple injuries, and the use of NSAIDs drugs are all factors that can contribute to the complexity of fracture treatment. \u0026nbsp;These factors are statistically significant in the context of fracture management (P\u0026lt;0.05). (Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 presents the univariate analysis of relevant data for fracture healing and nonunion groups, highlighting the importance of early diagnosis and intervention in managing nonunion.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eproject\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFracture healing group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=484,%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBone nonunion group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=77,%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eX2 or T values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP price\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ewoman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e125（25.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e22（28.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e359（74.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e55（71.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e43.17\u0026plusmn;16.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e43.58\u0026plusmn;17.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e40.53\u0026plusmn;15.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003et=1.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026ge;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e14（2.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e6（7.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e24\u0026le;BMI＜28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e130（26.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e18（23.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e＜24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e340（70.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e53（68.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esmoke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e364（75.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e32（41.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e36.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e120（24.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e45（58.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edrink\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e372（76.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e40（51.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e21.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e112（23.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e37（48.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eosteoporosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e380（78.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e62（80.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e104（21.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e15（19.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypoproteinemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e434（89.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e68（88.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e50（10.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e9（11.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ediabetes mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e426（88.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e67（87.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e58（12.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e10（13.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ehypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e409（84.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e63（81.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e75（15.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e14（18.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003efracture site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eUp to 1 / 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e53（11.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e6（7.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e5.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eIn 1 / 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e289（59.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e57（74.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eThe next 1 / 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e142（29.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e14（18.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFracture side farewell\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eright\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e230（47.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e32（41.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ethe left side\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e254（52.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e45（58.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh energy damage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e208（43.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e11（14.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e22.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e276（57.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e66（85.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecompound fracture\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e377（77.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e42（54.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e19.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e107（22.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e35（45.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAO somatotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eA mould\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e69（14.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e12（15.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e4.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eB mould\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e107（22.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e9（11.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eC mould\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e308（63.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e56（72.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003einitial condition mode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eclosed reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e95（19.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e11（14.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eOpen reset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e389（80.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e66（85.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emultiple injury\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e337（69.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e23（29.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e45.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e147（30.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e54（70.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOperation time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e230.10\u0026plusmn;77.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e233.03\u0026plusmn;74.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e224.81\u0026plusmn;94.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003et=0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal fixation mode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003ebone nail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e375（77.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e46（59.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e42.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eNail system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e81（16.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e9（11.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eExternal fixed frame\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e28（5.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e22（28.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal fixation failed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e461（95.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e63（81.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e19.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e23（4.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e14（18.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e471（97.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e71（92.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e5.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13（2.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e6（7.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEarly weight-bearing exercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eEndovirus for 12 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e321（66.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e58（75.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026lt;12 Weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e163（33.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e19（24.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe use of anticoagulants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e77（15.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e17（22.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e407（84.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e60（77.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSAIDS Drug use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e197（40.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e20（26.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e6.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e287（59.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e57（74）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUse of quinolone antibiotics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003edeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e326（67.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e52（67.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e158（32.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e25（32.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: BMI: constitution index, BMI= weight / height 2 (kg/m2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2.2 LASSO regression \u0026nbsp;Based on the demographics, trauma-related conditions, and iatrogenic conditions of the patients in the development cohort, LASSO regression analysis was used to screen out 5 predictive variables with non-zero coefficients from 24 variables (Figure 1). \u0026nbsp;The process involved drawing vertical lines at \u0026lambda; values corresponding to the minimum (\u0026lambda; = 0.008) and one standard error (\u0026lambda; = 0.035) as shown in Figure 2, to determine the optimal subset of predictors. When log (\u0026lambda;) = -3.347, the LASSO regression model was optimal. The predictor variables screened were smoking, high energy injury, multiple injuries, mode of internal fixation, failure of internal fixation.\u003c/p\u003e\n\u003cp\u003e2.3 A prediction model was developed with postoperative healing of femoral shaft fractures as the dependent variable (coded as: fracture healing = 0, bone union = 1). Using LASSO regression analysis (see Table 2 and Table 3), the results indicated that smoking, high-energy injury, multiple injuries, internal fixation mode, and internal fixation failure were significant predictors of femoral shaft fracture nonunion (P \u0026lt; 0.05). Based on these predictive variables, a nomogram was constructed (as shown in Figure 2) to visually depict the functional relationships among variables within the mathematical model. This tool facilitates rapid and accurate probability estimations, particularly in clinical and engineering contexts. Each variable can be assigned a score according to the nomogram, and the total score is calculated by summing the individual scores. The probability of nonunion can then be estimated by drawing a vertical line downward from the total score.\u003c/p\u003e\n\u003cp\u003eTable 2: multivariate Logistic regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"472\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 88px;\"\u003e\n \u003cp\u003efactor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eregression coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 52px;\"\u003e\n \u003cp\u003estandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eWald price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eP price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 44px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 51px;\"\u003e\n \u003cp\u003elower limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003esuperior limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003esmoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e16.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHigh energy damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003emultiple injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e11.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e2.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e5.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eIntramedullary nail (for reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e6.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eNail system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e5.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eExternal fixed frame\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e3.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eInternal fixation failed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e1.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e8.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e3.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e7.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003econstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e-2.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e26.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Multivariate Logistic Regression Analysis of Risk Factors for Nonunion after Femoral Shaft Fracture Surgery\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"382\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eassignment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eNo =0, Yes =1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh energy damage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eNo =0, Yes =1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emultiple injury\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eNo =0, Yes =1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal fixation mode\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eNail =1, nail system =2, external holder =3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal fixation failed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eNo =0, Yes =1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 176px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFracture healing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eNonunion =1, and fracture healing =0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: The 95%CI means the 95% confidence interval\u003c/p\u003e\n\u003cp\u003e2.4 Validation of the prediction model\u003c/p\u003e\n\u003cp\u003eThe validation of this predictive model was mainly based on the discrimination and calibration of the model. The discrimination of the model was evaluated by plotting the ROC curve of the predictive model for predicting the occurrence of nonunion in patients with femoral shaft fractures.The AUC values of the development and validation cohorts were 0.828 (95% CI: 0.783-0.872) and 0.835 (95% CI: 0.770-0.900), respectively, with corresponding cutoff values of 0.023 and 0.157. (fig.3).These results suggest a strong predictive performance of the model, as indicated by the high AUC values, which are indicative of the model\u0026apos;s ability to distinguish between positive and negative outcomes effectively and good discrimination ability of the prediction model. Meanwhile, The Hosmer-Lemeshow goodness-of-fit test indicated a good model fit, with P-values of 0.463 in the development cohort and 0.858 in the validation cohort, both above the conventional threshold of 0.05, suggesting that the observed differences are likely due to random variation.The model exhibits a high level of calibration, as evidenced by its prediction probability being largely consistent with the actual probability.In addition, the calibration curves of the development cohort and the validation cohort showed moderate consistency, indicating that the predictive model possesses good calibration ability（fig.4).In conclusion, the Nomogram of the predictive model has moderate predictive ability.\u003c/p\u003e\n\u003cp\u003e2.5 Clinical application\u003c/p\u003e\n\u003cp\u003eUsing decision curve analysis (DCA) to evaluate the clinical validity of the prediction model, we conducted DCA for the nomogram predicting postoperative nonunion risk in patients with femoral shaft fractures (Figure 5). The results indicated that if the threshold probabilities for both patients and clinicians exceed 15%, the risk of postoperative nonunion following fracture in the current study is more favorable compared to a universal intervention strategy for all patients. Within this range, the net benefit of the prediction model was significantly higher than the two extreme scenarios: either all patients receiving clinical intervention or none receiving it.\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eAt present, the Nomogram, as a disease prediction tool based on multivariate regression analysis[13], has gained widespread application in clinical research, particularly in the field of orthopedic disease risk prediction[14,15]. Its high reliability and practicality have been acknowledged by the medical community, leading to its extensive use in clinical practice. Nomogram-based prediction models are renowned for their simplicity, intuitiveness, and ease of comprehension[16]. These models incorporate clinical factors such as age, gender, and laboratory test results to predict the likelihood of disease occurrence, progression, and prognosis. They are especially valuable for visually illustrating the influence of various factors on prediction outcomes, thereby assisting clinicians in accurately assessing patient risks and devising personalized treatment strategies. In this study, 15.9% of patients with femoral shaft fracture in the development cohort experienced nonunion, aligning with the range of nonunion rates (8% to 23%) reported in recent literature\u0026nbsp;[17], indicating a significant risk for nonunion in femoral shaft fractures.\u0026nbsp;Using LASSO regression and multifactor Logistic regression analysis identified five independent risk factors for postoperative nonunion: smoking, high energy injury, multiple injuries, internal fixation, and internal fixation failure., a nomogram prediction model of femoral shaft fracture patients was constructed and verified according to these independent risk factors. The prediction model demonstrated robust predictive capabilities, with an AUC of 0.828 for the development cohort and an AUC of 0.835 for the external validation cohort, indicating a high level of prediction accuracy. Meanwhile, the calibration curves for both the development cohort and the validation cohort showed good agreement between the actual diagnosis and the predicted diagnosis. In addition, the DCA curve also suggested that the prediction model has a very good clinical validity. This prediction model shows that for patients with femoral shaft fracture, early targeted intervention measures for high-risk groups in clinical treatment can effectively reduce the risk of bone nonunion in patients with femoral shaft fracture.\u003c/p\u003e\n\u003cp\u003eStudies and clinical observations have consistently shown that cigarette smoking is significantly associated with bone nonunion\u0026nbsp;[18-20]. This study's findings, with an odds ratio (OR) of 3.094 for smoking and a 95% confidence interval (CI) ranging from 1.790 to 5.350, align with existing literature by confirming that smoking significantly increases the risk of nonunion. The results are consistent with the understanding that smoking impairs bone healing, as evidenced by the inhibition of collagen production and the disruption of oxygen supply to the fracture site. Boesmuller et al., in a retrospective study, analyzed the database\u0026nbsp;[21]\u0026nbsp;of ORIF patients receiving PHF at their institution, and found that 13% had nonunion and that smoking was the only risk factor associated with nonunion rate (p \u0026lt;0.002). Smolle et al\u0026nbsp;[22]\u0026nbsp;meta-analyzed the negative effects of smoking on orthopaedic and trauma patients, and found that the risk of postoperative nonunion was significantly higher than that of smokers, indicating that smoking had a harmful effect on the incidence of fractures and (subsequently) the development of nonunion. Smoking affects fracture healing because nicotine, the main component in tobacco, inhibits blood vessel growth, has adverse effects on local blood transport and osteoblast function, affects the fracture healing process, extends the fracture healing time, and leads to an increased risk of bone nonunion\u0026nbsp;[23]. In addition, nicotine can also reduce the blood oxygen content, increase the risk of wound infection, necrosis, and further affect the fracture healing\u0026nbsp;[24].\u003c/p\u003e\n\u003cp\u003eThe relationship between high energy injury and bone nonunion Local blood supply is one of the most important factors affecting fracture healing. All the factors affecting local blood supply will cause the slowdown of fracture healing speed. High-energy damage not only easily causes serious comminuted fractures and periosteum damage, but also often leads to vascular and nerve damage, impairing local blood supply to the fracture site, blocking neurotrophic effects, and thus making fracture healing more difficult\u0026nbsp;[25], at the same time, high Energy damage impairs the biological environment of fracture healing, causing larger hematoma and bleeding necrosis, which triggers a local traumatic inflammatory reaction of extended duration. The formation of larger hematoma disrupts local circulation, thereby affecting the repair of fracture tissue and the connection process of bone cells, have a negative impact on fracture healing. In a study by O'Halloran et al\u0026nbsp;[26], which examined 382 cases of tibial fractures, it was found that high-energy injuries resulted in nonunion at a rate of 14.7%. This is consistent with findings from other studies, such as one that reported a 39.1% nonunion rate in high-energy injuries (Reference 0), and another that highlighted the challenges of nonunion, particularly in the middle and distal thirds of the tibia (Reference 3). and through multifactorial Logistic analysis, high energy injury was an independent risk factor for nonunion. The results of this study also demonstrated that high energy injury (OR = 2.454,95%CI: 1.167~5.159) significantly increased the risk of nonunion, consistent with related studies reported\u0026nbsp;[27].\u003c/p\u003e\n\u003cp\u003eThe relationship between multiple injuries and nonunion has shown that the fracture has the risk of delayed healing and nonunion after multiple trauma[28]. Basic research has shown that the physiological disturbances resulting from hemorrhagic shock post-trauma can adversely affect fracture healing. This is supported by clinical studies, such as one which found that early and appropriate fluid resuscitation is crucial for the successful treatment of patients with multiple fractures and hemorrhagic shock. Using an animal model, Litche et al. have demonstrated that mice subjected to a closed femoral fracture followed by hemorrhagic shock exhibit poor and delayed fracture healing, as compared to controled\u0026nbsp;[29]. with mice receiving femoral fracture alone. Similarly, Bundkirchen et al[30]\u0026nbsp;found that hemorrhagic shock delayed callus formation in femoral fracture mice, altered callus composition and reduced fracture healing strength compared to control mice that did not develop hemorrhagic shock. Base excess (BD) can be used to quantify the common values of hypoperfusion, as a surrogate marker of hemorrhagic shock, Sardesai et al. confirmed in a retrospective clinical study that BD≥6\u0026nbsp;mmol/L is a risk factor for nonunion within 24 hours in multiple injuries. The results showed that local tissue hypoxia and increased anaerobic metabolism after acute injury may initiate pathological bone healing, leading to non-union\u0026nbsp;[31]. The study found that patients with multiple injuries had an odds ratio of 2.897 (95% CI: 1.580 to 5.312) for postoperative nonunion, indicating a significantly increased risk compared to those without multiple injuries, which aligns with previous research findings\u0026nbsp;[26,27].\u003c/p\u003e\n\u003cp\u003eAssociation between internal fixation method and nonunion Intramedullary nail fixation is deemed a significant risk factor for postoperative nonunion following femoral shaft fractures. Currently, intramedullary nailing stands as the established gold standard for treating such fractures.It is one of the most successful techniques in orthopedics for treating diaphyseal and metaphyseal fractures[32].\u0026nbsp;Facilitating fracture healing by providing a relative stability, While a limited incision maintains the blood supply, In particular, the periosteal blood supplies\u0026nbsp;[33]\u0026nbsp;.However, relevant studies show that, The relatively high incidence of nonunion after intramedullary nailing for femoral shaft fractures\u0026nbsp;[34]\u0026nbsp;.As is reported in the literature, The incidence of nonunion varies with age, ranging from 2.0% to 9.2% across different age groups\u0026nbsp;[35].\u0026nbsp;However, the incidence of cases with free bone fragments can increase to 12.5% to 34%\u0026nbsp;[36, 37]\u0026nbsp;which is significantly higher than that of simple fractures.\u0026nbsp;Therefor, With the increase in the severity of fractures, the incidence of fracture nonunion significantly rises. Some patients even require open reduction.\u0026nbsp;This may be attributed to insufficient medial cortical support in comminuted fractures, aligning with findings from relevant literature. The primary challenge with intramedullary nailing in femoral shaft fractures is the complexity of reducing the medial wall, which can lead to a lack of support against bending forces and varus torque, potentially increasing the risk of nonunion. Our results demonstrated a clear trend, with 77 cases of femoral nonunionons, 46 cases occurred after intramedullary nailing. The nail approach, as evidenced by multiple studies, has demonstrated a significant impact on postoperative outcomes, particularly in reducing the incidence of nonunion.\u0026nbsp;It may mainly be related to the condition of the patients admitted to the hospital. The majority of the patients have complex conditions, including open injuries and comminuted fractures, which consequently increases the risk of bone nonunion. And intramedullary nail treatment of Postoperative bone nonunion following femoral fracture is a multifactorial issue, with operator-related factors playing a significant roleSurgeons with insufficient clinical experience may have inadequate assessment and insufficient emphasis on the fracture type and bone structure reconstruction. Improper selection of internal fixation devices, insufficient strength or length of the internal fixation may lead to the failure of internal fixation, ultimately resulting in the nonunion of the fracture . Early weight-bearing and functional exercises without proper healing can exacerbate these issues, leading to same question\u0026nbsp;[38].\u0026nbsp;In medical activities, the patients’condition should be considered comprehensively, and the internal fixation should be reasonably selected, so as to reduce the risk of bone nonunion.\u003c/p\u003e\n\u003cp\u003eThe relationship between internal fixation failure and bone nonunion Internal fixation failure is a significant predictor of bone nonunion\u0026nbsp;[14].\u0026nbsp;In this study, internal fixation failure (n = 14, 18.2%) represented a significant proportion of bone nonunion cases, suggesting a strong correlation between the two. Indeed, our results indicate that the failure of internal fixation (OR = 3.437, 95% CI: 1.519~7.778) significantly elevates the risk of bone nonunion.t with related studies reporting\u0026nbsp;[39]. Johnson et al\u0026nbsp;[40]\u0026nbsp;conducted a Logistic regression analysis to identify risk factors for failure of proximal intramedullary nail fixation within 10 years. Their study concluded that subtrochanteric fracture was an independent risk factor for internal fixation failure after intramedullary nailing. The growth of a fracture requires local mechanical stability. Improper selection of internal fixation, failure of internal fixation, or insufficient strength of the internal fixation will all lead to an unstable environment for the fracture. Shear forces cause micro-movements at the fracture ends. Excessive micro-movements will increase exudation, resulting in the formation of fibrous cartilage at the fracture ends that is difficult to fully ossify. The widened distance between the fracture ends leads to the occurrence of nonunion\u0026nbsp;[41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, there are still some shortcomings in this study: First, this study is a retrospective study, The prediction efficiency of the nomogram prediction model still needs to be verified by more external data, In particular, the utilization of multicenter, large-sample prospective cohort studies across various regions and ethnic groups; next, The risk factor analysis did not include all potential risk factors affecting the development of nonunion after femoral shaft fracture, Such as laboratory indicators (blood routine, blood biochemistry, coagulation function), psychological factors, anxiety factors and other factors; last, The patients had a relatively younger mean age, and the injury mechanism was predominantly high-energy damage. Therefore, the results of this study have limited predictive power for low-energy nonunion in elderly patients., Further studies are needed.\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study developed a comprehensive personalized risk prediction model that integrates five key predictor variables: smoking, high-energy injury, multiple injuries, internal fixation mode, and internal fixation failure. The ROC curve, calibration curve, and decision curve analysis (DCA) of both the development cohort and the external validation cohort consistently demonstrated that the model exhibits excellent predictive performance, making it suitable for clinical application and capable of providing effective diagnostic and treatment guidance for clinicians. In clinical practice, patients can be actively encouraged to quit smoking through early-stage health education, while timely and precisely targeted interventions can be implemented for high-risk individuals to reduce the risk of non-union. These measures not only alleviate the economic burden associated with non-union but also enhance the quality of life for affected patients.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eROC \u0026nbsp; \u0026nbsp;Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp;Decision curve analysis\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp;Body mess index\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; Area under the ROC curve\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp;Odds ratio\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence interval\u003c/p\u003e\n\u003cp\u003eORIF \u0026nbsp; Open Reduction and Internal Fixation\u003c/p\u003e\n\u003cp\u003ePHF \u0026nbsp; \u0026nbsp; Proximal humeral fracture\u003c/p\u003e\n\u003cp\u003eBD \u0026nbsp; \u0026nbsp; Base excess\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors participated in the conception and design of this study.Yefan Zhang, Zhilong Hao, Jiahao Zeng and Chao Yang \u0026nbsp;contributed to data collection and analysis. Yefan Zhang writed the first draft of the manuscript. Junjun Fan, Donglin Li and Haifeng Dang revised the manuscript.All authors read and agreed to the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical Trial of 3D-Printed HA-TCP Bone Material for the Repair of Bone Defects and Strategic Research on the Treatment of Osteoarthritis with Adhesives (Project Number: 2023XJSM19)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of \u0026nbsp;Xijing Hospital of Military Medical University (approval No. QX20221008-1), and the need for informed consent was waived by the ethics committee. All procedures involving human participants in this study were conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Orthopedics, Xijing Hospital of Military Medical University (Xi \u0026apos;an, 710032),China . \u0026nbsp; Email:fanjunjunys＠163.com\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKA E, JH P, ZS R, et al. Healing delayed but generally reliable after bisphosphonate-associated complete femur fractures treated with IM nails.[J]. 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DOI: https://doi.org/10.1016/j.injury.2015.10.001.\u003c/li\u003e\n\u003cli\u003eMA S, L L, N B, et al. Fracture, nonunion and postoperative infection risk in the smoking orthopaedic patient: a systematic review and meta-analysis.[J]. EFORT open reviews, 2021,6(11):1006-1019. DOI: 10.1302/2058-5241.6.210058.\u003c/li\u003e\n\u003cli\u003eEJ H, J A, HS S, et al. Deleterious effect of smoking on healing of open tibia-shaft fractures.[J]. American journal of orthopedics (Belle Mead, N.J.), 2002,31(9):518-521.\u003c/li\u003e\n\u003cli\u003eH K, N Z, H T, et al. The effects of nicotine administration on the pathophysiology of rat aortic wall.[J]. Biotechnic \u0026amp; histochemistry : official publication of the Biological Stain Commission, 2017,92(2):141-148. DOI: 10.1080/10520295.2017.1287428.\u003c/li\u003e\n\u003cli\u003eKobbe, Philipp., Lichte, Philipp., Pape, Hans-Christoph.. 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DOI: 10.1016/j.injury.2015.06.020.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"femoral shaft fracture, postoperative complications, nonunion, risk factors, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6395838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6395838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective：To identify independent risk factors for nonunion following femoral shaft fracture surgery and develop a clinically applicable nomogram model for personalized risk prediction.\u003c/p\u003e\n\u003cp\u003eMethods：A retrospective cohort study included 804 patients with femoral shaft fractures treated at Xijing Hospital (2014–2020). Patients were divided into development (n=561) and validation (n=243) cohorts. Variables were screened via LASSO regression, and a nomogram was constructed using multivariate logistic regression. Model performance was assessed using ROC curves, calibration plots, Hosmer-Lemeshow tests, and decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003eResults：Five independent predictors of nonunion were identified: smoking (OR=3.094, 95% CI:1.790–5.350), high-energy injury (OR=2.454, 95% CI:1.167–5.159), multiple injuries (OR=2.897, 95% CI:1.580–5.312), internal fixation method (OR=3.437, 95% CI:1.519–7.778), and fixation failure (OR=3.437, 95% CI:1.519–7.778). The nomogram demonstrated excellent discrimination (AUC=0.828 in development, 0.835 in validation cohorts) and calibration (Hosmer-Lemeshow P=0.463 and P=0.858, respectively). DCA confirmed clinical utility at threshold probabilities \u0026gt;15%.\u003c/p\u003e\n\u003cp\u003eConclusion：This nomogram provides a practical tool for predicting nonunion risk in femoral shaft fractures, enabling early intervention for high-risk patients.\u003c/p\u003e\n\u003cp\u003eClinical trial number：Not applicable.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram Model for Predicting Postoperative Nonunion in Femoral Shaft Fractures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:07:46","doi":"10.21203/rs.3.rs-6395838/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T07:44:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T21:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104843737645909744636070848074077280292","date":"2025-05-12T06:06:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T16:40:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T08:04:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307978535609789418196769081914960792249","date":"2025-05-02T14:49:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296460020320435231622565180947615645073","date":"2025-05-02T11:26:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-18T02:02:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-14T08:37:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-11T13:44:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T13:43:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2025-04-07T15:34:36+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":"a71f887a-9459-4ea3-bbf9-64c259c37f68","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T16:00:08+00:00","versionOfRecord":{"articleIdentity":"rs-6395838","link":"https://doi.org/10.1186/s12891-025-09064-2","journal":{"identity":"bmc-musculoskeletal-disorders","isVorOnly":false,"title":"BMC Musculoskeletal Disorders"},"publishedOn":"2025-09-30 15:57:10","publishedOnDateReadable":"September 30th, 2025"},"versionCreatedAt":"2025-05-07 03:07:46","video":"","vorDoi":"10.1186/s12891-025-09064-2","vorDoiUrl":"https://doi.org/10.1186/s12891-025-09064-2","workflowStages":[]},"version":"v1","identity":"rs-6395838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6395838","identity":"rs-6395838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}