Development and validation of a clinical model of second fractures for hip fracture patients after surgery

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This study aims to investigate the associated risk factors and establish a prediction model. Methods Retrospectively data were obtained from two affiliated hospitals at Soochow University for older patients diagnosed with hip fractures who underwent surgical treatment between January 2019 and December 2021. The endpoint was a second fracture. Independent risk factors for second fractures in patients were identified through the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression. A nomogram was established and assessed for predictability, discriminatory ability, and clinical applicability using areas under the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both cohorts. Results : Among 1735 patients, 8.7% (115/1735) had second fractures within two years after surgery. Variables screened by LASSO, including age, hip joint function, neurovascular disease, eye disease, living alone, and regular exercise, were incorporated into the Cox regression model. The nomogram demonstrated favorable discriminatory ability, with areas under the ROC curves (AUC) of 0.832 (95% CI, 0.765-0.895) and 0.773 (95% CI, 0.727-0.818) after development and validation, respectively. The calibration curves showed good consistency between the actual second fracture incidence and the predicted probability. DCA of the nomogram demonstrated the model’s excellent clinical efficacy. Conclusions The nomogram model enabled accurate individualized prediction of second fractures in elderly patients within two years after surgical treatment, which might assist clinicians in precise perioperative management and rehabilitation education following initial hip surgery. Second fracture Predictive model Nomogram Hip fractures Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Hip fractures are prevalent and severe fragility fractures among older patients[ 1 ]. In recent years, the increasing elderly population has led to an annual increase in hip fracture cases. It is estimated that the number of hip fractures will reach 6.3 million by 2050 as global life expectancy increases[ 2 ]. Furthermore, prior osteoporotic fractures are considered clinical predictors that increase the risk of second fractures, and relevant research also indicates that older patients with an initial fracture face twice the risk of experiencing a second fracture within two years compared to their initial fracture[ 3 ]. Second fractures are associated with high mortality rates, increased patient expenses, prolonged hospital stays, and poor quality of life[ 4 – 6 ]. Thus, it is crucial to explore prognostic factors early to identify patients at high risk for second fractures. In recent years, several indicators have been proposed by previous studies to potentially predict second fractures, such as age, gender, high comorbidity burden, visual impairment, and neurovascular disease[ 7 – 9 ]. However, these studies focused solely on clinical variables to identify patients at greater risk of developing second fractures. In addition, some studies were limited to specific populations rather than the general population[ 10 ]. Hence, the distinction and priority of these indicators in increasing the risk of developing second fractures and their predictive value remain unknown. Given the importance of early identification of adverse prognoses for patient treatment and management, an increasing number of researchers and policymakers are turning to predictive models. For example, to the best of our knowledge, several research groups have developed hip fracture-specific risk prediction models, such as the FRAX and QFracture models. However, the FRAX was designed for predicting initial fracture risk, and its ability to predict second fractures remains to be further validated[ 11 ]; moreover, the QFracture was also designed for initial fracture assessment and contained many variables that might not facilitate clinical application[ 12 ]. Although predictors for postoperative second fractures have been extensively studied, research on risk prediction models remains limited. The nomogram has been accepted as a reliable tool for creating a simple intuitive graph of a statistical predictive model that quantifies the risk of a clinical event. Therefore, we conducted this retrospective study to investigate the predictors of two-year postoperative second fractures and developed a clinical nomogram to predict high-risk patients. By utilizing a model based on clinical and claims data, policymakers and clinicians can find effective methods to reduce fast-growing healthcare costs and target individualized treatment protocols. Methods Patients We collected the clinical data of 2402 patients with hip fractures who were hospitalized at two hospitals affiliated with Soochow University from Jan 1, 2019, to Dec 31, 2021. During the follow-up period from May 2023 to Jan 2024, data on postoperative mobility, survival status, occurrence and timing of second fractures, as well as the location of second fractures, were collected via telephone interviews. A total of 315 patients did not meet the inclusion criteria. Additionally, 352 patients were lost due to changes in phone numbers. Ultimately, 1735 patients were included in the study. All patients were followed up for at least two years or until death. The patient flow chart is shown in Fig. 1 . The inclusion criteria were as follows: (1) aged 60 years and older, (2) had first-time unilateral fragility hip fractures, and (3) underwent surgical treatment (internal fixation and/or hip arthroplasty). The exclusion criteria were as follows: (1) in-hospital mortality; (2) fractures due to pathological or high-energy injuries; or (3) declined follow-up or incomplete data. In this study, patients were randomly divided into a training cohort (70%) and a testing cohort (30%). Patients in the training cohort were used to develop the nomogram model, whereas patients in the testing cohort were used to validate the resulting nomogram. Informed consent was waived by the ethics committee, as this was a minimum-risk study. The study was approved by the ethics committee of Soochow University (SUDA20230115H07) and reported using the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement for prediction studies. Clinicopathological data collection In this study, factors influencing second fractures in patients were collected through the electronic medical records system. The demographic information included age, gender, and living alone; clinical characteristics included hip fracture type, cause of fracture, number of comorbidities, hypertension, heart disease (coronary artery disease, heart failure, arrhythmia, heart dysfunction, chronic pulmonary heart disease), cerebrovascular disease (Cerebral hemorrhage, stroke, and related sequelae), diabetes, neurovascular disease (Alzheimer’s disease, Parkinson’s disease), eye disease (cataract, glaucoma, diabetic eye disease), rheumatoid arthritis, respiratory disease (COPD, chronic bronchitis, emphysema, pulmonary fibrosis), and hip joint function (Harris score). Laboratory examinations revealed anemia and hypoalbuminemia (ALB). Additional factors included fracture type, dietary habits, regular sunlight exposure, regular exercise, and postoperative complications diagnosed by bone physicians according to the results of examination after consultation. These 23 factors were included in the subsequent analysis as candidate predictors, determining the minimum number of outcome events and sample size for the study. Statistical analysis Continuous variables that followed a normal distribution were expressed as the means ± standard deviations, and the t-test was used for comparisons between groups. Categorical variables were presented as n (%) and comparisons between groups were performed using the chi-square test. Continuous variables were categorized into distinct groups to simplify the model and enhance objectivity. We categorized ages into four subgroups: 60–69, 70–79, 80–89, and ≥ 90 years based on literature precedence[ 4 ]. To identify potential risk factors for second fractures, we employed the least absolute shrinkage and selection operator (LASSO) regression analysis to develop the final prediction model using Cox regression. LASSO was a regularization method that fits a single model for each tuning parameter in the contraction penalty term, greatly improving computational efficiency. Furthermore, it can be used to mitigate the overfitting problem and reduce the regression coefficient to zero, increasing model interpretability[ 13 ]. We used the minimum standard I-Standard Error method to determine the final number of predictors. This number was identified at one standard error to the right from the minimum mean square error, indicating good fitness of the model with the fewest predictors. Multivariate Cox regression was performed to assess the prognostic value of the variables selected through LASSO regression. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for two-year second fractures. To visualize and quantify the effect of each selected variable on the estimated 1- and 2-year second fracture probability, a nomogram was constructed and validated. For internal validation, the receiver operating characteristic (ROC) curve was calculated to evaluate the model’s ability to discriminate between individuals at high and low risk of second fractures. After internal validation by bootstrapping, a calibration curve was used to evaluate the calibration of the nomogram by contrasting the actual risk and predicted risk. The clinical usefulness of the nomogram was estimated using decision curve analysis (DCA) based on the net benefit and threshold probabilities. Survival curves were depicted using Kaplan-Meier analysis and compared with a log-rank test in both the development and validation cohorts. Statistical analyses were performed using IBM SPSS software (version 26.0) and R software (version 4.1.3). All tests were two-sided. A P value less than 0.05 was considered to indicate statistical significance. Results Patient characteristics A total of 1735 patients were included at different times according to the inclusion and exclusion criteria, 151 (8.7%) of whom experienced a second fracture within two years postoperatively. The most frequent site was the contralateral hip (32%, 49/151), followed by the spine (29%, 43/151), wrist (13%, 20/151), shoulder (8%,12/151), knee (6%,9/151), and other sites (12%,18/151). Additional file 1 provided relevant information. According to the discharge time, the patients were divided into a development cohort (n = 1215) and a validation cohort (n = 520). There were no statistically significant differences in all variables included between the two groups (p > 0.05), which showed that the data grouping was random and reasonable ( Table 1 ) . Table 1 Patient characteristics in the development and temporal validation cohorts Characteristic development validation P value N = 1215(%) N = 520(%) Gender 0.47 female 795(65.4) 331(63.6) male 420(34.6) 189(36.4) Age 0.68 60 ~ 69 392(32.3) 178(34.3) 70 ~ 79 440(36.2) 185(35.6) 80 ~ 89 362(29.8) 145(27.8) > 90 21(1.7) 12(2.2) Cause of fracture 0.79 fall 736(60.6) 330(63.4) accident 277(22.8) 104(20.0) others 202(16.6) 86(16.6) Fracture type 0.56 Intertrochanteric fracture 417(34.3) 186(35.8) Femoral neck fracture 798(65.7) 334(64.2) amenia 0.31 Yes 354(29.1) 113(21.8) No 861(70.9) 407(78.2) Living alone 0.86 Yes 149(12.3) 63(12.0) No 1066(87.7) 457(88.0) ALB 0.27 Yes 211(17.3) 66(12.6) No 1004(82.7) 454(87.4) Osteoporosis .73 Yes 1152(94.8) 491(94.4) No 63(5.2) 29(5.6) Comorbidities 0.45 Yes 1083(89.1) 457(87.8) No 132(10.9) 63(12.2) Neurovascular disease 0.07 Yes 26(2.1) 20(3.8) No 1189(97.9) 500(96.2) Respiratory diseases 0.63 Yes 164(13.5) 75(14.4) No 1051(86.5) 445(85.6) Characteristic development Temporal validation P value N = 1215(%) N = 520(%) Heart diseases 0.91 Yes 165(13.6) 70(13.4) No 1050(86.4) 450(86.6) Hypertension 0.35 Yes 685(56.4) 306(58.8) No 530(43.6) 214(41.2) Cerebrovascular disease 0.48 Yes 90(7.4) 33(6.4) No 1125(92.6) 487(93.6) Rheumatoid arthritis 0.08 Yes 163(13.4) 40(7.6) No 1052(86.6) 480(92.4) Diabetes 0.53 Yes 324(26.7) 147(28.2) No 891(73.3) 373(71.8) Anti-osteoporosis 0.53 Yes 312(25.7) 181(34.8) No 903(74.3) 339(65.2) Eye disease 0.44 Yes 55(4.5) 28(5.4) No 1160(95.5) 492(94.6) Regular exercise 0.56 Yes 1027(84.5) 434(83.4) No 188(15.5) 86(16.6) Dietary habit 0.68 Good 824(67.8) 358(68.8) Poor 391(32.2) 162(31.2) Sunlight 0.25 Yes 269(22.1) 128(24.6) No 946(77.9) 392(75.4) Perioperative complication 0.76 Yes 38(3.1) 15(2.8) No 1177(96.9) 505(97.2) Hip joint function 0.55 Excellent 199(16.4) 73(14.0) Good 463(38.1) 210(40.4) Ordinary 462(38.0) 193(37.2) Poor 91(7.5) 44(8.4) Variable selection LASSO regression analysis revealed that using the minimum standard I standard error method, six variables with nonzero coefficients were identified as critical factors associated with second fractures. These variables included age, hip joint function, neurovascular disease, eye disease, living alone, and regular exercise ( Fig. 2 ) . These optimal variables were further screened via multivariate Cox analysis, which revealed that all six variables were associated with the risk of second fractures in patients with hip fracture. Details are provided in Table 2 . Furthermore, the Kaplan–Meier method was used to estimate the cumulative incidences of second fractures between the groups during the follow-up period (Additional File 2). Table 2 Multivariate Cox regression analyses of variables related to second fracture Variables B P HR 95%CI Hip joint function Excellent NE <0.001 1 Good 0.557 0.230 1.745 0.703 4.331 Ordinary 1.569 <0.001 4.800 2.063 11.168 Poor 3.016 <0.001 20.412 8.660 48.110 Age 60 ~ 69 NE <0.001 1 70 ~ 79 2.154 90 3.336 0.001 28.093 9.793 80.594 Neurovascular disease No Yes 1.775 <0.001 5.899 3.645 9.545 Eye disease No Yes 1.411 <0.001 4.099 2.698 6.255 Living alone No Yes 0.543 0.007 1.722 1.160 2.556 Regular exercise No Yes -0.968 <0.001 0.380 0.270 0.535 Nomogram Construction and Validation To facilitate clinical use, we converted the predictive model based on age, hip joint function, neurovascular disease, eye disease, living alone, and regular exercise into a nomogram ( Fig. 3 ) . Scores corresponding to the different categories of independent risk factors were calculated by assigning point values to each risk factor category and summing these values for each patient. The total score scales represented the estimated probability of patients experiencing second fractures, and the nomogram model could be utilized to calculate this incidence. To assess the discriminatory ability of the nomogram, we plotted the ROC curves for the training and validation cohorts and calculated the area under the curve (AUC). The AUC, calculated for the training cohort and validation cohort using the ROC curve analysis, was 0.832 (95% CI: 0.765 ~ 0.895) and 0.773 (95% CI: 0.727 ~ 0.818), respectively, indicating high discriminatory ability ( Fig. 4 ) . In the calibration chart, the calibration curve closely coincided with the reference line in both the training and validation cohorts, demonstrating good consistency between the observed and predicted probabilities ( Fig. 5 ) . The decision curve analysis (DCA) of the nomogram, evaluating the clinical utility of the predictive model, is shown in Fig. 6 . The net benefit of using a nomogram to predict second fractures in elderly patients with hip fracture was high when the threshold probability was between 0.01 and 0.85 in the training cohort and between 0.01 and 0.75 in the validation cohort. Therefore, the nomogram demonstrated good clinical utility for predicting delayed discharge in elderly patients with hip fractures. Discussion Our investigation revealed a second fracture rate of 8.7% following hip fracture surgery, which aligns with rates reported in Korea[ 14 ] is notably lower than those observed in Finland and Spain[ 15 , 16 ]. These variations in the rate of second fractures can be attributed to differences in inclusion and exclusion criteria across studies. In elderly patients with hip fractures, increasing age is associated with a higher incidence of complications such as periprosthetic infection, pneumonia, urinary tract infection, and deteriorating nutritional status, all of which can contribute to secondary fractures. Moreover, some studies have small sample sizes and focus on specific populations, limiting their generalizability. In contrast, our study sample, which represents the general population aged 60 years or older living in Suzhou city, provides more broadly applicable results. Furthermore, the differences in healthcare systems, surgical techniques, and postoperative management practices between countries may also contribute to variations in second fracture rates. This study identified several independent risk factors for second fractures, including age, hip joint function, neurological disease, eye disease, living alone, and regular exercise. The significance of these factors lies not only in their predictive value but also in their accessibility and ease of assessment, highlighting the practical utility of our nomogram model in clinical settings. Advancing age independently increases the risk of second fractures, a trend that is consistent with previous research[ 17 ]. The underlying reasons for this are multifactorial. As individuals age, bone mineral density decreases, and bone architecture becomes more fragile, leading to osteoporosis, a condition characterized by reduced bone strength and increased fracture risk. Moreover, age-related changes in bone quality, such as decreased collagen content and alterations in bone microarchitecture, further contribute to bone fragility. These changes make older adults more susceptible to fractures even with minimal trauma or falls. In addition to bone health, aging is associated with an increased burden of comorbidities, such as cardiovascular disease, diabetes, and neurological diseases, which further elevate the risk of fractures. Notably, poorer hip joint function, as indicated by lower Harris scores, correlates with decreased physical activity and muscle strength, thereby increasing susceptibility to fractures[ 18 ]. The Harris Hip Score is a widely used clinical tool that assesses various aspects of hip function, including pain, mobility, and daily activities. Lower scores on this scale often reflect significant impairments in these areas, leading to a reduction in overall physical activity levels. Decreased physical activity due to hip dysfunction contributes to muscle atrophy and weakness, particularly in the lower extremities. This muscle deterioration compromises the body's ability to maintain balance and respond effectively to perturbations, increasing the likelihood of falls. Moreover, reduced mobility limits the weight-bearing activities that are crucial for maintaining bone density, further predisposing patients to fragility fractures. The association between neurovascular disease and second fractures underscores the role of impaired balance and proprioception in fall-related injuries. In our study, we referred to Parkinson's disease (PD) and Alzheimer's disease (AD) collectively as neurovascular disease. Most hip fractures occur following a fall, and impaired balance and postural instability in patients with PD contribute significantly to an increased risk of second fractures[ 19 ]. Nam JS et al. revealed that the risk of hip fracture was approximately two-fold greater in individuals with PD compared to healthy controls, supporting our findings [ 20 ]. AD involves degenerative changes in the central nervous system, characterized by a progressive loss of memory and cognition. The majority of Alzheimer's disease patients exhibit balance impairments and gait deficits, further increasing their fall risk[ 21 ]. Additionally, some studies have demonstrated an association between cognitive decline and an increased risk of hip fractures. Furthermore, our investigation identified eye disease as a significant predictor of second fractures post-hip surgery. Although eye disease has been commonly utilized to prognosticate fragility fractures, its association with second fractures remains understudied[ 22 , 23 ]. Patients with eye disease often experience poor balance and reduced muscle strength, making them more susceptible to falls. Visual impairments can hinder depth perception, peripheral vision, and overall spatial awareness, leading to an increased risk of accidents and falls. In addition to the physiological impacts, external environmental factors play a crucial role in the risk of second fractures. The absence of nonslip mats in bathrooms or uncleared water stains on floors can increase the risk of slipping, thereby elevating the chance of secondary fractures. Living alone is another significant factor related to second fractures in hip fracture patients. However, the exact relationship between living alone and fractures remains unclear. We hypothesize that patients living alone may face several challenges that contribute to this increased risk. Without the support and encouragement of family members or caregivers, individuals may be less likely to engage in regular physical activity or adhere to prescribed rehabilitation regimens. The absence of a support system can also lead to delays in seeking medical attention or assistance after a fall, exacerbating the severity of injuries and complicating recovery. Furthermore, living alone can contribute to social isolation and mental health issues such as depression and anxiety, which have been linked to decreased motivation and physical activity. These psychological factors can negatively impact overall health and increase the risk of secondary fractures. The lack of companionship and assistance in daily activities may also mean that home environments are not optimally adapted for safety, increasing the risk of falls. In contrast, regular exercise emerged as a protective predictor against second fractures in our study. regular exercise emerged as a protective predictor against second fractures in our study. Many scholars believe that in China, due to the lack of rehabilitation units and economic constraints, patients are often discharged directly to their private homes, with only a small number of wealthy patients admitted to nursing homes[ 24 , 25 ]. Consequently, limited access to rehabilitation services and resources outside the hospital setting means that most patients in China are unable to engage in regular exercise post-discharge, increasing the risk of second fractures. Additionally, for rural patients, the lack of effective home rehabilitation services to improve their physical function after returning home contributes to a poor prognosis. This situation is exacerbated by the geographical isolation and limited healthcare infrastructure in rural areas, which hinder access to follow-up care and professional guidance on rehabilitation exercises. We developed a nomogram for predicting second fractures in elderly patients within two years after hip surgery, incorporating demographic variables, clinical characteristics, and laboratory data, ultimately identifying six optimal predictors. Our prediction model exhibited good discrimination and calibration, both development and validation cohorts. Additionally, Decision Curve Analysis (DCA) confirmed the excellent clinical utility of the nomogram model. However, several limitations must be acknowledged. First, the data analyzed in this retrospective study were limited to medical records, which may introduce methodological bias. Moreover, our predictive model lacks validation in an external population. Future prospective, multicenter, or population-based large-sample cohort studies are needed to further validate our model. Conclusion Overall, we developed and validated a nomogram prediction model incorporating age, hip joint function, neurovascular disease status, eye disease status, living alone, and regular exercise to predict the occurrence of second fractures within two years after surgery in elderly patients. We anticipate that this straightforward and visual predictive model will assist clinicians in tailoring perioperative management strategies and designing personalized rehabilitation programs for patients undergoing initial hip surgery. By leveraging this model, healthcare providers can better identify high-risk individuals and implement targeted interventions to mitigate the risk of secondary fractures, ultimately improving patient outcomes and quality of life. Abbreviations K-S Kolmogorov–Smirnov OR Hazard ratio CI Confidence interval ROC Receiver operating characteristic C-index Concordance index DCA Decision curve analysis LASSO Least Absolute Shrink age and Selection Operator AUC Area under the curve TRIPOD Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis FRAX Fracture Risk Assessment Tool COPD Chronic obstructive pulmonary disease ALB hypoalbuminemia Declarations Ethics approval This study was approved by the Ethics Committee of the Medical Collage of Soochow University (Ethics Approval Number: SUDA20230115H07). Because this retrospective study was conducted in accordance with the Declaration of Helsinki, and we did not involve the privacy of patients, informed consent was waived from the patients. Consent for publication Written informed consent for publication of this paper was obtained from two hospitals affiliated with Soochow University and all authors. Data availability All data generated or used during the study are available from the corresponding author and first author upon reasonable request. Competing interests The authors have no relevant financial or nonfinancial interests to disclose. Funding There was no direct funding source aligned for this study. Author contributions LZ involved in the conception and design of the study and drafted the manuscript. YZ contributed to the study conception and design and critically revised the manuscript. WX, SL and WW collected and analyzed the patient data. All authors reviewed the manuscript. The author(s) read and approved the final manuscript. Acknowledgments Not applicable. References Parker M, Johansen A. Hip fracture. Bmj-British Med J. 2006;333:27–30. https://doi.org/10.1136/bmj.333.7557.27 . C. Cooper C, Cole ZA, Holroyd CR, et al. Secular trends in the incidence of hip and other osteoporotic fractures. Osteoporos Int. 2011;22:1277–88. https://doi.org/10.1007/s00198-011-1601-6 . 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Exercise Monitoring and Assessment System for Home-Based Respiratory Rehabilitation. IEEE Sens J. 2022;22:18890–902. https://doi.org/10.1109/jsen.2022.3200984 . Additional Declarations No competing interests reported. Supplementary Files SupplementFile1.docx Supplementfile2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Jun, 2024 Editor assigned by journal 20 Jun, 2024 Submission checks completed at journal 20 Jun, 2024 First submitted to journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4596878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316955188,"identity":"473aebda-c2e9-4f04-939b-0fb328d95a9c","order_by":0,"name":"Linlin Zhang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Zhang","suffix":""},{"id":316955189,"identity":"7a04c624-56a6-46cc-bd2b-b219dd484974","order_by":1,"name":"Yanling Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFCCxAcgUoafmfnwAyK1JBuASB7JdrY0A9K0GJznUZAgSoN8ezLjY54/h3mMD/MwGDDU2EQT1GJw5jGzMQ/PYR6zw7wHHjAcS8ttIKhFIv+YNI8ESAtfggFjw2HCWuRnJLP/5jEAOqyZx0CCKC0MN5LZmHkSDvMYMBOrBeQXyTkH0oFuAwZyAjF+AYXYhzd/rOX4+w8ffvChxoYIhwEBEw9DM4SVQIxyEGD8wVBHrNpRMApGwSgYiQAAdlo6effgZgQAAAAASUVORK5CYII=","orcid":"","institution":"Second Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Yanling","middleName":"","lastName":"Zhou","suffix":""},{"id":316955191,"identity":"226f0d11-e5a4-4734-9fca-58ebf24826f2","order_by":2,"name":"Wenping Xue","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Wenping","middleName":"","lastName":"Xue","suffix":""},{"id":316955192,"identity":"09da095c-7fc2-4cd4-bc83-e963522cf835","order_by":3,"name":"Wei Wang","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":316955194,"identity":"c2e824cf-2f0f-48b5-8b31-416983041768","order_by":4,"name":"Shuqiu Lin","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Shuqiu","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-06-18 02:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4596878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4596878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60615242,"identity":"7a2a6586-896b-4add-89dc-6738162825b9","added_by":"auto","created_at":"2024-07-18 20:11:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306940,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/b04a45830dbbc3d37991dace.jpg"},{"id":60614369,"identity":"64be56d2-aeca-4285-b9e1-e4626ec715c4","added_by":"auto","created_at":"2024-07-18 20:03:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138806,"visible":true,"origin":"","legend":"\u003cp\u003eClinical characteristics according to LASSO regression analysis. (a) Predictor coefficient graphic.\u003c/p\u003e\n\u003cp\u003eEach colored solid line in the figure represents a variable with decreasing variable coefficients as Log (λ) increases and part of the variable coefficients become zero. (b) LASSO regression model cross-validation graph. The dotted vertical lines on the left and right of the graph represent the values of log (λ. min) and\u003c/p\u003e\n\u003cp\u003e(λ.1 se), respectively.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/8ebe511e68ccc5f0a8311024.jpg"},{"id":60614364,"identity":"029e2e07-e3b8-4477-9e7e-7b4a4a1f7d72","added_by":"auto","created_at":"2024-07-18 20:03:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":84818,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting second fractures in elderly patients with hip fractures\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/3667b995101489669259b2fb.jpg"},{"id":60615239,"identity":"8ff5749c-cec2-421e-9263-f6a06f4c71f6","added_by":"auto","created_at":"2024-07-18 20:11:15","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":107436,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the area under the receiver operating characteristic curve between nomogram-independent predictors in the development cohort (a) and the validation cohort (b)\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/24ad4e81744da2a6e3e9eb0a.jpg"},{"id":60614367,"identity":"4b04b8ba-ac18-4f6d-a4a7-92b706b9d401","added_by":"auto","created_at":"2024-07-18 20:03:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116992,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of calibration curves between the development cohort (a) and the validation cohort (b)\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/b3001d50311083f3225fa724.jpg"},{"id":60615240,"identity":"abd50d08-f46f-420b-8779-92da0e828d8d","added_by":"auto","created_at":"2024-07-18 20:11:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117079,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of decision curve analyses between the development cohort (a) and the validation cohort (b)\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/1160436c73cbe0965c31ede5.jpg"},{"id":60617053,"identity":"3d3d8833-0809-4b66-9723-6d385a2ae827","added_by":"auto","created_at":"2024-07-18 20:27:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1552925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/ae57720e-5b9f-4545-aedb-0dadb63fb92e.pdf"},{"id":60615238,"identity":"778a058d-a6ac-41a3-8c0d-8e1282a08293","added_by":"auto","created_at":"2024-07-18 20:11:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":62988,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/814cdc4ad06e4eccb6ddbbf7.docx"},{"id":60616376,"identity":"5f6424c9-935d-41b1-bd4c-8e1bccf7ae29","added_by":"auto","created_at":"2024-07-18 20:19:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":682987,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4596878/v1/2d56d8c4fe6bf31265e6d3e5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a clinical model of second fractures for hip fracture patients after surgery","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHip fractures are prevalent and severe fragility fractures among older patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, the increasing elderly population has led to an annual increase in hip fracture cases. It is estimated that the number of hip fractures will reach 6.3\u0026nbsp;million by 2050 as global life expectancy increases[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, prior osteoporotic fractures are considered clinical predictors that increase the risk of second fractures, and relevant research also indicates that older patients with an initial fracture face twice the risk of experiencing a second fracture within two years compared to their initial fracture[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Second fractures are associated with high mortality rates, increased patient expenses, prolonged hospital stays, and poor quality of life[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, it is crucial to explore prognostic factors early to identify patients at high risk for second fractures.\u003c/p\u003e \u003cp\u003eIn recent years, several indicators have been proposed by previous studies to potentially predict second fractures, such as age, gender, high comorbidity burden, visual impairment, and neurovascular disease[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, these studies focused solely on clinical variables to identify patients at greater risk of developing second fractures. In addition, some studies were limited to specific populations rather than the general population[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hence, the distinction and priority of these indicators in increasing the risk of developing second fractures and their predictive value remain unknown. Given the importance of early identification of adverse prognoses for patient treatment and management, an increasing number of researchers and policymakers are turning to predictive models. For example, to the best of our knowledge, several research groups have developed hip fracture-specific risk prediction models, such as the FRAX and QFracture models. However, the FRAX was designed for predicting initial fracture risk, and its ability to predict second fractures remains to be further validated[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; moreover, the QFracture was also designed for initial fracture assessment and contained many variables that might not facilitate clinical application[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough predictors for postoperative second fractures have been extensively studied, research on risk prediction models remains limited. The nomogram has been accepted as a reliable tool for creating a simple intuitive graph of a statistical predictive model that quantifies the risk of a clinical event. Therefore, we conducted this retrospective study to investigate the predictors of two-year postoperative second fractures and developed a clinical nomogram to predict high-risk patients. By utilizing a model based on clinical and claims data, policymakers and clinicians can find effective methods to reduce fast-growing healthcare costs and target individualized treatment protocols.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe collected the clinical data of 2402 patients with hip fractures who were hospitalized at two hospitals affiliated with Soochow University from Jan 1, 2019, to Dec 31, 2021. During the follow-up period from May 2023 to Jan 2024, data on postoperative mobility, survival status, occurrence and timing of second fractures, as well as the location of second fractures, were collected via telephone interviews. A total of 315 patients did not meet the inclusion criteria. Additionally, 352 patients were lost due to changes in phone numbers. Ultimately, 1735 patients were included in the study. All patients were followed up for at least two years or until death. The patient flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) aged 60 years and older, (2) had first-time unilateral fragility hip fractures, and (3) underwent surgical treatment (internal fixation and/or hip arthroplasty). The exclusion criteria were as follows: (1) in-hospital mortality; (2) fractures due to pathological or high-energy injuries; or (3) declined follow-up or incomplete data. In this study, patients were randomly divided into a training cohort (70%) and a testing cohort (30%). Patients in the training cohort were used to develop the nomogram model, whereas patients in the testing cohort were used to validate the resulting nomogram.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003e was waived by the ethics committee, as this was a minimum-risk study. The study was approved by the ethics committee of Soochow University (SUDA20230115H07) and reported using the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement for prediction studies.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathological data collection\u003c/h2\u003e \u003cp\u003eIn this study, factors influencing second fractures in patients were collected through the electronic medical records system. The demographic information included age, gender, and living alone; clinical characteristics included hip fracture type, cause of fracture, number of comorbidities, hypertension, heart disease (coronary artery disease, heart failure, arrhythmia, heart dysfunction, chronic pulmonary heart disease), cerebrovascular disease (Cerebral hemorrhage, stroke, and related sequelae), diabetes, neurovascular disease (Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease), eye disease (cataract, glaucoma, diabetic eye disease), rheumatoid arthritis, respiratory disease (COPD, chronic bronchitis, emphysema, pulmonary fibrosis), and hip joint function (Harris score). Laboratory examinations revealed anemia and hypoalbuminemia (ALB). Additional factors included fracture type, dietary habits, regular sunlight exposure, regular exercise, and postoperative complications diagnosed by bone physicians according to the results of examination after consultation. These 23 factors were included in the subsequent analysis as candidate predictors, determining the minimum number of outcome events and sample size for the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables that followed a normal distribution were expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and the t-test was used for comparisons between groups. Categorical variables were presented as n (%) and comparisons between groups were performed using the chi-square test. Continuous variables were categorized into distinct groups to simplify the model and enhance objectivity. We categorized ages into four subgroups: 60\u0026ndash;69, 70\u0026ndash;79, 80\u0026ndash;89, and \u0026ge;\u0026thinsp;90 years based on literature precedence[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo identify potential risk factors for second fractures, we employed the least absolute shrinkage and selection operator (LASSO) regression analysis to develop the final prediction model using Cox regression. LASSO was a regularization method that fits a single model for each tuning parameter in the contraction penalty term, greatly improving computational efficiency. Furthermore, it can be used to mitigate the overfitting problem and reduce the regression coefficient to zero, increasing model interpretability[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We used the minimum standard I-Standard Error method to determine the final number of predictors. This number was identified at one standard error to the right from the minimum mean square error, indicating good fitness of the model with the fewest predictors.\u003c/p\u003e \u003cp\u003eMultivariate Cox regression was performed to assess the prognostic value of the variables selected through LASSO regression. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for two-year second fractures. To visualize and quantify the effect of each selected variable on the estimated 1- and 2-year second fracture probability, a nomogram was constructed and validated. For internal validation, the receiver operating characteristic (ROC) curve was calculated to evaluate the model\u0026rsquo;s ability to discriminate between individuals at high and low risk of second fractures. After internal validation by bootstrapping, a calibration curve was used to evaluate the calibration of the nomogram by contrasting the actual risk and predicted risk. The clinical usefulness of the nomogram was estimated using decision curve analysis (DCA) based on the net benefit and threshold probabilities. Survival curves were depicted using Kaplan-Meier analysis and compared with a log-rank test in both the development and validation cohorts. Statistical analyses were performed using IBM SPSS software (version 26.0) and R software (version 4.1.3). All tests were two-sided. A \u003cem\u003eP\u003c/em\u003e value less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 1735 patients were included at different times according to the inclusion and exclusion criteria, 151 (8.7%) of whom experienced a second fracture within two years postoperatively. The most frequent site was the contralateral hip (32%, 49/151), followed by the spine (29%, 43/151), wrist (13%, 20/151), shoulder (8%,12/151), knee (6%,9/151), and other sites (12%,18/151). Additional file 1 provided relevant information. According to the discharge time, the patients were divided into a development cohort (n\u0026thinsp;=\u0026thinsp;1215) and a validation cohort (n\u0026thinsp;=\u0026thinsp;520). There were no statistically significant differences in all variables included between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which showed that the data grouping was random and reasonable \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics in the development and temporal validation cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edevelopment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003evalidation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1215(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;520(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e795(65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331(63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189(36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e392(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178(34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026thinsp;~\u0026thinsp;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e440(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185(35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026thinsp;~\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362(29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e736(60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e330(63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaccident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277(22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eothers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntertrochanteric fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e417(34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemoral neck fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e798(65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334(64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eamenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e861(70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407(78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1066(87.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457(88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211(17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1004(82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e454(87.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1152(94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491(94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1083(89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e457(87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1189(97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500(96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1051(86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e445(85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003edevelopment\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTemporal validation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1215(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;520(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165(13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1050(86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450(86.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e685(56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e306(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530(43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1125(92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e487(93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163(13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1052(86.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e480(92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e891(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e373(71.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-osteoporosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e903(74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339(65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1160(95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e492(94.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1027(84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434(83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188(15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e824(67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358(68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391(32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e946(77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e392(75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerioperative complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1177(96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e505(97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip joint function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463(38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210(40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e462(38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVariable selection\u003c/h2\u003e \u003cp\u003eLASSO regression analysis revealed that using the minimum standard I standard error method, six variables with nonzero coefficients were identified as critical factors associated with second fractures. These variables included age, hip joint function, neurovascular disease, eye disease, living alone, and regular exercise \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These optimal variables were further screened via multivariate Cox analysis, which revealed that all six variables were associated with the risk of second fractures in patients with hip fracture. Details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Furthermore, the Kaplan\u0026ndash;Meier method was used to estimate the cumulative incidences of second fractures between the groups during the follow-up period (Additional File 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Cox regression analyses of variables related to second fracture\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip joint function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026thinsp;~\u0026thinsp;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026thinsp;~\u0026thinsp;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026thinsp;~\u0026thinsp;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNomogram Construction and Validation\u003c/p\u003e \u003cp\u003eTo facilitate clinical use, we converted the predictive model based on age, hip joint function, neurovascular disease, eye disease, living alone, and regular exercise into a nomogram \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Scores corresponding to the different categories of independent risk factors were calculated by assigning point values to each risk factor category and summing these values for each patient. The total score scales represented the estimated probability of patients experiencing second fractures, and the nomogram model could be utilized to calculate this incidence. To assess the discriminatory ability of the nomogram, we plotted the ROC curves for the training and validation cohorts and calculated the area under the curve (AUC). The AUC, calculated for the training cohort and validation cohort using the ROC curve analysis, was 0.832 (95% CI: 0.765\u0026thinsp;~\u0026thinsp;0.895) and 0.773 (95% CI: 0.727\u0026thinsp;~\u0026thinsp;0.818), respectively, indicating high discriminatory ability \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In the calibration chart, the calibration curve closely coincided with the reference line in both the training and validation cohorts, demonstrating good consistency between the observed and predicted probabilities \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The decision curve analysis (DCA) of the nomogram, evaluating the clinical utility of the predictive model, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The net benefit of using a nomogram to predict second fractures in elderly patients with hip fracture was high when the threshold probability was between 0.01 and 0.85 in the training cohort and between 0.01 and 0.75 in the validation cohort. Therefore, the nomogram demonstrated good clinical utility for predicting delayed discharge in elderly patients with hip fractures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur investigation revealed a second fracture rate of 8.7% following hip fracture surgery, which aligns with rates reported in Korea[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] is notably lower than those observed in Finland and Spain[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These variations in the rate of second fractures can be attributed to differences in inclusion and exclusion criteria across studies. In elderly patients with hip fractures, increasing age is associated with a higher incidence of complications such as periprosthetic infection, pneumonia, urinary tract infection, and deteriorating nutritional status, all of which can contribute to secondary fractures. Moreover, some studies have small sample sizes and focus on specific populations, limiting their generalizability. In contrast, our study sample, which represents the general population aged 60 years or older living in Suzhou city, provides more broadly applicable results. Furthermore, the differences in healthcare systems, surgical techniques, and postoperative management practices between countries may also contribute to variations in second fracture rates.\u003c/p\u003e \u003cp\u003eThis study identified several independent risk factors for second fractures, including age, hip joint function, neurological disease, eye disease, living alone, and regular exercise. The significance of these factors lies not only in their predictive value but also in their accessibility and ease of assessment, highlighting the practical utility of our nomogram model in clinical settings. Advancing age independently increases the risk of second fractures, a trend that is consistent with previous research[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The underlying reasons for this are multifactorial. As individuals age, bone mineral density decreases, and bone architecture becomes more fragile, leading to osteoporosis, a condition characterized by reduced bone strength and increased fracture risk. Moreover, age-related changes in bone quality, such as decreased collagen content and alterations in bone microarchitecture, further contribute to bone fragility. These changes make older adults more susceptible to fractures even with minimal trauma or falls. In addition to bone health, aging is associated with an increased burden of comorbidities, such as cardiovascular disease, diabetes, and neurological diseases, which further elevate the risk of fractures.\u003c/p\u003e \u003cp\u003eNotably, poorer hip joint function, as indicated by lower Harris scores, correlates with decreased physical activity and muscle strength, thereby increasing susceptibility to fractures[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The Harris Hip Score is a widely used clinical tool that assesses various aspects of hip function, including pain, mobility, and daily activities. Lower scores on this scale often reflect significant impairments in these areas, leading to a reduction in overall physical activity levels. Decreased physical activity due to hip dysfunction contributes to muscle atrophy and weakness, particularly in the lower extremities. This muscle deterioration compromises the body's ability to maintain balance and respond effectively to perturbations, increasing the likelihood of falls. Moreover, reduced mobility limits the weight-bearing activities that are crucial for maintaining bone density, further predisposing patients to fragility fractures.\u003c/p\u003e \u003cp\u003eThe association between neurovascular disease and second fractures underscores the role of impaired balance and proprioception in fall-related injuries. In our study, we referred to Parkinson's disease (PD) and Alzheimer's disease (AD) collectively as neurovascular disease. Most hip fractures occur following a fall, and impaired balance and postural instability in patients with PD contribute significantly to an increased risk of second fractures[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nam JS et al. revealed that the risk of hip fracture was approximately two-fold greater in individuals with PD compared to healthy controls, supporting our findings [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. AD involves degenerative changes in the central nervous system, characterized by a progressive loss of memory and cognition. The majority of Alzheimer's disease patients exhibit balance impairments and gait deficits, further increasing their fall risk[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, some studies have demonstrated an association between cognitive decline and an increased risk of hip fractures.\u003c/p\u003e \u003cp\u003eFurthermore, our investigation identified eye disease as a significant predictor of second fractures post-hip surgery. Although eye disease has been commonly utilized to prognosticate fragility fractures, its association with second fractures remains understudied[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Patients with eye disease often experience poor balance and reduced muscle strength, making them more susceptible to falls. Visual impairments can hinder depth perception, peripheral vision, and overall spatial awareness, leading to an increased risk of accidents and falls. In addition to the physiological impacts, external environmental factors play a crucial role in the risk of second fractures. The absence of nonslip mats in bathrooms or uncleared water stains on floors can increase the risk of slipping, thereby elevating the chance of secondary fractures.\u003c/p\u003e \u003cp\u003eLiving alone is another significant factor related to second fractures in hip fracture patients. However, the exact relationship between living alone and fractures remains unclear. We hypothesize that patients living alone may face several challenges that contribute to this increased risk. Without the support and encouragement of family members or caregivers, individuals may be less likely to engage in regular physical activity or adhere to prescribed rehabilitation regimens. The absence of a support system can also lead to delays in seeking medical attention or assistance after a fall, exacerbating the severity of injuries and complicating recovery. Furthermore, living alone can contribute to social isolation and mental health issues such as depression and anxiety, which have been linked to decreased motivation and physical activity. These psychological factors can negatively impact overall health and increase the risk of secondary fractures. The lack of companionship and assistance in daily activities may also mean that home environments are not optimally adapted for safety, increasing the risk of falls.\u003c/p\u003e \u003cp\u003eIn contrast, regular exercise emerged as a protective predictor against second fractures in our study. regular exercise emerged as a protective predictor against second fractures in our study. Many scholars believe that in China, due to the lack of rehabilitation units and economic constraints, patients are often discharged directly to their private homes, with only a small number of wealthy patients admitted to nursing homes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consequently, limited access to rehabilitation services and resources outside the hospital setting means that most patients in China are unable to engage in regular exercise post-discharge, increasing the risk of second fractures. Additionally, for rural patients, the lack of effective home rehabilitation services to improve their physical function after returning home contributes to a poor prognosis. This situation is exacerbated by the geographical isolation and limited healthcare infrastructure in rural areas, which hinder access to follow-up care and professional guidance on rehabilitation exercises.\u003c/p\u003e \u003cp\u003eWe developed a nomogram for predicting second fractures in elderly patients within two years after hip surgery, incorporating demographic variables, clinical characteristics, and laboratory data, ultimately identifying six optimal predictors. Our prediction model exhibited good discrimination and calibration, both development and validation cohorts. Additionally, Decision Curve Analysis (DCA) confirmed the excellent clinical utility of the nomogram model. However, several limitations must be acknowledged. First, the data analyzed in this retrospective study were limited to medical records, which may introduce methodological bias. Moreover, our predictive model lacks validation in an external population. Future prospective, multicenter, or population-based large-sample cohort studies are needed to further validate our model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, we developed and validated a nomogram prediction model incorporating age, hip joint function, neurovascular disease status, eye disease status, living alone, and regular exercise to predict the occurrence of second fractures within two years after surgery in elderly patients. We anticipate that this straightforward and visual predictive model will assist clinicians in tailoring perioperative management strategies and designing personalized rehabilitation programs for patients undergoing initial hip surgery. By leveraging this model, healthcare providers can better identify high-risk individuals and implement targeted interventions to mitigate the risk of secondary fractures, ultimately improving patient outcomes and quality of life.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eK-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eKolmogorov\u0026ndash;Smirnov\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eConfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eConcordance index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eLeast Absolute Shrink age and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eTRIPOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eTransparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eFRAX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eFracture Risk Assessment Tool\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.370705244122966%\" valign=\"top\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.62929475587704%\" valign=\"top\"\u003e\n \u003cp\u003ehypoalbuminemia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Medical Collage of Soochow University (Ethics Approval Number: SUDA20230115H07). Because this retrospective study was conducted in accordance with the Declaration of Helsinki, and we did not involve the privacy of patients, informed consent was waived from the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication of this paper was obtained from two hospitals affiliated with Soochow University and all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or used during the study are available from the corresponding author and first author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or nonfinancial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no direct funding source aligned for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLZ involved in the conception and design of the study and drafted the manuscript. YZ contributed to the study conception and design and critically revised the manuscript. WX, SL and WW collected and analyzed the patient data. All authors reviewed the manuscript. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParker M, Johansen A. Hip fracture. Bmj-British Med J. 2006;333:27\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj.333.7557.27\u003c/span\u003e\u003cspan address=\"10.1136/bmj.333.7557.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. C.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper C, Cole ZA, Holroyd CR, et al. Secular trends in the incidence of hip and other osteoporotic fractures. 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IEEE Sens J. 2022;22:18890\u0026ndash;902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/jsen.2022.3200984\u003c/span\u003e\u003cspan address=\"10.1109/jsen.2022.3200984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Second fracture, Predictive model, Nomogram, Hip fractures","lastPublishedDoi":"10.21203/rs.3.rs-4596878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4596878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eSecond fracture following initial hip fracture surgery poses a life-threatening risk in the elderly population. This study aims to investigate the associated risk factors and establish a prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Retrospectively data were obtained from two affiliated hospitals at Soochow University for older patients diagnosed with hip fractures who underwent surgical treatment between January 2019 and December 2021. The endpoint was a second fracture. Independent risk factors for second fractures in patients were identified through the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression. A nomogram was established and assessed for predictability, discriminatory ability, and clinical applicability using areas under the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among 1735 patients, 8.7% (115/1735) had second fractures within two years after surgery. Variables screened by LASSO, including age, hip joint function, neurovascular disease, eye disease, living alone, and regular exercise, were incorporated into the Cox regression model. The nomogram demonstrated favorable discriminatory ability, with areas under the ROC curves (AUC) of 0.832 (95% CI, 0.765-0.895) and 0.773 (95% CI, 0.727-0.818) after development and validation, respectively. The calibration curves showed good consistency between the actual second fracture incidence and the predicted probability. DCA of the nomogram demonstrated the model’s excellent clinical efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e The nomogram model enabled accurate individualized prediction of second fractures in elderly patients within two years after surgical treatment, which might assist clinicians in precise perioperative management and rehabilitation education following initial hip surgery.\u003c/p\u003e","manuscriptTitle":"Development and validation of a clinical model of second fractures for hip fracture patients after surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 20:03:10","doi":"10.21203/rs.3.rs-4596878/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-20T15:25:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-20T13:40:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-20T13:39:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2024-06-18T02:57:56+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":"4dc62e22-82dd-4f12-ae34-132803a3bae6","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T13:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 20:03:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4596878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4596878","identity":"rs-4596878","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}