Study on risk factors of preoperative deep vein thrombosis in patients with lower limb fractures and construction and validation of risk prediction nomogram model

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Methods The patients were divided into DVT group and non DVT group according to whether there was deep vein thrombosis of the lower extremity. The differences in the levels of D-D, FIB, FDP and platelets between the two groups were analyzed and compared. ROC curve was used to evaluate the levels of D-D, FIB, FDP and PLT in the peripheral blood of patients with lower extremity fracture to predict the formation of deep vein thrombosis of the lower extremity. Logistic regression analysis was used to analyze the related risk factors of deep vein thrombosis, and the corresponding nomogram risk prediction model of lower limb deep vein thrombosis in patients with lower limb fractures was drawn according to the regression coefficient, which was verified by calibration curve and consistency curve. Results The levels of D-D, FIB, FDP, and PLT in the DVT group were higher than those in the non DVT group, with statistical significance (P < 0.05); Moreover, FIB is superior to D-D, FDP, and PLT in predicting the risk of fractures and thrombosis, while PLT has the weakest predictive power. Multivariate logistic analysis showed that platelet, D-D, FIB and FDP were independent risk factors for deep vein thrombosis in patients with lower limb fractures (P < 0.05); Based on the independent risk factors mentioned above, the complex logistic regression formula was transformed into a visual column chart (with a C-index of 0.962), which has good discrimination and consistency. Conclusions The levels of D-D, FIB, FDP and PLT in peripheral blood of patients with lower limb fracture and DVT were significantly increased. Early monitoring of D-D, FIB, FDP and PLT levels in patients with lower limb fracture can effectively screen for lower limb deep vein thrombosis. lower limb fracture deep vein thrombosis of lower extremity risk factors nomogram fibrinogen Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Deep vein thrombosis (DVT) of lower limbs is caused by a variety of reasons, which lead to changes in the coagulation mechanism of the body to promote thrombosis, and then lead to stenosis or occlusion of the vascular lumen, which is more likely to occur in lower limb blood vessels (including femoral vein and deep leg vein) 1,2 . It has been reported that deep vein thrombosis is a common complication after traumatic fracture. Among hospitalized patients, the risk of deep vein thrombosis in trauma patients is 13 times higher than that in non trauma patients 3 . Considering that fractures can damage the vascular wall to make the blood in a hypercoagulable state, which is easy to lead to thrombosis 4 . If the patient is complicated with deep vein thrombosis, the fracture healing will be greatly affected; In addition, pulmonary embolism (PE) caused by lower limb deep vein thrombosis poses a pathogenic threat to the health of patients 5 . In addition, lower limb fractures, as one of the common types of fractures, account for one-third of fracture patients and are mostly concentrated in the elderly population. With the increasing aging of the population, the proportion of patients with lower limb fractures is showing an increasing trend 6 . Clinically, the early symptoms of lower limb deep vein thrombosis are lack of specificity, and the symptoms are relatively hidden. Only 10–17% of DVT patients have obvious clinical symptoms (such as lower limb swelling, local deep tenderness, back flexion pain), which is easy to cause misdiagnosis and missed diagnosis. Therefore, early diagnosis and intervention for high-risk patients has important clinical significance 7, 8 . At present, the commonly used methods for diagnosis of deep vein thrombosis include imaging detection methods such as vascular color ultrasound and venography. Although these technologies have certain specificity and sensitivity, they cannot be used to predict the occurrence of DVT (the high price and complicated operation limit their application in clinical disease screening). Therefore, it is very important for clinical diagnosis and treatment of lower limb deep vein thrombosis to explore new and effective predictive indicators for early diagnosis. According to reports, levels of D-dimer (D-D), fibrinogen (FIB), fibrinogen degradation products (FDP), and platelets (PLT) can to some extent reflect the hypercoagulable state of the blood 9,10 . FIB, as a glycoprotein synthesized and secreted by hepatocytes, can induce secondary fibrinolysis hyperfunction by increasing the activity of plasmin, and then participate in the process of coagulation and hemostasis, which has a certain correlation with the occurrence of DVT; While D-D, as plasmin hydrolyzed cross-linked fibrinogen, can also be used as an indicator of body thrombosis. When there is thrombosis in the vascular lumen, its indicator can significantly rise 11 ; FDP have a certain correlation with D-D, which can reflect the coagulation function of the patient's body and are recognized as biochemical indicators of thrombotic diseases in clinical practice; Platelets mainly participate in the body's coagulation process by promoting hemostasis and accelerating coagulation. This study retrospectively analyzed 145 patients with lower limb fractures admitted to the emergency department of our hospital, analyzed the correlation and predictive value of D-D, FIB, FDP and PLT with lower limb deep vein thrombosis, analyzed the risk factors of lower limb fractures complicated with lower limb deep vein thrombosis, and constructed a nomogram of the risk probability of lower limb deep vein thrombosis, in order to effectively guide clinical diagnosis and treatment. 2. Materials and Methods 2.1 Data collection Retrospective analysis of the clinical data of 145 patients with traumatic fractures of the lower limbs who visited the department of General Surgery of our hospital from September 2022 to March 2023, including 70 males and 75 females, aged 26 to 76 years, with an average age of 53.78 ± 11.76 years; The patients were divided into DVT group and non DVT group according to whether there was deep vein thrombosis of lower limbs. Inclusion criteria: (1) All fracture patients were diagnosed in accordance with the clinical diagnostic criteria for limb fractures in the "Chinese Open Fracture Diagnosis and Treatment Guidelines (2019 Edition)" 12 ; (2) All patients underwent lower limb venous ultrasound examination, and patients with lower limb thrombosis were diagnosed by completing lower limb angiography (DSA) if necessary; (3) The patient has no previous history of lower limb thrombosis. Exclusion criteria: (1) Previous history of pulmonary embolism; (2) Previous history of atrial fibrillation; (3) Patients who have undergone vascular related surgery in the past; (4) Patients with other organ tumors; (5) Patients with abnormal coagulation function. This study followed the ethical standards of Helsinki Declaration, and all patients were informed and signed the relevant consent before treatment. 2.2 Data collection General and clinical data of all patients were collected, including gender, age, body mass index (BMI), trauma site, history of diabetes, history of hypertension, cause of injury, platelet, D-D, FIB and FDP levels. Compare the differences in D-D, FIB, FDP, and platelet levels between DVT and non DVT groups. 2.3 Diagnostic criteria of DVT According to the "Guidelines for Diagnosis and Treatment of Deep Venous Thrombosis" 13 : (1) The lower limbs are swollen and painful; (2) The blood coagulation function is in hypercoagulable state; (3) D-D level > 500µg/L; (4) B-ultrasound examination can directly see that the blood vessels can not be squashed by the probe, and there is uneven echo shadow in the lumen; (5) The blood vessels can't be completely filled or appear defect after filling. During the examination, 1 ~ 4 items were screened first, and those whose first four items met the requirements were screened for the fifth item, and the diagnosis was DVT according to the results. 2.4 therapeutic method When each fracture patient is admitted to the hospital, he will be diagnosed by color Doppler ultrasound of lower limbs immediately, and then he will be selectively intervened to prevent or treat deep venous thrombosis of lower limbs. For patients without thrombus, it is considered that fracture can make blood in hypercoagulable state by damaging blood vessel wall, which will easily lead to thrombosis and increase the risk of thrombosis, so low dose anticoagulation can be selected to prevent it, and the dose is 0.2 ml of low molecular weight heparin (2/ day); For patients with thrombus, they can be treated according to their weight. The routine dose is 0.6ml of low molecular weight heparin (2/ day). Both patients stopped anticoagulation one day before operation and resumed anticoagulation treatment as soon as possible after operation. In addition, for patients with deep venous thrombosis of lower limbs, pneumatic pump can be used to contract the muscles of the ankle and calf after operation to promote local blood circulation. Improving the venous return of lower limbs can also play a therapeutic role to some extent. 2.5 Statistical analysis SPSS (version 26.0) and R language (version 4.2.1) software was used for statistical analysis of the data. The counting data is represented by n (%), and inter group comparisons are performed using χ 2 tests or Fisher tests (n < 5); The measurement data is expressed as mean ± standard deviation (x ± s), and t-test is used for inter group comparison. The predictive value of using receiver operating characteristic curve (ROC curve) to evaluate D-D, FIB, FDP, and PLT levels in DVT patients with lower limb fractures. Logistic regression analysis was used to analyze the related risk factors of deep vein thrombosis. Multifactor Logistic regression was used to establish a prediction model for the risk of deep vein thrombosis of lower limbs. The model was visualized by nomogram, and the prediction accuracy and discrimination ability of the model were determined by calibration curve, ROC curve and consistency index (C index), α=0.05 is the inspection level. 3. Results 3.1 Comparison of D-D, FIB, FDP, PLT levels between two groups The levels of D-dimer, FIB, FDP, and PLT in the DVT group were higher than those in the non DVT group, with statistical significance (P < 0.05), as shown in Table 1 . Table 1 Comparison of D-dimer, FIB, FDP levels between two groups (x ± s). Indicator DVT group (n = 60) Non-DVT group(n = 60) t P D-D (mg/L) 2.06 ± 2.94 0.87 ± 1.43 −3.180 0.002 FIB (g/L) 6.05 ± 2.09 3.55 ± 1.45 −8.482 < 0.001 FDP (mg/L) 6.96 ± 2.03 4.27 ± 2.15 −7.681 < 0.001 PLT (×10 9 /L) 289.83 ± 90.79 203.50 ± 84.96 −5.906 < 0.001 3.2 Comparison of D-dimer, FIB, FDP, and PLT in predicting the risk of fracture combined with thrombosis Draw ROC curves of D-D, FIB, FDP, and PLT to evaluate their ability to predict the risk of lower limb thrombosis. The results showed that the AUC of D-D, FIB, FDP, and PLT were 0.810 (95%CI: 0.725–0.894, P < 0.001), 0.861 (95%CI: 0.800−0.923, P < 0.001), 0.834 (95%CI: 0.755–0.913, P < 0.001), and 0.752 (95%CI: 0.670–0.834, P < 0.001), respectively. It can be seen that FIB is superior to D-D, FDP in predicting the risk of fracture complicated thrombosis PLT has the weakest predictive performance, as shown in Fig. 1 . 3.3 Single factor analysis of deep vein thrombosis in patients with lower limb fractures This study included 145 patients with lower limb fractures, including 66 patients with lower limb deep vein thrombosis and 79 patients with non deep vein thrombosis. By comparing the differences of general information between the two groups, we found that BMI, trauma site, whether diabetes was combined with history, platelets, D-D, FIB and FDP were related to lower limb deep vein thrombosis (P 0.05), as shown in Table 2 . Table 2 Comparison of general information between two groups of patients (n). Indicator n DVT group n = 66 Non-DVT group n = 79 χ 2 P Gender 0.509 0.476 Male 70 34 36 Female 75 32 43 Age (years) 2.166 0.141 ≤ 60 94 47 47 > 60 51 19 32 BMI (kg/m 2 ) 4.140 0.042 ≤ 24 92 36 56 > 24 53 30 23 Fracture site 9.539 0.008 Tibiofibular 27 18 9 femur 102 38 64 hip bone 16 10 6 History of diabetes 17.235 < 0.001 No 100 34 66 Yes 45 32 13 History of hypertension 0.541 0.462 No 92 44 48 Yes 53 22 31 Energy 0.417 0.518 Low 107 47 60 High 38 19 19 PLT (×10 9 /L) 31.979 300 56 42 14 D-D (mg/L) 85.283 0.5 71 60 11 FIB (g/L) 59.624 4 70 55 15 FDP (mg/L) 78.851 5 69 58 11 3.4 Multifactor analysis of deep vein thrombosis in patients with lower limb fractures Single factor meaningful indicators were included in the multivariate logistic regression model for analysis. The results showed that platelets (OR = 19.233, 95%CI: 2.890-127.975, P = 0.002), D-D (OR = 10.457, 95%CI: 1.492–73.270, P = 0.018), FIB (OR = 5.220, 95%CI: 1.315–20.715, P = 0.019), FDP (OR = 11.634, 95%CI: 1.710-79.168, P = 0.012) were independent risk factors for deep vein thrombosis in patients with lower limb fractures (P 0.05), but they can still be used as important reference indicators for predicting deep vein thrombosis in patients with lower limb fractures, as shown in Table 3 . Table 3 Multivariate logistic regression analysis on the occurrence of DVT in fracture patients. Risk factor Coef (β) S.E. Wald OR 95% CI P Intercept −5.498 1.845 8.882 0.004 - 0.003 BMI 0.944 0.716 1.741 2.571 0.632–10.456 0.187 Fracture site −0.502 0.629 0.637 0.605 0.176–2.078 0.425 History of diabetes 1.410 0.776 3.300 4.095 0.895–18.743 0.069 PLT 2.957 0.967 9.349 19.233 2.890−127.975 0.002 D-D 2.347 0.993 5.584 10.457 1.492–73.270 0.018 FIB 1.652 0.703 5.521 5.220 1.315–20.715 0.019 FDP 2.454 0.978 6.290 11.634 1.710−79.168 0.012 3.5 Construction of risk model for deep vein thrombosis in patients with lower limb fractures We converted the complex logistic regression formula into a visual nomogram based on independent risk factors, with a C index of 0.962 (0.936–0.988), suggesting that the nomogram has good discrimination and consistency, and the calibration curve shows that the risk probability of deep vein thrombosis of the lower extremity predicted by the nomogram model is consistent with the actual observation results in the study cohort (χ 2 = 5.242, P = 0.731), the AUC value of the ROC curve is 0.962, indicating that the predicted value of this column chart is high, as shown in Figs. 2 , 3 , and 4 . 4. Discussion Lower limb deep vein thrombosis is a kind of abnormal blood coagulation in the deep vein lumen, resulting in vascular stenosis or occlusion, which has been proved to be a common complication of lower limb fractures 1 . Considering that the pain at the fracture site in the early stage of trauma and local edema limit the movement of the patient's limbs, thereby slowing down the blood flow of the affected limb, coupled with the stress response caused by fractures, platelet adhesion may increase 14, 15 . Considering that deep vein thrombosis can bring bad prognosis to fracture patients and prolong the fracture healing time, in order to improve the quality of life of patients, it is clinically necessary to increase the assessment of early diagnosis and treatment of fracture patients with deep vein thrombosis risk 16 . However, in the past, a large number of studies have focused on exploring the prevention and treatment of thrombosis, and there have been relatively few studies related to the prediction of risk factors for lower limb thrombosis. At present, the commonly used method for diagnosing deep vein thrombosis is phlebography, which has high specificity and sensitivity. However, due to its high price, complicated operation, invasive operation and other reasons, it is not conducive to the general survey of clinical diseases. At this stage, some researchers have proposed that the laboratory test results can be applied to the diagnosis of lower limb deep vein thrombosis to a certain extent 17 . D-D, FIB and FDP belong to fibrin products, which can effectively reflect the hypercoagulable state of the body blood, and have a good role in predicting lower limb deep vein thrombosis. PLT is involved in the hemostasis and coagulation process of the body. Therefore, in this study, we analyzed the clinical data of 120 patients with lower limb fractures treated in our hospital, analyzed the correlation and predictive value of D-D, FIB, FDP and PLT with lower limb deep vein thrombosis, and established a clinical prediction model for the risk of DVT in fracture patients. In this study, we divided the fracture patients into DVT group and non DVT group according to whether they were complicated with lower limb deep vein thrombosis. By comparing the levels of D-D, FIB, FDP and platelets between the two groups, we found that the levels of D-dimer, FIB, FDP and PLT in DVT group were higher than those in non DVT group. The following reasons were considered: (1) Fibrinolysis system is the key anticoagulant mechanism in the body, which can maintain the permeability Blood flow status and involvement in tissue repair 18 ; As the degradation product of fibrin after plasmin is activated, D-D has strong antigenic specificity. Relevant research reports that its level increases with the activation of the fibrinolytic system 19 , and its level change can be used to evaluate the balance between patients' coagulation and fibrinolytic systems, so it can be used as an important monitoring indicator of thrombotic diseases 20, 21 . (2) FIB, as a protein synthesized in the liver and involved in coagulation, belongs to a class of coagulation factors (coagulation factor I). It increases blood viscosity and peripheral resistance by promoting platelet aggregation, endothelial cell growth, proliferation, and enhancing smooth muscle contraction to accelerate thrombosis formation 22 . (3) FDP can reflect the activation of the fibrinolytic system and thrombosis of the body to a certain extent, and the FDP content is significantly higher than the normal value during primary fibrinolysis and venous thrombosis 23 . (4) PLT, as one of the common blood cells in the human body, are mainly involved in the hemostasis and coagulation processes of the body. People with higher levels of PLT in their peripheral blood are more likely to cause thrombosis than other populations (activated platelets can induce platelet aggregation by producing thromboxanes, leading to thrombosis) 10 . We used ROC curve to evaluate the levels of D-D, FIB, FDP and PLT in the peripheral blood of patients with lower limb fractures to predict lower limb deep vein thrombosis. The results showed that the four levels had a certain predictive value, and FIB was better than D-D, FDP and PLT in predicting the risk of fracture with thrombosis, while PLT had the weakest predictive effect. We further confirmed that the levels of D-D, FIB, FDP and PLT are independent risk factors for deep vein thrombosis in patients with lower limb fractures by using multivariate logistic regression model analysis (P < 0.05). This study constructed a line chart risk prediction model based on independent risk factors screened by logistic regression models. The predictive factors used in this model are common and easy to identify in clinical practice. The line chart shows strong predictive ability, good discrimination and consistency (with a C-index of 0.962), And during the validation process, the calibration curve showed that there was no significant difference (P > 0.05) between the predicted risk probability of DVT using the column plot model and the actual observation results, indicating that the predictive model had a good fit. Therefore, clinical doctors can effectively predict and screen patients with lower limb fractures based on this model, and develop corresponding personalized treatment plans to improve the quality of life of patients. There are certain limitations to this study. The sample size included in this study is small and the time span is long. Some patients are excluded because of lack of data, so selection bias cannot be ruled out. In the future research, we will further expand the sample size in order to provide more accurate data support for clinic. In summary, patients with lower limb fractures combined with DVT have significantly increased levels of D-D, FIB, FDP, and PLT in their peripheral blood, which is a good predictive indicator for lower limb fractures combined with DVT. In clinical practice, dynamic detection of peripheral blood D-D, FIB, FDP, and PLT levels can be used for early screening and diagnosis of patients with lower limb fractures to avoid affecting their poor prognosis. In addition, the column chart constructed based on this has high clinical application value, which can help clinical doctors formulate or adjust reasonable diagnosis and treatment plans in a timely manner. Declarations Declaration of Competing Interest The authors declare no conflicts of interest. Ethical approval This study was conducted according to the ethical principles of medical research involving human subjects in the Declaration of Helsinki and have been approved by the biomedical ethics committee of the 900th Hospital of Joint Logistics Support Force (Number: 2024-093). Patients and their families signed informed consent before operation. Consent for publication Written informed consent for publication was obtained from the participant. Funding This work was supported by the Natural Science Fund of Fujian Province (2017J01327); the Special Research Project of Training Injury Prevention and Treatment in the 900th Hospital of Joint Logistics Support Force (2023XL02), the Key Project of No.900th Hospital of Joint Logistics Support Force (2023ZS01). Author Contribution F Z: study design, data collection and manuscript writing. Xb C, Jq H: data collection and manuscript review. Xb C and C L: data collection and analysis. Acknowledgements The authors would like to thank all colleagues for data collection from the Department of General Surgery, No.900th Hospital of China People's Liberation Army Joint Logistics Support Force. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Sugimura 1OR, Kawamura M. Sensitivity to activated protein C in patients with deep vein thrombosis during early puerperium period. Acta Obstet Gynecol Scand. 2005;84(8):799–801. 10.1111/j.0001-6349.2005.00762.x . Cao Wenya is always red Padua score combined with serum D? Analysis of early prediction of deep vein thrombosis in patients with acute ischemic stroke by dimer [J]. Nurs Res. 2019;33(16):2841–4. 10.12102/j.issn.1009-6493.2019.16.024 . Cipolle 3RFB, Velmahos MD. 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Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Surgery → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Reviews received at journal 18 Nov, 2024 Reviews received at journal 17 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers agreed at journal 25 Oct, 2024 Reviewers invited by journal 25 Oct, 2024 Editor invited by journal 23 Oct, 2024 Editor assigned by journal 22 Oct, 2024 Submission checks completed at journal 22 Oct, 2024 First submitted to journal 18 Oct, 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. 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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-5292114","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372058389,"identity":"c6ae79f2-1ff9-478b-8016-4994c625bb5a","order_by":0,"name":"Fan Zheng","email":"","orcid":"","institution":"No.900th Hospital of China People's Liberation Army Joint Logistics Support Force","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Zheng","suffix":""},{"id":372058390,"identity":"e1dfaad7-7332-45fc-944f-8d53ec83e526","order_by":1,"name":"Xiaobin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYFACHoYDCTwScvzsjY0PPhCphfHABxkLY8mew82GM4jUwnxwhk1F4oYb6W3SHMRo4J+Re+AwT46EscHNhw3SDAx2croNBLRInDmXcJjnjISc5O3EBuMChmRjswMEtBiw9xgc5u2RMOYDakmewXAgcRtBLcw8QC3/JBIbbh5sOMxDlBagLQdn8EgkTrjB2NhMlBaQXw584JEABnJiM+MMAyL8Agyxwx8SeOqAUXn8+Y8PFXZyBLWgu5M05aNgFIyCUTAKcAAACBRHLICBk08AAAAASUVORK5CYII=","orcid":"","institution":"No.900th Hospital of China People's Liberation Army Joint Logistics Support Force","correspondingAuthor":true,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Chen","suffix":""},{"id":372058391,"identity":"fbd6ba95-1d29-4689-9559-4cb6fc6fbe15","order_by":2,"name":"Jianqiang Huang","email":"","orcid":"","institution":"No.900th Hospital of China People's Liberation Army Joint Logistics Support Force","correspondingAuthor":false,"prefix":"","firstName":"Jianqiang","middleName":"","lastName":"Huang","suffix":""},{"id":372058392,"identity":"64b376a4-3e31-41f5-b80c-c384d304c085","order_by":3,"name":"Chen Lin","email":"","orcid":"","institution":"No.900th Hospital of China People's Liberation Army Joint Logistics Support Force","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-10-19 02:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5292114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5292114/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12893-024-02718-3","type":"published","date":"2024-12-21T15:58:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68561900,"identity":"5f754db7-ddc2-4f91-a388-e941b0ff1880","added_by":"auto","created_at":"2024-11-08 14:35:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":302648,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curve for Predicting the Risk of Fracture Complication with Thrombosis in D-D、FIB、FDP and PLT.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5292114/v1/40b9d3898fbed3f369ad92bc.png"},{"id":68563450,"identity":"0f3ca174-3f79-44bf-a21c-5295c9cc460e","added_by":"auto","created_at":"2024-11-08 14:43:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":355749,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model of risk probability of lower limb deep vein thrombosis in fracture patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5292114/v1/a233c6dc82c4562cd1d90c9b.png"},{"id":68561903,"identity":"2723d416-d46b-4066-9736-889b7db4ede3","added_by":"auto","created_at":"2024-11-08 14:35:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299080,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of column chart prediction model.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5292114/v1/089b23d4225d5af3ef0f8120.png"},{"id":68561901,"identity":"6da14eaa-a2e5-43ec-a799-8ba5846e5c51","added_by":"auto","created_at":"2024-11-08 14:35:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":231493,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve used to verify the predictive ability of the model.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5292114/v1/b75925dcb1e7979aa2a1cd70.png"},{"id":72201934,"identity":"6dba5c87-0033-483d-8e8f-81b1961f9820","added_by":"auto","created_at":"2024-12-23 16:12:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1769299,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5292114/v1/7797b1e8-4c99-402d-a6d8-d8913bad9d8d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on risk factors of preoperative deep vein thrombosis in patients with lower limb fractures and construction and validation of risk prediction nomogram model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDeep vein thrombosis (DVT) of lower limbs is caused by a variety of reasons, which lead to changes in the coagulation mechanism of the body to promote thrombosis, and then lead to stenosis or occlusion of the vascular lumen, which is more likely to occur in lower limb blood vessels (including femoral vein and deep leg vein) \u003csup\u003e1,2\u003c/sup\u003e. It has been reported that deep vein thrombosis is a common complication after traumatic fracture. Among hospitalized patients, the risk of deep vein thrombosis in trauma patients is 13 times higher than that in non trauma patients \u003csup\u003e3\u003c/sup\u003e. Considering that fractures can damage the vascular wall to make the blood in a hypercoagulable state, which is easy to lead to thrombosis \u003csup\u003e4\u003c/sup\u003e. If the patient is complicated with deep vein thrombosis, the fracture healing will be greatly affected; In addition, pulmonary embolism (PE) caused by lower limb deep vein thrombosis poses a pathogenic threat to the health of patients \u003csup\u003e5\u003c/sup\u003e. In addition, lower limb fractures, as one of the common types of fractures, account for one-third of fracture patients and are mostly concentrated in the elderly population. With the increasing aging of the population, the proportion of patients with lower limb fractures is showing an increasing trend \u003csup\u003e6\u003c/sup\u003e. Clinically, the early symptoms of lower limb deep vein thrombosis are lack of specificity, and the symptoms are relatively hidden. Only 10\u0026ndash;17% of DVT patients have obvious clinical symptoms (such as lower limb swelling, local deep tenderness, back flexion pain), which is easy to cause misdiagnosis and missed diagnosis. Therefore, early diagnosis and intervention for high-risk patients has important clinical significance \u003csup\u003e7, 8\u003c/sup\u003e. At present, the commonly used methods for diagnosis of deep vein thrombosis include imaging detection methods such as vascular color ultrasound and venography. Although these technologies have certain specificity and sensitivity, they cannot be used to predict the occurrence of DVT (the high price and complicated operation limit their application in clinical disease screening). Therefore, it is very important for clinical diagnosis and treatment of lower limb deep vein thrombosis to explore new and effective predictive indicators for early diagnosis.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAccording to reports, levels of D-dimer (D-D), fibrinogen (FIB), fibrinogen degradation products (FDP), and platelets (PLT) can to some extent reflect the hypercoagulable state of the blood \u003csup\u003e9,10\u003c/sup\u003e. FIB, as a glycoprotein synthesized and secreted by hepatocytes, can induce secondary fibrinolysis hyperfunction by increasing the activity of plasmin, and then participate in the process of coagulation and hemostasis, which has a certain correlation with the occurrence of DVT; While D-D, as plasmin hydrolyzed cross-linked fibrinogen, can also be used as an indicator of body thrombosis. When there is thrombosis in the vascular lumen, its indicator can significantly rise \u003csup\u003e11\u003c/sup\u003e; FDP have a certain correlation with D-D, which can reflect the coagulation function of the patient's body and are recognized as biochemical indicators of thrombotic diseases in clinical practice; Platelets mainly participate in the body's coagulation process by promoting hemostasis and accelerating coagulation. This study retrospectively analyzed 145 patients with lower limb fractures admitted to the emergency department of our hospital, analyzed the correlation and predictive value of D-D, FIB, FDP and PLT with lower limb deep vein thrombosis, analyzed the risk factors of lower limb fractures complicated with lower limb deep vein thrombosis, and constructed a nomogram of the risk probability of lower limb deep vein thrombosis, in order to effectively guide clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eRetrospective analysis of the clinical data of 145 patients with traumatic fractures of the lower limbs who visited the department of General Surgery of our hospital from September 2022 to March 2023, including 70 males and 75 females, aged 26 to 76 years, with an average age of 53.78\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76 years; The patients were divided into DVT group and non DVT group according to whether there was deep vein thrombosis of lower limbs. Inclusion criteria: (1) All fracture patients were diagnosed in accordance with the clinical diagnostic criteria for limb fractures in the \"Chinese Open Fracture Diagnosis and Treatment Guidelines (2019 Edition)\" \u003csup\u003e12\u003c/sup\u003e; (2) All patients underwent lower limb venous ultrasound examination, and patients with lower limb thrombosis were diagnosed by completing lower limb angiography (DSA) if necessary; (3) The patient has no previous history of lower limb thrombosis. Exclusion criteria: (1) Previous history of pulmonary embolism; (2) Previous history of atrial fibrillation; (3) Patients who have undergone vascular related surgery in the past; (4) Patients with other organ tumors; (5) Patients with abnormal coagulation function. This study followed the ethical standards of Helsinki Declaration, and all patients were informed and signed the relevant consent before treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eGeneral and clinical data of all patients were collected, including gender, age, body mass index (BMI), trauma site, history of diabetes, history of hypertension, cause of injury, platelet, D-D, FIB and FDP levels. Compare the differences in D-D, FIB, FDP, and platelet levels between DVT and non DVT groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Diagnostic criteria of DVT\u003c/h2\u003e \u003cp\u003eAccording to the \"Guidelines for Diagnosis and Treatment of Deep Venous Thrombosis\" \u003csup\u003e13\u003c/sup\u003e: (1) The lower limbs are swollen and painful; (2) The blood coagulation function is in hypercoagulable state; (3) D-D level\u0026thinsp;\u0026gt;\u0026thinsp;500\u0026micro;g/L; (4) B-ultrasound examination can directly see that the blood vessels can not be squashed by the probe, and there is uneven echo shadow in the lumen; (5) The blood vessels can't be completely filled or appear defect after filling. During the examination, 1\u0026thinsp;~\u0026thinsp;4 items were screened first, and those whose first four items met the requirements were screened for the fifth item, and the diagnosis was DVT according to the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 therapeutic method\u003c/h2\u003e \u003cp\u003eWhen each fracture patient is admitted to the hospital, he will be diagnosed by color Doppler ultrasound of lower limbs immediately, and then he will be selectively intervened to prevent or treat deep venous thrombosis of lower limbs. For patients without thrombus, it is considered that fracture can make blood in hypercoagulable state by damaging blood vessel wall, which will easily lead to thrombosis and increase the risk of thrombosis, so low dose anticoagulation can be selected to prevent it, and the dose is 0.2 ml of low molecular weight heparin (2/ day); For patients with thrombus, they can be treated according to their weight. The routine dose is 0.6ml of low molecular weight heparin (2/ day). Both patients stopped anticoagulation one day before operation and resumed anticoagulation treatment as soon as possible after operation. In addition, for patients with deep venous thrombosis of lower limbs, pneumatic pump can be used to contract the muscles of the ankle and calf after operation to promote local blood circulation. Improving the venous return of lower limbs can also play a therapeutic role to some extent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eSPSS (version 26.0) and R language (version 4.2.1) software was used for statistical analysis of the data. The counting data is represented by n (%), and inter group comparisons are performed using χ\u003csup\u003e2\u003c/sup\u003e tests or Fisher tests (n\u0026thinsp;\u0026lt;\u0026thinsp;5); The measurement data is expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and t-test is used for inter group comparison. The predictive value of using receiver operating characteristic curve (ROC curve) to evaluate D-D, FIB, FDP, and PLT levels in DVT patients with lower limb fractures. Logistic regression analysis was used to analyze the related risk factors of deep vein thrombosis. Multifactor Logistic regression was used to establish a prediction model for the risk of deep vein thrombosis of lower limbs. The model was visualized by nomogram, and the prediction accuracy and discrimination ability of the model were determined by calibration curve, ROC curve and consistency index (C index), α=0.05 is the inspection level.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparison of D-D, FIB, FDP, PLT levels between two groups\u003c/h2\u003e \u003cp\u003eThe levels of D-dimer, FIB, FDP, and PLT in the DVT group were higher than those in the non DVT group, with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of D-dimer, FIB, FDP levels between two groups (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDVT group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-DVT group(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-D (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;8.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.96\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;7.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e289.83\u0026thinsp;\u0026plusmn;\u0026thinsp;90.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e203.50\u0026thinsp;\u0026plusmn;\u0026thinsp;84.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;5.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Comparison of D-dimer, FIB, FDP, and PLT in predicting the risk of fracture combined with thrombosis\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eDraw ROC curves of D-D, FIB, FDP, and PLT to evaluate their ability to predict the risk of lower limb thrombosis. The results showed that the AUC of D-D, FIB, FDP, and PLT were 0.810 (95%CI: 0.725\u0026ndash;0.894, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.861 (95%CI: 0.800\u0026minus;0.923, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 0.834 (95%CI: 0.755\u0026ndash;0.913, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 0.752 (95%CI: 0.670\u0026ndash;0.834, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. It can be seen that FIB is superior to D-D, FDP in predicting the risk of fracture complicated thrombosis PLT has the weakest predictive performance, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Single factor analysis of deep vein thrombosis in patients with lower limb fractures\u003c/h2\u003e \u003cp\u003eThis study included 145 patients with lower limb fractures, including 66 patients with lower limb deep vein thrombosis and 79 patients with non deep vein thrombosis. By comparing the differences of general information between the two groups, we found that BMI, trauma site, whether diabetes was combined with history, platelets, D-D, FIB and FDP were related to lower limb deep vein thrombosis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while gender, age The history of hypertension and the cause of injury were not related to the occurrence of deep vein thrombosis in the lower limbs (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eComparison of general information between two groups of patients (n).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDVT group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-DVT group n\u0026thinsp;=\u0026thinsp;79\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47\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\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\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\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\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\u003e\u0026gt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\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\u003eFracture site\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibiofibular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\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\u003efemur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\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\u003ehip bone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\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\u003eHistory of diabetes\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\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\u003eHistory of hypertension\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\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\u003eEnergy\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\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\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\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\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\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\u003e\u0026gt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\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\u003eD-D (mg/L)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\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\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\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\u003eFIB (g/L)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\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\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\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\u003eFDP (mg/L)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\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\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Multifactor analysis of deep vein thrombosis in patients with lower limb fractures\u003c/h2\u003e \u003cp\u003eSingle factor meaningful indicators were included in the multivariate logistic regression model for analysis. The results showed that platelets (OR\u0026thinsp;=\u0026thinsp;19.233, 95%CI: 2.890-127.975, P\u0026thinsp;=\u0026thinsp;0.002), D-D (OR\u0026thinsp;=\u0026thinsp;10.457, 95%CI: 1.492\u0026ndash;73.270, P\u0026thinsp;=\u0026thinsp;0.018), FIB (OR\u0026thinsp;=\u0026thinsp;5.220, 95%CI: 1.315\u0026ndash;20.715, P\u0026thinsp;=\u0026thinsp;0.019), FDP (OR\u0026thinsp;=\u0026thinsp;11.634, 95%CI: 1.710-79.168, P\u0026thinsp;=\u0026thinsp;0.012) were independent risk factors for deep vein thrombosis in patients with lower limb fractures (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); BMI, fracture site and diabetes are not independent risk factors for deep vein thrombosis in patients with lower limb fractures (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but they can still be used as important reference indicators for predicting deep vein thrombosis in patients with lower limb fractures, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis on the occurrence of DVT in fracture patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;5.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.632\u0026ndash;10.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.176\u0026ndash;2.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.895\u0026ndash;18.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.890\u0026minus;127.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.492\u0026ndash;73.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.315\u0026ndash;20.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.710\u0026minus;79.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Construction of risk model for deep vein thrombosis in patients with lower limb fractures\u003c/h2\u003e \u003cp\u003eWe converted the complex logistic regression formula into a visual nomogram based on independent risk factors, with a C index of 0.962 (0.936\u0026ndash;0.988), suggesting that the nomogram has good discrimination and consistency, and the calibration curve shows that the risk probability of deep vein thrombosis of the lower extremity predicted by the nomogram model is consistent with the actual observation results in the study cohort (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.242, P\u0026thinsp;=\u0026thinsp;0.731), the AUC value of the ROC curve is 0.962, indicating that the predicted value of this column chart is high, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLower limb deep vein thrombosis is a kind of abnormal blood coagulation in the deep vein lumen, resulting in vascular stenosis or occlusion, which has been proved to be a common complication of lower limb fractures \u003csup\u003e1\u003c/sup\u003e. Considering that the pain at the fracture site in the early stage of trauma and local edema limit the movement of the patient\u0026apos;s limbs, thereby slowing down the blood flow of the affected limb, coupled with the stress response caused by fractures, platelet adhesion may increase \u003csup\u003e14, 15\u003c/sup\u003e. Considering that deep vein thrombosis can bring bad prognosis to fracture patients and prolong the fracture healing time, in order to improve the quality of life of patients, it is clinically necessary to increase the assessment of early diagnosis and treatment of fracture patients with deep vein thrombosis risk \u003csup\u003e16\u003c/sup\u003e. However, in the past, a large number of studies have focused on exploring the prevention and treatment of thrombosis, and there have been relatively few studies related to the prediction of risk factors for lower limb thrombosis. At present, the commonly used method for diagnosing deep vein thrombosis is phlebography, which has high specificity and sensitivity. However, due to its high price, complicated operation, invasive operation and other reasons, it is not conducive to the general survey of clinical diseases. At this stage, some researchers have proposed that the laboratory test results can be applied to the diagnosis of lower limb deep vein thrombosis to a certain extent \u003csup\u003e17\u003c/sup\u003e. D-D, FIB and FDP belong to fibrin products, which can effectively reflect the hypercoagulable state of the body blood, and have a good role in predicting lower limb deep vein thrombosis. PLT is involved in the hemostasis and coagulation process of the body. Therefore, in this study, we analyzed the clinical data of 120 patients with lower limb fractures treated in our hospital, analyzed the correlation and predictive value of D-D, FIB, FDP and PLT with lower limb deep vein thrombosis, and established a clinical prediction model for the risk of DVT in fracture patients.\u003c/p\u003e\n\u003cp\u003eIn this study, we divided the fracture patients into DVT group and non DVT group according to whether they were complicated with lower limb deep vein thrombosis. By comparing the levels of D-D, FIB, FDP and platelets between the two groups, we found that the levels of D-dimer, FIB, FDP and PLT in DVT group were higher than those in non DVT group. The following reasons were considered: (1) Fibrinolysis system is the key anticoagulant mechanism in the body, which can maintain the permeability Blood flow status and involvement in tissue repair \u003csup\u003e18\u003c/sup\u003e; As the degradation product of fibrin after plasmin is activated, D-D has strong antigenic specificity. Relevant research reports that its level increases with the activation of the fibrinolytic system \u003csup\u003e19\u003c/sup\u003e, and its level change can be used to evaluate the balance between patients\u0026apos; coagulation and fibrinolytic systems, so it can be used as an important monitoring indicator of thrombotic diseases \u003csup\u003e20, 21\u003c/sup\u003e. (2) FIB, as a protein synthesized in the liver and involved in coagulation, belongs to a class of coagulation factors (coagulation factor I). It increases blood viscosity and peripheral resistance by promoting platelet aggregation, endothelial cell growth, proliferation, and enhancing smooth muscle contraction to accelerate thrombosis formation \u003csup\u003e22\u003c/sup\u003e. (3) FDP can reflect the activation of the fibrinolytic system and thrombosis of the body to a certain extent, and the FDP content is significantly higher than the normal value during primary fibrinolysis and venous thrombosis \u003csup\u003e23\u003c/sup\u003e. (4) PLT, as one of the common blood cells in the human body, are mainly involved in the hemostasis and coagulation processes of the body. People with higher levels of PLT in their peripheral blood are more likely to cause thrombosis than other populations (activated platelets can induce platelet aggregation by producing thromboxanes, leading to thrombosis) \u003csup\u003e10\u003c/sup\u003e. We used ROC curve to evaluate the levels of D-D, FIB, FDP and PLT in the peripheral blood of patients with lower limb fractures to predict lower limb deep vein thrombosis. The results showed that the four levels had a certain predictive value, and FIB was better than D-D, FDP and PLT in predicting the risk of fracture with thrombosis, while PLT had the weakest predictive effect. We further confirmed that the levels of D-D, FIB, FDP and PLT are independent risk factors for deep vein thrombosis in patients with lower limb fractures by using multivariate logistic regression model analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eThis study constructed a line chart risk prediction model based on independent risk factors screened by logistic regression models. The predictive factors used in this model are common and easy to identify in clinical practice. The line chart shows strong predictive ability, good discrimination and consistency (with a C-index of 0.962), And during the validation process, the calibration curve showed that there was no significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the predicted risk probability of DVT using the column plot model and the actual observation results, indicating that the predictive model had a good fit. Therefore, clinical doctors can effectively predict and screen patients with lower limb fractures based on this model, and develop corresponding personalized treatment plans to improve the quality of life of patients. There are certain limitations to this study. The sample size included in this study is small and the time span is long. Some patients are excluded because of lack of data, so selection bias cannot be ruled out. In the future research, we will further expand the sample size in order to provide more accurate data support for clinic.\u003c/p\u003e\n\u003cp\u003eIn summary, patients with lower limb fractures combined with DVT have significantly increased levels of D-D, FIB, FDP, and PLT in their peripheral blood, which is a good predictive indicator for lower limb fractures combined with DVT. In clinical practice, dynamic detection of peripheral blood D-D, FIB, FDP, and PLT levels can be used for early screening and diagnosis of patients with lower limb fractures to avoid affecting their poor prognosis. In addition, the column chart constructed based on this has high clinical application value, which can help clinical doctors formulate or adjust reasonable diagnosis and treatment plans in a timely manner.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003e This study was conducted according to the ethical principles of medical research involving human subjects in the Declaration of Helsinki and have been approved by the biomedical ethics committee of the 900th Hospital of Joint Logistics Support Force (Number: 2024-093). Patients and their families signed informed consent before operation.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003e Written informed consent for publication was obtained from the participant.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Natural Science Fund of Fujian Province (2017J01327); the Special Research Project of Training Injury Prevention and Treatment in the 900th Hospital of Joint Logistics Support Force (2023XL02), the Key Project of No.900th Hospital of Joint Logistics Support Force (2023ZS01).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF Z: study design, data collection and manuscript writing. Xb C, Jq H: data collection and manuscript review. Xb C and C L: data collection and analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank all colleagues for data collection from the Department of General Surgery, No.900th Hospital of China People's Liberation Army Joint Logistics Support Force.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSugimura 1OR, Kawamura M. Sensitivity to activated protein C in patients with deep vein thrombosis during early puerperium period. 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Clin Appl Thromb Hemost 2021 Jan-Dec;27:1076029620986862. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1076029620986862\u003c/span\u003e\u003cspan address=\"10.1177/1076029620986862\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiaolin 21Z, Ying Z, Lin M. etc The application effect of D-dimer and ultrasound in evaluating the stability of lower limb deep vein thrombosis [J] Guangxi Medicine, 2018,40 (23): 2798\u0026ndash;2801 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11675/j.issn.0253-4304.2018.23.14\u003c/span\u003e\u003cspan address=\"10.11675/j.issn.0253-4304.2018.23.14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShian 22WJ. Deep Venous Thrombosis and Pulmonary Embolism: Current Therapy. Am Fam Physician. 2017;95(5):295\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBo 23H. Wang Ke The significance of plasma D dimer and FDP detection in the diagnosis of DIC [J]. Chongqing Med J. 2004;33(11):1666\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3969/j.issn.1671-8348.2004.11.032\u003c/span\u003e\u003cspan address=\"10.3969/j.issn.1671-8348.2004.11.032\" 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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lower limb fracture, deep vein thrombosis of lower extremity, risk factors, nomogram, fibrinogen","lastPublishedDoi":"10.21203/rs.3.rs-5292114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5292114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo explore the correlation between the levels of D-dimer (D-D), fibrinogen (FIB), fibrinogen degradation products (FDP) and platelets (PLT) in peripheral blood of patients with lower limb fractures and the formation of deep vein thrombosis in lower limbs, and to establish a new thrombosis prediction model for patients with lower limb fractures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe patients were divided into DVT group and non DVT group according to whether there was deep vein thrombosis of the lower extremity. The differences in the levels of D-D, FIB, FDP and platelets between the two groups were analyzed and compared. ROC curve was used to evaluate the levels of D-D, FIB, FDP and PLT in the peripheral blood of patients with lower extremity fracture to predict the formation of deep vein thrombosis of the lower extremity. Logistic regression analysis was used to analyze the related risk factors of deep vein thrombosis, and the corresponding nomogram risk prediction model of lower limb deep vein thrombosis in patients with lower limb fractures was drawn according to the regression coefficient, which was verified by calibration curve and consistency curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe levels of D-D, FIB, FDP, and PLT in the DVT group were higher than those in the non DVT group, with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Moreover, FIB is superior to D-D, FDP, and PLT in predicting the risk of fractures and thrombosis, while PLT has the weakest predictive power. Multivariate logistic analysis showed that platelet, D-D, FIB and FDP were independent risk factors for deep vein thrombosis in patients with lower limb fractures (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Based on the independent risk factors mentioned above, the complex logistic regression formula was transformed into a visual column chart (with a C-index of 0.962), which has good discrimination and consistency.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe levels of D-D, FIB, FDP and PLT in peripheral blood of patients with lower limb fracture and DVT were significantly increased. Early monitoring of D-D, FIB, FDP and PLT levels in patients with lower limb fracture can effectively screen for lower limb deep vein thrombosis.\u003c/p\u003e","manuscriptTitle":"Study on risk factors of preoperative deep vein thrombosis in patients with lower limb fractures and construction and validation of risk prediction nomogram model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 14:34:57","doi":"10.21203/rs.3.rs-5292114/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-18T20:47:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-18T17:17:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-17T15:40:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71323634931151985254919463658627770190","date":"2024-10-29T08:17:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240388706875824417848556395040739801660","date":"2024-10-25T07:23:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-25T04:31:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-23T12:50:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-22T09:41:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-22T09:38:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2024-10-19T02:18:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f143134d-87cb-45db-a69d-b9ee6a22f0a8","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:05:20+00:00","versionOfRecord":{"articleIdentity":"rs-5292114","link":"https://doi.org/10.1186/s12893-024-02718-3","journal":{"identity":"bmc-surgery","isVorOnly":false,"title":"BMC Surgery"},"publishedOn":"2024-12-21 15:58:20","publishedOnDateReadable":"December 21st, 2024"},"versionCreatedAt":"2024-11-08 14:34:57","video":"","vorDoi":"10.1186/s12893-024-02718-3","vorDoiUrl":"https://doi.org/10.1186/s12893-024-02718-3","workflowStages":[]},"version":"v1","identity":"rs-5292114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5292114","identity":"rs-5292114","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}