Deep venous thrombosis in Polytrauma patients with Traumatic Brain Injury： development and validation of a predictive model

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Methods: A retrospective and observationaltrails were performed from November,2021 to May,2023. The prediction model was developed in a training cohort that consisted of 349 polytrauma patients with traumatic brain injury and data was gathered from November,2021 to August,2022. The baseline clinical characteristics from the electronic medical and nursing records of each patient which include demographics, medical records, laboratory parameters, and clinical outcomes were collected. Multivariable logistic regression analysis was used to develop the predicting model, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 298 consecutive patients from August,2022 to May,2023. Result: A total of 647 trauma patients who met the inclusion criteria. Among these, 349 patients were in training cohort and 298 patients were in validation cohort. The incidence of DVT was 32.1% and 31.9% in the trainingand validation cohorts, respectively. Predictors contained in the individualized prediction nomogram the Age, Smoking, ISS, GCS, D-dimer, MV and AVD. The model showed a good discrimination, with a C-index of 0.783 and a good calibration. Calibration curves and decision curve analysis of the DVT-predicting nomogram demonstrated that the nomogram was clinically useful. Conclusion: This study presents a nomogram that incorporates both the demographic characteristics and clinical risk factors, and can be conveniently used to individualized prediction of DVT in polytrauma patients with traumatic brain injury Health sciences/Risk factors Health sciences/Diseases/Trauma Polytrauma Traumatic Brain Injury Deep Venous Thrombosis Clinical Risk Score Figures Figure 1 Figure 2 1. Introduction Polytrauma, characterized by poor outcomes and high mortality due to fatal damage and tricky complications, represents a complex disease and continues to be a sustained health issue. Trauma-induced coagulopathy (TIC) is a common direct cause of early post-injury mortality (30–50%),from which deep venous thrombosis(DVT) is a pathological outcome related to a coagulation disorder originating[ 1 ].Previous studies found the incidence of lower extremity DVT to be as high as 20% in polytrauma patients [ 2 ], and DVT is also the primary cause of death in patients who survive the initial traumatic insults[ 3 ]. Direct mechanical venous injury, systemic hypercoagulability, and immobilization by skeletal fixation contribute to the high incidence of DVT in polytrauma and the immobility arising from the underlying neurologic lesion itself, and use of sedatives and neuromuscular blocking agents[ 4 ] in traumatic brain injury(TBI) with which more than 50% of polytrauma patients are accompanied [ 5 ],is also associated with a high risk of thromboembolic complications[ 6 , 7 ].It is consistent with our own clinical practice experience that polytrauma patients with TBI were at an even higher risk of developing DVT than those polytrauma patients without TBI. The management of hemorrhage and disordered coagulation presents a prevalent and crucial challenge in cases of polytrauma, particularly in traumatic brain injury (TBI), where surgeons must carefully weigh the potential risks of ongoing brain hemorrhage against the development of secondary thrombotic complications such as deep venous thrombosis (DVT). The absence of conclusive evidence to inform decisions regarding the use of anticoagulant prophylaxis in polytrauma patients with TBI necessitates that surgeons rely on their own clinical judgment to assess the potential risks and benefits[ 4 ]. The timely identification of polytrauma patients with traumatic brain injury (TBI) at risk for developing deep vein thrombosis (DVT) is crucial for effective treatment and resource utilization. Currently, DVT diagnosis relies on the assessment of D-dimer (DD) levels and venous Doppler ultrasound (VDU)[ 8 ]. Numerous studies have demonstrated the sensitivity and accuracy of DD in detecting not only the presence but also the extent of thrombus formation[ 9 ] .DVT is typically diagnosed based on VDU findings, including dilated, noncompressible veins or intraluminal shadows indicative of thrombosiss. Studies have shown that the weighted mean sensitivity and specificity of venous Doppler ultrasound (VDU) in diagnosing proximal deep vein thrombosis (DVT) are estimated to be 97% and 94%, respectively[ 8 ].However, D-dimer (DD) testing has demonstrated low specificity in diagnosing DVT, particularly in polytrauma patients with complicating factors such as infection. Additionally, VDU may not be readily available for polytrauma patients requiring mechanical ventilation[ 10 ].Due to the lack of a specific and convenient diagnostic method, this study aims to develop a nomogram for estimating the risk of DVT in polytrauma patients with traumatic brain injury (TBI). 2. Patients and methods 2.1 Study design and Patients This was an institutional review board-approved, retrospective, observational trial with informed consent. All patients admitted to the traumatic intensive care unit (TICU) or intensive care unit (ICU) of the Advanced Trauma Center(Level I, verified by China Trauma Rescue &Treatment Association (CTRTA)) at the Tongji Hospital (Wuhan) from November,2021 to May,2023. A total of 647 consecutive patients met the eligibility requirements, which divided into DVT group and N-DVT group. Eligible patients between November,2021 to August,2022 were included into the training cohort for development of the nomogram, and those between August,2022 to May,2023 were entered into the validation cohort. The present study was approved by the ethics committee at Tongji Hospital (TJIRB20200720). Patient consent for data collection was obtained from each patient or the patient’s legally authorized representative. The entry criteria were: 1)Consistent with polytrauma with traumatic brain injury diagnosis;2) Admission within 24 hours;3) age over 18 years; Exclusions were patients who were pregnant and mentally ill. Patients with a history of VTE or treated with delayed presentation due to transfer were also excluded from our study. 2.2.Data Collection Retrospectively collected the baseline clinical characteristics from the electronic medical and nursing records of each patient which include demographics (such as age and sex), medical records (such as ISS score, GCS score ,Cause and location of injury and so on), laboratory parameters (such as level of D-dimer, Lactate ,APTT and so on at admission), and clinical outcomes (such as mechanical ventilation). Furthermore, a VDU of both lower extremities was performed from the ankle to the inguinal ligament to evaluate the deep venous system and determine the presence of a DVT. 2.3.Diagnosis The new “Berlin definition” of polytrauma: a patient with AIS ≥ 3 for two or more different body regions and with one or more additional parameters from the following: hypotension (systolic blood pressure ≤ 90 mmHg), unconsciousness (Glasgow Coma Scale [GCS] score ≤ 8), acidosis (base deficit ≤-6.0), coagulopathy (partial thromboplastin time [PTT] ≤ 40 s or international normalized ratio [INR] ≥ 1.4), and age ≥ 70 years[ 11 ]. DVT was defined as an abnormality seen on venous Doppler ultrasound (VDU), such as the presence of dilated, noncompressible veins or intraluminal shadows consistent with thrombosis[ 12 ]. TBI was defined by the presence of subdural hemorrhage, epidural hemorrhage, focal contusion, or diffuse axonal injury on computed tomography (CT) according to recommended guidelines[ 13 ]. All enrolled subjects received standardized treatment and management per established TBI and polytrauma guidelines. 2.4 Statistical Analysis Percentages are typically utilized in the analysis of categorical variables, while mean plus standard deviation (mean ± SD) are commonly employed for continuous variables. In the context of categorical and continuous variables, the chi-squared test or Fisher test and the Mann–Whitney U test or t-test are utilized separately. The significance of each variable in the training cohort was evaluated through univariate analysis to identify independent risk factors associated with the presence of DVT. Multivariate logistic regression analysis was conducted to analyze the independent risk factors of DVT in polytrauma patients with traumatic brain injury. A nomogram was created using multivariate logistic regression results and the rms package in R. Each regression coefficient was converted to a 0-100 point scale, with the variable having the highest β coefficient assigned 100 points. Total points were calculated by adding points across variables to predict probabilities. The C-index was used to assess model accuracy, bootstrap validation for overfitting, calibration plot for nomogram performance, ROC curve analysis for predictive accuracy. Statistical analysis was done using R software, with significance set at P < .05. 3. Result 3.1 Demographics and characteristics of patients The study cohort consisted of 647 trauma patients who met the inclusion criteria. Among these, 349 patients were in training cohort and 298 patients were in validation cohort. Patient characteristics in the training and validation cohorts are given in Table 1 .There are no significant differences between the two cohorts in DVT prevalence(P = 0.99). The incidence of DVT was 32.1% and 31.9% in the training and validation cohorts, respectively. There were no significant differences in the clinical characteristics between the training and the validation cohort, either within the DVT-positive cohort or in the DVT negative cohort, which justified their use as training and validation cohorts . In univariate analysis, older Age, Fat, Smoking, higher D-dimer levels, higher ISS, lower GCS score, Mechanical ventilation and Application of vasoactive drugs were associated with a higher risk of DVT in the training cohort. Particularly, delayed mechanical prophylaxis and delayed anticoagulant therapy were more susceptible to developing a DVT in the training cohort. Significant predictive factors for DVT are listed in Table 1 . Table 1 Characteristics of Patients in the Training and Validation Cohorts Variables Training(n = 349) Validation(n = 298) DVT(+,112) DVT(-,237) P DVT(+,95) DVT(-203) P Age 56.1 ± 12.3 47.6 ± 11.3 < 0.01 57.3 ± 11.5 46.2 ± 10.4 < 0.01 Male 84(75.0) 177(74.7) 0.95 70(73.7) 153(75.4) 0.87 BMI 24.3 ± 2.6 22.5 ± 1.7 < 0.01 24.9 ± 2.7 23.2 ± 2.4 < 0.01 Smoking 76(67.9) 86(36.3) < 0.01 67(70.5) 84(41.4) < 0.01 Anatomical location of injury - - - - - - Thoracic injury 58(51.8) 129(54.4) 0.73 51(53.7) 114(56.2) 0.78 Abdominal injury 50(44.6) 94(39.7) 0.44 45(47.4) 104(51.2) 0.62 Pelvic and limbs fracture 81(72.3) 116(48.9) < 0.01 73(76.8) 124(61.1) 0.01 ISS 32.8 ± 6.9 30.4 ± 6.1 < 0.01 33.5 ± 6.5 31.2 ± 7.1 < 0.01 GCS 8.2 ± 2.1 10.4 ± 1.7 < 0.01 8.9 ± 2.4 10.1 ± 1.8 < 0.01 Blood transfusions - - - - - - Red cells 39(34.8) 56(23.6) 0.04 34(35.8) 46(22.7) 0.02 Platelets 13(11.6) 21(8.9) 0.54 9(9.5) 15(7.4) 0.70 Fresh frozen plasma 27(24.1) 30(12.7) 0.01 25(26.3) 19(9.4) < 0.01 Lactate 1.9 ± 0.8 1.8 ± 0.7 0.24 1.7 ± 0.6 1.8 ± 0.8 0.28 PT 15.1 ± 2.4 14.5 ± 3.2 0.08 16.3 ± 2.6 16.7 ± 2.4 0.19 APTT 40.1 ± 11.8 38.6 ± 10.5 0.23 41.6 ± 12.5 39.8 ± 11.3 0.22 D-dimer 15.3 ± 6.2 3.6 ± 1.3 < 0.01 17.6 ± 6.4 3.7 ± 1.8 < 0.01 Mechanical ventilation 34(30.4) 19(8.2) < 0.01 33(34.7) 13(6.4) < 0.01 First day of mechanical prophylaxis ≤ 3d 25(22.3) 140(60.1) < 0.01 27(28.4) 126(62.1) < 0.01 Application of vasoactive drugs 42(37.5) 11(4.7) < 0.01 33(34.7) 7(3.4) < 0.01 All values are presented as mean ± standard deviation or number(percentage);DVT: deep vein thrombosis; GCS: Glasgow coma scale; ISS: injury severity score; P < 0.05 is statistically significant 3.2.Multivariate analyses of relative factors for DVT After univariate analysis, those variables with a P value < 0.05 were selected for multivariate analysis using a multiple logistic regression model. For a logistic regression analysis to detect risk factors of DVT in polytrauma patients with traumatic brain injury in the training cohort, these variables: Age, Smoking, ISS, GCS, D-dime, Mechanical ventilation, Application of vasoactive drugs were included. M Multivariate logistic regression analysis showed that an increasing Age(OR1.725, 95%CI 1.215–5.314 ,P = 0.016),Smoking(OR 1.976, 95%CI1.142-4.642,P = 0.003), ISS(OR 3.612, 95%CI1.354-8.437,P = 0.006), D-dimer (OR2.847, 95% CI 1.243–6.831, P = 0.014), Mechanical ventilation (OR 1.824, 95% CI, 1.011–5.835, P = 0.024), Application of vasoactive drugs (OR 2.018, 95% CI 1.164–6.312, P = 0.013) and a decreasing GCS (OR0.425 95% CI 0.237–0.863, P = 0.009) were each associated with an increasing risk of DVT.(Table 2 ) Table 2 Multivariable Logistic Regression Model for Predicting Development of deep venous thrombosis in polytrauma patients with traumatic brain injury in the Training Cohort. Variables B OR odds ratio (95% CI) P Age 0.532 1.725 1.215–5.314 0.016 Smoking 1.026 1.976 1.142–4.642 0.003 ISS 1.283 3.612 1.354–8.437 0.006 GCS -0.856 0.425 0.237–0.863 0.009 D-dimer 1.206 2.847 1.243–6.831 0.014 Mechanical ventilation 0.601 1.824 1.011–5.835 0.024 Application of vasoactive drugs 0.702 2.018 1.164–6.312 0.013 3.3. Construction of the Risk Score and Nomogram-Based Calculator These independently associated risk factors were used to form an DVT risk estimation nomogram in the training cohort. To use the nomogram, find the position of each variable on the corresponding axis, draw a line to the points axis for the number of points, add the points from all of the variables, and draw a line from the total points axis to determine the DVT probabilities at the lower line of the nomogram (Fig. 1 ). The resulting model was internally validated using the training method. The calibration curve of the nomogram for the probability of DVT demonstrated good agreement between prediction and observation in the training cohort .The Hosmer-Leme show test yielded a nonsignificant statistic (P = 0.216), which suggested that there was no departure from perfect fit. (Fig. 2 A). In the training cohort, AUC of ROC curve analysis for the sensitivity and specificity of the nomogram was 0.783. (Fig. 2 B).In addition, in the range of threshold 0.0–1.0, the net benefit rate of the training model was almost the same as that of the validation model. (Fig. 2 C) Figure 1 . Figure 2 A Fig. 2 B 4. Discussion The increased risk of DVT is classically caused by stasis, hypercoagulable state, and endothelial injury, known as Virchow’s Triad, which is common in polytrauma patients [ 14 ]. Meanwhile, in polytrauma, bleeding causes the release of coagulation factors that can lead to DVT[ 15 ]. After the survival after the acute phase on the first day, the greatest concerns in these patients are life-threatening complications, such as deep venous thrombosis(DVT)[ 16 ].The incidences of DVT in trauma patients are reported between 7%and 60%,depending on the patient demographics, the methods of detection, and the type of prophylaxis[ 17 , 18 ]. In our literature, the incidences of DVT were 32.1% in polytrauma patients with traumatic brain injury. In our previous literature, the DVT rate in polytrauma patients with TBI and patients isolated TBI showed great difference (31.9% vs 20.2%, P < 0.01) although two groups share the similar GCS. Correspondingly, the DVT rate is significantly higher in polytrauma patients with TBI compared to polytrauma patients without TBI (31.9% vs 22.0%, p < 0.05) although the ISS showed no difference in the two groups[ 19 ].One probable mechanism is that individuals with traumatic brain injuries (TBI) frequently experience immobility, weakness, and confinement to bed, as well as other traumatic injuries that elevate the likelihood of venous stasis. A study conducted in 2009 revealed a three-to fourfold heightened risk of deep vein thrombosis (DVT) in TBI patients[ 20 ]. Several risk factors were identified for the occurrence of deep vein thrombosis (DVT) in patients with traumatic brain injury (TBI), including multiple injuries, advanced age, male gender, comorbidities, craniotomies, subarachnoid hemorrhage (SAH), and lower limb injuries[ 21 ].In our study, older age, smoking, higher level of D-dimmer, higher ISS scores, lower GCS scores, longer duration of ventilator use, and higher rate of vasoactive drugs use were identified as risk factors for DVT in polytrauma patients with traumatic brain injury. It has been widely acknowledged that DVT is predominantly a disease of older age, with a significantly increased risk of DVT in patients>40 years old compared with younger patients[ 22 ]. Moreover, the risk of DVT approximately doubles with each subsequent decade. D-dimer is a biomarker of fibrin formation and degradation. A retrospective bioinformatics analysis identified neurosurgical inpatients who underwent a protocol assessing serum D-dimer levels and had a VDU study evaluating the presence of DVT and reached the conclusion that the D-dimer protocol was efficient in screening for DVT during hospitalization [ 10 ]. Polytrauma patients with higher ISS scores who are related to coagulopathy, increased risk of bleeding, delayed thromboprophylaxis (such as active bleeding, hemodynamic instability, solid organ injury) are at higher risk of DVT [ 23 ]. Traumatic brain injury (TBI) commonly results in significant shock, acidosis, and hypothermia, exacerbating the already compromised coagulation system in polytrauma patients. Additionally, research has indicated a distinct TBI-related coagulopathy linked to the systemic dissemination of tissue factor and brain phospholipids following blood-brain barrier disruption[ 24 ].There exists an inherent correlation between deep vein thrombosis (DVT) and the length of time a patient utilizes a ventilator, as prolonged use of a ventilator results in increased time spent in a supine position. Gibbs' research revealed that the prevalence of venous thrombosis at autopsy was 15% in patients who were on bed rest for less than one week before death, but increased to 80% in patients who were immobile for an extended period of time[ 25 ]. We developed and validated a nomogram for the individualized prediction of DVT in polytrauma patients with traumatic brain injury. The nomogram incorporates 7 items of the Age, Smoking, ISS, GCS, D-dimer, MV and AVD. The performance of this risk score was satisfactory with accuracy based on AUC in the training cohort of 0.78. The DVT-predicting nomogram can be used by clinicians to estimate an individual hospitalized patient’s risk of developing DVT. The 7 variables required for calculation of the risk of developing DVT are generally readily available at hospital admission, and the nomogram is easy to use. Calibration curves and decision curve analysis of the DVT-predicting nomogram demonstrated that the nomogram was clinically useful. If the patient’s estimated risk for DVT is low, the clinician may choose to monitor, whereas high-risk estimates might support aggressive treatment or admission to the ICU. The study is subject to certain limitations, notably its retrospective nature which imposes constraints on the study design. In our investigation, the diagnosis of deep vein thrombosis (DVT) primarily relied on ultrasound examinations conducted during the patient's hospitalization, potentially leading to the oversight of DVT occurrences post-discharge and clinically insignificant DVT events. Furthermore, the study omitted data pertaining to the timing of mobilization and the presence of active infection or inflammation, factors that may influence the onset of DVT. Lastly, the inclusion of both surgical and non-surgical patients in the study introduces an additional source of potential bias. 5. Conclusion In conclusion ,this study presents a nomogram that incorporates both the demographic characteristics and clinical risk factors, and can be conveniently used to individualized prediction of DVT in polytrauma patients with traumatic brain injury Declarations Author Contributions: H.L. participated in the designing of the experiment, collection, analysis, and interpretation of data, and drafting the manuscript. C.Z. contributed to the collection and analysis of data and drafted the manuscript. All authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee at Tongji Hospital (approval date: 22 July 2020). Informed Consent Statement: Informed consent was obtained from each patient or the patient’s legally authorized representative involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical, legal and privacy issues. Acknowledgments: We thank the staff at the Department of Clinical Statistics for their assistance in statistical analysis. Conflicts of Interest: The authors declare no conflict of interest. References Brohi, K., et al., Acute traumatic coagulopathy . J Trauma, 2003. 54(6): p. 1127–30. Geerts, W.H., et al., A prospective study of venous thromboembolism after major trauma . N Engl J Med, 1994. 331(24): p. 1601–6. Ruskin, K.J., Deep vein thrombosis and venous thromboembolism in trauma . Curr Opin Anaesthesiol, 2018. 31(2): p. 215–218. Scales, D.C., et al., Prophylactic anticoagulation to prevent venous thromboembolism in traumatic intracranial hemorrhage: a decision analysis . Crit Care, 2010. 14(2): p. R72. van Wessem, K.J.P., D. Jochems, and L.P.H. Leenen, The effect of prehospital tranexamic acid on outcome in polytrauma patients with associated severe brain injury . Eur J Trauma Emerg Surg, 2022. 48(3): p. 1589–1599. Knudson, M.M., et al., Thromboembolism after trauma: an analysis of 1602 episodes from the American College of Surgeons National Trauma Data Bank. Ann Surg, 2004. 240(3): p. 490-6; discussion 496-8. Broughton, G., 2nd, et al., Deep venous thrombosis prophylaxis practice and treatment strategies among plastic surgeons: survey results . Plast Reconstr Surg, 2007. 119(1): p. 157–174. Mousa, A.Y., et al., Appropriate Use of Venous Imaging and Analysis of the D-Dimer/Clinical Probability Testing Paradigm in the Diagnosis and Location of Deep Venous Thrombosis . Ann Vasc Surg, 2018. 50: p. 21–29. Riva, N., et al., Biomarkers for the diagnosis of venous thromboembolism: D-dimer, thrombin generation, procoagulant phospholipid and soluble P-selectin . J Clin Pathol, 2018. 71(11): p. 1015–1022. Ordieres-Ortega, L., et al., Predictive value of D-dimer testing for the diagnosis of venous thrombosis in unusual locations: A systematic review . Thromb Res, 2020. 189: p. 5–12. Rau, C.S., et al., Polytrauma Defined by the New Berlin Definition: A Validation Test Based on Propensity-Score Matching Approach . Int J Environ Res Public Health, 2017. 14(9). Allen, C.J., et al., Surveillance and Early Management of Deep Vein Thrombosis Decreases Rate of Pulmonary Embolism in High-Risk Trauma Patients . J Am Coll Surg, 2016. 222(1): p. 65–72. Schweitzer, A.D., et al., Traumatic Brain Injury: Imaging Patterns and Complications . Radiographics, 2019. 39(6): p. 1571–1595. Vazquez-Garza, E., et al., Venous thromboembolism: thrombosis, inflammation, and immunothrombosis for clinicians . J Thromb Thrombolysis, 2017. 44(3): p. 377–385. Moore, E.E., et al., Trauma-induced coagulopathy . Nat Rev Dis Primers, 2021. 7(1): p. 30. Paydar, S., et al., Management of Deep Vein Thrombosis (DVT) Prophylaxis in Trauma Patients . Bull Emerg Trauma, 2016. 4(1): p. 1–7. Badireddy, M. and V.R. Mudipalli, Deep Venous Thrombosis Prophylaxis , in StatPearls . 2023: Treasure Island (FL). Alsheikh, M., et al., The Incidence of Venous Thromboembolism and Practice of Deep Venous Thrombosis Prophylaxis Among Hospitalized Cirrhotic Patients . Gastroenterology Res, 2022. 15(2): p. 67–74. Chen, D., et al., Venous Thrombus Embolism in Polytrauma: Special Attention to Patients with Traumatic Brain Injury . J Clin Med, 2023. 12(5). Selby, R., et al., Hypercoagulability after trauma: hemostatic changes and relationship to venous thromboembolism . Thromb Res, 2009. 124(3): p. 281–7. Jamous, M.A., The Safety of Early Thromboembolic Prophylaxis in Closed Traumatic Intracranial Hemorrhage . Open Access Emerg Med, 2020. 12: p. 81–85. Skrifvars, M.B., et al., Venous thromboembolic events in critically ill traumatic brain injury patients . Intensive Care Med, 2017. 43(3): p. 419–428. Roberts, L., et al., A preliminary study of intensivist-performed DVT ultrasound screening in trauma ICU patients (APSIT Study) . Ann Intensive Care, 2020. 10(1): p. 122. Maegele, M., et al., Changes in Coagulation following Brain Injury . Semin Thromb Hemost, 2020. 46(2): p. 155–166. Rethinasamy, R., et al., Deep Vein Thrombosis and the Neurosurgical Patient . Malays J Med Sci, 2019. 26(5): p. 139–147. Additional Declarations No competing interests reported. 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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-3958430","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":277370597,"identity":"8d7a4a25-ee9f-4be1-b314-28a349bb7f0f","order_by":0,"name":"Cong Zhang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Zhang","suffix":""},{"id":277370598,"identity":"b376f76b-6051-4dcc-8561-6d55e09940f2","order_by":1,"name":"Hui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPmYeBobEBgY5AzDXwIKwFjaoFmMDBmaQFgkitDAAtTA2MCRuAGthIEYLO+8xiYc77NK3s/cf3fCjQIKBv707gYDD+NIkEs8k5+7sOcx2swfoMIkzZzcQ8ouZRGLbgdwNN5LZbvAAtRhI5BKnJd0AqOXmH1K0JIC03CbWFmMLoF8MN5w5bHZbxkCCh6Bf+PnPGN78ucNO3uB447Obb/7YyPG39+LXggF4SFM+CkbBKBgFowArAAB6ez9HiOiW6gAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-02-15 11:02:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3958430/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3958430/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52450838,"identity":"511595bd-51de-40ad-9eda-6bb1f9c11dbc","added_by":"auto","created_at":"2024-03-11 19:10:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49621,"visible":true,"origin":"","legend":"\u003cp\u003eDeveloped DVT-predicting nomogram. The DVT-predicting nomogram was developed in the training cohort. MV: Mechanical ventilation ,AVD: Application of vasoactive drugs\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3958430/v1/1f95120ed7e14e609d020dc6.png"},{"id":52450839,"identity":"e156013f-08db-4694-9b6b-61d212de7630","added_by":"auto","created_at":"2024-03-11 19:10:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA:\u003c/strong\u003e Calibration curves of the DVT-predicting nomogram . Calibration curves depict the agreement between the predicted risks of DVT and observed outcomes of DVT.\u003cstrong\u003e B:\u003c/strong\u003eROC curve analysis for the sensitivity and specificity of the nomogram in the training cohort. \u003cstrong\u003eC:\u003c/strong\u003e Decision curve analysis for the DVT-predicting nomogram. The y-axis measures the net benefit. The pink line represents the nomogram of training cohort. The blue line represents the nomogram of validation cohort. Thin black line represents the assumption that no patients have DVT. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3958430/v1/3f27eaaeaaac85cb8d46dc0e.png"},{"id":60048675,"identity":"d50d75d3-166e-49ad-a901-3e2deb660624","added_by":"auto","created_at":"2024-07-11 05:29:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":763632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3958430/v1/dd2b99c0-0507-460d-8f85-2d580abd57c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep venous thrombosis in Polytrauma patients with Traumatic Brain Injury： development and validation of a predictive model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePolytrauma, characterized by poor outcomes and high mortality due to fatal damage and tricky complications, represents a complex disease and continues to be a sustained health issue. Trauma-induced coagulopathy (TIC) is a common direct cause of early post-injury mortality (30\u0026ndash;50%),from which deep venous thrombosis(DVT) is a pathological outcome related to a coagulation disorder originating[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Previous studies found the incidence of lower extremity DVT to be as high as 20% in polytrauma patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and DVT is also the primary cause of death in patients who survive the initial traumatic insults[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Direct mechanical venous injury, systemic hypercoagulability, and immobilization by skeletal fixation contribute to the high incidence of DVT in polytrauma and the immobility arising from the underlying neurologic lesion itself, and use of sedatives and neuromuscular blocking agents[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] in traumatic brain injury(TBI) with which more than 50% of polytrauma patients are accompanied [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],is also associated with a high risk of thromboembolic complications[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].It is consistent with our own clinical practice experience that polytrauma patients with TBI were at an even higher risk of developing DVT than those polytrauma patients without TBI.\u003c/p\u003e \u003cp\u003eThe management of hemorrhage and disordered coagulation presents a prevalent and crucial challenge in cases of polytrauma, particularly in traumatic brain injury (TBI), where surgeons must carefully weigh the potential risks of ongoing brain hemorrhage against the development of secondary thrombotic complications such as deep venous thrombosis (DVT). The absence of conclusive evidence to inform decisions regarding the use of anticoagulant prophylaxis in polytrauma patients with TBI necessitates that surgeons rely on their own clinical judgment to assess the potential risks and benefits[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe timely identification of polytrauma patients with traumatic brain injury (TBI) at risk for developing deep vein thrombosis (DVT) is crucial for effective treatment and resource utilization. Currently, DVT diagnosis relies on the assessment of D-dimer (DD) levels and venous Doppler ultrasound (VDU)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Numerous studies have demonstrated the sensitivity and accuracy of DD in detecting not only the presence but also the extent of thrombus formation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] .DVT is typically diagnosed based on VDU findings, including dilated, noncompressible veins or intraluminal shadows indicative of thrombosiss. Studies have shown that the weighted mean sensitivity and specificity of venous Doppler ultrasound (VDU) in diagnosing proximal deep vein thrombosis (DVT) are estimated to be 97% and 94%, respectively[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].However, D-dimer (DD) testing has demonstrated low specificity in diagnosing DVT, particularly in polytrauma patients with complicating factors such as infection. Additionally, VDU may not be readily available for polytrauma patients requiring mechanical ventilation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Due to the lack of a specific and convenient diagnostic method, this study aims to develop a nomogram for estimating the risk of DVT in polytrauma patients with traumatic brain injury (TBI).\u003c/p\u003e"},{"header":"2. Patients and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and Patients\u003c/h2\u003e \u003cp\u003e This was an institutional review board-approved, retrospective, observational trial with informed consent. All patients admitted to the traumatic intensive care unit (TICU) or intensive care unit (ICU) of the Advanced Trauma Center(Level I, verified by China Trauma Rescue \u0026amp;Treatment Association (CTRTA)) at the Tongji Hospital (Wuhan) from November,2021 to May,2023. A total of 647 consecutive patients met the eligibility requirements, which divided into DVT group and N-DVT group. Eligible patients between November,2021 to August,2022 were included into the training cohort for development of the nomogram, and those between August,2022 to May,2023 were entered into the validation cohort. The present study was approved by the ethics committee at Tongji Hospital (TJIRB20200720). Patient consent for data collection was obtained from each patient or the patient\u0026rsquo;s legally authorized representative. The entry criteria were: 1)Consistent with polytrauma with traumatic brain injury diagnosis;2) Admission within 24 hours;3) age over 18 years; Exclusions were patients who were pregnant and mentally ill. Patients with a history of VTE or treated with delayed presentation due to transfer were also excluded from our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Data Collection\u003c/h2\u003e \u003cp\u003eRetrospectively collected the baseline clinical characteristics from the electronic medical and nursing records of each patient which include demographics (such as age and sex), medical records (such as ISS score, GCS score ,Cause and location of injury and so on), laboratory parameters (such as level of D-dimer, Lactate ,APTT and so on at admission), and clinical outcomes (such as mechanical ventilation). Furthermore, a VDU of both lower extremities was performed from the ankle to the inguinal ligament to evaluate the deep venous system and determine the presence of a DVT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3.Diagnosis\u003c/h2\u003e \u003cp\u003eThe new \u0026ldquo;Berlin definition\u0026rdquo; of polytrauma: a patient with AIS\u0026thinsp;\u0026ge;\u0026thinsp;3 for two or more different body regions and with one or more additional parameters from the following: hypotension (systolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;90 mmHg), unconsciousness (Glasgow Coma Scale [GCS] score\u0026thinsp;\u0026le;\u0026thinsp;8), acidosis (base deficit \u0026le;-6.0), coagulopathy (partial thromboplastin time [PTT]\u0026thinsp;\u0026le;\u0026thinsp;40 s or international normalized ratio [INR]\u0026thinsp;\u0026ge;\u0026thinsp;1.4), and age\u0026thinsp;\u0026ge;\u0026thinsp;70 years[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. DVT was defined as an abnormality seen on venous Doppler ultrasound (VDU), such as the presence of dilated, noncompressible veins or intraluminal shadows consistent with thrombosis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. TBI was defined by the presence of subdural hemorrhage, epidural hemorrhage, focal contusion, or diffuse axonal injury on computed tomography (CT) according to recommended guidelines[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. All enrolled subjects received standardized treatment and management per established TBI and polytrauma guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003ePercentages are typically utilized in the analysis of categorical variables, while mean plus standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) are commonly employed for continuous variables. In the context of categorical and continuous variables, the chi-squared test or Fisher test and the Mann\u0026ndash;Whitney U test or t-test are utilized separately. The significance of each variable in the training cohort was evaluated through univariate analysis to identify independent risk factors associated with the presence of DVT. Multivariate logistic regression analysis was conducted to analyze the independent risk factors of DVT in polytrauma patients with traumatic brain injury. A nomogram was created using multivariate logistic regression results and the rms package in R. Each regression coefficient was converted to a 0-100 point scale, with the variable having the highest β coefficient assigned 100 points. Total points were calculated by adding points across variables to predict probabilities. The C-index was used to assess model accuracy, bootstrap validation for overfitting, calibration plot for nomogram performance, ROC curve analysis for predictive accuracy. Statistical analysis was done using R software, with significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographics and characteristics of patients\u003c/h2\u003e \u003cp\u003eThe study cohort consisted of 647 trauma patients who met the inclusion criteria. Among these, 349 patients were in training cohort and 298 patients were in validation cohort. Patient characteristics in the training and validation cohorts are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.There are no significant differences between the two cohorts in DVT prevalence(P\u0026thinsp;=\u0026thinsp;0.99). The incidence of DVT was 32.1% and 31.9% in the training and validation cohorts, respectively. There were no significant differences in the clinical characteristics between the training and the validation cohort, either within the DVT-positive cohort or in the DVT negative cohort, which justified their use as training and validation cohorts .\u003c/p\u003e \u003cp\u003eIn univariate analysis, older Age, Fat, Smoking, higher D-dimer levels, higher ISS, lower GCS score, Mechanical ventilation and Application of vasoactive drugs were associated with a higher risk of DVT in the training cohort. Particularly, delayed mechanical prophylaxis and delayed anticoagulant therapy were more susceptible to developing a DVT in the training cohort. Significant predictive factors for DVT are listed 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\u003eCharacteristics of Patients in the Training and Validation Cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTraining(n\u0026thinsp;=\u0026thinsp;349)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eValidation(n\u0026thinsp;=\u0026thinsp;298)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDVT(+,112)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDVT(-,237)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDVT(+,95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDVT(-203)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e153(75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67(70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84(41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnatomical location of injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoracic injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129(54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e114(56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePelvic and limbs fracture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116(48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(76.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124(61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46(22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh frozen plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst day of mechanical prophylaxis\u0026thinsp;\u0026le;\u0026thinsp;3d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140(60.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126(62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplication of vasoactive drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAll values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number(percentage);DVT: deep vein thrombosis; GCS: Glasgow coma scale; ISS: injury severity score; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is statistically significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Multivariate analyses of relative factors for DVT\u003c/h2\u003e \u003cp\u003eAfter univariate analysis, those variables with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for multivariate analysis using a multiple logistic regression model. For a logistic regression analysis to detect risk factors of DVT in polytrauma patients with traumatic brain injury in the training cohort, these variables: Age, Smoking, ISS, GCS, D-dime, Mechanical ventilation, Application of vasoactive drugs were included. M Multivariate logistic regression analysis showed that an increasing Age(OR1.725, 95%CI 1.215\u0026ndash;5.314 ,P\u0026thinsp;=\u0026thinsp;0.016),Smoking(OR 1.976, 95%CI1.142-4.642,P\u0026thinsp;=\u0026thinsp;0.003), ISS(OR 3.612, 95%CI1.354-8.437,P\u0026thinsp;=\u0026thinsp;0.006), D-dimer (OR2.847, 95% CI 1.243\u0026ndash;6.831, P\u0026thinsp;=\u0026thinsp;0.014), Mechanical ventilation (OR 1.824, 95% CI, 1.011\u0026ndash;5.835, P\u0026thinsp;=\u0026thinsp;0.024), Application of vasoactive drugs (OR 2.018, 95% CI 1.164\u0026ndash;6.312, P\u0026thinsp;=\u0026thinsp;0.013) and a decreasing GCS (OR0.425 95% CI 0.237\u0026ndash;0.863, P\u0026thinsp;=\u0026thinsp;0.009) were each associated with an increasing risk of DVT.(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\u003eMultivariable Logistic Regression Model for Predicting Development of deep venous thrombosis in polytrauma patients with traumatic brain injury in the Training Cohort.\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=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eodds ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.215\u0026ndash;5.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.142\u0026ndash;4.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eISS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.354\u0026ndash;8.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.237\u0026ndash;0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.243\u0026ndash;6.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.011\u0026ndash;5.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplication of vasoactive drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.164\u0026ndash;6.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3. Construction of the Risk Score and Nomogram-Based Calculator\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThese independently associated risk factors were used to form an DVT risk estimation nomogram in the training cohort. To use the nomogram, find the position of each variable on the corresponding axis, draw a line to the points axis for the number of points, add the points from all of the variables, and draw a line from the total points axis to determine the DVT probabilities at the lower line of the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The resulting model was internally validated using the training method. The calibration curve of the nomogram for the probability of DVT demonstrated good agreement between prediction and observation in the training cohort .The Hosmer-Leme show test yielded a nonsignificant statistic (P\u0026thinsp;=\u0026thinsp;0.216), which suggested that there was no departure from perfect fit. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the training cohort, AUC of ROC curve analysis for the sensitivity and specificity of the nomogram was 0.783. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).In addition, in the range of threshold 0.0\u0026ndash;1.0, the net benefit rate of the training model was almost the same as that of the validation model. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe increased risk of DVT is classically caused by stasis, hypercoagulable state, and endothelial injury, known as Virchow\u0026rsquo;s Triad, which is common in polytrauma patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Meanwhile, in polytrauma, bleeding causes the release of coagulation factors that can lead to DVT[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. After the survival after the acute phase on the first day, the greatest concerns in these patients are life-threatening complications, such as deep venous thrombosis(DVT)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].The incidences of DVT in trauma patients are reported between 7%and 60%,depending on the patient demographics, the methods of detection, and the type of prophylaxis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In our literature, the incidences of DVT were 32.1% in polytrauma patients with traumatic brain injury. In our previous literature, the DVT rate in polytrauma patients with TBI and patients isolated TBI showed great difference (31.9% vs 20.2%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) although two groups share the similar GCS. Correspondingly, the DVT rate is significantly higher in polytrauma patients with TBI compared to polytrauma patients without TBI (31.9% vs 22.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) although the ISS showed no difference in the two groups[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].One probable mechanism is that individuals with traumatic brain injuries (TBI) frequently experience immobility, weakness, and confinement to bed, as well as other traumatic injuries that elevate the likelihood of venous stasis. A study conducted in 2009 revealed a three-to fourfold heightened risk of deep vein thrombosis (DVT) in TBI patients[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral risk factors were identified for the occurrence of deep vein thrombosis (DVT) in patients with traumatic brain injury (TBI), including multiple injuries, advanced age, male gender, comorbidities, craniotomies, subarachnoid hemorrhage (SAH), and lower limb injuries[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].In our study, older age, smoking, higher level of D-dimmer, higher ISS scores, lower GCS scores, longer duration of ventilator use, and higher rate of vasoactive drugs use were identified as risk factors for DVT in polytrauma patients with traumatic brain injury. It has been widely acknowledged that DVT is predominantly a disease of older age, with a significantly increased risk of DVT in patients\u0026gt;40 years old compared with younger patients[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, the risk of DVT approximately doubles with each subsequent decade. D-dimer is a biomarker of fibrin formation and degradation. A retrospective bioinformatics analysis identified neurosurgical inpatients who underwent a protocol assessing serum D-dimer levels and had a VDU study evaluating the presence of DVT and reached the conclusion that the D-dimer protocol was efficient in screening for DVT during hospitalization [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Polytrauma patients with higher ISS scores who are related to coagulopathy, increased risk of bleeding, delayed thromboprophylaxis (such as active bleeding, hemodynamic instability, solid organ injury) are at higher risk of DVT [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Traumatic brain injury (TBI) commonly results in significant shock, acidosis, and hypothermia, exacerbating the already compromised coagulation system in polytrauma patients. Additionally, research has indicated a distinct TBI-related coagulopathy linked to the systemic dissemination of tissue factor and brain phospholipids following blood-brain barrier disruption[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].There exists an inherent correlation between deep vein thrombosis (DVT) and the length of time a patient utilizes a ventilator, as prolonged use of a ventilator results in increased time spent in a supine position. Gibbs' research revealed that the prevalence of venous thrombosis at autopsy was 15% in patients who were on bed rest for less than one week before death, but increased to 80% in patients who were immobile for an extended period of time[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe developed and validated a nomogram for the individualized prediction of DVT in polytrauma patients with traumatic brain injury. The nomogram incorporates 7 items of the Age, Smoking, ISS, GCS, D-dimer, MV and AVD. The performance of this risk score was satisfactory with accuracy based on AUC in the training cohort of 0.78. The DVT-predicting nomogram can be used by clinicians to estimate an individual hospitalized patient\u0026rsquo;s risk of developing DVT. The 7 variables required for calculation of the risk of developing DVT are generally readily available at hospital admission, and the nomogram is easy to use. Calibration curves and decision curve analysis of the DVT-predicting nomogram demonstrated that the nomogram was clinically useful. If the patient\u0026rsquo;s estimated risk for DVT is low, the clinician may choose to monitor, whereas high-risk estimates might support aggressive treatment or admission to the ICU.\u003c/p\u003e \u003cp\u003eThe study is subject to certain limitations, notably its retrospective nature which imposes constraints on the study design. In our investigation, the diagnosis of deep vein thrombosis (DVT) primarily relied on ultrasound examinations conducted during the patient's hospitalization, potentially leading to the oversight of DVT occurrences post-discharge and clinically insignificant DVT events. Furthermore, the study omitted data pertaining to the timing of mobilization and the presence of active infection or inflammation, factors that may influence the onset of DVT. Lastly, the inclusion of both surgical and non-surgical patients in the study introduces an additional source of potential bias.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion ,this study presents a nomogram that incorporates both the demographic characteristics and clinical risk factors, and can be conveniently used to individualized prediction of DVT in polytrauma patients with traumatic brain injury\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e H.L. participated in the designing of the experiment, collection, analysis, and interpretation of data, and drafting the manuscript. C.Z. contributed to the collection and analysis of data and drafted the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee at Tongji Hospital (approval date: 22 July 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from each patient or the patient\u0026rsquo;s legally authorized representative involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical, legal and privacy issues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank the staff at the Department of Clinical Statistics for their assistance in statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrohi, K., et al., \u003cem\u003eAcute traumatic coagulopathy\u003c/em\u003e. J Trauma, 2003. 54(6): p. 1127\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeerts, W.H., et al., \u003cem\u003eA prospective study of venous thromboembolism after major trauma\u003c/em\u003e. N Engl J Med, 1994. 331(24): p. 1601\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuskin, K.J., \u003cem\u003eDeep vein thrombosis and venous thromboembolism in trauma\u003c/em\u003e. Curr Opin Anaesthesiol, 2018. 31(2): p. 215\u0026ndash;218.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScales, D.C., et al., \u003cem\u003eProphylactic anticoagulation to prevent venous thromboembolism in traumatic intracranial hemorrhage: a decision analysis\u003c/em\u003e. Crit Care, 2010. 14(2): p. R72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Wessem, K.J.P., D. Jochems, and L.P.H. Leenen, \u003cem\u003eThe effect of prehospital tranexamic acid on outcome in polytrauma patients with associated severe brain injury\u003c/em\u003e. Eur J Trauma Emerg Surg, 2022. 48(3): p. 1589\u0026ndash;1599.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnudson, M.M., et al., \u003cem\u003eThromboembolism after trauma: an analysis of 1602 episodes from the American College of Surgeons National Trauma Data Bank.\u003c/em\u003e Ann Surg, 2004. 240(3): p. 490-6; discussion 496-8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroughton, G., 2nd, et al., \u003cem\u003eDeep venous thrombosis prophylaxis practice and treatment strategies among plastic surgeons: survey results\u003c/em\u003e. Plast Reconstr Surg, 2007. 119(1): p. 157\u0026ndash;174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMousa, A.Y., et al., \u003cem\u003eAppropriate Use of Venous Imaging and Analysis of the D-Dimer/Clinical Probability Testing Paradigm in the Diagnosis and Location of Deep Venous Thrombosis\u003c/em\u003e. Ann Vasc Surg, 2018. 50: p. 21\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiva, N., et al., \u003cem\u003eBiomarkers for the diagnosis of venous thromboembolism: D-dimer, thrombin generation, procoagulant phospholipid and soluble P-selectin\u003c/em\u003e. J Clin Pathol, 2018. 71(11): p. 1015\u0026ndash;1022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrdieres-Ortega, L., et al., \u003cem\u003ePredictive value of D-dimer testing for the diagnosis of venous thrombosis in unusual locations: A systematic review\u003c/em\u003e. Thromb Res, 2020. 189: p. 5\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRau, C.S., et al., \u003cem\u003ePolytrauma Defined by the New Berlin Definition: A Validation Test Based on Propensity-Score Matching Approach\u003c/em\u003e. Int J Environ Res Public Health, 2017. 14(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen, C.J., et al., \u003cem\u003eSurveillance and Early Management of Deep Vein Thrombosis Decreases Rate of Pulmonary Embolism in High-Risk Trauma Patients\u003c/em\u003e. J Am Coll Surg, 2016. 222(1): p. 65\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchweitzer, A.D., et al., \u003cem\u003eTraumatic Brain Injury: Imaging Patterns and Complications\u003c/em\u003e. Radiographics, 2019. 39(6): p. 1571\u0026ndash;1595.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVazquez-Garza, E., et al., \u003cem\u003eVenous thromboembolism: thrombosis, inflammation, and immunothrombosis for clinicians\u003c/em\u003e. J Thromb Thrombolysis, 2017. 44(3): p. 377\u0026ndash;385.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore, E.E., et al., \u003cem\u003eTrauma-induced coagulopathy\u003c/em\u003e. Nat Rev Dis Primers, 2021. 7(1): p. 30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaydar, S., et al., \u003cem\u003eManagement of Deep Vein Thrombosis (DVT) Prophylaxis in Trauma Patients\u003c/em\u003e. Bull Emerg Trauma, 2016. 4(1): p. 1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadireddy, M. and V.R. Mudipalli, \u003cem\u003eDeep Venous Thrombosis Prophylaxis\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. 2023: Treasure Island (FL).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsheikh, M., et al., \u003cem\u003eThe Incidence of Venous Thromboembolism and Practice of Deep Venous Thrombosis Prophylaxis Among Hospitalized Cirrhotic Patients\u003c/em\u003e. Gastroenterology Res, 2022. 15(2): p. 67\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, D., et al., \u003cem\u003eVenous Thrombus Embolism in Polytrauma: Special Attention to Patients with Traumatic Brain Injury\u003c/em\u003e. J Clin Med, 2023. 12(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelby, R., et al., \u003cem\u003eHypercoagulability after trauma: hemostatic changes and relationship to venous thromboembolism\u003c/em\u003e. Thromb Res, 2009. 124(3): p. 281\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamous, M.A., \u003cem\u003eThe Safety of Early Thromboembolic Prophylaxis in Closed Traumatic Intracranial Hemorrhage\u003c/em\u003e. Open Access Emerg Med, 2020. 12: p. 81\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkrifvars, M.B., et al., \u003cem\u003eVenous thromboembolic events in critically ill traumatic brain injury patients\u003c/em\u003e. Intensive Care Med, 2017. 43(3): p. 419\u0026ndash;428.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts, L., et al., \u003cem\u003eA preliminary study of intensivist-performed DVT ultrasound screening in trauma ICU patients (APSIT Study)\u003c/em\u003e. Ann Intensive Care, 2020. 10(1): p. 122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaegele, M., et al., \u003cem\u003eChanges in Coagulation following Brain Injury\u003c/em\u003e. Semin Thromb Hemost, 2020. 46(2): p. 155\u0026ndash;166.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRethinasamy, R., et al., \u003cem\u003eDeep Vein Thrombosis and the Neurosurgical Patient\u003c/em\u003e. Malays J Med Sci, 2019. 26(5): p. 139\u0026ndash;147.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polytrauma, Traumatic Brain Injury, Deep Venous Thrombosis, Clinical Risk Score","lastPublishedDoi":"10.21203/rs.3.rs-3958430/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3958430/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e To develop and validate a nomogram for prediction of the occurrence of deep venous thrombosis in polytrauma patients with traumatic brain injury.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective and observationaltrails were performed from November,2021 to May,2023. The prediction model was developed in a training cohort that consisted of 349 polytrauma patients with traumatic brain injury and data was gathered from November,2021 to August,2022. The baseline clinical characteristics from the electronic medical and nursing records of each patient which include demographics, medical records, laboratory parameters, and clinical outcomes were collected. Multivariable logistic regression analysis was used to develop the predicting model, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 298 consecutive patients from August,2022 to May,2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e A total of 647 trauma patients who met the inclusion criteria. Among these, 349 patients were in training cohort and 298 patients were in validation cohort. The incidence of DVT was 32.1% and 31.9% in the trainingand validation cohorts, respectively. Predictors contained in the individualized prediction nomogram the Age, Smoking, ISS, GCS, D-dimer, MV and AVD. The model showed a good discrimination, with a C-index of 0.783 and a good calibration. Calibration curves and decision curve analysis of the DVT-predicting nomogram demonstrated that the nomogram was clinically useful.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study presents a nomogram that incorporates both the demographic characteristics and clinical risk factors, and can be conveniently used to individualized prediction of DVT in polytrauma patients with traumatic brain injury\u003c/p\u003e","manuscriptTitle":"Deep venous thrombosis in Polytrauma patients with Traumatic Brain Injury： development and validation of a predictive model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:09:57","doi":"10.21203/rs.3.rs-3958430/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee3cffd4-1b98-410a-b0fe-c753d3096e30","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29230967,"name":"Health sciences/Risk factors"},{"id":29230968,"name":"Health sciences/Diseases/Trauma"}],"tags":[],"updatedAt":"2024-07-11T05:21:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 19:09:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3958430","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3958430","identity":"rs-3958430","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}