Correlation analysis between serum NETs, FIB, miR-374a-5p levels and deep vein thrombosis in the lower limb after knee arthroplasty

Correlation analysis between serum NETs, FIB, miR-374a-5p levels and deep vein thrombosis in the lower limb after knee arthroplasty | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Correlation analysis between serum NETs, FIB, miR-374a-5p levels and deep vein thrombosis in the lower limb after knee arthroplasty zhongshan gui, XianZong Ning, BeiYue Wang, XiaoQiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4789246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the changes in the levels of serum neutrophil extracellular traps (NETs), fibrinogen (FIB), and micro RNA-374a-5p (miR-374a-5p) after knee arthroplasty, and to analyze their correlation with postoperative deep vein thrombosis (DVT) and their predictive efficacy. Methods 108 patients who underwent knee arthroplasty in Nanjing Pukou People's Hospital from September 2020 to May 2023 were selected for the study, and 32 patients in the DVT group and 76 patients in the non-DVT group were categorized according to the occurrence of lower limb DVT at 7 days after surgery. The clinical data of the two groups and the levels of serum NETs, FIB and miR-374a-5p in the preoperative period, 1 day postoperative period and 3 days postoperative period were compared and analyzed. Pearson's method was used to analyze the correlation between preoperative serum NETs, FIB, miR-374a-5p levels and the venous thromboembolism risk assessment scale (Caprini) score. Multifactorial logistic regression was used to analyze the factors influencing the occurrence of lower limb DVT after surgery. The predictive value of serum NETs, FIB, and miR-374a-5p levels on the occurrence of postoperative lower limb DVT was evaluated by using receiver operating characteristic curve(ROC) and area under the curve (AUC). Results The levels of serum NETs, FIB, and miR-374a-5p were higher in the DVT group than in the non-DVT group at 1 day and 3 days postoperatively (P<0.05); the levels of preoperative serum NETs, FIB, and miR- 374a-5p were positively correlated with the Caprini score (P<0.05); The Caprini score and elevated levels of serum NETs,FIB,and miR-374a-5p at 3 days after surgery were independent risk factors for postoperative lower limb DVT (P<0.05);The combined prediction of serum NETs,FIB,and miR-374a-5p levels at 3 days after surgery for AUC in lower limb DVT was greater than that predicted by single indicators ( P<0.05). Conclusion The levels of serum NETs,FIB, and miR-374a-5p in patients with DVT after knee arthroplasty increase.Combined detection of their levels has certain predictive value for the occurrence of postoperative lower limb DVT. Knee arthroplasty Lower limb deep vein thrombosis Neutrophil extracellular traps Fibrinogen MicroRNA-374a-5p Introduction Deep vein thrombosis (DVT) is a common complication after knee arthroplasty, and the incidence of DVT in the lower limbs after surgery is 40%~60%. Deep venography is the gold standard for diagnosis of DVT, but it is an invasive examination and the operation process is complicated, while the serological indexes are easy to operate, and it has become a common method of clinical auxiliary diagnosis of DVT [1] . Neutrophil extracellular traps (NETs) are composed of histones, neutrophil intracellular granules which can bind vascular willebrand factor (VWF), coagulation factor Ⅻ (FⅫ), promote fibrin formation and platelet agglutination, and reduce venous thrombosis after inhibiting their expression [ 2 ] . Fibrinogen (FIB) has a certain predictive value for intravascular coagulation and thrombosis [ 3] . Dysregulation of microRNA (miRNA) expression is closely related to the development of various diseases such as DVT, Micro RNA-374a-5p (miR-374a-5p) is expressed at elevated levels in mixed-type DVT, and its high expression may aggravate the severity of DVT [4] . At present, there are relatively few reports on the correlation between lower limb DVT and serum NETs, FIB, and miR-374a-5p after knee arthroplasty. therefore, the present study was conducted to investigate the changes in the levels of serum NETs, FIB, and miR-374a-5p in patients with lower limb DVT after knee arthroplasty, and to analyze the predictive value of these levels in lower limb DVT after surgery, in order to provide a reference for the clinical diagnosis and treatment. 1 Information and methodology 1.1 General information From September 2020 to May 2023, 108 patients who underwent knee arthroplasty in Nanjing Pukou People's Hospital were selected, including 73 males and 35 females; Age 49-78 years, mean age (63.29±7.02) years; Body mass index 18-30 kg/m2 , mean body mass index was (24.01±2.61)kg/m2; Primary diseases: traumatic knee osteoarthritis in 48 cases, rheumatoid knee osteoarthritis in 43 cases, necrosis of the knee joint in 13 cases, and synovial chondroma in 4 cases. The study was approved by the Ethics Committee of the hospital (approval number: YCYY20200815001), and informed consent was signed. 1.2 Selection Criteria Inclusion criteria: patients who meet the indications for knee arthroplasty; no abnormal coagulation function; no anticoagulation; no previous lower limb vascular surgery; no significant hemorrhagic tendency; unilateral limb; duration <15 d. Exclusion criteria: perioperative fever and respiratory infections; COVID-19 infections; long-term use of immune modulators; postoperative wound redness or superficial infections; preoperative lower limb DVT; previous history of cardiovascular and cerebrovascular obstruction; combined with active lower limb ulcers. 1.3 Methods 1.3.1 Treatment methods and grouping criteria The study subjects were treated with knee arthroplasty and underwent surgical procedures according to the references [5] .After the operation, conventional treatments including anticoagulation and antithrombotic therapy were given. According to the 7 days postoperative whether lower limb DVT occurred or not was divided into DVT group and non-DVT group. DVT standard [6] : after compression of the distal limb, ultrasound did not detect blood flow signals at the lesion; the internal diameter of the venous lumen was widened,and the lumen was not fully compressed after the pressure; the lumen of the vein was widened and a solid mass with different echoes was seen. 1.3.2 Clinical data The clinical data of the study subjects were collected, including gender, age, operation time, intraoperative blood loss, tourniquet application time, body mass index, venous thromboembolism risk assessment scale (Caprini) score, underlying disease, primary disease, Caprini score included 40 risk factors for thrombosis, each risk factor severity recorded as 1,2,3,4,5 respectively, the higher the score, the higher the risk of DVT [7] . 1.3.3 Detection of serum NETs, FIB, miR-374a-5p level 5 mL of fasting peripheral venous blood was collected from the two study groups before surgery, 1 d after surgery and 3 d after surgery respectively,and then centrifuged at 3,500 r/min for 10 minutes after standing for 30 min at room temperature. The level of serum NETs was measured by capture ELISA of MPO-DNA complex (provided by Shanghai Huabang Biological Company). Serum FIB levels were measured by the ACL7000 automatic coagulation analyzer and its accompanying test kit (Beckman Coulter). Serum levels of miR-374a-5p were measured by quantitative real-time PCR (qRT-PCR): Total RNA was extracted from serum by the Trizol method (Invitrogen, USA), Synthetic cDNA according to miRNA 1st Strand cDNA Synthesis Kit (Shanghai Zeha Biotechnology Co., Ltd.), qRT-PCR amplification using cDNA as a template, the experimental procedure was performed according to SYBR Green SuperReal fluorescence quantitative premixed reagent enhanced version (Beijing Tiangen Biochemical Company), The data were detected by StepOne TM fluorescence analyzer (Applied Biosystems, USA), and the level of miR-374a-5p was calculated by using the 2 - ΔΔCt method with U6 as the internal reference. The miR-374a-5p primer was designed and synthesized by Beijing BaoRi Physics Technology Company, with miR-374a-5p forward primer 5 ′ -GCCGGTTATAATACAACCTGATAAG-3 ′ and reverse primer 5 ′ -TATGGTTGTTCTCTGCTCTGTCTC-3 ′ and U6 forward primer 5 ′ -CAGCACATATACTAAAATTGGAAC G-3 ′ and reverse primer 5 ′ -ACGAATTTGCGTGTCATCC-3 ′. 1.4 Observation indicators ① Compare and analyze the levels of serum NETs, FIB, and miR-374a-5p in the two groups at preoperative, 1d postoperative and 3d postoperative, and analyze the correlation with Caprini score.② Analyze the factors influencing the occurrence of DVT in lower limbs, and evaluate the predictive efficacy of serum NETs, FIB, and miR-374a-5p levels on the occurrence of DVT in lower limbs at 3d postoperatively. 1.5 Statistical methods Data The data were analyzed by SPSS 25.0, and the measurement data conformed to normal distribution were expressed as mean±standard deviation (x±s), and the comparisons between two groups were made by independent samples t-test; count data were expressed as rate [n(%)],and the comparisons between the two groups were performed by theχ 2 test; Pearson's method was used to analyze the correlation between the levels of serum NETs, FIB, miR-374a-5p and Caprini scores; Multivariate Logistic regression was performed to analyze the factors influencing the occurrence of postoperative lower limb DVT; Drawing the receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC)To evaluate the value of different serum indexes in predicting the occurrence of lower limb DVT, and the difference was considered statistically significant with P<0.05. 2 Results 2.1 Comparison of clinical data between the two groups The incidence of DVT in the lower limb 7 days after surgery was 29.63% (32/108). There were no statistically significant differences between the two groups in terms of gender, operation time, intraoperative blood loss, tourniquet application time, and body mass index,underlying disease, and primary disease (P> 0.05). The age and Caprini score of the DVT group were higher than those of the non-DVT group (P<0.05), as shown in Table 1. 2.2 Comparison of serum NETs, FIB, and miR-374a-5p levels There was no statistically significant difference in the levels of serum NETs, FIB and miR-374a-5p between the two groups before surgery (P>0.05). The levels of serum NETs, FIB and miR-374a-5p were higher in both groups in the 1 d and 3 d postoperative than in the preoperative period; the levels of serum NETs, FIB and miR-374a-5p were higher in the DVT group than in the non-DVT group in the 1 d and 3 d postoperative period (P<0.05), shown in Table 2. 2.3 Correlation analysis of preoperative serum NETs, FIB, miR-374a-5p levels and Caprini score Correlation analysis by Pearson's method showed that preoperative serum NETs, FIB, and miR-374a-5p levels were positively correlated with Caprini scores (P<0.05), as shown in Table 3. 2.4 Multifactorial Logistic Regression Analysis of Factors Influencing the Occurrence of Postoperative Lower limb DVT The results of the multicollinearity diagnosis showed that there might be multicollinearity in the serum levels of NETs, FIB, and miR-374a-5p at 1 d and 3 d postoperatively, and that trauma and pain at 1 d postoperatively could cause a stress response, which might affect the changes in serum levels of the indexes, and thus the serum levels of NETs, FIB, and miR-374a-5p at 1 d postoperatively were excluded.Logistic stepwise regression analysis was performed with the occurrence of postoperative lower limb DVT as the dependent variable ( no=0, yes=1) ,and age, Caprini score, and 3d postoperative serum NETs, FIB, miR-374a-5p levels as the independent variables. The results showed that Caprini score, 3d postoperative serum NETs, FIB, and miR-374a-5p levels were independent influences on the occurrence of postoperative lower limb DVT (P<0.05),as shown in Table 4. 2.5 Predictive value of ROC analysis of serum NETs, FIB, and miR-374a-5p levels for the development of postoperative lower limb DVT Using 32 cases in the DVT group as positive samples and 76 cases in the non-DVT group as negative samples, ROC analysis showed that the combination of serum NETs, FIB, and miR-374a-5p levels at 3 d postoperatively predicted a greater AUC for the occurrence of postoperative lower limb DVT than that predicted by a single index (P<0.05), as shown in Table 5. 3 Discussion The generation of DVT in the lower limb after knee arthroplasty may be related to inflammatory reactions, inflammatory damage to vascular endothelial cells, damage to the inner wall of blood vessels, and hypercoagulability of blood. During knee arthroplasty, the body may activate the coagulation system, increase the level of thrombin, and the blood becomes hypercoagulable, and the prolonged lying in bed after the operation leads to the restriction of lower limb venous return, which increases the risk of the generation of DVT [8] . Platelets can promote the production of NETs which can directly bind to FⅫ, converting it to activated coagulation factors, activating the endogenous coagulation pathway, inactivating the degradation of natural anticoagulant substances and inducing blood platelet aggregation, and involving in the process of venous thrombosis [9] .The results of this study showed that the serum NETs levels were higher in the DVT group than in the non-DVT group, and were positively correlated with the Caprini score, suggesting that higher levels of NETs were closely related to the development of postoperative DVT in the lower limb. Intraoperative stimulation of neutrophils releases mitochondrial DNA, which increases the production of NETs, and the slowing of blood flow in the lower limb can cause blood stasis, which can also be involved in the formation of NETs, and at the same time, NETs are associated with platelet activation and activation of the coagulation waterfall reaction, which causes a state of hypercoagulability in the blood and promotes thrombosis, and these processes indicate that NETs are one of the most important factors in the generation of DVT. In a study conducted by Chenglin Chen et al. [10] , it was shown that elevated levels of postoperative serum NETs may be an influential factor in the development of lower limb DVT after knee arthroplasty, and the AUC for the level of serum NETs at 3 d postoperatively in predicting the development of postoperative lower limb DVT was 0.831. In this study, we found that the AUC of serum NETs levels at 3 d postoperatively for predicting the generation of postoperative lower limb DVT was 0.778, and the reason for this analysis may be related to the different sample sizes. FIB is a plasma glycoprotein synthesized by the liver, which is a specific molecular marker for fibrin cleavage by fibrinolytic enzymes, and its level is closely related to the generation of DVT, and the enhanced coagulation activity of the body, the elevated level of FIB can reflect the secondary hyperfibrinolytic and hypercoagulable state, which can inhibit the fibrinolytic process of the body [11] . The results of this study showed that serum FIB levels were higher in the DVT group than in the non-DVT group, which was similar to the results reported by Chen Xia et al [12] . At the same time, the present study found that the level of FIB was positively correlated with the Caprini score, and its elevated level was a risk factor for the occurrence of postoperative lower limb DVT, suggesting that the elevated level of FIB may be involved in the process of postoperative lower limb DVT. The reason for this may be that after knee arthroplasty, the broken tiny soft tissues can enter the blood circulation system through blood vessels, activate the coagulation mechanism, and promote the release of inflammatory mediators into the blood, and the hypercoagulation mechanism can cause DVT, and the surgical operation can aggravate the stress reaction. the reduction of limb activity after operation can reduce the blood flow rate and aggravate the degree of DVT. After vascular injury, FIB is converted to fibrin, which can activate coagulation factors, accelerate blood clotting and participate in the process of DVT formation. miRNAs can participate in the regulation of cell proliferation and differentiation by binding to downstream target genes, and their aberrant expression is closely related to the development of many diseases, such as rheumatoid arthritis. miR-374a-5p can inhibit the expression of interleukin-10 (IL-10), which can lead to an imbalance in the homeostasis of the body's internal environment, causing inflammatory injury to the vascular endothelial cells, and ultimately leading to the formation of DVT. However, miR-374a-5p has only been preliminarily demonstrated in animal and cellular experiments to be an effective target for the treatment of DVT [13] . In the present study, we found that serum miR-374a-5p levels were higher in the DVT group than in the non-DVT group, and were closely associated with the development of DVT. It is known that miR-374a-5p can bind to IL-10, cause inflammatory damage to the vascular endothelium, and participate in the process of thrombosis and coronary artery recirculation [14] . Therefore, it is hypothesized that increased levels of miR-374a-5p can inhibit the expression of IL-10, regulate the expression of inflammatory factors, exacerbate inflammation in the affected limbs, and affect the process of venous thrombus dissolution and recanalization, which leads to the generation of DVT in the lower limb after surgery. The present study also found that the AUC of the combination of serum NETs, FIB, and miR-374a-5p levels at 3 d postoperatively predicted the occurrence of postoperative DVT in the lower limb was greater than that predicted by a single indicator, which may provide evidence-based support for the prevention of DVT in the clinic. In conclusion, serum levels of NETs, FIB, and miR-374a-5p are elevated in patients with lower limb DVT after knee arthroplasty and are closely related to the formation of DVT, and the combined detection of these levels can predict the risk of lower limb DVT after surgery, which may provide a new idea for the prevention and treatment of DVT. Declarations Author Contribution LXQ and WBY designed the study. GZS was responsible for data collection and analysis and wrote the manuscript. gzs was responsible for data collection and analysis. NXZ was responsible for follow-up. The fnal manuscript was read and approved by all authors. References Yao M, Ma J, Wu D, Fang C, Wang Z, Guo T, Mo J. Neutrophil extracellular traps mediate deep vein thrombosis: from mechanism to therapy. Front Immunol. 2023 Aug 23;14:1198952. doi: 10.3389/fimmu.2023.1198952. Campos J, Ponomaryov T, De Prendergast A, Whitworth K, Smith CW, Khan AO, Kavanagh D, Brill A. Neutrophil extracellular traps and inflammasomes cooperatively promote venous thrombosis in mice. Blood Adv. 2021 May 11;5(9):2319-2324. doi: 10.1182/bloodadvances.2020003377. Iding AFJ, Alkarithi G, Cate HT, Ariëns RAS, Ten Cate-Hoek AJ. Fibrinogen levels and clot properties identify patients who benefit from catheter-directed thrombolysis after DVT. Blood Adv. 2024 Jun 11;8(11):2924-2932. doi: 10.1182/bloodadvances.2023012493. Tang J, Li C, Zhang P, Lin W, Yang Y. Predictive value of miR-374a-5p of peripheral blood mononuclear cells in deep venous thrombosis for elderly patients after total hip arthroplasty. Am J Transl Res. 2021 Sep 15;13(9):10670-10675. Matassi F, Pettinari F, Frasconà F, Innocenti M, Civinini R. Coronal alignment in total knee arthroplasty: a review. J Orthop Traumatol. 2023 May 22;24(1):24. doi: 10.1186/s10195-023-00702-w. Kim KA, Choi SY, Kim R. Endovascular Treatment for Lower Extremity Deep Vein Thrombosis: An Overview. 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Tables Table 1 Comparison of clinical data between the two groups [n (%), (x ± s)] Clinical parameter DVT group (n=32) Non-DVT group (n=76) statistic P-value gender 0.028 0.868 male 22(68.75) 51(67.11) female 10(31.25) 25(32.89) Age / year 65.21±6.07 62.48±5.72 2.224 0.028 Operation time / min 122.63±20.87 126.27±22.09 0.795 0.429 Intra-operative blood loss / mL 244.19±51.32 238.57±55.63 0.490 0.625 Tourniquet application time / min 63.26±11.08 65.28±12.71 0.782 0.436 Body mass index / (kg/m 2 ) 23.65±2.08 24.16±3.05 0.864 0.390 Caprini Score 7.22±1.62 3.16±0.93 16.404 <0.001 Basic disease hypertension 12(37.50) 19(25.00) 1.719 0.190 diabetes 10(31.25) 17(22.37) 0.947 0.330 hyperlipoidemia 9(28.13) 15(19.74) 0.917 0.338 Primary disease 1.076 0.783 Traumatic knee arthritis 14(43.75) 34(44.74) Rheumatoid knee arthritis 13(40.63) 30(39.47) Knee necrosis 3(9.38) 10(13.16) synovial chondroma 2(6.25) 2(2.63) Note: DVT = deep vein thrombosis of lower limbs, Caprini= risk assessment scale of venous thromboembolism, the same below. Table 2 Comparison of serum NETs, FIB, and miR-374a-5p levels in the two groups (x ± s) ggroup Example number NETs (Absorbance) FIB/(g·L-1) miR-374a-5p Pre- 1d 3d Operative after after surgery surgery Pre- 1d 3d Operative after after surgery surgery Pre- 1d 3d Operative after after surgery surgery DDVT group 32 0.20±0.04 0.56±0.10 0.45±0.11 2.23±0.44 5.96±0.61 4.62±0.64 1.00±0.06 2.85±0.41 2.41±0.50 NNon-DVT group 76 0.21±0.05 0.37±0.08 0.36±0.10 2.18±0.51 3.31±0.53 3.28±0.76 1.01±0.04 1.88±0.42 1.83±0.56 t-value 1.003 10.444 4.145 0.484 22.675 8.747 1.015 11.036 5.067 P-value 0.318 <0.001 <0.001 0.630 <0.001 <0.001 0.312 <0.001 <0.001 Note: NETs = neutrophil extracellular traps, FIB = fibrinogen, miR-374a-5p= Micro RNA-374a-5p, the same below. Table 3 Correlation analysis of preoperative serum NETs, FIB, miR-374a-5p levels and Caprini score gindex NETs FIB miR-374a-5p r-value P-value r-value P-value r-value P-value Caprini Score 0.526 <0.05 0.471 <0.05 0.602 <0.05 Table 4 Multifactorial Logistic Regression Analysis of Factors Influencing the Occurrence of Postoperative Lower limb DVT influence factor The independent variable assignment β SE The Waldχ2 value P-value OR(95%CI) age Continuous variables, original values in -0.280 0.236 1.412 >0.05 0.755(0.526，1.085) Caprini Score Continuous variables, original values in 1.527 0.524 8.490 <0.05 4.604(1.958，10.82) NETs Continuous variables, original values in 2.013 0.714 7.946 <0.05 7.484(3.416，16.39) FIB Continuous variables, original values in 2.491 0.619 16.197 <0.05 12.07(6.113，23.85) miR-374a-5p Continuous variables, original values in 3.192 0.638 25.029 <0.05 24.33(5.662，18.47) Table 5 Predictive value of ROC analysis of serum NETs, FIB, and miR-374a-5p levels for the development of postoperative lower limb DVT index AUC 95%CI cut-off value sensitivity /% specificity /% P-value NETs 0.778 0.653，0.816 > 0.53 absorbance 71.88 73.68 4.76 g/L 62.50 72.37 2.22 75.00 60.53 <0.001 combination 0.895 0.722，0.913 87.50 81.58 <0.001 Note: ROC = receiver operating characteristic curve, AUC = area under the curve. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4789246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344753166,"identity":"9c49408f-9dfe-41f6-a101-75a32856d8a8","order_by":0,"name":"zhongshan gui","email":"","orcid":"","institution":"Department of Vascular Surgery, Gulou Clinical Medical College affiliated to Nanjing University of Chinese Medicine, Nanjing City 210000, Jiangsu Province","correspondingAuthor":false,"prefix":"","firstName":"zhongshan","middleName":"","lastName":"gui","suffix":""},{"id":344753167,"identity":"2a146446-2e62-47a5-b43b-70d706008f15","order_by":1,"name":"XianZong Ning","email":"","orcid":"","institution":"Department of Orthopedics,Nanjing Pukou People's Hospital, Nanjing City 211800, Jiangsu Province","correspondingAuthor":false,"prefix":"","firstName":"XianZong","middleName":"","lastName":"Ning","suffix":""},{"id":344753168,"identity":"028494da-5289-404a-8c1e-43fb5e6a5c1e","order_by":2,"name":"BeiYue Wang","email":"","orcid":"","institution":"Department of Orthopedics,Nanjing Pukou People's Hospital, Nanjing City 211800, Jiangsu Province","correspondingAuthor":false,"prefix":"","firstName":"BeiYue","middleName":"","lastName":"Wang","suffix":""},{"id":344753169,"identity":"7215c87f-1d84-4c95-8708-26ae74c03342","order_by":3,"name":"XiaoQiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFACxgZmMC0BxB8MJOr7gSLEa2GcUWHDOLOBoBYGBrgWZp4zaYwbDhBQLh+R3Py5oOKO3fzZzc8e8LYdZjY+f7jtwQ8GOzldHJYZ3khsMJ5x5lnyhjvHzA0k2w6zmd1IbDfsYUg2NsNhneGMxIZkoOHJBhIJZhKGbYd5zG4wtknwMBxI3IZHy2GQFvkZ6d8kEtsOSxj3H2yT/INHi7xEYmMzUIsdw40cM4kDZ9IMDBgS26Tx2WLA87AZGFCHEwxu5JRJNlTYJEjcAGqRMcDtF/n29MefeSoO2wMdtk36D9BH/P3Hn0m+qbCTw6XFACqe2IAmjl052BaoUnvcSkbBKBgFo2DEAwDpNGNBIFKW4wAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Vascular Surgery, Gulou Clinical Medical College affiliated to Nanjing University of Chinese Medicine, Nanjing City 210000, Jiangsu Province","correspondingAuthor":true,"prefix":"","firstName":"XiaoQiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-23 13:51:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4789246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4789246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67822468,"identity":"48436970-3839-45c5-a922-41cde336d078","added_by":"auto","created_at":"2024-10-30 05:39:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":382029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4789246/v1/e7027576-552e-4efb-9390-0fbb4ea1c970.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation analysis between serum NETs, FIB, miR-374a-5p levels and deep vein thrombosis in the lower limb after knee arthroplasty","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeep vein thrombosis (DVT) is a common complication after knee arthroplasty, and the incidence of DVT in the lower limbs after surgery is 40%~60%. Deep venography is the gold standard for diagnosis of DVT, but it is an invasive examination and the operation process is complicated, while the serological indexes are easy to operate, and it has become a common method of clinical auxiliary diagnosis of DVT\u003csup\u003e[1]\u003c/sup\u003e. Neutrophil extracellular traps (NETs) are composed of histones, neutrophil intracellular granules which can bind vascular willebrand factor (VWF), coagulation factor Ⅻ (FⅫ), promote fibrin formation and platelet agglutination, and \u0026nbsp;reduce venous thrombosis after inhibiting their expression\u003csup\u003e[ 2 ]\u003c/sup\u003e. Fibrinogen (FIB) has a certain predictive value for intravascular coagulation and thrombosis\u003csup\u003e\u0026nbsp;[ 3]\u003c/sup\u003e. Dysregulation of microRNA (miRNA) expression is closely related to the development of various diseases such as DVT, Micro RNA-374a-5p (miR-374a-5p) is expressed at elevated levels in mixed-type DVT, and its high expression may aggravate the severity of DVT\u003csup\u003e[4]\u0026nbsp;\u003c/sup\u003e. At present, there are relatively few reports on the correlation between lower limb DVT and serum NETs, FIB, and miR-374a-5p after knee arthroplasty. therefore, the present study was conducted to investigate the changes in the levels of serum NETs, FIB, and miR-374a-5p in patients with lower limb DVT after knee arthroplasty, and to analyze the predictive value of these levels in lower limb DVT after surgery, in order to provide a reference for the clinical diagnosis and treatment.\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"1 Information and methodology ","content":"\u003cp\u003e1.1 General information\u003c/p\u003e\u003cp\u003eFrom September 2020 to May 2023, 108 patients who underwent knee arthroplasty in Nanjing Pukou People's Hospital were selected, including 73 males and 35 females; Age 49-78 years, mean age (63.29±7.02) years; Body mass index 18-30 kg/m2 , mean body mass index was (24.01±2.61)kg/m2; Primary diseases: traumatic knee osteoarthritis in 48 cases, rheumatoid knee osteoarthritis in 43 cases, necrosis of the knee joint in 13 cases, and synovial chondroma in 4 cases. The study was approved by the Ethics Committee of the hospital (approval number: YCYY20200815001), and informed consent was signed.\u003c/p\u003e\u003cp\u003e1.2 Selection Criteria\u003c/p\u003e\u003cp\u003eInclusion criteria: patients who meet the indications for knee arthroplasty; no abnormal coagulation function; no anticoagulation; no previous lower limb vascular surgery; no significant hemorrhagic tendency; unilateral limb; duration \u0026lt;15 d.\u003c/p\u003e\u003cp\u003eExclusion criteria: perioperative fever and respiratory infections; COVID-19 infections; long-term use of immune modulators; postoperative wound redness or superficial infections; preoperative lower limb DVT; previous history of cardiovascular and cerebrovascular obstruction; combined with active lower limb ulcers.\u003c/p\u003e\u003cp\u003e1.3 Methods\u003c/p\u003e\u003cp\u003e1.3.1 Treatment methods and grouping criteria\u003c/p\u003e\u003cp\u003eThe study subjects were treated with knee arthroplasty and underwent surgical procedures according to the references\u003csup\u003e[5]\u003c/sup\u003e.After the operation, conventional treatments including anticoagulation and antithrombotic therapy were given. According to the 7 days postoperative whether lower limb DVT occurred or not was divided into DVT group and non-DVT group. DVT standard \u003csup\u003e[6]\u003c/sup\u003e: after compression of the distal limb, ultrasound did not detect blood flow signals at the lesion; the internal diameter of the venous lumen was widened,and the lumen was not fully compressed after the pressure; the lumen of the vein was widened and a solid mass with different echoes was seen.\u003c/p\u003e\u003cp\u003e1.3.2 Clinical data\u003c/p\u003e\u003cp\u003eThe clinical data of the study subjects were collected, including gender, age, operation time, intraoperative blood loss, tourniquet application time, body mass index, venous thromboembolism risk assessment scale (Caprini) score, underlying disease, primary disease, Caprini score included 40 risk factors for thrombosis, each risk factor severity recorded as 1,2,3,4,5 respectively, the higher the score, the higher the risk of DVT\u003csup\u003e\u0026nbsp;[7]\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\u003cp\u003e1.3.3 Detection of serum NETs, FIB, miR-374a-5p level\u003c/p\u003e\u003cp\u003e5 mL of fasting peripheral venous blood was collected from the two study groups before surgery, 1 d after surgery and 3 d after surgery respectively,and then centrifuged at 3,500 r/min for 10 minutes after standing for 30 min at room temperature. The level of serum NETs was measured by capture ELISA of MPO-DNA complex (provided by Shanghai Huabang Biological Company). Serum FIB levels were measured by the ACL7000 automatic coagulation analyzer and its accompanying test kit (Beckman Coulter). Serum levels of miR-374a-5p were measured by quantitative real-time PCR (qRT-PCR): Total RNA was extracted from serum by the Trizol method \u0026nbsp;(Invitrogen, USA), Synthetic cDNA according to miRNA 1st Strand cDNA Synthesis Kit (Shanghai Zeha Biotechnology Co., Ltd.), qRT-PCR amplification using cDNA as a template, the experimental procedure was performed according to SYBR Green SuperReal fluorescence quantitative premixed reagent enhanced version (Beijing Tiangen Biochemical Company), The data were detected by StepOne TM fluorescence analyzer (Applied Biosystems, USA), and the level of miR-374a-5p was calculated by using the 2\u003csup\u003e-\u003c/sup\u003e\u003csup\u003eΔΔCt\u003c/sup\u003e method with U6 as the internal reference. The miR-374a-5p primer was designed and synthesized by Beijing BaoRi Physics Technology Company, with miR-374a-5p forward primer 5 ′ -GCCGGTTATAATACAACCTGATAAG-3 ′ and reverse primer 5 ′ -TATGGTTGTTCTCTGCTCTGTCTC-3 ′ and U6 forward primer 5 ′ -CAGCACATATACTAAAATTGGAAC G-3 ′ and reverse primer 5 ′ -ACGAATTTGCGTGTCATCC-3 ′.\u003c/p\u003e\u003cp\u003e1.4 Observation indicators\u003c/p\u003e\u003cp\u003e① Compare and analyze the levels of serum NETs, FIB, and miR-374a-5p in the two groups at preoperative, 1d postoperative and 3d postoperative, and analyze the correlation with Caprini score.② Analyze the factors influencing the occurrence of DVT in lower limbs, and evaluate the predictive efficacy of serum NETs, FIB, and miR-374a-5p levels on the occurrence of DVT in lower limbs at 3d postoperatively.\u003c/p\u003e\u003cp\u003e1.5 Statistical methods Data\u003c/p\u003e\u003cp\u003eThe data were analyzed by SPSS 25.0, and the measurement data conformed to normal distribution were expressed as mean±standard deviation (x±s), and the comparisons between two groups were made by independent samples t-test; count data were expressed as rate [n(%)],and the comparisons between the two groups were performed by theχ\u003csup\u003e2\u003c/sup\u003e test; Pearson's method was used to analyze the correlation between the levels of serum NETs, FIB, miR-374a-5p and Caprini scores; Multivariate Logistic regression was performed to analyze the factors influencing the occurrence of postoperative lower limb DVT; Drawing the receiver operating characteristic curve (ROC) and calculating the area under the curve (AUC)To evaluate the value of different serum indexes in predicting the occurrence of lower limb DVT, and the difference was considered statistically significant with P\u0026lt;0.05.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003e2.1 Comparison of clinical data between the two groups\u003c/p\u003e\n\u003cp\u003eThe incidence of DVT in the lower limb 7 days after surgery was 29.63% (32/108). There were no statistically significant differences between the two groups in terms of gender, operation time, intraoperative blood loss, tourniquet application time, and body mass index,underlying disease, and primary disease (P\u0026gt; 0.05). The age and Caprini score of the DVT group were higher than those of the non-DVT group (P\u0026lt;0.05), as shown in Table 1.\u003c/p\u003e\n\u003cp\u003e2.2 Comparison of serum NETs, FIB, and miR-374a-5p levels\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There was no statistically significant difference in the levels of serum NETs, FIB and miR-374a-5p between the two groups before surgery (P\u0026gt;0.05). The levels of serum NETs, FIB and miR-374a-5p were higher in both groups in the 1 d and 3 d postoperative than in the preoperative period; the levels of serum NETs, FIB and miR-374a-5p were higher in the DVT group than in the non-DVT group in the 1 d and 3 d postoperative period (P\u0026lt;0.05), shown in Table 2.\u003c/p\u003e\n\u003cp\u003e2.3 Correlation analysis of preoperative serum NETs, FIB, miR-374a-5p levels and Caprini score\u003c/p\u003e\n\u003cp\u003eCorrelation analysis by Pearson\u0026apos;s method showed that preoperative serum NETs, FIB, and miR-374a-5p levels were positively correlated with Caprini scores (P\u0026lt;0.05), as shown in Table 3.\u003c/p\u003e\n\u003cp\u003e2.4 Multifactorial Logistic Regression Analysis of Factors Influencing the Occurrence of Postoperative Lower limb DVT \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the multicollinearity diagnosis showed that there might be multicollinearity in the serum levels of NETs, FIB, and miR-374a-5p at 1 d and 3 d postoperatively, and that trauma and pain at 1 d postoperatively could cause a stress response, which might affect the changes in serum levels of the indexes, and thus the serum levels of NETs, FIB, and miR-374a-5p at 1 d postoperatively were excluded.Logistic stepwise regression analysis was performed with the occurrence of postoperative lower limb DVT as the dependent variable ( no=0, yes=1) ,and age, Caprini score, and 3d postoperative serum NETs, FIB, miR-374a-5p levels as the independent variables. The results showed that Caprini score, 3d postoperative serum NETs, FIB, and miR-374a-5p levels were independent influences on the occurrence of postoperative lower limb DVT (P\u0026lt;0.05),as shown in Table 4.\u003c/p\u003e\n\u003cp\u003e2.5 Predictive value of ROC analysis of serum NETs, FIB, and miR-374a-5p levels for the development of postoperative lower limb DVT\u003c/p\u003e\n\u003cp\u003eUsing 32 cases in the DVT group as positive samples and 76 cases in the non-DVT group as negative samples, ROC analysis showed that the combination of serum NETs, FIB, and miR-374a-5p levels at 3 d postoperatively predicted a greater AUC for the occurrence of postoperative lower limb DVT than that predicted by a single index (P\u0026lt;0.05), as shown in Table 5.\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThe generation of DVT in the lower limb after knee arthroplasty may be related to inflammatory reactions, inflammatory damage to vascular endothelial cells, damage to the inner wall of blood vessels, and hypercoagulability of blood. During knee arthroplasty, the body may activate the coagulation system, increase the level of thrombin, and the blood becomes hypercoagulable, and the prolonged lying in bed after the operation leads to the restriction of lower limb venous return, which increases the risk of the generation of DVT\u003csup\u003e\u0026nbsp;[8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePlatelets can promote the production of NETs which can directly bind to FⅫ, converting it to activated coagulation factors, activating the endogenous coagulation pathway, inactivating the degradation of natural anticoagulant substances and inducing blood platelet aggregation, and \u0026nbsp;involving in the process of venous thrombosis\u003csup\u003e\u0026nbsp;[9]\u003c/sup\u003e.The results of this study showed that the serum NETs levels were higher in the DVT group than in the non-DVT group, and were positively correlated with the Caprini score, suggesting that higher levels of NETs were closely related to the development of postoperative DVT in the lower limb. Intraoperative stimulation of neutrophils releases mitochondrial DNA, which increases the production of NETs, and the slowing of blood flow in the lower limb can cause blood stasis, which can also be involved in the formation of NETs, and at the same time, NETs are associated with platelet activation and activation of the coagulation waterfall reaction, which causes a state of hypercoagulability in the blood and promotes thrombosis, and these processes indicate that NETs are one of the most important factors in the generation of DVT. In a study conducted by Chenglin Chen et al. \u003csup\u003e[10]\u0026nbsp;\u003c/sup\u003e, it was shown that elevated levels of postoperative serum NETs may be an influential factor in the development of lower limb DVT after knee arthroplasty, and the AUC for the level of serum NETs at 3 d postoperatively in predicting the development of postoperative lower limb DVT was 0.831. In this study, we found that the AUC of serum NETs levels at 3 d postoperatively for predicting the generation of postoperative lower limb DVT was 0.778, and the reason for this analysis may be related to the different sample sizes. FIB is a plasma glycoprotein synthesized by the liver, which is a specific molecular marker for fibrin cleavage by fibrinolytic enzymes, and its level is closely related to the generation of DVT, and the enhanced coagulation activity of the body, the elevated level of FIB can reflect the secondary hyperfibrinolytic and hypercoagulable state, which can inhibit the fibrinolytic process of the body \u003csup\u003e[11]\u003c/sup\u003e. The results of this study showed that serum FIB levels were higher in the DVT group than in the non-DVT group, which was similar to the results reported by Chen Xia et al \u003csup\u003e[12]\u003c/sup\u003e. At the same time, the present study found that the level of FIB was positively correlated with the Caprini score, and its elevated level was a risk factor for the occurrence of postoperative lower limb DVT, suggesting that the elevated level of FIB may be involved in the process of postoperative lower limb DVT. The reason for this may be that after knee arthroplasty, the broken tiny soft tissues can enter the blood circulation system through blood vessels, activate the coagulation mechanism, and promote the release of inflammatory mediators into the blood, and the hypercoagulation mechanism can cause DVT, and the surgical operation can aggravate the stress reaction. the reduction of limb activity after operation can reduce the blood flow rate and aggravate the degree of DVT. After vascular injury, FIB is converted to fibrin, which can activate coagulation factors, accelerate blood clotting and participate in the process of DVT formation.\u003c/p\u003e\n\u003cp\u003emiRNAs can participate in the regulation of cell proliferation and differentiation by binding to downstream target genes, and their aberrant expression is closely related to the development of many diseases, such as rheumatoid arthritis. miR-374a-5p can inhibit the expression of interleukin-10 (IL-10), which can lead to an imbalance in the homeostasis of the body\u0026apos;s internal environment, causing inflammatory injury to the vascular endothelial cells, and ultimately leading to the formation of DVT. However, miR-374a-5p has only been preliminarily demonstrated in animal and cellular experiments to be an effective target for the treatment of DVT \u003csup\u003e[13]\u003c/sup\u003e. In the present study, we found that serum miR-374a-5p levels were higher in the DVT group than in the non-DVT group, and were closely associated with the development of DVT. It is known that miR-374a-5p can bind to IL-10, cause inflammatory damage to the vascular endothelium, and participate in the process of thrombosis and coronary artery recirculation\u003csup\u003e\u0026nbsp;[14]\u003c/sup\u003e. Therefore, it is hypothesized that increased levels of miR-374a-5p can inhibit the expression of IL-10, regulate the expression of inflammatory factors, exacerbate inflammation in the affected limbs, and affect the process of venous thrombus dissolution and recanalization, which leads to the generation of DVT in the lower limb after surgery. The present study also found that the AUC of the combination of serum NETs, FIB, and miR-374a-5p levels at 3 d postoperatively predicted the occurrence of postoperative DVT in the lower limb was greater than that predicted by a single indicator, which may provide evidence-based support for the prevention of DVT in the clinic.\u003c/p\u003e\n\u003cp\u003eIn conclusion, serum levels of NETs, FIB, and miR-374a-5p are elevated in patients with lower limb DVT after knee arthroplasty and are closely related to the formation of DVT, and the combined detection of these levels can predict the risk of lower limb DVT after surgery, which may provide a new idea for the prevention and treatment of DVT.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLXQ and WBY designed the study. GZS was responsible for data collection and analysis and wrote the manuscript. gzs was responsible for data collection and analysis. NXZ was responsible for follow-up. The fnal manuscript was read and approved by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYao M, Ma J, Wu D, Fang C, Wang Z, Guo T, Mo J. Neutrophil extracellular traps mediate deep vein thrombosis: from mechanism to therapy. Front Immunol. 2023 Aug 23;14:1198952. doi: 10.3389/fimmu.2023.1198952. \u003c/li\u003e\n\u003cli\u003eCampos J, Ponomaryov T, De Prendergast A, Whitworth K, Smith CW, Khan AO, Kavanagh D, Brill A. Neutrophil extracellular traps and inflammasomes cooperatively promote venous thrombosis in mice. Blood Adv. 2021 May 11;5(9):2319-2324. doi: 10.1182/bloodadvances.2020003377. \u003c/li\u003e\n\u003cli\u003eIding AFJ, Alkarithi G, Cate HT, Ari\u0026euml;ns RAS, Ten Cate-Hoek AJ. Fibrinogen levels and clot properties identify patients who benefit from catheter-directed thrombolysis after DVT. 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Systematic review of venous thromboembolism risk categories derived from Caprini score. J Vasc Surg Venous Lymphat Disord. 2022 Nov;10(6):1401-1409.e7. doi: 10.1016/j.jvsv.2022.05.003. Epub 2022 Aug 2. \u003c/li\u003e\n\u003cli\u003eLieberman JR, Bell JA. Venous Thromboembolic Prophylaxis After Total Hip and Knee Arthroplasty. J Bone Joint Surg Am. 2021 Aug 18;103(16):1556-1564. doi: 10.2106/JBJS.20.02250. \u003c/li\u003e\n\u003cli\u003eXu X, Wu Y, Xu S, Yin Y, Ageno W, De Stefano V, Zhao Q, Qi X. Clinical significance of neutrophil extracellular traps biomarkers in thrombosis. Thromb J. 2022 Oct 12;20(1):63. doi: 10.1186/s12959-022-00421-y. \u003c/li\u003e\n\u003cli\u003eCHEN Cheng-lin,HUI Shu-guo,WANG Zhi-yuan,ZHI Li-qiang.Influence factors of deep venous thromboembolism after knee arthroplasty and significance of changes of serum nets and sVCAM-1 levels[J].zhong guo gushang / China J Orthop Trauma,2022,35(11):1053~1059. DOI:10.12200/j.issn.1003-0034.2022.11.009.\u003c/li\u003e\n\u003cli\u003eMlačo A, Mlačo N, Begić E, Mekić M, Džubur A. D-Dimer and Fibrinogen Values according to the Localization of Deep Venous Thrombosis. Int J Angiol. 2023 Jan 13;32(4):243-247. doi: 10.1055/s-0042-1759819. \u003c/li\u003e\n\u003cli\u003eCHEN Xiao, LI Ling-li, HE Ling-xiao, et al. Prediction of Thrombolysis Parameters Combined with D-Dimer and Fibrinogen on Deep Vein Thrombosis in Elder-ly Patients with Hip Fracture[J]. Sichuan Medical Journal,2022,43(11):1091-1096. DOI:10.16252/j.cnki.issn1004-0501-2022.11.006.\u003c/li\u003e\n\u003cli\u003eHembrom AA, Srivastava S, Garg I, Kumar B. MicroRNAs in venous thrombo-embolism. Clin Chim Acta. 2020 May;504:66-72. doi: 10.1016/j.cca.2020.01.034. Epub 2020 Feb 1. \u003c/li\u003e\n\u003cli\u003eTang J, Li C, Zhang P, Lin W, Yang Y. Predictive value of miR-374a-5p of peripheral blood mononuclear cells in deep venous thrombosis for elderly patients after total hip arthroplasty. Am J Transl Res. 2021 Sep 15;13(9):10670-10675. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Comparison of clinical data between the two groups [n (%), (x \u0026plusmn; s)]\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eClinical parameter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003eDVT group (n=32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003eNon-DVT group (n=76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003estatistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e22(68.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e51(67.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e10(31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e25(32.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eAge / year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e65.21\u0026plusmn;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e62.48\u0026plusmn;5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e2.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eOperation time / min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e122.63\u0026plusmn;20.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e126.27\u0026plusmn;22.09\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eIntra-operative blood loss / mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e244.19\u0026plusmn;51.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e238.57\u0026plusmn;55.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eTourniquet application time / min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e63.26\u0026plusmn;11.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e65.28\u0026plusmn;12.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index / (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e23.65\u0026plusmn;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e24.16\u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eCaprini Score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e7.22\u0026plusmn;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e3.16\u0026plusmn;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e16.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eBasic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003ehypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e12(37.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e19(25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003ediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e10(31.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e17(22.37)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003ehyperlipoidemia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e9(28.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e15(19.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eTraumatic knee arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e14(43.75)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e34(44.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eRheumatoid knee arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e13(40.63)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e30(39.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003eKnee necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e3(9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e10(13.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.686067019400355%\" valign=\"top\"\u003e\n \u003cp\u003esynovial chondroma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.63668430335097%\" valign=\"top\"\u003e\n \u003cp\u003e2(6.25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.045855379188712%\" valign=\"top\"\u003e\n \u003cp\u003e2(2.63)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.932980599647266%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.698412698412698%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: DVT = deep vein thrombosis of lower limbs, Caprini= risk assessment scale of venous thromboembolism, the same below.\u003c/p\u003e\n\u003cp\u003eTable 2 Comparison of serum NETs, FIB, and miR-374a-5p levels in the two groups (x\u0026nbsp;\u0026plusmn;\u0026nbsp;s)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.963093145869948%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eggroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.13884007029877%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eExample number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.010544815465728%\" valign=\"top\"\u003e\n \u003cp\u003eNETs (Absorbance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.81195079086116%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.725834797891036%\" valign=\"top\"\u003e\n \u003cp\u003eFIB/(g\u0026middot;L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.272407732864675%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003emiR-374a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.23809523809524%\" valign=\"top\"\u003e\n \u003cp\u003ePre- \u0026nbsp; \u0026nbsp; \u0026nbsp; 1d \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3d\u003c/p\u003e\n \u003cp\u003eOperative \u0026nbsp;after \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; after \u0026nbsp;\u003c/p\u003e\n \u003cp\u003esurgery \u0026nbsp; \u0026nbsp; \u0026nbsp; surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.142857142857146%\" valign=\"top\"\u003e\n \u003cp\u003ePre- \u0026nbsp; \u0026nbsp; \u0026nbsp; 1d \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3d\u003c/p\u003e\n \u003cp\u003eOperative \u0026nbsp;after \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; after \u0026nbsp;\u003c/p\u003e\n \u003cp\u003esurgery \u0026nbsp; \u0026nbsp; \u0026nbsp; surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.61904761904762%\" valign=\"top\"\u003e\n \u003cp\u003ePre- \u0026nbsp; \u0026nbsp; \u0026nbsp; 1d \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3d\u003c/p\u003e\n \u003cp\u003eOperative \u0026nbsp;after \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; after \u0026nbsp;\u003c/p\u003e\n \u003cp\u003esurgery \u0026nbsp; \u0026nbsp; \u0026nbsp; surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.963093145869948%\" valign=\"top\"\u003e\n \u003cp\u003eDDVT group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.13884007029877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.010544815465728%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.20\u0026plusmn;0.04 \u0026nbsp;0.56\u0026plusmn;0.10 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.45\u0026plusmn;0.11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.81195079086116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.725834797891036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.23\u0026plusmn;0.44 \u0026nbsp;5.96\u0026plusmn;0.61 \u0026nbsp; \u0026nbsp; \u0026nbsp;4.62\u0026plusmn;0.64\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.272407732864675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.00\u0026plusmn;0.06 \u0026nbsp;2.85\u0026plusmn;0.41 \u0026nbsp; \u0026nbsp; \u0026nbsp;2.41\u0026plusmn;0.50\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.963093145869948%\" valign=\"top\"\u003e\n \u003cp\u003eNNon-DVT group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.13884007029877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.010544815465728%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.21\u0026plusmn;0.05 \u0026nbsp;0.37\u0026plusmn;0.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.36\u0026plusmn;0.10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.81195079086116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.725834797891036%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.18\u0026plusmn;0.51 \u0026nbsp; \u0026nbsp; 3.31\u0026plusmn;0.53 \u0026nbsp; 3.28\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.272407732864675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.01\u0026plusmn;0.04 \u0026nbsp;1.88\u0026plusmn;0.42 \u0026nbsp; \u0026nbsp; \u0026nbsp;1.83\u0026plusmn;0.56\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.963093145869948%\" valign=\"top\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.13884007029877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.010544815465728%\" valign=\"top\"\u003e\n \u003cp\u003e1.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;10.444 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 4.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.81195079086116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.725834797891036%\" valign=\"top\"\u003e\n \u003cp\u003e0.484 \u0026nbsp; \u0026nbsp; \u0026nbsp; 22.675 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 8.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.272407732864675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e1.015 \u0026nbsp; \u0026nbsp; \u0026nbsp; 11.036 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;5.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.963093145869948%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.13884007029877%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.010544815465728%\" valign=\"top\"\u003e\n \u003cp\u003e0.318 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.81195079086116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.725834797891036%\" valign=\"top\"\u003e\n \u003cp\u003e0.630 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.272407732864675%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.077328646748683%\" valign=\"top\"\u003e\n \u003cp\u003e0.312 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: NETs = neutrophil extracellular traps, FIB = fibrinogen, miR-374a-5p= Micro RNA-374a-5p, the same below.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Correlation analysis of preoperative serum NETs, FIB, miR-374a-5p levels and Caprini score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.864603481624759%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003egindex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.62669245647969%\" valign=\"top\"\u003e\n \u003cp\u003eNETs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.094777562862669%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.11218568665377%\" valign=\"top\"\u003e\n \u003cp\u003eFIB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.499032882011605%\" valign=\"top\"\u003e\n \u003cp\u003emiR-374a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.23809523809524%\" valign=\"top\"\u003e\n \u003cp\u003er-value \u0026nbsp; \u0026nbsp; \u0026nbsp; P-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.142857142857146%\" valign=\"top\"\u003e\n \u003cp\u003er-value \u0026nbsp; \u0026nbsp; \u0026nbsp; P-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.61904761904762%\" valign=\"top\"\u003e\n \u003cp\u003er-value \u0026nbsp; \u0026nbsp; \u0026nbsp; P-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.864603481624759%\" valign=\"top\"\u003e\n \u003cp\u003eCaprini Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.62669245647969%\" valign=\"top\"\u003e\n \u003cp\u003e0.526 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.05\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.094777562862669%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.11218568665377%\" valign=\"top\"\u003e\n \u003cp\u003e0.471 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;0.05\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.499032882011605%\" valign=\"top\"\u003e\n \u003cp\u003e0.602 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;0.05\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Multifactorial Logistic Regression Analysis of Factors Influencing the Occurrence of Postoperative Lower limb DVT\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.26056338028169%\" valign=\"top\"\u003e\n \u003cp\u003einfluence factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eThe independent variable assignment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.570422535211268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.964788732394366%\" valign=\"top\"\u003e\n \u003cp\u003eThe Wald\u0026chi;2 value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.26056338028169%\" valign=\"top\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variables, original values in\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.570422535211268%\" valign=\"top\"\u003e\n \u003cp\u003e-0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"top\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.964788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e0.755(0.526，1.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.26056338028169%\" valign=\"top\"\u003e\n \u003cp\u003eCaprini Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variables, original values in\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.570422535211268%\" valign=\"top\"\u003e\n \u003cp\u003e1.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"top\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.964788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e8.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e4.604(1.958，10.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.26056338028169%\" valign=\"top\"\u003e\n \u003cp\u003eNETs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variables, original values in\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.570422535211268%\" valign=\"top\"\u003e\n \u003cp\u003e2.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"top\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.964788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e7.946\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e7.484(3.416，16.39)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.26056338028169%\" valign=\"top\"\u003e\n \u003cp\u003eFIB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variables, original values in\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.570422535211268%\" valign=\"top\"\u003e\n \u003cp\u003e2.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"top\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.964788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e16.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e12.07(6.113，23.85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.26056338028169%\" valign=\"top\"\u003e\n \u003cp\u003emiR-374a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variables, original values in\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.570422535211268%\" valign=\"top\"\u003e\n \u003cp\u003e3.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.514084507042254%\" valign=\"top\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.964788732394366%\" valign=\"top\"\u003e\n \u003cp\u003e25.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e24.33(5.662，18.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5 Predictive value of ROC analysis of serum NETs, FIB, and miR-374a-5p levels for the development of postoperative lower limb DVT\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003eindex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.028169014084508%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003ecut-off \u0026nbsp; \u0026nbsp; value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.204225352112676%\" valign=\"top\"\u003e\n \u003cp\u003esensitivity /%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.795774647887324%\" valign=\"top\"\u003e\n \u003cp\u003especificity /%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003eNETs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e0.778\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.028169014084508%\" valign=\"top\"\u003e\n \u003cp\u003e0.653，0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt; 0.53 absorbance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.204225352112676%\" valign=\"top\"\u003e\n \u003cp\u003e71.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.795774647887324%\" valign=\"top\"\u003e\n \u003cp\u003e73.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003eFIB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e0.724\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.028169014084508%\" valign=\"top\"\u003e\n \u003cp\u003e0.618，0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;4.76 g/L\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.204225352112676%\" valign=\"top\"\u003e\n \u003cp\u003e62.50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.795774647887324%\" valign=\"top\"\u003e\n \u003cp\u003e72.37\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003emiR-374a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e0.728\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.028169014084508%\" valign=\"top\"\u003e\n \u003cp\u003e0.629，0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;2.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.204225352112676%\" valign=\"top\"\u003e\n \u003cp\u003e75.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.795774647887324%\" valign=\"top\"\u003e\n \u003cp\u003e60.53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003ecombination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.028169014084508%\" valign=\"top\"\u003e\n \u003cp\u003e0.722，0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.661971830985916%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.204225352112676%\" valign=\"top\"\u003e\n \u003cp\u003e87.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.795774647887324%\" valign=\"top\"\u003e\n \u003cp\u003e81.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.549295774647888%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ROC = receiver operating characteristic curve, AUC = area under the curve.\u003c/p\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":"Knee arthroplasty, Lower limb deep vein thrombosis, Neutrophil extracellular traps, Fibrinogen, MicroRNA-374a-5p ","lastPublishedDoi":"10.21203/rs.3.rs-4789246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4789246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the changes in the levels of serum neutrophil extracellular traps (NETs), fibrinogen (FIB), and micro RNA-374a-5p (miR-374a-5p) after knee arthroplasty, and to analyze their correlation with postoperative deep vein thrombosis (DVT) and their predictive efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e108 patients who underwent knee arthroplasty in Nanjing Pukou People's Hospital from September 2020 to May 2023 were selected for the study, and 32 patients in the DVT group and 76 patients in the non-DVT group were categorized according to the occurrence of lower limb DVT at 7 days after surgery. The clinical data of the two groups and the levels of serum NETs, FIB and miR-374a-5p in the preoperative period, 1 day postoperative period and 3 days postoperative period were compared and analyzed. Pearson's method was used to analyze the correlation between preoperative serum NETs, FIB, miR-374a-5p levels and the venous thromboembolism risk assessment scale (Caprini) score. Multifactorial logistic regression was used to analyze the factors influencing the occurrence of lower limb DVT after surgery. The predictive value of serum NETs, FIB, and miR-374a-5p levels on the occurrence of postoperative lower limb DVT was evaluated by using receiver operating characteristic curve(ROC) and area under the curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe levels of serum NETs, FIB, and miR-374a-5p were higher in the DVT group than in the non-DVT group at 1 day and 3 days postoperatively (P\u0026lt;0.05); the levels of preoperative serum NETs, FIB, and miR- 374a-5p were positively correlated with the Caprini score (P\u0026lt;0.05); The Caprini score and elevated levels of serum NETs,FIB,and miR-374a-5p at 3 days after surgery were independent risk factors for postoperative lower limb DVT (P\u0026lt;0.05);The combined prediction of serum NETs,FIB,and miR-374a-5p levels at 3 days after surgery for AUC in lower limb DVT was greater than that predicted by single indicators ( P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe levels of serum NETs,FIB, and miR-374a-5p in patients with DVT after knee arthroplasty increase.Combined detection of their levels has certain predictive value for the occurrence of postoperative lower limb DVT.\u003c/p\u003e","manuscriptTitle":"Correlation analysis between serum NETs, FIB, miR-374a-5p levels and deep vein thrombosis in the lower limb after knee arthroplasty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-28 15:15:56","doi":"10.21203/rs.3.rs-4789246/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":"1c52a628-151a-4cd6-b029-6bf168ed25ae","owner":[],"postedDate":"August 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-16T05:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-28 15:15:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4789246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4789246","identity":"rs-4789246","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}