Comparative Analysis of DVT Incidence After Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF): Identifying Risk Factors and Developing a Predictive Model

Comparative Analysis of DVT Incidence After Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF): Identifying Risk Factors and Developing a Predictive Model | 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 Comparative Analysis of DVT Incidence After Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF): Identifying Risk Factors and Developing a Predictive Model Yong-zhuang Ma, Wei Zhang, Chen Chen, Zhuo Ma, Li-ming He, Xiao-ming Guan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7347462/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: This study aims to compare the incidence of deep vein thrombosis (DVT) between patients undergoing Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF) , identify independent risk factors, and develop a predictive model for postoperative DVT risk. Methods: This retrospective study included 207 patients who underwent PLIF and PELIF at our hospital from April 2024 to June 2025. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for postoperative DVT. A predictive model was constructed based on these factors, and its discriminative ability was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Results: The incidence of DVT was significantly higher in the PELIF group compared to the PLIF group (P < 0.001). Univariate analysis revealed that age, intraoperative fluid administration, monocyte percentage, and D-dimer levels were significantly associated with postoperative DVT (P < 0.05). Multivariate logistic regression analysis further confirmed that age, intraoperative fluid administration, and monocyte percentage were independent risk factors. The predictive model constructed based on these factors had an AUC of 0.89 (95% CI: 0.82 - 0.95), indicating good discriminative ability. Conclusion: This study successfully identified independent risk factors for postoperative DVT in lumbar surgery patients and developed a predictive model with high discriminative ability. This model can serve as a practical tool for clinicians to assess postoperative DVT risk, optimize perioperative management, and reduce the incidence of DVT. Registry : Ethics Committee of Shanxi Bethune Hospital, Approval notice number: YXLL-2025-094, Registration date: 1st April 2025. Deep vein thrombosis (DVT) Lumbar surgery Risk factors Predictive model percutaneous endoscopic lumbar interbody fusion (PELIF) Figures Figure 1 Figure 2 1. Introduction Deep vein thrombosis (DVT) remains a critical concern in spinal surgery, with potentially fatal sequelae such as pulmonary embolism (PE) ( 1 , 2 ). Recent research has highlighted that among patients with lumbar degenerative diseases (LDD), a notable proportion, ranging from 6.1–9.2%, present with DVT prior to surgery. This condition appears to have a more pronounced impact on elderly individuals and those with elevated D-dimer levels. These findings are consistent with previous research indicating that preoperative DVT is not uncommon in LDD patients, with incidence rates varying across different studies. One study found that the incidence of preoperative DVT in LDD patients can reach 9.2%, and is associated with advanced age, high ASA physical status, and malignancy ( 3 ). Elevated preoperative D-dimer levels have also been identified as an important predictive indicator for DVT ( 4 ). In the context of LDD, it is important to recognize that preoperative DVT may already be present in patients upon admission. Early detection of DVT is essential for effective perioperative management. If left untreated, preoperative DVT can lead to serious complications, including thrombus extension and pulmonary embolism (PE). The progression rates of DVT to more severe conditions postoperatively can be as high as 24–40.6%, with PE incidence rates varying between 0% and 4.6% ( 5 ). In lumbar surgery, intraoperative fluid management has been shown to be associated with the occurrence of postoperative DVT. A retrospective analysis showed that patients who received less than 2000 milliliters of intravenous fluid had a significantly increased risk of pulmonary complications, which may be related to the occurrence of DVT ( 5 ). Therefore, optimizing intraoperative fluid management may be an important strategy for preventing postoperative DVT. Recent advances in risk stratification highlight the predictive value of biomarkers and clinical variables. Preoperative D-dimer elevation consistently signals thrombotic susceptibility ( 4 ), while machine learning approaches like LASSO regression have identified novel predictors, including lower limb weakness and intraoperative blood loss ( 6 ). Nomogram-based tools further enhance clinical utility by translating multivariate models into visual scoring systems ( 3 ). However, PELIF refers to a percutaneous minimally invasive technique that utilizes single-port or biportal spinal endoscopy to achieve lumbar spinal canal decompression and interbody fusion, combined with percutaneous pedicle screw fixation. existing algorithms lack specificity for percutaneous endoscopic lumbar interbody fusion (PELIF) techniques and fail to incorporate dynamic intraoperative factors such as real-time fluid administration or procedure duration-critical gaps given PELIF’s distinct hemodynamic impacts. In summary, this study aims to fill the knowledge gap by identifying independent risk factors for DVT in patients undergoing lumbar surgery and developing a predictive model to estimate the risk of postoperative DVT. Specifically, we will quantitatively compare DVT incidence between PLIF and PELIF approaches in a rigorously characterized cohort (n = 207). Additionally, we will develop and validate an PELIF-specific predictive model incorporating novel surgical parameters. Our approach extends beyond conventional biomarkers by evaluating understudied variables, including monocyte percentages and granular fluid management data, which preliminary evidence suggests may modulate thrombotic pathways. Through multivariable modeling and external validation, we aim to establish a clinically actionable tool that accounts for PELIF’s unique risk architecture. This work responds to urgent calls for procedure-specific DVT prevention frameworks while advancing understanding of how intraoperative decisions-particularly fluid administration-influence thrombogenesis in minimally invasive spine surgery. 2. Patients and Methods 2.1 Study Design and Population This retrospective study collected data from patients who underwent open lumbar surgery (PLIF) and PELIF at our hospital from April 2024 to June 2025, aiming to identify risk factors for DVT and construct a predictive model. To verify the external validity of the model, we also gathered data from patients who underwent PELIF at other hospitals during the same period. The study protocol was approved by our hospital's medical ethics committee : YXLL-2025-094. We included patients aged ≥ 18 years who underwent PLIF and PELIF, with complete clinical data and no preoperative DVT detected by lower limb venous ultrasound. Exclusion criteria included: a history of long-term anticoagulant use; preoperative detection of lower limb DVT by ultrasound; a history of DVT; no surgery during hospitalization or multiple surgeries before PELIF; coagulation-related diseases; severe underlying diseases or unstable vital signs; and lower limb paralysis. These strict inclusion and exclusion criteria ensured the homogeneity of the study population and the reliability of the results. 2.2 Data Collection We comprehensively collected clinical data from patients undergoing PELIF, covering general information (age, gender, height, weight, etc.), medical history (duration of main symptoms, walking difficulties, lower limb muscle strength, etc.), personal history (smoking, alcohol consumption, etc.), past medical history (hypertension, diabetes, etc.), primary diagnosis, pre- and postoperative lower limb venous ultrasound results, preoperative laboratory tests (complete blood count, coagulation function, biochemical indicators, etc.), and surgical information (surgical time, intraoperative blood loss, etc.). Specifically, preoperative laboratory tests detailed white blood cell count, red blood cell count, platelet count, etc. C-reactive protein levels were categorized (≤ 5 mg/L and > 5 mg/L), while other laboratory indicators were included as continuous variables. Surgical information precisely recorded surgical time, number of intervertebral discs involved, and intraoperative blood loss. Postoperative lower limb venous ultrasound results were used to determine whether DVT occurred, the core outcome of this study. Through these comprehensive and meticulous data collections, we aimed to build an accurate predictive model to assess the risk of DVT after PELIF. 2.3 Risk Factor Analysis for DVT After PELIF Patients were divided into DVT and non-DVT groups based on whether DVT occurred postoperatively, and intergroup comparisons were made for each indicator. With postoperative DVT occurrence as the dependent variable, we performed univariate logistic regression analysis on each indicator. Indicators with P < 0.05 were considered statistically significant and eligible for inclusion in the predictive model construction. 2.4 Development and Validation of the Predictive Model for DVT After PELIF Variable Selection: In the univariate logistic regression analysis, indicators statistically associated with postoperative DVT (P < 0.05) were included in the multivariate regression analysis. These indicators were incorporated into a multivariate logistic regression model, with stepwise regression analysis (bidirectional selection) used to further identify independent risk factors. The variable selection process was entirely based on the statistical analysis results from SPSS software. Model Construction and Evaluation: Using SPSS software, we constructed a multivariate logistic regression model with the indicators selected in the univariate analysis. Model parameters were estimated using the maximum likelihood estimation method, calculating the regression coefficients (β), standard errors (SE), Wald statistics, corresponding P-values, and odds ratios (OR) with their 95% confidence intervals (CI) for each independent variable. A P-value < 0.05 indicated a significant impact of the variable on the dependent variable. We drew receiver operating characteristic (ROC) curves using the training set data and calculated the area under the curve (AUC). An AUC value closer to 1 indicated stronger model discrimination ability. Subsequently, we drew ROC curves and calculated AUC values using the validation set and external validation data to verify the model's robustness and generalizability. 2.5 Statistical Methods All statistical analyses in this study were completed using SPSS software (version 26.0, IBM Corporation, USA). Continuous variables conforming to a normal distribution were expressed as mean ± standard deviation (mean ± SD), while those not conforming were expressed as median and interquartile range (median, IQR). Categorical data were expressed as frequency and percentage (n, %). For continuous variables, t-tests or nonparametric rank-sum tests were used based on sample distribution characteristics; for categorical variables, chi-square tests or Fisher's exact tests were used based on distribution characteristics. Univariate and multivariate logistic regression analyses and mediation analysis were all completed using SPSS software. The significance level for statistical analysis was set at P < 0.05. 3. Results 3.1 Patient Characteristics Comparison: PLIF vs. PELIF The study enrolled a total of 207 participants, with 108 undergoing PLIF (52.17%) and 99 undergoing PELIF (47.83%). Significant differences were observed between the two groups in terms of symptom duration (months), surgical time (minutes), intraoperative fluid administration (mL), intraoperative blood loss, systolic and diastolic blood pressure, postoperative ambulation time (days), preoperative and postoperative C-reactive protein (CRP) levels, high-density lipoprotein cholesterol (HDL-C), glucose levels, gender, and hypertension status (P < 0.05). However, no significant differences were found in pulse pressure, albumin, total cholesterol, low-density lipoprotein cholesterol (LDL-C), uric acid, potassium, platelet distribution width (PLT), platelet mean volume, age, height, weight, BMI, follow-up D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides, blood urea nitrogen, serum creatinine, chloride, sodium, calcium, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, lymphocyte percentage, monocyte count, monocyte percentage, red blood cell count, platelet count, prothrombin time (PT), D-dimer, thrombus formation group, primary diagnosis, smoking history, alcohol consumption history, drug allergy history, food allergy history, diabetes mellitus, history of cerebral infarction, history of coronary heart disease, and coagulation function abnormalities (P > 0.05) (Supplementary table 1). Specifically, the PLIF group had a higher proportion of males (53.7% vs. 35.4%; P = 0.008) and a lower prevalence of hypertension (31.5% vs. 52.5%; P = 0.002). Significant differences were noted in clinical parameters: the PLIF group had longer symptom duration (median 18.0 months [IQR 6.0–96.0] vs. 4.0 months [1.0–11.5]; P < 0.001), shorter surgical time (166.5 minutes [151.0–180.0] vs. 191.0 minutes [153.5–236.5]; P < 0.001), higher intraoperative fluid administration (2000 mL [1600–2420] vs. 1500 mL [1000–1500]; P < 0.001), greater blood loss (800 mL [500–1000] vs. 100 mL [80–100]; P < 0.001), higher systolic blood pressure (120 mmHg [115–125] vs. 115 mmHg [109.5–120]; P < 0.001), higher diastolic blood pressure (72 mmHg [66–78] vs. 65 mmHg [60–72]; P < 0.001), longer postoperative ambulation time (7 days [6–9] vs. 2 days [2–3]; P < 0.001), higher preoperative CRP levels (2.80 mg/L [2.00–4.30] vs. 2.00 mg/L [1.00–3.12]; P < 0.001), and higher postoperative CRP levels (32.36 mg/L [25.73–43.90] vs. 14.70 mg/L [7.45–29.73]; P < 0.001). In contrast, the PELIF group had lower HDL-C (1.15 mmol/L [1.04–1.33] vs. 1.23 mmol/L [1.17–1.38]; P = 0.010) and lower glucose levels (5.06 mmol/L [4.69–5.73] vs. 5.34 mmol/L [4.83–6.82]; P = 0.012) (Supplementary table 1). The postoperative DVT rates in the pelif group and plif group were 42.42% and 27.78%, respectively. Notably, the results of this study indicate that the incidence of DVT in the PELIF group is significantly higher than that in the PLIF group (P < 0.001). This finding suggests that in our subsequent work, there is a need to further explore the evaluation of risk factors for DVT and the construction of predictive models in PELIF, so as to better optimize perioperative management and reduce the risk of DVT occurrence. 3.2 Patient Characteristics and DVT Incidence in PELIF: Univariate Analysis A total of 99 patients who underwent PEPLIF were included in this study, of whom 57 (57.58%) did not develop DVT postoperatively, while 42 (42.42%) did. The mean age of the study population was 63.22 years (range: 35–78 years), with 52 males and 47 females. The basic characteristics of the study population are summarized in Table 1 . Table 1 Basic Characteristics and Differences in Patients Undergoing percutaneous endoscopic lumbar interbody fusion (PELIF). Variables Total (n = 99) Non-DVT (n = 57) DVT (n = 42) Statistic P Age, Mean ± SD 63.22 ± 9.30 60.47 ± 10.17 66.95 ± 6.38 t=-3.88 < 0.001 Height (cm), Mean ± SD 163.56 ± 6.82 164.57 ± 6.43 162.22 ± 7.17 t = 1.65 0.102 Weight (kg), Mean ± SD 68.91 ± 9.33 70.48 ± 9.60 66.83 ± 8.64 t = 1.89 0.062 Albumin (g/L), Mean ± SD 40.47 ± 3.03 40.36 ± 3.19 40.62 ± 2.84 t=-0.42 0.673 Total Cholesterol (mmol/L), Mean ± SD 4.78 ± 1.06 4.79 ± 0.93 4.76 ± 1.25 t = 0.12 0.906 LDL-C (mmol/L), Mean ± SD 3.01 ± 0.78 3.05 ± 0.68 2.94 ± 0.92 t = 0.55 0.584 Uric Acid (µmol/L), Mean ± SD 300.79 ± 66.25 299.01 ± 65.32 303.25 ± 68.29 t=-0.30 0.763 Potassium (mmol/L), Mean ± SD 3.89 ± 0.34 3.87 ± 0.34 3.91 ± 0.34 t=-0.53 0.598 Calcium (mmol/L), Mean ± SD 2.30 ± 0.11 2.29 ± 0.10 2.32 ± 0.13 t=-1.41 0.163 Lymphocyte %, Mean ± SD 31.13 ± 8.72 30.92 ± 8.80 31.43 ± 8.71 t=-0.29 0.773 Red Blood Cell Count (×10¹²/L), Mean ± SD 4.39 ± 0.50 4.42 ± 0.52 4.34 ± 0.49 t = 0.77 0.443 PLT Distribution Width (fL), Mean ± SD 16.83 ± 0.48 16.75 ± 0.48 16.95 ± 0.46 t=-2.01 0.047 PLT Mean Volume (fL), Mean ± SD 8.84 ± 1.00 8.79 ± 1.00 8.92 ± 1.01 t=-0.63 0.53 PT (seconds), Mean ± SD 10.61 ± 0.67 10.61 ± 0.69 10.61 ± 0.66 t = 0.02 0.981 BMI, M (Q 1 , Q 3 ) 25.39 (24.14, 27.34) 25.48 (24.17, 27.34) 25.31 (23.97, 26.76) Z=-0.84 0.4 Duration of Symptoms (months), M (Q 1 , Q 3 ) 4.00 (1.00, 11.75) 4.00 (1.00, 10.00) 4.00 (1.25, 12.00) Z=-0.45 0.652 Surgical Time (minutes), M (Q 1 , Q 3 ) 185.00 (159.50, 216.00) 180.00 (150.00, 215.00) 187.00 (169.00, 246.75) Z=-1.68 0.093 Intraoperative Fluid Administration (ml), M (Q₁, Q₃) 1500.00 (1000.00, 1500.00) 1500.00 (1200.00, 1600.00) 1000.00 (800.00, 1425.00) Z=-4.88 < 0.001 Intraoperative Blood Loss (ml), M (Q 1 , Q 3 ) 100.00 (80.00, 100.00) 100.00 (100.00, 100.00) 100.00 (80.00, 100.00) Z=-2.10 0.036 Systolic Blood Pressure (mmHg), M (Q 1 , Q 3 ) 115.00 (109.50, 120.00) 115.00 (109.00, 120.00) 110.00 (110.00, 120.00) Z=-0.20 0.844 Diastolic Blood Pressure (mmHg), M (Q 1 , Q 3 ) 65.00 (60.00, 72.00) 69.00 (60.00, 75.00) 65.00 (62.00, 70.00) Z=-0.49 0.626 Pulse Pressure (mmHg), M (Q 1 , Q 3 ) 45.00 (40.00, 50.00) 45.00 (40.00, 50.00) 45.00 (40.00, 53.75) Z=-0.22 0.825 Postoperative Ambulation Time (days), M (Q 1 , Q 3 ) 2.00 (2.00, 3.00) 2.00 (2.00, 3.00) 2.00 (2.00, 3.00) Z=-0.14 0.885 Follow-up D-dimer (µg/ml), M (Q 1 , Q 3 ) 767.00 (397.00, 1220.00) 762.00 (369.00, 1220.75) 831.00 (453.50, 1217.00) Z=-0.49 0.627 Preoperative CRP (mg/L), M (Q 1 , Q 3 ) 2.00 (1.00, 3.12) 2.00 (1.00, 2.98) 2.00 (1.00, 3.20) Z=-0.06 0.951 Postoperative CRP (mg/L), M (Q 1 , Q 3 ) 14.70 (7.45, 29.73) 13.46 (7.45, 25.32) 15.29 (7.42, 32.46) Z=-0.58 0.56 ALT (U/L), M (Q 1 , Q 3 ) 19.70 (14.30, 30.50) 23.00 (16.00, 32.70) 18.75 (13.30, 25.43) Z=-1.31 0.191 AST (U/L), M (Q 1 , Q 3 ) 21.10 (16.80, 24.48) 21.65 (17.60, 24.50) 19.95 (16.72, 24.40) Z=-0.68 0.495 Triglycerides (mmol/L), M (Q 1 , Q 3 ) 1.58 (1.14, 1.98) 1.69 (1.36, 1.99) 1.45 (1.06, 1.95) Z=-0.99 0.322 HDL-C (mmol/L), M (Q 1 , Q 3 ) 1.15 (1.04, 1.33) 1.14 (1.04, 1.30) 1.17 (1.06, 1.40) Z=-0.24 0.811 Glucose (mmol/L), M (Q 1 , Q 3 ) 5.06 (4.69, 5.73) 5.01 (4.67, 5.73) 5.06 (4.73, 5.84) Z=-0.28 0.779 Urea (mmol/L), M (Q 1 , Q 3 ) 5.75 (4.70, 7.10) 5.60 (4.90, 7.10) 5.90 (4.60, 6.80) Z=-0.10 0.92 Serum Creatinine (µmol/L), M (Q 1 , Q 3 ) 71.10 (65.40, 80.00) 71.10 (67.20, 79.00) 70.55 (61.10, 80.22) Z=-1.25 0.21 Chloride (mmol/L), M (Q 1 , Q 3 ) 106.50 (104.65, 108.35) 106.50 (104.60, 108.40) 106.40 (104.85, 107.87) Z=-0.13 0.896 Sodium (mmol/L), M (Q 1 , Q 3 ) 140.80 (139.15, 141.95) 140.80 (139.10, 141.80) 140.75 (139.22, 142.00) Z=-0.10 0.924 White Blood Cell Count (×10⁹/L), M (Q 1 , Q 3 ) 5.90 (5.15, 6.85) 6.00 (5.10, 7.40) 5.80 (5.20, 6.60) Z=-0.43 0.671 Neutrophil Count (×10⁹/L), M (Q 1 , Q 3 ) 3.20 (2.67, 4.17) 3.14 (2.65, 4.39) 3.30 (2.71, 4.00) Z=-0.13 0.899 Neutrophil %, M (Q 1 , Q 3 ) 57.60 (51.20, 63.10) 57.70 (51.10, 62.80) 56.25 (52.00, 63.63) Z=-0.36 0.721 Lymphocyte Count (×10⁹/L), M (Q 1 , Q 3 ) 1.91 (1.51, 2.26) 1.92 (1.46, 2.34) 1.90 (1.57, 2.14) Z=-0.01 0.989 Monocytes (×10⁹/L), M (Q 1 , Q 3 ) 0.45 (0.34, 0.58) 0.49 (0.35, 0.60) 0.41 (0.32, 0.50) Z=-1.57 0.116 Monocyte %, M (Q 1 , Q 3 ) 7.40 (6.10, 8.95) 8.10 (6.80, 9.40) 6.80 (5.93, 7.80) Z=-2.24 0.025 Platelet Count (×10⁹/L), M (Q 1 , Q 3 ) 216.00 (179.50, 254.00) 219.00 (191.00, 249.00) 209.50 (171.00, 270.75) Z=-0.64 0.522 D-dimer (µg/ml), M (Q 1 , Q 3 ) 115.00 (74.00, 171.00) 98.00 (65.50, 154.00) 129.00 (99.50, 192.25) Z=-2.08 0.037 Gender, n(%) χ²=6.19 0.013 Female 64 (64.65) 31 (54.39) 33 (78.57) Male 35 (35.35) 26 (45.61) 9 (21.43) Primary Diagnosis, n(%) - 0.388 Lumbar Spondylolisthesis 32 (32.32) 19 (33.33) 13 (30.95) Lumbar Spinal Stenosis 32 (32.32) 15 (26.32) 17 (40.48) Lumbar Disc Herniation 34 (34.34) 22 (38.60) 12 (28.57) Lumbar Infection 1 (1.01) 1 (1.75) 0 (0.00) Smoking History, n(%) χ²=1.76 0.184 No 76 (76.77) 41 (71.93) 35 (83.33) Yes 23 (23.23) 16 (28.07) 7 (16.67) CHistory, n(%) χ²=5.16 0.023 No 79 (79.80) 41 (71.93) 38 (90.48) Yes 20 (20.20) 16 (28.07) 4 (9.52) Medication Allergy History, n(%) χ²=0.01 0.914 No 88 (88.89) 50 (87.72) 38 (90.48) Yes 11 (11.11) 7 (12.28) 4 (9.52) Food Allergy History, n(%) - 0.506 No 97 (97.98) 55 (96.49) 42 (100.00) Yes 2 (2.02) 2 (3.51) 0 (0.00) Diabetes Mellitus, n(%) χ²=0.03 0.87 No 77 (77.78) 44 (77.19) 33 (78.57) Yes 22 (22.22) 13 (22.81) 9 (21.43) Hypertension, n(%) χ²=1.43 0.231 No 47 (47.47) 30 (52.63) 17 (40.48) Yes 52 (52.53) 27 (47.37) 25 (59.52) History of Cerebral Infarction, n(%) χ²=0.00 0.969 No 93 (93.94) 53 (92.98) 40 (95.24) Yes 6 (6.06) 4 (7.02) 2 (4.76) History of Coronary Heart Disease, n(%) χ²=0.00 0.969 No 93 (93.94) 53 (92.98) 40 (95.24) Yes 6 (6.06) 4 (7.02) 2 (4.76) Abnormal Coagulation Function, n(%) - 1 No 98 (98.99) 56 (98.25) 42 (100.00) Yes 1 (1.01) 1 (1.75) 0 (0.00) t: t-test, Z: Mann-Whitney test, χ²: Chi-square test, -: Fisher exact; SD: standard deviation, M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile Univariate analysis revealed that age, PLT, intraoperative fluid administration, intraoperative blood loss, monocyte percentage, D-dimer, gender, and alcohol consumption history were significantly associated with postoperative DVT formation (P 0.05). 3.3 Collinearity Diagnosis The collinearity diagnosis results indicated that the variance inflation factor (VIF) values for all variables were less than 10, suggesting no severe multicollinearity issues, and thus no special handling was required (Table 2 ). Table 2 Collinearity Diagnostics Variable Tolerance VIF Age 0.776 1.289 PLT Distribution Width 0.893 1.12 Intraoperative Fluid Administration 0.826 1.211 Intraoperative Blood Loss 0.85 1.176 Monocyte % 0.821 1.218 D-dimer 0.833 1.2 Gender 0.553 1.81 Alcohol Consumption History 0.586 1.706 3.4 Multivariate Logistic Regression Analysis: Identification of Independent Risk Factors Multivariate logistic regression analysis identified age, intraoperative fluid administration, and monocyte percentage as independent risk factors for DVT formation after minimally invasive lumbar surgery (Table 3 ). The beta coefficient for age was 0.159 (P < 0.001), with an OR of 1.173 (95% CI: 1.074–1.281), indicating that the risk of postoperative DVT increased with age. The beta coefficient for intraoperative fluid administration was − 0.004 (P < 0.001), with an OR of 0.996 (95% CI: 0.995–0.998), suggesting that higher intraoperative fluid administration was associated with a lower risk of postoperative DVT. The beta coefficient for monocyte percentage was − 0.538 (P = 0.002), with an OR of 0.584 (95% CI: 0.418–0.817), indicating that patients with lower monocyte percentages had a higher risk of postoperative DVT. Table 3 Univariate and Multivariate Logistic Regression Results Variables Univariate Multivariate β S.E Z P OR (95%CI) β S.E Wald P OR (95%CI) Gender Female 1.00 (Reference) Male -1.12 0.46 -2.44 0.015 0.33 (0.13 ~ 0.80) Alcohol Consumption History No 1.00 (Reference) Yes -1.31 0.6 -2.17 0.03 0.27 (0.08 ~ 0.88) Age 0.09 0.03 3.24 0.001 1.09 (1.04 ~ 1.16) 0.159 0.045 12.571 < 0.001 1.173 (1.074–1.281) PLT Distribution Width 0.88 0.45 1.95 0.051 2.42 (0.99 ~ 5.86) D-dimer 0 0 1.67 0.095 1.00 (1.00 ~ 1.01) Intraoperative Fluid Administration -0.01 0 -4.53 < 0.001 0.99 (0.99 ~ 0.99) -0.004 0.001 17.57 < 0.001 0.996 (0.995–0.998) Intraoperative Blood Loss -0.01 0.01 -2.05 0.04 0.99 (0.98 ~ 0.99) Monocyte % -0.21 0.1 -2.07 0.038 0.81 (0.66 ~ 0.99) -0.538 0.171 9.894 0.002 0.584 (0.418–0.817) OR: Odds Ratio, CI: Confidence Interval Supplementary Table 1 Comparison of Baseline Characteristics Between PLIF and PELIF Groups To facilitate clinical application, these findings were incorporated into a nomogram (Fig. 1 ). The nomogram integrates the three independent predictive factors: age, intraoperative fluid administration (ml), and monocyte percentage. It allows clinicians to calculate the probability of postoperative DVT occurrence in patients. By locating the specific values of each variable on the nomogram and summing up the corresponding scores, a total score is obtained. Converting this total score into a probability value enables the prediction of the risk of postoperative DVT. This nomogram provides a practical tool for assessing DVT risk in patients undergoing PELIF surgery, enhancing the ability to implement targeted prophylactic measures. 3.5 Prediction Model Construction and Validation Based on the multivariate logistic regression analysis, we constructed a risk prediction model for postoperative DVT formation following PELIF surgery: ln (p/1-p) = -1.938 + 0.159×age − 0.004×intraoperative fluid administration − 0.538×monocyte percentage. This model enables the calculation of the probability of postoperative DVT occurrence in patients. The Hosmer-Lemeshow goodness-of-fit test showed a chi-square value of 1.854 with 8 degrees of freedom and a significance level (P) of 0.985, indicating no significant difference between the model's predicted probabilities and the actual observed frequencies, and thus the model fits the data well. The ROC curve analysis revealed an AUC of 0.89 (95% CI: 0.82–0.95), demonstrating excellent discriminatory power (Fig. 2 ). The model also exhibited sensitivity of 0.81 (95% CI: 0.70–0.91), specificity of 0.83 (95% CI: 0.72–0.95), accuracy of 0.82 (95% CI: 0.73–0.89), positive predictive value (PPV) of 0.87 (95% CI: 0.78–0.96), negative predictive value (NPV) of 0.76 (95% CI: 0.64–0.88), and an optimal cut-off value of 0.395, further confirming its practical utility in predicting postoperative DVT formation. 3.6 Assessment of Potential Mediators Mediation analysis results showed that although D-dimer, PLT distribution width, and postoperative ambulation time were statistically associated with DVT formation after minimally invasive lumbar surgery in univariate analysis, they did not exhibit significant mediating effects in the relationship between the main risk factors (age, intraoperative fluid administration, and monocyte percentage) and postoperative DVT formation. Specifically, D-dimer did not play a significant mediating role between age, intraoperative fluid administration, and monocyte percentage and postoperative DVT formation, as its indirect effect 95% confidence intervals all included zero, and the corresponding P-values were all greater than 0.05. PLT distribution width also did not show a significant mediating effect between intraoperative fluid administration and monocyte percentage and postoperative DVT formation. Similarly, postoperative ambulation time did not significantly mediate the relationship between intraoperative fluid administration and postoperative DVT formation. This indicates that in this study, these potential mediating factors did not significantly explain the impact pathway of the main risk factors on postoperative DVT formation (Supplementary table 2). 4. Discussion Our study successfully identified independent risk factors for postoperative DVT in patients undergoing lumbar surgery through a retrospective analysis of patient data and constructed a predictive model based on multivariate logistic regression analysis. This model integrates three key factors: age, intraoperative fluid administration, and monocyte percentage, effectively predicting the risk of postoperative DVT. The findings not only provide clinicians with a practical prediction tool but also offer scientific evidence for optimizing perioperative management and reducing the incidence of DVT. Additionally, the predictive model is visualized in the form of a nomogram, facilitating quick assessment of postoperative DVT risk by clinicians and further validating its clinical applicability. Age emerged as an independent risk factor for postoperative DVT, consistent with previous studies ( 7 – 10 ). As individuals age, their blood vessel walls thin, blood flow becomes sluggish, and the prevalence of comorbidities such as hypertension and diabetes increase, all of which contribute to a heightened risk of DVT ( 11 ). Numerous studies have also highlighted age as a significant risk factor for DVT, particularly in patients undergoing lumbar surgery ( 12 – 14 ). Intraoperative fluid administration was found to have a negative correlation with postoperative DVT risk, likely because adequate intraoperative fluid management helps maintain stable hemodynamics ( 15 ). Proper fluid administration can reduce the incidence of postoperative hypotension and, consequently, the risk of blood stasis ( 16 ). However, excessive fluid administration may lead to tissue edema, which could paradoxically increase the risk of DVT ( 17 – 19 ). Therefore, intraoperative fluid management must strike a balance between maintaining hemodynamic stability and avoiding fluid overload. Patients with a lower percentage of monocytes had a higher risk of postoperative DVT, possibly due to the role of monocytes in inflammatory responses and endothelial repair ( 20 , 21 ). A lower monocyte percentage may indicate weaker postoperative inflammatory and endothelial repair capabilities, thereby increasing the risk of DVT ( 21 ). This finding offers clinicians a new perspective, suggesting that the inflammatory state and immune function of patients should be monitored in postoperative management. The predictive model constructed in this study, visualized as a nomogram, enables clinicians to quickly assess the risk of postoperative DVT. The model's AUC value of 0.89 indicates its strong discriminative ability to distinguish between high-risk and low-risk patients. Moreover, the model's sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were all at high levels, further validating its clinical utility. By utilizing this model, clinicians can perform precise risk stratification of patients preoperatively and postoperatively, thereby devising personalized prevention strategies, reducing unnecessary anticoagulant treatments, and minimizing associated complications. Despite the significant progress made in identifying DVT risk factors and constructing a predictive model, our study has certain limitations. First, as a retrospective study, it is susceptible to selection and information biases. Second, the relatively limited sample size, especially in the external validation part, may affect the generalizability of the model. Additionally, not all potential DVT risk factors, such as genetic factors and medication use, were considered in the study, which may limit the comprehensiveness of the model. Future research should adopt a prospective design, expand the sample size, and include more potential risk factors to further validate and optimize the predictive model. Future research could delve deeper into the mechanisms underlying DVT formation, particularly the roles of inflammatory responses and immune function. Combining multicenter data to develop a more comprehensive predictive model and validating its applicability in different populations through prospective studies would be beneficial. Additionally, research could focus on postoperative DVT prevention measures, such as the optimal combination of mechanical and pharmacological prophylaxis, as well as how to reduce the incidence of DVT while avoiding bleeding complications. These studies would help further optimize perioperative management for patients undergoing lumbar surgery and improve patient outcomes. In summary, by identifying independent risk factors for postoperative DVT in lumbar surgery patients and constructing a predictive model, our study provides clinicians with a practical tool to optimize perioperative management, reduce the incidence of DVT, and improve patient outcomes. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Data availability statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Author Contributions Zhen-wu Gao and Hao-yu Feng were responsible for the manuscript revision and research design. Yong-zhuang Ma performed the data analysis and drafted the manuscript. Wei Zhang and Lei-min Xi were responsible for the data collection. Zhuo Ma ,Xiao-ming Guan, and Li-ming He participated in data verification and manuscript revision. Qiang Chang provided administrative support. Funding This study was supported by the National Key Research and Development Plan (2022YFC3601900 and 2022YFC3601904). Ethics statement This study was approved by the Ethics Committee of Shanxi Bethune Hospital (approval number: YXLL-2025-094). Informed Consent Statement All authors declare that they have no conflict of interest. References Ojima M, Takegawa R, Hirose T, Ohnishi M, Shiozaki T, Shimazu T. Hemodynamic effects of electrical muscle stimulation in the prophylaxis of deep vein thrombosis for intensive care unit patients: a randomized trial. J Intensive Care (2017) 5:9. doi: 10.1186/s40560-016-0206-8 Wei M, Yang W, Qiao Y, Ma L, Xu W, Dong J. A nomogram predicting the risk of venous thromboembolism in patients following urologic surgeries. Sci Rep (2025) 15:238. doi: 10.1038/s41598-024-84681-w Yang T, Wei J, Wang Z, Li S, Zhang T, Jia S, Meng C. Analysis of risk factors and prediction model construction of deep vein thrombosis in patients with lumbar degenerative diseases before surgery. Sci Rep (2025) 15:26069. doi: 10.1038/s41598-025-10752-1 Sato K, Date H, Michikawa T, Morita M, Hayakawa K, Kaneko S, Fujita N. Preoperative prevalence of deep vein thrombosis in patients scheduled to have surgery for degenerative musculoskeletal disorders. BMC Musculoskelet Disord (2021) 22:513. doi: 10.1186/s12891-021-04405-3 Ongaigui C, Fiorda-Diaz J, Dada O, Mavarez-Martinez A, Echeverria-Villalobos M, Bergese SD. Intraoperative Fluid Management in Patients Undergoing Spine Surgery: A Narrative Review. Front Surg (2020) 7: doi: 10.3389/fsurg.2020.00045 Zhao Y, Kong X, Song K, Liu Z, Zhang Y, Cheng L. Analysis of risk factors and establishment of prediction model for lower extremity deep vein thrombosis after lumbar fusion surgery. BMC Surg (2024) 24:392. doi: 10.1186/s12893-024-02689-5 Yang D, Chen S, Zhuo C, Chen H. Analysis of Risk Factors for Postoperative Deep Vein Thrombosis in Traumatic Spinal Fracture Complicated with Spinal Cord Injury. Clinical and Applied Thrombosis/Hemostasis (2024) 30: doi: 10.1177/10760296241271331 Chen X, Sui Z, Ting J, Qi M, Yin Y, He F. Analysis of the current status and influencing factors of LEDVT in patients with acute hemorrhagic stroke. Medicine (2025) 104:e41759. doi: 10.1097/MD.0000000000041759 Qu S-W, Cong Y-X, Wang P-F, Fei C, Li Z, Yang K, Shang K, Ke C, Huang H, Zhuang Y, et al. Deep Vein Thrombosis in the Uninjured Lower Extremity: A Retrospective Study of 1454 Patients With Lower Extremity Fractures. Clinical and Applied Thrombosis/Hemostasis (2021) 27: doi: 10.1177/1076029620986862 Chang R, Chen A, Zhang L, Li X, Deng W, Li X. Effect of unicompartmental knee arthroplasty combined with arthroscopic debridement on knee osteoarthritis and analysis of risk factors of deep venous thrombosis. Am J Transl Res (2023) 15:1343–1351. Yu X, Wu Y, Ning R. The deep vein thrombosis of lower limb after total hip arthroplasty: what should we care. BMC Musculoskelet Disord (2021) 22:547. doi: 10.1186/s12891-021-04417-z Wei J, Li W, Pei Y, Shen Y, Li J. Clinical analysis of preoperative risk factors for the incidence of deep venous thromboembolism in patients undergoing posterior lumbar interbody fusion. J Orthop Surg Res (2016) 11:68. doi: 10.1186/s13018-016-0403-0 Li Q, Yu Z, Chen X, Zhang W. Analysis of risk factors for lower limb deep vein thrombosis in patients after Lumbar Fusion Surgery. Pak J Med Sci (2020) 37: doi: 10.12669/pjms.37.1.3041 Zhang L, Cao H, Chen Y, Jiao G. Risk factors for venous thromboembolism following spinal surgery. Medicine (2020) 99:e20954. doi: 10.1097/MD.0000000000020954 Virág M, Rottler M, Gede N, Ocskay K, Leiner T, Tuba M, Ábrahám S, Farkas N, Hegyi P, Molnár Z. Goal-Directed Fluid Therapy Enhances Gastrointestinal Recovery after Laparoscopic Surgery: A Systematic Review and Meta-Analysis. J Pers Med (2022) 12:734. doi: 10.3390/jpm12050734 Yu S, Zou T, Wei S, Yu Y, Ding G. Comprehensive case report and literature review on perioperative management of multiple pheochromocytoma in a pediatric patient. Front Pediatr (2025) 13: doi: 10.3389/fped.2025.1439186 Ahuja S, de Grooth H-J, Paulus F, van der Ven FL, Serpa Neto A, Schultz MJ, Tuinman PR, Ahuja S, van Akkeren JP, Algera AG, et al. Association between early cumulative fluid balance and successful liberation from invasive ventilation in COVID-19 ARDS patients — insights from the PRoVENT-COVID study: a national, multicenter, observational cohort analysis. Crit Care (2022) 26:157. doi: 10.1186/s13054-022-04023-y Dietrich M, Hölle T, Piredda M, Feißt M, Rehn P, von der Forst M, Fischer D, Hackert T, Larmann J, Michalski CW, et al. Intraoperative hemodynamic management during pancreatoduodenectomy – an analysis of 525 patients. Langenbecks Arch Surg (2025) 410:123. doi: 10.1007/s00423-025-03669-w Hansen B. Fluid Overload. Front Vet Sci (2021) 8: doi: 10.3389/fvets.2021.668688 Xiong X, Hu P, Li T, Yu S, Mao Q. Association between inflammatory indices and preoperative deep vein thrombosis in patients undergoing total joint arthroplasty: a retrospective study. Thromb J (2025) 23:6. doi: 10.1186/s12959-024-00682-9 Wang Z, Zhou Q, Liu H, Zhang J, Zhu Z, Wu J, Chen X, Liu Y. Association Between Monocyte Count and Preoperative Deep Venous Thrombosis in Older Patients with hip Fracture: A Retrospective Study. Clinical and Applied Thrombosis/Hemostasis (2022) 28: doi: 10.1177/10760296221100806 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx 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. 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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-7347462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506943816,"identity":"d8fada06-b7b9-45be-b202-879c5aaf9be0","order_by":0,"name":"Yong-zhuang Ma","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yong-zhuang","middleName":"","lastName":"Ma","suffix":""},{"id":506943817,"identity":"bbd4afdb-5108-4f5f-9ddb-303bf233f84e","order_by":1,"name":"Wei Zhang","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":506943818,"identity":"c56fdf96-b7a4-4434-b08f-8e5a8b9cae5a","order_by":2,"name":"Chen Chen","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Chen","suffix":""},{"id":506943819,"identity":"c0b85e4a-5a6f-4921-8fc5-f3ce6e53fece","order_by":3,"name":"Zhuo Ma","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical 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14:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7347462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7347462/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90464969,"identity":"baf79624-b9a5-40e1-8aaf-e3ecb65364fd","added_by":"auto","created_at":"2025-09-03 05:12:38","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for Predicting the Risk of Postoperative DVT After PELIF Surgery\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7347462/v1/484892759f39d9034461c1a2.jpeg"},{"id":90465576,"identity":"0d5ea6cb-277f-4f83-a49d-d9b710afa9e5","added_by":"auto","created_at":"2025-09-03 05:31:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve Analysis of the Prediction Model for Postoperative DVT After PELIF Surgery\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7347462/v1/1c11f47a00ab2aaa406a20e7.jpeg"},{"id":98430596,"identity":"27f60986-0e03-4bb9-a801-cbf0ddc21497","added_by":"auto","created_at":"2025-12-17 16:45:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1405777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7347462/v1/4e6a92d2-8dde-474e-9c67-4b45005f67f2.pdf"},{"id":90464968,"identity":"bfd2c48d-867e-4677-9b53-3e8ab0ad51d7","added_by":"auto","created_at":"2025-09-03 05:12:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32444,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7347462/v1/439f62532f411aff837cde7d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of DVT Incidence After Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF): Identifying Risk Factors and Developing a Predictive Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDeep vein thrombosis (DVT) remains a critical concern in spinal surgery, with potentially fatal sequelae such as pulmonary embolism (PE) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Recent research has highlighted that among patients with lumbar degenerative diseases (LDD), a notable proportion, ranging from 6.1\u0026ndash;9.2%, present with DVT prior to surgery. This condition appears to have a more pronounced impact on elderly individuals and those with elevated D-dimer levels. These findings are consistent with previous research indicating that preoperative DVT is not uncommon in LDD patients, with incidence rates varying across different studies. One study found that the incidence of preoperative DVT in LDD patients can reach 9.2%, and is associated with advanced age, high ASA physical status, and malignancy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Elevated preoperative D-dimer levels have also been identified as an important predictive indicator for DVT (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the context of LDD, it is important to recognize that preoperative DVT may already be present in patients upon admission. Early detection of DVT is essential for effective perioperative management. If left untreated, preoperative DVT can lead to serious complications, including thrombus extension and pulmonary embolism (PE). The progression rates of DVT to more severe conditions postoperatively can be as high as 24\u0026ndash;40.6%, with PE incidence rates varying between 0% and 4.6% (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn lumbar surgery, intraoperative fluid management has been shown to be associated with the occurrence of postoperative DVT. A retrospective analysis showed that patients who received less than 2000 milliliters of intravenous fluid had a significantly increased risk of pulmonary complications, which may be related to the occurrence of DVT (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, optimizing intraoperative fluid management may be an important strategy for preventing postoperative DVT.\u003c/p\u003e\u003cp\u003eRecent advances in risk stratification highlight the predictive value of biomarkers and clinical variables. Preoperative D-dimer elevation consistently signals thrombotic susceptibility (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), while machine learning approaches like LASSO regression have identified novel predictors, including lower limb weakness and intraoperative blood loss (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Nomogram-based tools further enhance clinical utility by translating multivariate models into visual scoring systems (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, PELIF refers to a percutaneous minimally invasive technique that utilizes single-port or biportal spinal endoscopy to achieve lumbar spinal canal decompression and interbody fusion, combined with percutaneous pedicle screw fixation. existing algorithms lack specificity for percutaneous endoscopic lumbar interbody fusion (PELIF) techniques and fail to incorporate dynamic intraoperative factors such as real-time fluid administration or procedure duration-critical gaps given PELIF\u0026rsquo;s distinct hemodynamic impacts.\u003c/p\u003e\u003cp\u003eIn summary, this study aims to fill the knowledge gap by identifying independent risk factors for DVT in patients undergoing lumbar surgery and developing a predictive model to estimate the risk of postoperative DVT. Specifically, we will quantitatively compare DVT incidence between PLIF and PELIF approaches in a rigorously characterized cohort (n\u0026thinsp;=\u0026thinsp;207). Additionally, we will develop and validate an PELIF-specific predictive model incorporating novel surgical parameters. Our approach extends beyond conventional biomarkers by evaluating understudied variables, including monocyte percentages and granular fluid management data, which preliminary evidence suggests may modulate thrombotic pathways. Through multivariable modeling and external validation, we aim to establish a clinically actionable tool that accounts for PELIF\u0026rsquo;s unique risk architecture. This work responds to urgent calls for procedure-specific DVT prevention frameworks while advancing understanding of how intraoperative decisions-particularly fluid administration-influence thrombogenesis in minimally invasive spine surgery.\u003c/p\u003e"},{"header":"2. Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e\u003cp\u003eThis retrospective study collected data from patients who underwent open lumbar surgery (PLIF) and PELIF at our hospital from April 2024 to June 2025, aiming to identify risk factors for DVT and construct a predictive model. To verify the external validity of the model, we also gathered data from patients who underwent PELIF at other hospitals during the same period. The study protocol was approved by our hospital's medical ethics committee : YXLL-2025-094.\u003c/p\u003e\u003cp\u003eWe included patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years who underwent PLIF and PELIF, with complete clinical data and no preoperative DVT detected by lower limb venous ultrasound. Exclusion criteria included: a history of long-term anticoagulant use; preoperative detection of lower limb DVT by ultrasound; a history of DVT; no surgery during hospitalization or multiple surgeries before PELIF; coagulation-related diseases; severe underlying diseases or unstable vital signs; and lower limb paralysis. These strict inclusion and exclusion criteria ensured the homogeneity of the study population and the reliability of the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\u003cp\u003eWe comprehensively collected clinical data from patients undergoing PELIF, covering general information (age, gender, height, weight, etc.), medical history (duration of main symptoms, walking difficulties, lower limb muscle strength, etc.), personal history (smoking, alcohol consumption, etc.), past medical history (hypertension, diabetes, etc.), primary diagnosis, pre- and postoperative lower limb venous ultrasound results, preoperative laboratory tests (complete blood count, coagulation function, biochemical indicators, etc.), and surgical information (surgical time, intraoperative blood loss, etc.). Specifically, preoperative laboratory tests detailed white blood cell count, red blood cell count, platelet count, etc. C-reactive protein levels were categorized (\u0026le;\u0026thinsp;5 mg/L and \u0026gt;\u0026thinsp;5 mg/L), while other laboratory indicators were included as continuous variables. Surgical information precisely recorded surgical time, number of intervertebral discs involved, and intraoperative blood loss. Postoperative lower limb venous ultrasound results were used to determine whether DVT occurred, the core outcome of this study. Through these comprehensive and meticulous data collections, we aimed to build an accurate predictive model to assess the risk of DVT after PELIF.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Risk Factor Analysis for DVT After PELIF\u003c/h2\u003e\u003cp\u003ePatients were divided into DVT and non-DVT groups based on whether DVT occurred postoperatively, and intergroup comparisons were made for each indicator. With postoperative DVT occurrence as the dependent variable, we performed univariate logistic regression analysis on each indicator. Indicators with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant and eligible for inclusion in the predictive model construction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Development and Validation of the Predictive Model for DVT After PELIF\u003c/h2\u003e\u003cp\u003eVariable Selection: In the univariate logistic regression analysis, indicators statistically associated with postoperative DVT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were included in the multivariate regression analysis. These indicators were incorporated into a multivariate logistic regression model, with stepwise regression analysis (bidirectional selection) used to further identify independent risk factors. The variable selection process was entirely based on the statistical analysis results from SPSS software.\u003c/p\u003e\u003cp\u003eModel Construction and Evaluation: Using SPSS software, we constructed a multivariate logistic regression model with the indicators selected in the univariate analysis. Model parameters were estimated using the maximum likelihood estimation method, calculating the regression coefficients (β), standard errors (SE), Wald statistics, corresponding P-values, and odds ratios (OR) with their 95% confidence intervals (CI) for each independent variable. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated a significant impact of the variable on the dependent variable.\u003c/p\u003e\u003cp\u003eWe drew receiver operating characteristic (ROC) curves using the training set data and calculated the area under the curve (AUC). An AUC value closer to 1 indicated stronger model discrimination ability. Subsequently, we drew ROC curves and calculated AUC values using the validation set and external validation data to verify the model's robustness and generalizability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Methods\u003c/h2\u003e\u003cp\u003eAll statistical analyses in this study were completed using SPSS software (version 26.0, IBM Corporation, USA). Continuous variables conforming to a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), while those not conforming were expressed as median and interquartile range (median, IQR). Categorical data were expressed as frequency and percentage (n, %). For continuous variables, t-tests or nonparametric rank-sum tests were used based on sample distribution characteristics; for categorical variables, chi-square tests or Fisher's exact tests were used based on distribution characteristics. Univariate and multivariate logistic regression analyses and mediation analysis were all completed using SPSS software. The significance level for statistical analysis was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Patient Characteristics Comparison: PLIF vs. PELIF\u003c/h2\u003e\u003cp\u003eThe study enrolled a total of 207 participants, with 108 undergoing PLIF (52.17%) and 99 undergoing PELIF (47.83%). Significant differences were observed between the two groups in terms of symptom duration (months), surgical time (minutes), intraoperative fluid administration (mL), intraoperative blood loss, systolic and diastolic blood pressure, postoperative ambulation time (days), preoperative and postoperative C-reactive protein (CRP) levels, high-density lipoprotein cholesterol (HDL-C), glucose levels, gender, and hypertension status (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant differences were found in pulse pressure, albumin, total cholesterol, low-density lipoprotein cholesterol (LDL-C), uric acid, potassium, platelet distribution width (PLT), platelet mean volume, age, height, weight, BMI, follow-up D-dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglycerides, blood urea nitrogen, serum creatinine, chloride, sodium, calcium, white blood cell count, neutrophil count, neutrophil percentage, lymphocyte count, lymphocyte percentage, monocyte count, monocyte percentage, red blood cell count, platelet count, prothrombin time (PT), D-dimer, thrombus formation group, primary diagnosis, smoking history, alcohol consumption history, drug allergy history, food allergy history, diabetes mellitus, history of cerebral infarction, history of coronary heart disease, and coagulation function abnormalities (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Supplementary table 1).\u003c/p\u003e\u003cp\u003eSpecifically, the PLIF group had a higher proportion of males (53.7% vs. 35.4%; P\u0026thinsp;=\u0026thinsp;0.008) and a lower prevalence of hypertension (31.5% vs. 52.5%; P\u0026thinsp;=\u0026thinsp;0.002). Significant differences were noted in clinical parameters: the PLIF group had longer symptom duration (median 18.0 months [IQR 6.0\u0026ndash;96.0] vs. 4.0 months [1.0\u0026ndash;11.5]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shorter surgical time (166.5 minutes [151.0\u0026ndash;180.0] vs. 191.0 minutes [153.5\u0026ndash;236.5]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher intraoperative fluid administration (2000 mL [1600\u0026ndash;2420] vs. 1500 mL [1000\u0026ndash;1500]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), greater blood loss (800 mL [500\u0026ndash;1000] vs. 100 mL [80\u0026ndash;100]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher systolic blood pressure (120 mmHg [115\u0026ndash;125] vs. 115 mmHg [109.5\u0026ndash;120]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher diastolic blood pressure (72 mmHg [66\u0026ndash;78] vs. 65 mmHg [60\u0026ndash;72]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), longer postoperative ambulation time (7 days [6\u0026ndash;9] vs. 2 days [2\u0026ndash;3]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher preoperative CRP levels (2.80 mg/L [2.00\u0026ndash;4.30] vs. 2.00 mg/L [1.00\u0026ndash;3.12]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher postoperative CRP levels (32.36 mg/L [25.73\u0026ndash;43.90] vs. 14.70 mg/L [7.45\u0026ndash;29.73]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the PELIF group had lower HDL-C (1.15 mmol/L [1.04\u0026ndash;1.33] vs. 1.23 mmol/L [1.17\u0026ndash;1.38]; P\u0026thinsp;=\u0026thinsp;0.010) and lower glucose levels (5.06 mmol/L [4.69\u0026ndash;5.73] vs. 5.34 mmol/L [4.83\u0026ndash;6.82]; P\u0026thinsp;=\u0026thinsp;0.012) (Supplementary table 1). The postoperative DVT rates in the pelif group and plif group were 42.42% and 27.78%, respectively. Notably, the results of this study indicate that the incidence of DVT in the PELIF group is significantly higher than that in the PLIF group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding suggests that in our subsequent work, there is a need to further explore the evaluation of risk factors for DVT and the construction of predictive models in PELIF, so as to better optimize perioperative management and reduce the risk of DVT occurrence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Patient Characteristics and DVT Incidence in PELIF: Univariate Analysis\u003c/h2\u003e\u003cp\u003eA total of 99 patients who underwent PEPLIF were included in this study, of whom 57 (57.58%) did not develop DVT postoperatively, while 42 (42.42%) did. The mean age of the study population was 63.22 years (range: 35\u0026ndash;78 years), with 52 males and 47 females. The basic characteristics of the study population are summarized 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\u003eBasic Characteristics and Differences in Patients Undergoing percutaneous endoscopic lumbar interbody fusion (PELIF).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\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\u003eTotal (n\u0026thinsp;=\u0026thinsp;99)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-DVT (n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDVT (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (cm), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164.57\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162.22\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.83\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Cholesterol (mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUric Acid (\u0026micro;mol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e300.79\u0026thinsp;\u0026plusmn;\u0026thinsp;66.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e299.01\u0026thinsp;\u0026plusmn;\u0026thinsp;65.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e303.25\u0026thinsp;\u0026plusmn;\u0026thinsp;68.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mmol/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte %, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed Blood Cell Count (\u0026times;10\u0026sup1;\u0026sup2;/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT Distribution Width (fL), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT Mean Volume (fL), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et=-0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT (seconds), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et\u0026thinsp;=\u0026thinsp;0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.39 (24.14, 27.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.48 (24.17, 27.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.31 (23.97, 26.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of Symptoms (months), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00 (1.00, 11.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00 (1.00, 10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 (1.25, 12.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical Time (minutes), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185.00 (159.50, 216.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180.00 (150.00, 215.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187.00 (169.00, 246.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative Fluid Administration (ml), M (Q₁, Q₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500.00 (1000.00, 1500.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1500.00 (1200.00, 1600.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1000.00 (800.00, 1425.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative Blood Loss (ml), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.00 (80.00, 100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00 (100.00, 100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00 (80.00, 100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic Blood Pressure (mmHg), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115.00 (109.50, 120.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115.00 (109.00, 120.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.00 (110.00, 120.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic Blood Pressure (mmHg), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.00 (60.00, 72.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.00 (60.00, 75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.00 (62.00, 70.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulse Pressure (mmHg), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.00 (40.00, 50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.00 (40.00, 50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.00 (40.00, 53.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative Ambulation Time (days), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.885\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow-up D-dimer (\u0026micro;g/ml), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e767.00 (397.00, 1220.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e762.00 (369.00, 1220.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e831.00 (453.50, 1217.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative CRP (mg/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.00, 3.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (1.00, 2.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (1.00, 3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative CRP (mg/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.70 (7.45, 29.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.46 (7.45, 25.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.29 (7.42, 32.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.70 (14.30, 30.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.00 (16.00, 32.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.75 (13.30, 25.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.10 (16.80, 24.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.65 (17.60, 24.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.95 (16.72, 24.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58 (1.14, 1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.69 (1.36, 1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45 (1.06, 1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15 (1.04, 1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14 (1.04, 1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.17 (1.06, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mmol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.06 (4.69, 5.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.01 (4.67, 5.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.06 (4.73, 5.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea (mmol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.75 (4.70, 7.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.60 (4.90, 7.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.90 (4.60, 6.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum Creatinine (\u0026micro;mol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.10 (65.40, 80.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.10 (67.20, 79.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.55 (61.10, 80.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloride (mmol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106.50 (104.65, 108.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106.50 (104.60, 108.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.40 (104.85, 107.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (mmol/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140.80 (139.15, 141.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140.80 (139.10, 141.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140.75 (139.22, 142.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite Blood Cell Count (\u0026times;10⁹/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.90 (5.15, 6.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 (5.10, 7.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.80 (5.20, 6.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil Count (\u0026times;10⁹/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.20 (2.67, 4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.14 (2.65, 4.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.30 (2.71, 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil %, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.60 (51.20, 63.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.70 (51.10, 62.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.25 (52.00, 63.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte Count (\u0026times;10⁹/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91 (1.51, 2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.92 (1.46, 2.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.90 (1.57, 2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes (\u0026times;10⁹/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45 (0.34, 0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.49 (0.35, 0.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41 (0.32, 0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte %, M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.40 (6.10, 8.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.10 (6.80, 9.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.80 (5.93, 7.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet Count (\u0026times;10⁹/L), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216.00 (179.50, 254.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219.00 (191.00, 249.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e209.50 (171.00, 270.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-dimer (\u0026micro;g/ml), M (Q\u003csub\u003e1\u003c/sub\u003e, Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115.00 (74.00, 171.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.00 (65.50, 154.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.00 (99.50, 192.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZ=-2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 (64.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (54.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (78.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (35.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (45.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (21.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary Diagnosis, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbar Spondylolisthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (32.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (33.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (30.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbar Spinal Stenosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (32.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (26.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (40.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbar Disc Herniation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (34.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (38.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (28.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLumbar Infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking History, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (76.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (71.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (83.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (23.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (28.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (16.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHistory, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=5.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79 (79.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (71.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (90.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (20.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (28.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (9.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedication Allergy History, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (88.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (87.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (90.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (12.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (9.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Allergy History, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (97.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (96.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (77.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (77.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (78.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (22.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (22.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (21.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=1.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (47.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (52.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (40.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (52.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (47.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (59.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Cerebral Infarction, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (93.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (92.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (95.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (6.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (7.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of Coronary Heart Disease, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;=0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (93.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (92.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (95.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (6.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (7.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (4.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal Coagulation Function, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (98.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (98.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003et: t-test, Z: Mann-Whitney test, χ\u0026sup2;: Chi-square test, -: Fisher exact; SD: standard deviation, M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUnivariate analysis revealed that age, PLT, intraoperative fluid administration, intraoperative blood loss, monocyte percentage, D-dimer, gender, and alcohol consumption history were significantly associated with postoperative DVT formation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while height, weight, albumin, total cholesterol, and other indicators showed no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Collinearity Diagnosis\u003c/h2\u003e\u003cp\u003eThe collinearity diagnosis results indicated that the variance inflation factor (VIF) values for all variables were less than 10, suggesting no severe multicollinearity issues, and thus no special handling was required (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\u003eCollinearity Diagnostics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVIF\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\u003e0.776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.289\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT Distribution Width\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative Fluid Administration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative Blood Loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.218\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\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Consumption History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Multivariate Logistic Regression Analysis: Identification of Independent Risk Factors\u003c/h2\u003e\u003cp\u003eMultivariate logistic regression analysis identified age, intraoperative fluid administration, and monocyte percentage as independent risk factors for DVT formation after minimally invasive lumbar surgery (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The beta coefficient for age was 0.159 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an OR of 1.173 (95% CI: 1.074\u0026ndash;1.281), indicating that the risk of postoperative DVT increased with age. The beta coefficient for intraoperative fluid administration was \u0026minus;\u0026thinsp;0.004 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an OR of 0.996 (95% CI: 0.995\u0026ndash;0.998), suggesting that higher intraoperative fluid administration was associated with a lower risk of postoperative DVT. The beta coefficient for monocyte percentage was \u0026minus;\u0026thinsp;0.538 (P\u0026thinsp;=\u0026thinsp;0.002), with an OR of 0.584 (95% CI: 0.418\u0026ndash;0.817), indicating that patients with lower monocyte percentages had a higher risk of postoperative DVT.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and Multivariate Logistic Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\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=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eUnivariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e\u003cp\u003eMultivariate\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eS.E\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33 (0.13\u0026thinsp;~\u0026thinsp;0.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Consumption History\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.27 (0.08\u0026thinsp;~\u0026thinsp;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09 (1.04\u0026thinsp;~\u0026thinsp;1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e12.571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1.173 (1.074\u0026ndash;1.281)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT Distribution Width\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.42 (0.99\u0026thinsp;~\u0026thinsp;5.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\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\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00 (1.00\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative Fluid Administration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.99\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e17.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.996 (0.995\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative Blood Loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.98\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.81 (0.66\u0026thinsp;~\u0026thinsp;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e9.894\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.584 (0.418\u0026ndash;0.817)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eOR: Odds Ratio, CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eSupplementary Table\u0026nbsp;1 Comparison of Baseline Characteristics Between PLIF and PELIF Groups\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo facilitate clinical application, these findings were incorporated into a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The nomogram integrates the three independent predictive factors: age, intraoperative fluid administration (ml), and monocyte percentage. It allows clinicians to calculate the probability of postoperative DVT occurrence in patients. By locating the specific values of each variable on the nomogram and summing up the corresponding scores, a total score is obtained. Converting this total score into a probability value enables the prediction of the risk of postoperative DVT. This nomogram provides a practical tool for assessing DVT risk in patients undergoing PELIF surgery, enhancing the ability to implement targeted prophylactic measures.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Prediction Model Construction and Validation\u003c/h2\u003e\u003cp\u003eBased on the multivariate logistic regression analysis, we constructed a risk prediction model for postoperative DVT formation following PELIF surgery: ln (p/1-p) = -1.938\u0026thinsp;+\u0026thinsp;0.159\u0026times;age \u0026minus;\u0026thinsp;0.004\u0026times;intraoperative fluid administration \u0026minus;\u0026thinsp;0.538\u0026times;monocyte percentage. This model enables the calculation of the probability of postoperative DVT occurrence in patients.\u003c/p\u003e\u003cp\u003eThe Hosmer-Lemeshow goodness-of-fit test showed a chi-square value of 1.854 with 8 degrees of freedom and a significance level (P) of 0.985, indicating no significant difference between the model's predicted probabilities and the actual observed frequencies, and thus the model fits the data well. The ROC curve analysis revealed an AUC of 0.89 (95% CI: 0.82\u0026ndash;0.95), demonstrating excellent discriminatory power (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model also exhibited sensitivity of 0.81 (95% CI: 0.70\u0026ndash;0.91), specificity of 0.83 (95% CI: 0.72\u0026ndash;0.95), accuracy of 0.82 (95% CI: 0.73\u0026ndash;0.89), positive predictive value (PPV) of 0.87 (95% CI: 0.78\u0026ndash;0.96), negative predictive value (NPV) of 0.76 (95% CI: 0.64\u0026ndash;0.88), and an optimal cut-off value of 0.395, further confirming its practical utility in predicting postoperative DVT formation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Assessment of Potential Mediators\u003c/h2\u003e\u003cp\u003eMediation analysis results showed that although D-dimer, PLT distribution width, and postoperative ambulation time were statistically associated with DVT formation after minimally invasive lumbar surgery in univariate analysis, they did not exhibit significant mediating effects in the relationship between the main risk factors (age, intraoperative fluid administration, and monocyte percentage) and postoperative DVT formation. Specifically, D-dimer did not play a significant mediating role between age, intraoperative fluid administration, and monocyte percentage and postoperative DVT formation, as its indirect effect 95% confidence intervals all included zero, and the corresponding P-values were all greater than 0.05. PLT distribution width also did not show a significant mediating effect between intraoperative fluid administration and monocyte percentage and postoperative DVT formation. Similarly, postoperative ambulation time did not significantly mediate the relationship between intraoperative fluid administration and postoperative DVT formation. This indicates that in this study, these potential mediating factors did not significantly explain the impact pathway of the main risk factors on postoperative DVT formation (Supplementary table 2).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study successfully identified independent risk factors for postoperative DVT in patients undergoing lumbar surgery through a retrospective analysis of patient data and constructed a predictive model based on multivariate logistic regression analysis. This model integrates three key factors: age, intraoperative fluid administration, and monocyte percentage, effectively predicting the risk of postoperative DVT. The findings not only provide clinicians with a practical prediction tool but also offer scientific evidence for optimizing perioperative management and reducing the incidence of DVT. Additionally, the predictive model is visualized in the form of a nomogram, facilitating quick assessment of postoperative DVT risk by clinicians and further validating its clinical applicability.\u003c/p\u003e\u003cp\u003eAge emerged as an independent risk factor for postoperative DVT, consistent with previous studies (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). As individuals age, their blood vessel walls thin, blood flow becomes sluggish, and the prevalence of comorbidities such as hypertension and diabetes increase, all of which contribute to a heightened risk of DVT (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Numerous studies have also highlighted age as a significant risk factor for DVT, particularly in patients undergoing lumbar surgery (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Intraoperative fluid administration was found to have a negative correlation with postoperative DVT risk, likely because adequate intraoperative fluid management helps maintain stable hemodynamics (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Proper fluid administration can reduce the incidence of postoperative hypotension and, consequently, the risk of blood stasis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, excessive fluid administration may lead to tissue edema, which could paradoxically increase the risk of DVT (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Therefore, intraoperative fluid management must strike a balance between maintaining hemodynamic stability and avoiding fluid overload. Patients with a lower percentage of monocytes had a higher risk of postoperative DVT, possibly due to the role of monocytes in inflammatory responses and endothelial repair (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A lower monocyte percentage may indicate weaker postoperative inflammatory and endothelial repair capabilities, thereby increasing the risk of DVT (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This finding offers clinicians a new perspective, suggesting that the inflammatory state and immune function of patients should be monitored in postoperative management.\u003c/p\u003e\u003cp\u003eThe predictive model constructed in this study, visualized as a nomogram, enables clinicians to quickly assess the risk of postoperative DVT. The model's AUC value of 0.89 indicates its strong discriminative ability to distinguish between high-risk and low-risk patients. Moreover, the model's sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were all at high levels, further validating its clinical utility. By utilizing this model, clinicians can perform precise risk stratification of patients preoperatively and postoperatively, thereby devising personalized prevention strategies, reducing unnecessary anticoagulant treatments, and minimizing associated complications.\u003c/p\u003e\u003cp\u003eDespite the significant progress made in identifying DVT risk factors and constructing a predictive model, our study has certain limitations. First, as a retrospective study, it is susceptible to selection and information biases. Second, the relatively limited sample size, especially in the external validation part, may affect the generalizability of the model. Additionally, not all potential DVT risk factors, such as genetic factors and medication use, were considered in the study, which may limit the comprehensiveness of the model. Future research should adopt a prospective design, expand the sample size, and include more potential risk factors to further validate and optimize the predictive model.\u003c/p\u003e\u003cp\u003eFuture research could delve deeper into the mechanisms underlying DVT formation, particularly the roles of inflammatory responses and immune function. Combining multicenter data to develop a more comprehensive predictive model and validating its applicability in different populations through prospective studies would be beneficial. Additionally, research could focus on postoperative DVT prevention measures, such as the optimal combination of mechanical and pharmacological prophylaxis, as well as how to reduce the incidence of DVT while avoiding bleeding complications. These studies would help further optimize perioperative management for patients undergoing lumbar surgery and improve patient outcomes.\u003c/p\u003e\u003cp\u003eIn summary, by identifying independent risk factors for postoperative DVT in lumbar surgery patients and constructing a predictive model, our study provides clinicians with a practical tool to optimize perioperative management, reduce the incidence of DVT, and improve patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhen-wu Gao and Hao-yu Feng\u0026nbsp;were responsible for the manuscript revision and research design.\u0026nbsp;Yong-zhuang Ma performed the data analysis and drafted the manuscript.\u0026nbsp;Wei Zhang and Lei-min\u0026nbsp;Xi were responsible for the data collection.\u0026nbsp;Zhuo Ma ,Xiao-ming Guan, and Li-ming\u0026nbsp;He participated in data verification and manuscript revision.\u0026nbsp;Qiang Chang\u0026nbsp;provided administrative support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Plan (2022YFC3601900 and 2022YFC3601904).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Shanxi\u0026nbsp;Bethune\u0026nbsp;Hospital (approval number: YXLL-2025-094).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOjima M, Takegawa R, Hirose T, Ohnishi M, Shiozaki T, Shimazu T. 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Intraoperative hemodynamic management during pancreatoduodenectomy \u0026ndash; an analysis of 525 patients. \u003cem\u003eLangenbecks Arch Surg\u003c/em\u003e (2025) 410:123. doi: 10.1007/s00423-025-03669-w\u003c/li\u003e\n\u003cli\u003eHansen B. Fluid Overload. \u003cem\u003eFront Vet Sci\u003c/em\u003e (2021) 8: doi: 10.3389/fvets.2021.668688\u003c/li\u003e\n\u003cli\u003eXiong X, Hu P, Li T, Yu S, Mao Q. Association between inflammatory indices and preoperative deep vein thrombosis in patients undergoing total joint arthroplasty: a retrospective study. \u003cem\u003eThromb J\u003c/em\u003e (2025) 23:6. doi: 10.1186/s12959-024-00682-9\u003c/li\u003e\n\u003cli\u003eWang Z, Zhou Q, Liu H, Zhang J, Zhu Z, Wu J, Chen X, Liu Y. Association Between Monocyte Count and Preoperative Deep Venous Thrombosis in Older Patients with hip Fracture: A Retrospective Study. \u003cem\u003eClinical and Applied Thrombosis/Hemostasis\u003c/em\u003e (2022) 28: doi: 10.1177/10760296221100806\u003c/li\u003e\n\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":"Deep vein thrombosis (DVT), Lumbar surgery, Risk factors, Predictive model, percutaneous endoscopic lumbar interbody fusion (PELIF)","lastPublishedDoi":"10.21203/rs.3.rs-7347462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7347462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aims to compare the incidence of deep vein thrombosis (DVT) between patients undergoing Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF) , identify independent risk factors, and develop a predictive model for postoperative DVT risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective study included 207 patients who underwent PLIF and PELIF at our hospital from April 2024 to June 2025. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for postoperative DVT. A predictive model was constructed based on these factors, and its discriminative ability was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe incidence of DVT was significantly higher in the PELIF group compared to the PLIF group (P \u0026lt; 0.001). Univariate analysis revealed that age, intraoperative fluid administration, monocyte percentage, and D-dimer levels were significantly associated with postoperative DVT (P \u0026lt; 0.05). Multivariate logistic regression analysis further confirmed that age, intraoperative fluid administration, and monocyte percentage were independent risk factors. The predictive model constructed based on these factors had an AUC of 0.89 (95% CI: 0.82 - 0.95), indicating good discriminative ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study successfully identified independent risk factors for postoperative DVT in lumbar surgery patients and developed a predictive model with high discriminative ability. This model can serve as a practical tool for clinicians to assess postoperative DVT risk, optimize perioperative management, and reduce the incidence of DVT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistry\u003c/strong\u003e: Ethics Committee of Shanxi Bethune Hospital, Approval notice number: YXLL-2025-094, Registration date: 1st April 2025.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of DVT Incidence After Posterior Lumbar Interbody Fusion（PLIF）and percutaneous endoscopic lumbar interbody fusion (PELIF): Identifying Risk Factors and Developing a Predictive Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 05:12:33","doi":"10.21203/rs.3.rs-7347462/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":"c4991e20-770a-4dfa-896c-1f3d31d281df","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-14T22:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 05:12:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7347462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7347462","identity":"rs-7347462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}