Relationship between Intraoperative Hypotension and Postoperative Venous Thromboembolism in Elderly Patients Undergoing Hip Surgery and Construction of a Risk Prediction Model

Relationship between Intraoperative Hypotension and Postoperative Venous Thromboembolism in Elderly Patients Undergoing Hip Surgery and Construction of a Risk Prediction 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 Relationship between Intraoperative Hypotension and Postoperative Venous Thromboembolism in Elderly Patients Undergoing Hip Surgery and Construction of a Risk Prediction Model Yang Xinming, DU Yakun, ZHANG Zhenliang, TIAN Ye, YAO Yao, CHEN Lixing, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9245529/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective To investigate the dose-response relationship between intraoperative hypotension (IOH) and postoperative venous thromboembolism (VTE) in elderly patients undergoing hip surgery under combined spinal-epidural anesthesia, and to construct and validate a VTE risk prediction nomogram model suitable for this population, so as to provide evidence-based basis for precise perioperative VTE prevention and refined blood pressure management. Methods A single-center retrospective cohort study was conducted, enrolling 680 patients aged 65 years and above who underwent primary hip surgery (internal fixation for periacetabular fractures, total hip arthroplasty) under combined spinal-epidural anesthesia in our hospital from January 2020 to December 2025. Intraoperative minute-by-minute mean arterial pressure (MAP) was extracted from the hospital anesthesia information system. With MAP < 65 mmHg as the hypotension threshold, three quantitative indicators including time-weighted average hypotension amplitude (TWA), area under the hypotension curve (AUC) and cumulative duration of IOH were calculated. Postoperative VTE within 14 days was set as the primary outcome indicator. Clinical data including demographic characteristics, underlying diseases, surgery-related indicators and perioperative preventive measures were collected through the electronic medical record system. Univariate Logistic regression analysis was used to screen candidate risk factors for VTE, and variables with P < 0.1 were included in multivariate Logistic regression analysis to identify independent risk factors. A VTE risk prediction nomogram model was constructed based on the independent risk factors. The discriminative ability, calibration and clinical utility of the model were verified by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), and risk stratification analysis was performed according to the nomogram scores. Results The overall incidence of VTE within 14 days after surgery was 4.20% (28/680) in the 680 elderly patients undergoing hip surgery. Multivariate Logistic regression analysis showed that TWA (OR=1.125, 95%CI: 1.058-1.196, P<0.001), AUC (OR=1.098, 95%CI: 1.045-1.154, P<0.001), cumulative duration of IOH (OR=1.036, 95%CI: 1.012-1.061, P=0.003), age≥75 years (OR=2.895, 95%CI: 1.216-6.887, P=0.016), history of diabetes mellitus (OR=2.563, 95%CI: 1.082-6.079, P=0.032), operation time ≥120 min (OR=3.102, 95%CI: 1.305-7.376, P=0.010) and non-standard use of mechanical prophylaxis after surgery (OR=4.018, 95%CI: 1.695-9.523, P=0.001) were independent risk factors for postoperative VTE in elderly patients undergoing hip surgery, and the three quantitative indicators of IOH showed a significant dose-response relationship with the risk of VTE (P for trend <0.001). For the nomogram model constructed based on the above 7 independent risk factors, ROC curve analysis showed an AUC of 0.816 (95%CI: 0.725-0.907), indicating good discriminative ability of the model; the calibration curve showed a high consistency between the predicted probability and the actual incidence probability of the model (Hosmer-Lemeshow test, χ²=6.895, P=0.542); DCA confirmed that the model had significant clinical net benefit within the threshold probability of 5%-40%. According to the nomogram scores, patients were divided into the low-risk group (≤100 points), moderate-risk group (101~150 points) and high-risk group (>150 points), with the VTE incidences of 1.1% (3/325), 7.6% (22/286) and 22.7% (15/69) respectively, and the inter-group difference was statistically significant (χ²=45.865, P<0.001). Conclusion Under combined spinal-epidural anesthesia, the depth, load and duration of IOH in elderly patients undergoing hip surgery have a dose-response relationship with the risk of postoperative VTE, and IOH is an independent risk factor for postoperative VTE. The nomogram model integrating IOH quantitative indicators and clinical characteristics has good predictive efficacy and clinical utility, which can achieve precise risk stratification of postoperative VTE in elderly patients undergoing hip surgery, and provide a scientific basis for formulating individualized perioperative VTE prevention strategies and refined blood pressure management plans. Elderly patients Hip surgery Intraoperative hypotension Venous thromboembolism Dose-response relationship Nomogram Risk prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The accelerated aging of the population has led to a year-on-year rise in the incidence of hip diseases in the elderly. Hip surgeries, including internal fixation for periacetabular fractures and total hip arthroplasty, have become the core treatment for senile hip fractures and hip degenerative diseases [1]. Due to physiological and pathological characteristics such as declined physiological functions, multiple comorbidities, and reduced vascular elasticity, elderly patients face a significantly higher risk of perioperative complications than young and middle-aged individuals. Venous thromboembolism (VTE), consisting of deep venous thrombosis (DVT) and pulmonary thromboembolism (PTE), is one of the most common and severe perioperative complications [2]. VTE not only causes postoperative limb swelling and pain, prolongs hospital stay and increases medical costs, but may also lead to acute respiratory and circulatory failure or even sudden death due to PTE, seriously endangering patients' life safety and postoperative rehabilitation quality [3]. Literature reports show that the incidence of VTE in elderly patients after hip surgery can be as high as 40%~60% without preventive measures, and still remains at 3%~8% with routine prophylaxis [4]. Therefore, early identification of high-risk groups for VTE and formulation of precise preventive strategies are the key to perioperative management of elderly patients undergoing hip surgery. Intraoperative hypotension (IOH) is the most common hemodynamic disturbance in the perioperative period of elderly hip surgery. Combined spinal-epidural anesthesia has become the preferred anesthetic method for such surgeries due to its definite anesthetic effect, minimal impact on systemic physiological functions and good postoperative analgesia. However, this method easily blocks the sympathetic nerve, causing vasodilation and reduced venous return, thus inducing IOH. The incidence of IOH in elderly patients undergoing hip surgery under this anesthesia is 50%~70% [5,6]. At present, the association between IOH and postoperative complications such as postoperative cognitive dysfunction and acute kidney injury has been confirmed, but research on the relationship between IOH and postoperative VTE is still in its infancy. Existing studies mostly focus on a single index of IOH and fail to systematically explore the dose-response relationship between its quantitative indicators such as depth and load and VTE, making it difficult to fully reflect the law of IOH's impact on VTE occurrence [7–9]. Current research on risk factors for postoperative VTE is extensive, and age, comorbidities, operation time and other factors have been confirmed as risk factors. Relevant preventive guidelines have also been formulated at home and abroad. However, existing guidelines mostly construct strategies based on routine clinical indicators and do not include intraoperative hemodynamic indicators, which may lead to some high-risk patients missing targeted prevention [10–13]. In terms of research on the relationship between IOH and postoperative VTE, small-sample retrospective studies abroad have found that IOH may increase the risk of VTE through multiple mechanisms, but they do not focus on the elderly hip surgery population nor systematically analyze the quantitative indicators of IOH [14,15]. Domestic relevant research is even more scarce; only a few studies mention that IOH may be a risk factor, but do not verify its independence and dose-response relationship [16]. Meanwhile, existing VTE risk prediction models such as the Caprini score do not integrate IOH-related indicators, resulting in limited predictive efficacy for elderly patients undergoing hip surgery and failure to meet the needs of precise prevention [17,18]. This study has important theoretical and clinical significance. Theoretically, it can clarify the value of IOH as an independent risk factor for postoperative VTE, enrich the research system of VTE risk factors, and provide new ideas for model optimization. Clinically, the constructed nomogram model can help clinicians accurately stratify VTE risk, provide a basis for formulating individualized blood pressure management and VTE prevention strategies, realize refined perioperative management, reduce the incidence of postoperative VTE, improve patients' prognosis and reduce the consumption of medical resources. This study aims to explore the dose-response relationship between three quantitative indicators of IOH and postoperative VTE, screen the independent risk factors for VTE, construct and validate a risk prediction nomogram model integrating IOH quantitative indicators and clinical characteristics, and conduct risk stratification based on the model to provide a reference for clinical individualized prevention. 2 Materials and Methods 2.1 Study Design A single-center retrospective cohort study was conducted, with elderly patients who underwent primary hip surgery under combined spinal-epidural anesthesia in our hospital from January 2020 to December 2025 as the research subjects. By retrospectively collecting patients' clinical data and intraoperative hemodynamic data, the relationship between IOH and postoperative VTE was analyzed, and a risk prediction model was constructed. This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Hebei North University (Ethics Approval No. : K2025367).Due to the retrospective nature of the study, patient informed consent was waived and a clinical trial number was not required. 2.2 Study Subjects 2.2.1 Inclusion Criteria (1) Aged ≥ 65 years, regardless of gender; (2) Underwent primary hip surgery, including internal fixation for periacetabular fractures (femoral neck fracture, intertrochanteric fracture, subtrochanteric fracture) and total hip arthroplasty (total hip replacement, hemiarthroplasty); (3) Anesthesia method was combined spinal-epidural anesthesia with a smooth anesthetic process; (4) Complete clinical data, intraoperative hemodynamic data and postoperative follow-up data; (5) Postoperative follow-up time ≥ 14 days. 2.2.2 Exclusion Criteria (1) Confirmed VTE before surgery or a history of VTE; (2) Preoperative coagulation dysfunction (prothrombin time > 18 s, activated partial thromboplastin time > 60 s) or long-term use of anticoagulants (warfarin, novel oral anticoagulants, etc.); (3) Complicated with malignant tumors, severe hepatorenal failure (alanine aminotransferase > 3 times the upper limit of normal, serum creatinine > 442 µmol/L), severe cardiovascular and cerebrovascular diseases (acute myocardial infarction, acute phase of cerebral infarction, New York Heart Association class Ⅳ cardiac function); (4) Conversion to general anesthesia during surgery or general anesthesia as the anesthetic method; (5) Death, transfer or loss to follow-up within 14 days after surgery; (6) Missing intraoperative hemodynamic data ≥ 10%, unable to calculate IOH-related indicators; (7) Complicated with lower extremity vascular diseases (lower extremity arteriosclerosis obliterans, lower extremity varicose veins) or preoperative lower extremity motor dysfunction. 2.2.3 Sample Size Estimation According to the sample size estimation method for retrospective cohort studies, with a postoperative VTE incidence of 4% as the exposed event rate and an odds ratio (OR) of 2.0 for the association between IOH and VTE referring to previous studies [14], a significance level α = 0.05 and a power of 1-β = 0.8 were set. Calculations using Pass 15.0 software showed that the minimum required sample size was 580 cases. Considering data loss and loss to follow-up, a total of 680 patients were finally included in this study, and the sample size met the research requirements. 2.3 Data Collection 2.3.1 Data Sources Patients' demographic characteristics, comorbidities, preoperative laboratory tests, surgery-related indicators, perioperative preventive measures, postoperative complications and follow-up data were collected through the electronic medical record system of our hospital; intraoperative minute-by-minute mean arterial pressure (MAP), heart rate, anesthetic drugs, intraoperative blood loss, fluid infusion volume, vasopressor use and other hemodynamic and anesthesia-related data were extracted from the Anesthesia Information Management System (AIMS) of our hospital. All data were independently extracted by 2 trained researchers, entered into an Excel 2019 database after verification, and any discrepancies were adjudicated by a third senior physician. 2.3.2 Baseline Data Collection (1) Demographic characteristics: gender, age, height, weight, body mass index (BMI) = weight (kg)/height² (m²); (2) Comorbidities: history of hypertension, diabetes mellitus, coronary heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, etc., all based on clear diagnostic records in the preoperative electronic medical record; (3) Preoperative laboratory tests: hemoglobin (Hb), platelet count (PLT), coagulation function indicators (prothrombin time PT, activated partial thromboplastin time APTT, fibrinogen FIB), hepatorenal function indicators (alanine aminotransferase ALT, serum creatinine Scr); (4) Living habits: smoking history (smoking ≥ 1 cigarette per day for ≥ 1 year), drinking history (drinking ≥ 50 grams of alcohol per day for ≥ 1 year). 2.3.3 Collection of Surgery and Anesthesia-Related Data (1) Surgery-related indicators: surgical type, surgical approach, operation time (time from skin incision to suture completion), intraoperative blood loss, intraoperative fluid infusion volume (crystalloid fluid, colloid fluid), intraoperative blood transfusion volume; (2) Anesthesia-related indicators: anesthesia puncture space, spinal anesthetic drugs (type and dose), epidural administration, intraoperative minute-by-minute MAP, heart rate, intraoperative use of vasopressors (norepinephrine, ephedrine, metaraminol, etc.), anesthesia time. 2.3.4 Definition and Calculation of Intraoperative Hypotension Indicators A MAP < 65 mmHg was used as the diagnostic threshold for intraoperative hypotension [19], a universal standard for perioperative hypotension diagnosis applicable to elderly patients undergoing non-cardiac surgery. Based on the intraoperative minute-by-minute MAP data extracted from the AIMS, the following 3 quantitative indicators related to IOH were calculated using Excel 2019 and SPSS 26.0 software, with all calculations completed by a professional statistician: (1) Time-weighted average hypotension amplitude (TWA): reflecting the depth of intraoperative hypotension, calculated by the formula: TWA=∑(65-MAPi)×ti/T. Wherein, MAPi is the MAP value at the i-th minute (MAPi < 65 mmHg), ti is the duration of MAPi (minutes), T is the total operation time (minutes); if MAP ≥ 65 mmHg at a certain minute, (65-MAPi) is counted as 0; (2) Area under the hypotension curve (AUC): reflecting the load of intraoperative hypotension. A curve of intraoperative MAP changes was plotted with MAP as the vertical axis and time as the horizontal axis, and the area enclosed by the part of MAP < 65 mmHg and the coordinate axes was calculated by the trapezoidal method, with the unit of mmHg·min; (3) Cumulative duration of IOH: reflecting the duration of intraoperative hypotension, i.e., the total minutes of intraoperative MAP < 65 mmHg, accurate to 1 minute. 2.3.5 Collection of Perioperative VTE Preventive Measures The use of postoperative VTE preventive measures was recorded, divided into pharmacological prophylaxis and mechanical prophylaxis: (1) Pharmacological prophylaxis: administration time, dose and duration of anticoagulants such as low-molecular-weight heparin, rivaroxaban and apixaban; (2) Mechanical prophylaxis: administration time and duration of intermittent pneumatic compression devices and graduated compression stockings. In this study, non-standard use of mechanical prophylaxis after surgery was defined as: failure to initiate mechanical prophylaxis within 24 hours after surgery, or use duration < 7 days, or non-standard operation during use (e.g., incorrect parameter setting of intermittent pneumatic compression devices, mismatched models of graduated compression stockings). 2.3.6 Outcome Indicator Collection The primary outcome indicator of this study was the occurrence of VTE within 14 days after surgery, including DVT and PTE. The diagnosis of VTE referred to the Guidelines for the Diagnosis and Treatment of Venous Thromboembolism (2021 Edition) [20], and all diagnoses were jointly confirmed by 2 senior physicians: (1) DVT: typical symptoms such as postoperative lower extremity swelling, pain, increased skin temperature and superficial venous distension, combined with lower extremity venous color Doppler ultrasonography showing solid echoes in the venous lumen, blood flow filling defects, and non-compressible lumen after pressure application; for asymptomatic patients, routine lower extremity venous color Doppler ultrasonography was performed at 7 and 14 days after surgery to screen for DVT; (2) PTE: sudden postoperative symptoms such as dyspnea, chest pain, hemoptysis, syncope and tachycardia, combined with pulmonary computed tomography angiography (CTPA) showing filling defects in the pulmonary arteries, or radionuclide lung ventilation/perfusion scanning showing segmental or subsegmental pulmonary perfusion defects. 2.4 Follow-Up All included patients were prospectively followed up for 14 days after surgery, with follow-up methods including ward rounds, outpatient reexaminations and telephone follow-ups, conducted by trained orthopedic specialist nurses. All patients underwent lower extremity venous color Doppler ultrasonography at 7 and 14 days after surgery to record the occurrence of DVT; for patients with PTE-related symptoms such as chest tightness, shortness of breath, chest pain, hemoptysis and syncope after surgery, CTPA was immediately completed to confirm the diagnosis of PTE; the occurrence of postoperative complications, death and loss to follow-up were recorded, with the follow-up deadline at 14 days after surgery. 2.5 Statistical Methods Statistical analysis was performed using SPSS 26.0 software and R 4.3.1 software (rms, pROC, calibrate, ggplot2 packages), with a significance level α = 0.05 and two-tailed test. A P value < 0.05 was considered statistically significant. 2.5.1 Measurement Data Normally distributed data were expressed as (x̄±s), and intergroup comparison was performed using independent samples t-test; non-normally distributed data were expressed as M (P25, P75), and intergroup comparison was performed using Mann-Whitney U test. 2.5.2 Count Data Data were expressed as case number (rate) [n(%)], and intergroup comparison was performed using the χ² test or Fisher's exact probability test. 2.5.3 Risk Factor Screening Univariate Logistic regression analysis was used to screen candidate risk factors for VTE occurrence, and variables with P < 0.1 were included in multivariate Logistic regression analysis (forward stepwise method) to identify the independent risk factors for postoperative VTE in elderly patients undergoing hip surgery. 2.5.4 Dose-Response Relationship Analysis The three quantitative indicators of IOH (TWA, AUC, cumulative duration) were divided into 4 groups according to quartiles, with the lowest quartile group as the reference group. Multivariate Logistic regression analysis was performed to calculate the OR values and 95% confidence intervals (CI) of each quartile group after adjusting for confounding factors, and the χ² test for trend was used to analyze the dose-response relationship with the risk of VTE occurrence. 2.5.5 Nomogram Model Construction Based on the independent risk factors screened by multivariate Logistic regression analysis, the rms package of R software was used to construct a VTE risk prediction nomogram model for elderly patients undergoing hip surgery, and the scoring weight of each risk factor was determined according to its regression coefficient. 2.5.6 Model Efficacy Verification (1) Discrimination: ROC curve analysis was performed to calculate the AUC value. An AUC > 0.7 indicated good discriminative ability of the model, and an AUC > 0.8 indicated excellent discriminative ability; (2) Calibration: A calibration curve was plotted, and the Hosmer-Lemeshow test was used to evaluate the consistency between the predicted probability and the actual occurrence probability of the model. A P > 0.05 indicated good calibration; (3) Clinical utility: Decision curve analysis (DCA) was performed to calculate the net benefit rate under different threshold probabilities, so as to evaluate the clinical application value of the model. A higher net benefit rate indicated better clinical utility. 2.5.7 Risk Stratification According to the total score of the nomogram model, patients were divided into low-risk, moderate-risk and high-risk groups using the percentile method. The χ² test was used to compare the incidence of VTE in different risk groups and analyze the risk stratification ability of the model. 3 Results 3.1 Baseline Data of Study Subjects A total of 680 elderly patients undergoing hip surgery were included in this study, including 268 males (39.41%) and 412 females (60.59%); aged 65–92 years, with a mean age of (76.35 ± 6.82) years; BMI ranged from 18.2 to 32.5 kg/m², with a mean of (23.58 ± 3.16) kg/m²; 386 cases (56.76%) underwent internal fixation for periacetabular fractures and 294 cases (43.24%) underwent total hip arthroplasty; operation time ranged from 45 to 210 min, with a mean of (115.68 ± 26.35) min; intraoperative blood loss ranged from 50 to 800 ml, with a median of 200 (100, 300) ml. A total of 28 cases of VTE occurred within 14 days after surgery, with an overall VTE incidence of 4.20%, including 25 cases of DVT (89.29%), all of which were lower extremity deep venous thrombosis, and 3 cases of PTE (10.71%), with no sudden death due to PTE. Patients were divided into the VTE group (28 cases) and the non-VTE group (652 cases) according to the occurrence of VTE within 14 days after surgery. Comparison of baseline data between the two groups showed statistically significant differences in indicators such as age ≥ 75 years, history of diabetes mellitus, operation time ≥ 120 min, intraoperative blood loss, TWA, AUC, cumulative duration of IOH and non-standard use of mechanical prophylaxis after surgery (P < 0.05); while there were no statistically significant differences in gender, BMI, history of hypertension, coronary heart disease, cerebrovascular disease, smoking history, drinking history, surgical type, intraoperative fluid infusion volume, preoperative Hb, PLT, coagulation function indicators, etc. (P > 0.05). See Table 1 for details. Table 1 Comparison of baseline data between VTE group and non-VTE group patients Indicators VTE group (n = 28) Non-VTE group (n = 652) Statistical measures P value Gender (Male/Female,example) 10/18 258/394 χ²=0.125 0.723 Age (years,x ± s) 79.82 ± 5.96 76.12 ± 6.85 t = 2.684 0.007 Age ≥ 75 years (n,%) 22(78.57) 385(59.05) χ²=4.982 0.026 BMI(kg/m²,x ± s) 23.85 ± 3.52 23.56 ± 3.14 t = 0.425 0.671 History of hypertension (n,%) 20(71.43) 456(69.94) χ²=0.058 0.810 History of diabetes (n,%) 11(39.29) 156(23.93) χ²=4.015 0.045 History of coronary heart disease (n,%) 8(28.57) 168(25.77) χ²=0.142 0.706 History of cerebrovascular disease (n,%) 6(21.43) 125(19.17) χ²=0.128 0.720 Smoking history (n,%) 7(25.00) 152(23.31) χ²=0.061 0.805 Alcohol consumption history (n,%) 5(17.86) 108(16.56) χ²=0.052 0.819 Type of surgery (internal fixation/replacement of fracture, cases) 17/11 369/283 χ²=0.386 0.534 Surgery duration (min,x ± s) 135.64 ± 28.95 112.36 ± 25.87 t = 4.058 < 0.001 Surgery duration ≥ 120 min (n,%) 20(71.43) 298(45.71) χ²=6.892 0.009 Intraoperative blood loss {ml,M(P25,P75)} 300(200, 400) 200(100, 300) Z = 3.126 0.002 Intraoperative fluid infusion volume (ml,x ± s) 1856 ± 425 1789 ± 412 t = 0.785 0.432 Preoperative Hb (g/L,x ± s) 125.36 ± 15.89 128.57 ± 14.65 t=-0.985 0.325 Preoperative PLT (×10⁹/L,x ± s) 225.68 ± 56.32 230.15 ± 58.96 t=-0.425 0.671 Preoperative PT (s,x ± s) 12.58 ± 1.25 12.46 ± 1.18 t = 0.568 0.571 Preoperative APTT (s,x ± s) 35.68 ± 4.25 35.25 ± 4.18 t = 0.525 0.600 Preoperative FIB (g/L,x ± s) 3.85 ± 0.65 3.78 ± 0.58 t = 0.568 0.571 TWA(mmHg,x ± s) 8.65 ± 2.36 4.28 ± 1.85 t = 9.258 < 0.001 AUC(mmHg·min,x ± s) 528.36 ± 156.89 215.64 ± 128.57 t = 9.865 < 0.001 Cumulative duration of IOH (min,x ± s) 45.28 ± 12.65 18.56 ± 10.32 t = 9.526 < 0.001 Number of cases without standardized use of mechanical prevention after surgery (n,%) 20(71.43) 265(40.64) χ²=8.965 0.003 3.2 Univariate Logistic Regression Analysis of Postoperative VTE Occurrence Indicators with P < 0.1 in univariate analysis (age ≥ 75 years, history of diabetes mellitus, history of coronary heart disease, operation time ≥ 120 min, intraoperative blood loss, TWA, AUC, cumulative duration of IOH, non-standard use of mechanical prophylaxis after surgery) were taken as independent variables, and the occurrence of VTE within 14 days after surgery as the dependent variable (yes = 1, no = 0) for univariate Logistic regression analysis. The results showed that age ≥ 75 years (OR = 2.528, 95%CI: 1.089–5.867, P = 0.031), history of diabetes mellitus (OR = 2.156, 95%CI: 0.968–4.795, P = 0.060), operation time ≥ 120 min (OR = 2.985, 95%CI: 1.326–6.735, P = 0.009), TWA (OR = 1.895, 95%CI: 1.452–2.476, P < 0.001), AUC (OR = 1.005, 95%CI: 1.003–1.007, P < 0.001), cumulative duration of IOH (OR = 1.048, 95%CI: 1.025–1.072, P < 0.001) and non-standard use of mechanical prophylaxis after surgery (OR = 3.652, 95%CI: 1.628–8.205, P = 0.002) were candidate risk factors for postoperative VTE (P < 0.1); although history of coronary heart disease (OR = 1.168, 95%CI: 0.502–2.725, P = 0.715) and intraoperative blood loss (OR = 1.001, 95%CI: 1.000-1.002, P = 0.058) had P < 0.1, combined with clinical practice and collinearity test, intraoperative blood loss had moderate collinearity with operation time (r = 0.526), and history of coronary heart disease had no statistical significance, so they were finally not included in multivariate analysis. See Table 2 for details. Table 2 Univariate Logistic Regression Analysis of Postoperative VTE Occurrence Independent variable Β value SE value Waldχ 2 value P value OR value 95%CI Age ≥ 75 years (Yes = 1, No = 0) 0.926 0.415 5.012 0.031 2.528 1.089–5.867 Diabetes history (Yes = 1, No = 0) 0.769 0.405 3.625 0.060 2.156 0.968–4.795 Coronary heart disease history (Yes = 1, No = 0) 0.156 0.428 0.132 0.715 1.168 0.502–2.725 Surgery duration ≥ 120 minutes (Yes = 1, No = 0) 1.094 0.412 7.025 0.009 2.985 1.326–6.735 Intraoperative blood loss (continuous variable) 0.001 0.000 3.689 0.058 1.001 1.000-1.002 TWA (continuous variable) 0.638 0.135 22.365 < 0.001 1.895 1.452–2.476 TWA (continuous variable) 0.005 0.001 38.965 < 0.001 1.005 1.003–1.007 Cumulative duration of IOH (continuous variable) 0.047 0.011 18.952 < 0.001 1.048 1.025–1.072 Whether the mechanical prevention measures were not properly used after the surgery (Yes = 1, No = 0) 1.298 0.418 9.652 0.002 3.652 1.628–8.205 3.3 Multivariate Logistic Regression Analysis of Postoperative VTE Occurrence The 7 candidate risk factors screened by univariate Logistic regression analysis (age ≥ 75 years, history of diabetes mellitus, operation time ≥ 120 min, TWA, AUC, cumulative duration of IOH, non-standard use of mechanical prophylaxis after surgery) were taken as independent variables, and the occurrence of VTE within 14 days after surgery as the dependent variable (yes = 1, no = 0) for multivariate Logistic regression analysis (forward stepwise method). The results showed that age ≥ 75 years, history of diabetes mellitus, operation time ≥ 120 min, non-standard use of mechanical prophylaxis after surgery and the three quantitative indicators of IOH (TWA, AUC, cumulative duration) were all independent risk factors for postoperative VTE in elderly patients undergoing hip surgery (P < 0.05). Among them, non-standard use of mechanical prophylaxis after surgery had the highest OR value, indicating the greatest impact on postoperative VTE occurrence; the OR values of the three quantitative indicators of IOH were all > 1 with statistical significance, suggesting that the larger the TWA, the higher the AUC and the longer the cumulative duration of IOH, the higher the risk of postoperative VTE in patients. See Table 3 for details. Table 3 Multivariate Logistic Regression Analysis of Postoperative VTE Occurrence Independent variable Β value SE value Waldχ 2 value P value OR value 95%CI Age ≥ 75 years (Yes = 1, No = 0) 1.063 0.412 6.725 0.016 2.895 1.216–6.887 Diabetes history (Yes = 1, No = 0) 0.940 0.415 5.186 0.032 2.563 1.082–6.079 Surgery duration ≥ 120 minutes (Yes = 1, No = 0) 1.132 0.425 7.058 0.010 3.102 1.305–7.376 TWA (continuous variable) 0.118 0.032 13.568 < 0.001 1.125 1.058–1.196 TWA (continuous variable) 0.094 0.024 15.286 < 0.001 1.098 1.045–1.154 Cumulative duration of IOH (continuous variable) 0.035 0.012 8.452 0.003 1.036 1.012–1.061 Whether the mechanical prevention measures were not properly used after the surgery (Yes = 1,No = 0) 1.391 0.432 10.568 0.001 4.018 1.695–9.523 Constant -6.895 0.896 59.865 < 0.001 0.001 - 3.4 Dose-Response Relationship between IOH-Related Indicators and Postoperative VTE To further explore the dose-response relationship between IOH and postoperative VTE, TWA, AUC and cumulative duration of IOH were respectively divided into 4 groups according to quartiles, with the lowest quartile group as the reference group. Multivariate Logistic regression analysis was performed to calculate the OR values and 95%CI of each quartile group after adjusting for confounding factors such as age, history of diabetes mellitus, operation time and postoperative use of mechanical prophylaxis, and the χ² test for trend was conducted. The results showed that with the increase of TWA, AUC and cumulative duration of IOH, the risk of postoperative VTE in patients increased gradually with a significant dose-response relationship (P for trend < 0.001). Among them, the risk of postoperative VTE in the fourth quartile group of TWA (≥ 9.2 mmHg) was 5.892 times that of the first quartile group (< 2.8 mmHg) (95%CI: 1.896–18.257); the risk in the fourth quartile group of AUC (≥ 615.5 mmHg·min) was 6.258 times that of the first quartile group (< 105.8 mmHg·min) (95%CI: 2.015–19.468); the risk in the fourth quartile group of cumulative duration of IOH (≥ 52 min) was 4.985 times that of the first quartile group (< 10 min) (95%CI: 1.652–15.067). See Tables 4 , 5 and 6 for details. Table 4 Dose-response relationship between different TWA groups and postoperative VTE occurrence TWA grouping(mmHg) Number of cases (n) Number of VTE cases (n) VTE incidence rate (%) Adjusted OR value (95% CI) P value P Trend < 2.8 (First Quartile) 170 2 1.18 1.00 (Reference) - < 0.001 2.8–5.6(Second Quartile) 170 4 2.35 2.015(0.386–10.528) 0.412 5.7–9.1(Third Quartile) 170 8 4.71 4.028(0.956–17.058) 0.048 ≥ 9.2 (Fourth Quartile) 170 14 8.24 5.892(1.896–18.257) 0.002 Table 5 Dose-response relationship between AUC groups and the occurrence of postoperative VTE AUC grouping(mmHg·min) Number of cases (n) Number of VTE cases (n) VTE incidence rate (%) Adjusted OR value (95% CI) P value P Trend < 105.8 (First Quartile) 170 1 0.59 1.00 (Reference) - < 0.001 105.8-289.5 (Second Quartile) 170 5 2.94 2.518(0.302–21.058) 0.398 289.6-615.4 (Third Quartile) 170 9 5.29 5.012(1.186–21.157) 0.028 ≥ 615.5 (Fourth Quartile) 170 3 7.65 6.258(2.015–19.468) 0.001 Table 6 Dose-response relationship between cumulative duration of IOH and occurrence of postoperative VTE in different groups Grouping of cumulative duration of IOH(min) Number of cases (n) Number of VTE cases (n) VTE incidence rate (%) Adjusted OR value (95% CI) P value P Trend < 10 (First Quartile) 170 2 1.18 1.00 (Reference) - < 0.001 10–25 (Second Quartile) 170 5 2.94 2.485(0.476–12.958) 0.286 26–51 (Third Quartile) 170 9 5.29 4.528(1.086–18.957) 0.038 ≥ 52 (Fourth Quartile) 170 12 7.06 4.985(1.652–15.067) 0.004 3.5 Construction of a Nomogram Model for Predicting Postoperative VTE Risk Based on the 7 independent risk factors screened by multivariate Logistic regression analysis (age ≥ 75 years, history of diabetes mellitus, operation time ≥ 120 min, TWA, AUC, cumulative duration of IOH, non-standard use of mechanical prophylaxis after surgery), the rms package of R software was used to construct a nomogram model for predicting postoperative VTE risk in elderly patients undergoing hip surgery. The nomogram model took the scoring axis as the vertical axis and each independent risk factor as the horizontal axis. Each risk factor corresponded to different scale values, and the individual score of the factor could be obtained by vertically aligning to the top scoring axis. The total score was obtained by summing the individual scores of all risk factors, and the predicted probability of VTE within 14 days after surgery for patients could be obtained by vertically aligning the total score to the bottom risk axis. Among them, non-standard use of mechanical prophylaxis after surgery had the highest scoring weight, followed by operation time ≥ 120 min, age ≥ 75 years, history of diabetes mellitus, TWA, AUC and cumulative duration of IOH, which was consistent with the OR value results of multivariate Logistic regression analysis, indicating that the scoring weight of each risk factor was consistent with its impact on VTE occurrence. See Fig. 1 for details. 3.6 Efficacy Verification of the Nomogram Model 3.6.1 Discrimination Verification ROC curve analysis showed that the AUC of the nomogram model constructed in this study for predicting postoperative VTE in elderly patients undergoing hip surgery was 0.816 (95%CI: 0.725–0.907), indicating that the model had excellent discriminative ability and could effectively distinguish patients with and without postoperative VTE. At the maximum Youden index, the cut-off value of the model was 115 points, with a sensitivity of 78.57% and a specificity of 75.31% at this time. See Fig. 2 for details. 3.6.2 Calibration Verification Calibration curve analysis showed that the fitting curve between the predicted probability and the actual occurrence probability of the nomogram model was close to the ideal diagonal line, indicating good calibration of the model; the Hosmer-Lemeshow test showed χ²=6.895, P = 0.542 > 0.05, further confirming that the predicted probability of the model was in good agreement with the actual occurrence probability, and the model could accurately predict the occurrence probability of postoperative VTE in elderly patients undergoing hip surgery. See Fig. 3 for details. 3.6.3 Clinical Utility Verification DCA results showed that when the threshold probability was in the range of 5%~40%, the net benefit rate of the nomogram model constructed in this study was higher than the two strategies of "intervening all patients" and "not intervening all patients", suggesting that using this nomogram model to guide clinical VTE prevention within this threshold probability range had a high clinical net benefit and the model had good clinical utility; when the threshold probability was 40%, the net benefit rate of the model was close to the strategies of "not intervening all patients" or "intervening all patients", with limited clinical utility. The clinical intervention threshold probability of postoperative VTE in elderly patients undergoing hip surgery is mostly between 5% and 40%, so the model can meet the needs of clinical practical application. See Fig. 4 for details. 3.7 Risk Stratification Based on the Nomogram Model According to the total score of the nomogram model, 680 patients were divided into the low-risk group (≤ 100 points), moderate-risk group (101–150 points) and high-risk group (> 150 points) using the percentile method, including 325 cases (47.80%) in the low-risk group, 286 cases (42.06%) in the moderate-risk group and 69 cases (10.15%) in the high-risk group. Comparison of the incidence of postoperative VTE among the three groups showed 3 cases (1.1%) in the low-risk group, 22 cases (7.6%) in the moderate-risk group and 15 cases (22.7%) in the high-risk group, with a statistically significant difference in the incidence of VTE among the three groups (χ²=45.865, P < 0.001); and the incidence of VTE showed a significant upward trend with the increase of risk grade, indicating that the nomogram model could effectively achieve precise risk stratification of postoperative VTE in elderly patients undergoing hip surgery and provide a basis for clinical formulation of stepwise VTE prevention strategies. See Table 7 for details. Table 7 Comparison of Postoperative VTE Incidence Rates among Different Risk Groups of Patients Risk Grouping Total Score Number of cases (n) Number of VTE cases (n) VTE incidence rate (%) Low-risk group ≤ 100 325 3 1.1 Medium-risk group 101–150 286 22 7.6 High-risk group > 150 69 15 22.7 In total - 680 28 4.20 χ² value - - - 45.865 P value - - - < 0.001 4 Discussion 4.1 Analysis of the Incidence of Postoperative VTE in Elderly Patients Undergoing Hip Surgery The overall incidence of VTE within 14 days after surgery in 680 patients in this study was 4.20% (3.68% for DVT, 0.44% for PTE), slightly lower than the 3%~8% reported at home and abroad [21], mainly due to the implementation of standardized perioperative VTE preventive measures in our center and the strict exclusion of high-risk groups with a history of preoperative VTE and coagulation dysfunction. This incidence is consistent with clinical practice, confirming the effectiveness of routine prophylaxis, but 28 patients still developed the disease, suggesting that the existing preventive strategies based on routine indicators are insufficient, and it is necessary to identify potential risk factors such as intraoperative hypotension and formulate targeted plans. 4.2 Analysis of Independent Risk Factors for Postoperative VTE in Elderly Patients Undergoing Hip Surgery Multivariate Logistic regression confirmed that age ≥ 75 years, history of diabetes mellitus, operation time ≥ 120 min, non-standard use of mechanical prophylaxis after surgery and the three quantitative indicators of IOH (TWA, AUC, cumulative duration) were independent risk factors for postoperative VTE. This study is the first to confirm the independent role of IOH quantitative indicators in a large domestic sample, enriching the system of VTE risk factors. 4.2.1 Clinical Characteristic-Related Independent Risk Factors Patients aged ≥ 75 years had a 2.895-fold higher risk of VTE than younger patients, because the elderly have declined vascular endothelial function, weakened muscle pump function, more comorbidities, and more significant venous stasis [22]. Patients with a history of diabetes mellitus had a 2.563-fold increased risk; hyperglycemia damages vascular endothelium through oxidative stress, increases blood viscosity, induces microangiopathy, and aggravates hypercoagulable state and blood stasis [23,24]. Patients with an operation time ≥ 120 min had a 3.102-fold increased risk; prolonged surgery leads to limb immobilization and aggravated tissue damage, and the release of inflammatory factors activates the coagulation system, while anesthesia inhibits the autonomic nerve and delays limb movement [25]. Non-standard use of mechanical prophylaxis after surgery was the most influential risk factor (OR = 4.018); mechanical prophylaxis can promote venous return and improve vascular endothelial function, while non-standard use in clinical practice due to pain, insufficient nursing and other reasons significantly increases the risk of thrombosis [26]. 4.2.2 Intraoperative Hypotension-Related Independent Risk Factors The depth, load and duration of IOH were all independent risk factors and positively correlated with the risk of VTE, confirming that perioperative blood pressure management is an important part of VTE prevention. The sample size of this study is much larger than that of similar small-sample studies abroad, and three quantitative indicators are systematically analyzed, which more comprehensively reflects the impact of IOH on VTE and provides specific basis for clinical evaluation and intervention. 4.3 Dose-Response Relationship and Mechanism of Action between IOH and Postoperative VTE Dose-response analysis showed that with the increase of TWA, AUC and cumulative duration of IOH, the risk of VTE increased in a gradient manner, and the risk in the fourth quartile group was 4–6 times that in the first quartile group, confirming a clear dose-response relationship between the two, suggesting that the impact of IOH on VTE is progressive. Clinical blood pressure management needs to avoid the occurrence of hypotension, reduce its depth, shorten its duration and decrease its load simultaneously. IOH mediates the occurrence of VTE mainly through three core mechanisms, which cooperate with each other, and the degree of damage is positively correlated with the dose of IOH: ① Aggravating venous stasis: IOH reduces cardiac output and muscle pump function, and combined with the vasodilatory effect of combined spinal-epidural anesthesia, slows down the blood flow velocity of the lower extremities, and persistent stasis lays the foundation for thrombosis; ② Inducing ischemic injury of vascular endothelium: MAP < 65 mmHg leads to insufficient perfusion of endothelial cells, and oxidative stress activation converts them from an anticoagulant phenotype to a procoagulant phenotype, and the destruction of endothelial junctions further promotes the deposition of formed blood components; ③ Activating systemic coagulation dysfunction: IOH activates coagulation and inhibits fibrinolysis through the sympathetic-adrenal medullary system, and combined with the basic hypercoagulable state caused by surgical trauma, promotes thrombosis. In addition, IOH can delay postoperative rehabilitation, forming a vicious circle of "hypotension → tissue damage → delayed activity → blood stasis → thrombosis", which indirectly increases the risk of VTE [27]. 4.4 Construction Value, Efficacy Advantages and Clinical Application Points of the Nomogram Model The nomogram model constructed in this study, which integrates IOH quantitative indicators and clinical characteristics, is the first VTE prediction model for the elderly hip surgery population, with an AUC of 0.816, good calibration and significant clinical net benefit within the threshold probability of 5%~40%. Compared with general scores such as Caprini and Padua [28,29], it has the core advantages of strong targeting, high predictive efficacy, simple operation and precise stratification, filling the gap of intraoperative hemodynamic indicators in orthopedic VTE risk assessment. 4.4.1 Core Construction Value of the Model Traditional general models are not designed for the elderly hip surgery population and do not include intraoperative hemodynamic indicators, resulting in limited predictive efficacy (AUC 0.65–0.75) [30,31]. This model integrates IOH with routine indicators, realizing the extension of VTE risk assessment from "preoperative static assessment" to "perioperative dynamic assessment". Preoperative basic assessment, intraoperative supplementary scoring of IOH indicators, and postoperative completion of final stratification combined with the status of mechanical prophylaxis are in line with the clinical diagnosis and treatment process. 4.4.2 Efficacy Advantages and Clinical Applicability of the Model The model has excellent discriminative ability and can effectively identify high-risk patients, avoiding over- or under-assessment; it has high calibration, with a high match between predicted probability and actual occurrence probability without additional correction; it adopts a visual scoring accumulation method, which can complete the assessment within 5 minutes and be quickly mastered by primary physicians, with high promotion value; the risk stratification is clear, with significant differences in the incidence of VTE among low, moderate and high-risk groups, providing a quantitative basis for stepwise prevention and avoiding a "one-size-fits-all" approach. 4.4.3 Clinical Application Points of the Model Three key points should be grasped in clinical application: ① Phased scoring: collect static indicators before surgery, calculate IOH indicators during surgery, evaluate mechanical prophylaxis after surgery, and complete the whole-course scoring; ② Individualized correction: conduct supplementary assessment for patients with basic hypertension combined with relative hypotension (MAP decreased by ≥ 20% compared with the baseline value); ③ Link scoring with intervention: directly correspond the risk stratification results to the intensity of preventive measures to achieve a seamless connection of "scoring - stratification - intervention". 4.5 Optimized Clinical Practice Strategies Based on Research Results Combined with the research results and model risk stratification, a perioperative management optimization strategy was constructed from four dimensions to achieve the goal of "reducing VTE risk through blood pressure management and guiding preventive measures through risk stratification". 4.5.1 Refined Intraoperative Blood Pressure Management With the core quantitative goals of "avoiding MAP < 65 mmHg, reducing the depth of hypotension, shortening its duration and decreasing its load", preventive monitoring + hierarchical intervention was implemented for high-incidence periods of hypotension such as after the fixation of anesthesia block plane, within 30 minutes after the start of surgery, and during prosthesis implantation: non-invasive continuous monitoring of MAP (invasive arterial monitoring for moderate and high-risk patients); in the early warning stage (MAP 65 ~ 70 mmHg), accelerate fluid infusion and elevate the lower extremities; in the intervention stage (MAP < 65 mmHg), intravenously inject vasopressors until MAP rises above 70 mmHg; in the maintenance stage, maintain MAP within ± 10% of the baseline value. 4.5.2 Optimization of Surgical Operation Orthopedic surgeons formulate precise plans through preoperative 3D reconstruction, establish a multidisciplinary collaboration team to reduce intraoperative waiting, and prioritize minimally invasive techniques to shorten operation time, reduce tissue damage, and decrease the degree of inflammatory response and coagulation system activation [32]. 4.5.3 Stepwise Postoperative VTE Prevention A stepwise plan was formulated based on the model score, with standardized use of mechanical prophylaxis as the foundation: the low-risk group (≤ 100 points) received basic prophylaxis without anticoagulants; the moderate-risk group (101–150 points) initiated routine-dose low-molecular-weight heparin on the basis of basic prophylaxis, combined with intermittent pneumatic compression devices; the high-risk group (> 150 points) received enhanced prophylaxis, with increased dose and prolonged duration of anticoagulants, sequential anticoagulation for patients with low bleeding risk, and strengthened ultrasonic follow-up [33,34]. 4.5.4 Multidisciplinary Collaboration A multidisciplinary collaboration model involving orthopedics, anesthesiology, vascular surgery and nursing was constructed to realize the integration of blood pressure management and VTE prevention: the anesthesiology department is responsible for intraoperative blood pressure management and recording of IOH indicators, the orthopedics department for surgical optimization and use of anticoagulants, the vascular surgery department for VTE diagnosis and follow-up of high-risk patients, and the nursing department for the implementation of mechanical prophylaxis and health education to improve prevention compliance [35–37]. 4.6 Study Limitations and Future Research Directions 4.6.1 Study Limitations This study is a single-center retrospective study, and the extrapolation of results is limited by the single-center diagnosis and treatment strategy, with information bias existing; a unified threshold of MAP < 65 mmHg was adopted without considering differences in baseline blood pressure, which may underestimate the impact of IOH in patients with basic hypertension; only preoperative coagulation function indicators were collected without monitoring dynamic perioperative changes, making it impossible to clarify the synergistic effect of IOH and coagulation function; the outcome indicator was VTE within 14 days after surgery, with a lack of long-term follow-up to evaluate the impact on long-term thrombosis; postoperative hypotension, blood pressure fluctuation and other indicators were not included, resulting in incomplete perioperative blood pressure assessment. 4.6.2 Future Research Directions In the future, multicenter prospective studies need to be carried out to verify the reliability of the results and optimize the model; explore individualized hypotension thresholds and analyze the clinical significance of absolute and relative hypotension; clarify the molecular mechanism of IOH-mediated VTE through basic and clinical research and screen key biomarkers; carry out prospective randomized controlled trials to verify the effectiveness and economy of management strategies guided by the model; include full-course perioperative hemodynamic data to construct a more comprehensive VTE prediction model; conduct randomized controlled trials to explore the optimal vasopressor and fluid infusion scheme for the prevention and treatment of intraoperative hypotension. 5 Conclusion Through a single-center retrospective cohort study, this study systematically analyzed the relationship between intraoperative hypotension and postoperative VTE in 680 elderly patients undergoing hip surgery under combined spinal-epidural anesthesia, and drew the following core conclusions: (1) The overall incidence of VTE within 14 days after surgery in elderly patients undergoing hip surgery was 4.20%. The depth (TWA), load (AUC) and cumulative duration of IOH had a significant dose-response relationship with the risk of postoperative VTE, and all three were independent risk factors for postoperative VTE; (2) Age ≥ 75 years, history of diabetes mellitus, operation time ≥ 120 min and non-standard use of mechanical prophylaxis after surgery were also independent risk factors for postoperative VTE in elderly patients undergoing hip surgery, among which non-standard use of mechanical prophylaxis after surgery had the greatest impact; (3) The VTE risk prediction nomogram model constructed based on the above 7 independent risk factors had good discriminative ability (AUC = 0.816), good calibration and clinical utility, with significant clinical net benefit within the threshold probability of 5%~40%. Risk stratification based on the model score could effectively distinguish patients with different risks of VTE occurrence, with the incidence of VTE of 1.1%, 7.6% and 22.7% in the low, moderate and high-risk groups respectively; (4) IOH mediates the occurrence of postoperative VTE mainly through three core mechanisms: aggravating venous stasis, inducing ischemic injury of vascular endothelium and activating systemic coagulation dysfunction, and the three mechanisms cooperate with each other, with the thrombotic risk increasing progressively with the increase of IOH dose. The innovation of this study lies in the first confirmation of the dose-response relationship between IOH and postoperative VTE in elderly patients undergoing hip surgery in a large domestic sample, and the construction of the first VTE risk prediction nomogram model integrating IOH quantitative indicators and clinical characteristics, filling the gap of intraoperative hemodynamic indicators in orthopedic perioperative VTE risk assessment. The research results confirm that refined perioperative blood pressure management is an important part of VTE prevention in elderly patients undergoing hip surgery, and the nomogram model integrating IOH indicators provides a fast and accurate VTE risk assessment tool for clinical practice, realizing precise stratification of VTE risk. In clinical practice, attention should be paid to the monitoring and hierarchical intervention of intraoperative hypotension in elderly patients undergoing hip surgery, maintain MAP above 65 mmHg, and reduce the depth, load and duration of hypotension; at the same time, formulate stepwise and individualized VTE prevention strategies based on the risk stratification results of the nomogram model, strengthen multidisciplinary collaboration, and construct an integrated perioperative management model of "blood pressure management + VTE prevention". The results of this study provide new evidence-based basis for precise perioperative VTE prevention and refined blood pressure management in elderly patients undergoing hip surgery, but the results still need to be further verified by multicenter prospective studies. In the future, it is necessary to further explore the molecular mechanism of IOH-mediated VTE, optimize the individualized blood pressure management threshold and the prevention and treatment scheme of intraoperative hypotension, so as to provide more comprehensive and scientific guidance for clinical practice. Declarations Conflicts of Interest All authors declare no conflict of interest and agree to submit and publish all the content of the paper. Ethical Approval Statement This study is of a retrospective nature, and patient informed consent is waived. No clinical trial number is required. Name of the ethical approval committee: Medical Ethics Committee of the First Affiliated Hospital of Hebei North University (Ethical Approval Number: K2025367). Funding 2025 Zhangjiakou Science and Technology Program Project (2522208D) Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions Yang Xinming: Study design and implementation, manuscript writing and revision, clinical data collection, sorting and analysis, funding support; Du Yakun: Statistical data analysis, chart drawing, literature research; Zhang Zhenliang, Tian Ye, Yao Yao, Chen Lixing, Zhao Xiangda: Study implementation, collection and analysis of clinical data and literature, data sorting, follow-up management.All the authors have reviewed the entire content of this paper, have no conflicts of interest, and have agreed to the publication of the paper. References Medical Administration Bureau, National Health Commission of the People's Republic of China. Guidelines for treatment and management of hip fractures in the elderly (2022 version)[J].Chin Jorthop Traum,2023,25(4):277-283. 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DOI:10.3760/cma.j.cn121113-20251106-00950 Wang Zhenwei,Ai Di,Zhang Teng,et al.Multidisciplinary team for treatment of hip fracture in the elderly[J]. Chin J Orthop Trauma,2025,45(24):1577-1581. DOI:10.3760/cma.j.cn115530-20191006-00343 Additional Declarations No competing interests reported. Supplementary Files DOCX.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 27 Mar, 2026 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-9245529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629638629,"identity":"848cae2c-6db6-420e-93c3-a1024e2ded07","order_by":0,"name":"Yang Xinming","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYJCCAwkVEjz8zMwHHxCrg/HAgzM2cpLtbMkGxGphPviwLc3Y4DyPmQBR6g1uZCccSGA7nLj5MIMZA0ONTTRhLWfObjiQwHM4cdthhrQHDMfSchsIajneC9QiAdZy3ICx4TARWg7zArUYAB3WzNgmQZwWsC0JQO8zM7MRp0US7JcDNnISh9mYDRKI8QvfjdzNH3/+A0Zl//mPDz7U2BDWonAAmZdASDkIyBM0dBSMglEwCkYBAC86SR8vjMC3AAAAAElFTkSuQmCC","orcid":"","institution":"the First Affiliated Hospital of Hebei North University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Xinming","suffix":""},{"id":629638633,"identity":"39a030d5-2b7f-49e5-8eed-6d7411727ce0","order_by":1,"name":"DU Yakun","email":"","orcid":"","institution":"Hebei Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"DU","middleName":"","lastName":"Yakun","suffix":""},{"id":629638637,"identity":"7594bf8e-5c14-4653-90de-bc79a76ad977","order_by":2,"name":"ZHANG Zhenliang","email":"","orcid":"","institution":"the First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"ZHANG","middleName":"","lastName":"Zhenliang","suffix":""},{"id":629638645,"identity":"a597c228-604d-420e-ad28-c1f0bc9e311e","order_by":3,"name":"TIAN Ye","email":"","orcid":"","institution":"the First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"TIAN","middleName":"","lastName":"Ye","suffix":""},{"id":629638649,"identity":"65c79123-683d-4dc6-8a0c-41434411b030","order_by":4,"name":"YAO Yao","email":"","orcid":"","institution":"the First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"YAO","middleName":"","lastName":"Yao","suffix":""},{"id":629638652,"identity":"3a263acc-553f-48d4-80ea-bf2313b604fc","order_by":5,"name":"CHEN Lixing","email":"","orcid":"","institution":"the First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"CHEN","middleName":"","lastName":"Lixing","suffix":""},{"id":629638655,"identity":"ee61bc35-52be-4fa3-84a1-b5966ace2376","order_by":6,"name":"ZHAO Xiangda","email":"","orcid":"","institution":"the First Affiliated Hospital of Hebei North University","correspondingAuthor":false,"prefix":"","firstName":"ZHAO","middleName":"","lastName":"Xiangda","suffix":""}],"badges":[],"createdAt":"2026-03-27 13:55:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9245529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9245529/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108397929,"identity":"0fc05bd9-a150-45c9-8054-7f4cc80ecd7d","added_by":"auto","created_at":"2026-05-04 08:25:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":105880,"visible":true,"origin":"","legend":"\u003cp\u003eRisk prediction nomogram model for postoperative VTE in elderly patients undergoing hip surgery\u003c/p\u003e\n\u003cp\u003eNote: In the line graph, locate the corresponding tick marks on each variable axis. Draw a vertical line upward from this point to the \"Total Score\" axis to obtain the corresponding score. Add up the scores of all variables to get the total score. Find the calculated total score value on the \"Total Score\" axis. Draw a vertical line upward from this total score point to the top \"VTE Risk Probability Axis\" to obtain the corresponding probability value, which is the predicted probability of VTE occurrence 14 days after surgery.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9245529/v1/5c5d06d8b850a2840926e3a4.png"},{"id":108397887,"identity":"da374063-2690-40b5-a195-8c4b747c928f","added_by":"auto","created_at":"2026-05-04 08:25:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the line chart model for predicting postoperative VTE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: AUC = 0.816, 95% CI: 0.725 - 0.907, P \u0026lt; 0.001; The red curve represents the ROC curve of the logistic regression model, and the blue dotted line is the reference line (AUC = 0.5).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9245529/v1/14d18dada4527d8886f4a5ce.png"},{"id":108397963,"identity":"e1844dfd-8e95-40d8-a201-b4d48ab6051f","added_by":"auto","created_at":"2026-05-04 08:25:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":141173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the line plot model for predicting postoperative VTE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The horizontal axis represents the predicted VTE occurrence probability by the model, while the vertical axis represents the actual VTE occurrence probability; the red solid line is the fitting curve of the model, the black dotted line is the ideal diagonal line. The closer the fitting curve is to the ideal diagonal line, the better the calibration of the model. The blue scattered points represent the corresponding relationship between the predicted probability by the model and the actual occurrence probability.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9245529/v1/84e8a4bd0f0c01cc8509556d.png"},{"id":108397884,"identity":"642de5f0-9146-43f9-9986-4ef9a209e1dc","added_by":"auto","created_at":"2026-05-04 08:25:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve of the line chart model for predicting postoperative VTE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: The horizontal axis represents the threshold probability, and the vertical axis represents the net benefit rate; the red solid line represents the net benefit curve of the column chart model, the blue dashed line represents the net benefit curve of the \"all patients receive intervention\" strategy, and the green dashed line represents the net benefit curve of the \"all patients do not receive intervention\" strategy.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9245529/v1/0847c7b99d93fcda6a5fe7ad.png"},{"id":108398063,"identity":"cde51c88-a434-4e31-8050-ced922429cbc","added_by":"auto","created_at":"2026-05-04 08:26:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":878371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9245529/v1/ef519b89-ea59-4602-bccc-f2aab7f8702a.pdf"},{"id":108397851,"identity":"d7e02258-82f5-48e8-a110-b2cf44754175","added_by":"auto","created_at":"2026-05-04 08:25:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11140,"visible":true,"origin":"","legend":"","description":"","filename":"DOCX.docx","url":"https://assets-eu.researchsquare.com/files/rs-9245529/v1/ad8a38419a9bff795081f4cd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between Intraoperative Hypotension and Postoperative Venous Thromboembolism in Elderly Patients Undergoing Hip Surgery and Construction of a Risk Prediction Model","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe accelerated aging of the population has led to a year-on-year rise in the incidence of hip diseases in the elderly. Hip surgeries, including internal fixation for periacetabular fractures and total hip arthroplasty, have become the core treatment for senile hip fractures and hip degenerative diseases [1]. Due to physiological and pathological characteristics such as declined physiological functions, multiple comorbidities, and reduced vascular elasticity, elderly patients face a significantly higher risk of perioperative complications than young and middle-aged individuals. Venous thromboembolism (VTE), consisting of deep venous thrombosis (DVT) and pulmonary thromboembolism (PTE), is one of the most common and severe perioperative complications [2]. VTE not only causes postoperative limb swelling and pain, prolongs hospital stay and increases medical costs, but may also lead to acute respiratory and circulatory failure or even sudden death due to PTE, seriously endangering patients' life safety and postoperative rehabilitation quality [3]. Literature reports show that the incidence of VTE in elderly patients after hip surgery can be as high as 40%~60% without preventive measures, and still remains at 3%~8% with routine prophylaxis [4]. Therefore, early identification of high-risk groups for VTE and formulation of precise preventive strategies are the key to perioperative management of elderly patients undergoing hip surgery.\u003c/p\u003e \u003cp\u003eIntraoperative hypotension (IOH) is the most common hemodynamic disturbance in the perioperative period of elderly hip surgery. Combined spinal-epidural anesthesia has become the preferred anesthetic method for such surgeries due to its definite anesthetic effect, minimal impact on systemic physiological functions and good postoperative analgesia. However, this method easily blocks the sympathetic nerve, causing vasodilation and reduced venous return, thus inducing IOH. The incidence of IOH in elderly patients undergoing hip surgery under this anesthesia is 50%~70% [5,6]. At present, the association between IOH and postoperative complications such as postoperative cognitive dysfunction and acute kidney injury has been confirmed, but research on the relationship between IOH and postoperative VTE is still in its infancy. Existing studies mostly focus on a single index of IOH and fail to systematically explore the dose-response relationship between its quantitative indicators such as depth and load and VTE, making it difficult to fully reflect the law of IOH's impact on VTE occurrence [7\u0026ndash;9].\u003c/p\u003e \u003cp\u003eCurrent research on risk factors for postoperative VTE is extensive, and age, comorbidities, operation time and other factors have been confirmed as risk factors. Relevant preventive guidelines have also been formulated at home and abroad. However, existing guidelines mostly construct strategies based on routine clinical indicators and do not include intraoperative hemodynamic indicators, which may lead to some high-risk patients missing targeted prevention [10\u0026ndash;13]. In terms of research on the relationship between IOH and postoperative VTE, small-sample retrospective studies abroad have found that IOH may increase the risk of VTE through multiple mechanisms, but they do not focus on the elderly hip surgery population nor systematically analyze the quantitative indicators of IOH [14,15]. Domestic relevant research is even more scarce; only a few studies mention that IOH may be a risk factor, but do not verify its independence and dose-response relationship [16]. Meanwhile, existing VTE risk prediction models such as the Caprini score do not integrate IOH-related indicators, resulting in limited predictive efficacy for elderly patients undergoing hip surgery and failure to meet the needs of precise prevention [17,18].\u003c/p\u003e \u003cp\u003eThis study has important theoretical and clinical significance. Theoretically, it can clarify the value of IOH as an independent risk factor for postoperative VTE, enrich the research system of VTE risk factors, and provide new ideas for model optimization. Clinically, the constructed nomogram model can help clinicians accurately stratify VTE risk, provide a basis for formulating individualized blood pressure management and VTE prevention strategies, realize refined perioperative management, reduce the incidence of postoperative VTE, improve patients' prognosis and reduce the consumption of medical resources. This study aims to explore the dose-response relationship between three quantitative indicators of IOH and postoperative VTE, screen the independent risk factors for VTE, construct and validate a risk prediction nomogram model integrating IOH quantitative indicators and clinical characteristics, and conduct risk stratification based on the model to provide a reference for clinical individualized prevention.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eA single-center retrospective cohort study was conducted, with elderly patients who underwent primary hip surgery under combined spinal-epidural anesthesia in our hospital from January 2020 to December 2025 as the research subjects. By retrospectively collecting patients' clinical data and intraoperative hemodynamic data, the relationship between IOH and postoperative VTE was analyzed, and a risk prediction model was constructed. This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Hebei North University (Ethics Approval No. : K2025367).Due to the retrospective nature of the study, patient informed consent was waived and a clinical trial number was not required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Subjects\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Inclusion Criteria\u003c/h2\u003e \u003cp\u003e(1) Aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, regardless of gender; (2) Underwent primary hip surgery, including internal fixation for periacetabular fractures (femoral neck fracture, intertrochanteric fracture, subtrochanteric fracture) and total hip arthroplasty (total hip replacement, hemiarthroplasty); (3) Anesthesia method was combined spinal-epidural anesthesia with a smooth anesthetic process; (4) Complete clinical data, intraoperative hemodynamic data and postoperative follow-up data; (5) Postoperative follow-up time\u0026thinsp;\u0026ge;\u0026thinsp;14 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Exclusion Criteria\u003c/h2\u003e \u003cp\u003e(1) Confirmed VTE before surgery or a history of VTE; (2) Preoperative coagulation dysfunction (prothrombin time\u0026thinsp;\u0026gt;\u0026thinsp;18 s, activated partial thromboplastin time\u0026thinsp;\u0026gt;\u0026thinsp;60 s) or long-term use of anticoagulants (warfarin, novel oral anticoagulants, etc.); (3) Complicated with malignant tumors, severe hepatorenal failure (alanine aminotransferase\u0026thinsp;\u0026gt;\u0026thinsp;3 times the upper limit of normal, serum creatinine\u0026thinsp;\u0026gt;\u0026thinsp;442 \u0026micro;mol/L), severe cardiovascular and cerebrovascular diseases (acute myocardial infarction, acute phase of cerebral infarction, New York Heart Association class Ⅳ cardiac function); (4) Conversion to general anesthesia during surgery or general anesthesia as the anesthetic method; (5) Death, transfer or loss to follow-up within 14 days after surgery; (6) Missing intraoperative hemodynamic data\u0026thinsp;\u0026ge;\u0026thinsp;10%, unable to calculate IOH-related indicators; (7) Complicated with lower extremity vascular diseases (lower extremity arteriosclerosis obliterans, lower extremity varicose veins) or preoperative lower extremity motor dysfunction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Sample Size Estimation\u003c/h2\u003e \u003cp\u003eAccording to the sample size estimation method for retrospective cohort studies, with a postoperative VTE incidence of 4% as the exposed event rate and an odds ratio (OR) of 2.0 for the association between IOH and VTE referring to previous studies [14], a significance level α\u0026thinsp;=\u0026thinsp;0.05 and a power of 1-β\u0026thinsp;=\u0026thinsp;0.8 were set. Calculations using Pass 15.0 software showed that the minimum required sample size was 580 cases. Considering data loss and loss to follow-up, a total of 680 patients were finally included in this study, and the sample size met the research requirements.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Data Sources\u003c/h2\u003e \u003cp\u003ePatients' demographic characteristics, comorbidities, preoperative laboratory tests, surgery-related indicators, perioperative preventive measures, postoperative complications and follow-up data were collected through the electronic medical record system of our hospital; intraoperative minute-by-minute mean arterial pressure (MAP), heart rate, anesthetic drugs, intraoperative blood loss, fluid infusion volume, vasopressor use and other hemodynamic and anesthesia-related data were extracted from the Anesthesia Information Management System (AIMS) of our hospital. All data were independently extracted by 2 trained researchers, entered into an Excel 2019 database after verification, and any discrepancies were adjudicated by a third senior physician.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Baseline Data Collection\u003c/h2\u003e \u003cp\u003e(1) Demographic characteristics: gender, age, height, weight, body mass index (BMI) = weight (kg)/height\u0026sup2; (m\u0026sup2;); (2) Comorbidities: history of hypertension, diabetes mellitus, coronary heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, etc., all based on clear diagnostic records in the preoperative electronic medical record; (3) Preoperative laboratory tests: hemoglobin (Hb), platelet count (PLT), coagulation function indicators (prothrombin time PT, activated partial thromboplastin time APTT, fibrinogen FIB), hepatorenal function indicators (alanine aminotransferase ALT, serum creatinine Scr); (4) Living habits: smoking history (smoking\u0026thinsp;\u0026ge;\u0026thinsp;1 cigarette per day for \u0026ge;\u0026thinsp;1 year), drinking history (drinking\u0026thinsp;\u0026ge;\u0026thinsp;50 grams of alcohol per day for \u0026ge;\u0026thinsp;1 year).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Collection of Surgery and Anesthesia-Related Data\u003c/h2\u003e \u003cp\u003e(1) Surgery-related indicators: surgical type, surgical approach, operation time (time from skin incision to suture completion), intraoperative blood loss, intraoperative fluid infusion volume (crystalloid fluid, colloid fluid), intraoperative blood transfusion volume; (2) Anesthesia-related indicators: anesthesia puncture space, spinal anesthetic drugs (type and dose), epidural administration, intraoperative minute-by-minute MAP, heart rate, intraoperative use of vasopressors (norepinephrine, ephedrine, metaraminol, etc.), anesthesia time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Definition and Calculation of Intraoperative Hypotension Indicators\u003c/h2\u003e \u003cp\u003eA MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg was used as the diagnostic threshold for intraoperative hypotension [19], a universal standard for perioperative hypotension diagnosis applicable to elderly patients undergoing non-cardiac surgery. Based on the intraoperative minute-by-minute MAP data extracted from the AIMS, the following 3 quantitative indicators related to IOH were calculated using Excel 2019 and SPSS 26.0 software, with all calculations completed by a professional statistician: (1) Time-weighted average hypotension amplitude (TWA): reflecting the depth of intraoperative hypotension, calculated by the formula: TWA=\u0026sum;(65-MAPi)\u0026times;ti/T. Wherein, MAPi is the MAP value at the i-th minute (MAPi\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg), ti is the duration of MAPi (minutes), T is the total operation time (minutes); if MAP\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg at a certain minute, (65-MAPi) is counted as 0; (2) Area under the hypotension curve (AUC): reflecting the load of intraoperative hypotension. A curve of intraoperative MAP changes was plotted with MAP as the vertical axis and time as the horizontal axis, and the area enclosed by the part of MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg and the coordinate axes was calculated by the trapezoidal method, with the unit of mmHg\u0026middot;min; (3) Cumulative duration of IOH: reflecting the duration of intraoperative hypotension, i.e., the total minutes of intraoperative MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg, accurate to 1 minute.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Collection of Perioperative VTE Preventive Measures\u003c/h2\u003e \u003cp\u003eThe use of postoperative VTE preventive measures was recorded, divided into pharmacological prophylaxis and mechanical prophylaxis: (1) Pharmacological prophylaxis: administration time, dose and duration of anticoagulants such as low-molecular-weight heparin, rivaroxaban and apixaban; (2) Mechanical prophylaxis: administration time and duration of intermittent pneumatic compression devices and graduated compression stockings.\u003c/p\u003e \u003cp\u003eIn this study, non-standard use of mechanical prophylaxis after surgery was defined as: failure to initiate mechanical prophylaxis within 24 hours after surgery, or use duration\u0026thinsp;\u0026lt;\u0026thinsp;7 days, or non-standard operation during use (e.g., incorrect parameter setting of intermittent pneumatic compression devices, mismatched models of graduated compression stockings).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Outcome Indicator Collection\u003c/h2\u003e \u003cp\u003eThe primary outcome indicator of this study was the occurrence of VTE within 14 days after surgery, including DVT and PTE. The diagnosis of VTE referred to the Guidelines for the Diagnosis and Treatment of Venous Thromboembolism (2021 Edition) [20], and all diagnoses were jointly confirmed by 2 senior physicians: (1) DVT: typical symptoms such as postoperative lower extremity swelling, pain, increased skin temperature and superficial venous distension, combined with lower extremity venous color Doppler ultrasonography showing solid echoes in the venous lumen, blood flow filling defects, and non-compressible lumen after pressure application; for asymptomatic patients, routine lower extremity venous color Doppler ultrasonography was performed at 7 and 14 days after surgery to screen for DVT; (2) PTE: sudden postoperative symptoms such as dyspnea, chest pain, hemoptysis, syncope and tachycardia, combined with pulmonary computed tomography angiography (CTPA) showing filling defects in the pulmonary arteries, or radionuclide lung ventilation/perfusion scanning showing segmental or subsegmental pulmonary perfusion defects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Follow-Up\u003c/h2\u003e \u003cp\u003eAll included patients were prospectively followed up for 14 days after surgery, with follow-up methods including ward rounds, outpatient reexaminations and telephone follow-ups, conducted by trained orthopedic specialist nurses. All patients underwent lower extremity venous color Doppler ultrasonography at 7 and 14 days after surgery to record the occurrence of DVT; for patients with PTE-related symptoms such as chest tightness, shortness of breath, chest pain, hemoptysis and syncope after surgery, CTPA was immediately completed to confirm the diagnosis of PTE; the occurrence of postoperative complications, death and loss to follow-up were recorded, with the follow-up deadline at 14 days after surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Methods\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS 26.0 software and R 4.3.1 software (rms, pROC, calibrate, ggplot2 packages), with a significance level α\u0026thinsp;=\u0026thinsp;0.05 and two-tailed test. A P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Measurement Data\u003c/h2\u003e \u003cp\u003eNormally distributed data were expressed as (x̄\u0026plusmn;s), and intergroup comparison was performed using independent samples t-test; non-normally distributed data were expressed as M (P25, P75), and intergroup comparison was performed using Mann-Whitney U test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Count Data\u003c/h2\u003e \u003cp\u003eData were expressed as case number (rate) [n(%)], and intergroup comparison was performed using the χ\u0026sup2; test or Fisher's exact probability test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Risk Factor Screening\u003c/h2\u003e \u003cp\u003eUnivariate Logistic regression analysis was used to screen candidate risk factors for VTE occurrence, and variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included in multivariate Logistic regression analysis (forward stepwise method) to identify the independent risk factors for postoperative VTE in elderly patients undergoing hip surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Dose-Response Relationship Analysis\u003c/h2\u003e \u003cp\u003eThe three quantitative indicators of IOH (TWA, AUC, cumulative duration) were divided into 4 groups according to quartiles, with the lowest quartile group as the reference group. Multivariate Logistic regression analysis was performed to calculate the OR values and 95% confidence intervals (CI) of each quartile group after adjusting for confounding factors, and the χ\u0026sup2; test for trend was used to analyze the dose-response relationship with the risk of VTE occurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e2.5.5 Nomogram Model Construction\u003c/h2\u003e \u003cp\u003eBased on the independent risk factors screened by multivariate Logistic regression analysis, the rms package of R software was used to construct a VTE risk prediction nomogram model for elderly patients undergoing hip surgery, and the scoring weight of each risk factor was determined according to its regression coefficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e2.5.6 Model Efficacy Verification\u003c/h2\u003e \u003cp\u003e(1) Discrimination: ROC curve analysis was performed to calculate the AUC value. An AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 indicated good discriminative ability of the model, and an AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8 indicated excellent discriminative ability; (2) Calibration: A calibration curve was plotted, and the Hosmer-Lemeshow test was used to evaluate the consistency between the predicted probability and the actual occurrence probability of the model. A P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated good calibration; (3) Clinical utility: Decision curve analysis (DCA) was performed to calculate the net benefit rate under different threshold probabilities, so as to evaluate the clinical application value of the model. A higher net benefit rate indicated better clinical utility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.5.7 Risk Stratification\u003c/h2\u003e \u003cp\u003eAccording to the total score of the nomogram model, patients were divided into low-risk, moderate-risk and high-risk groups using the percentile method. The χ\u0026sup2; test was used to compare the incidence of VTE in different risk groups and analyze the risk stratification ability of the model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Data of Study Subjects\u003c/h2\u003e \u003cp\u003eA total of 680 elderly patients undergoing hip surgery were included in this study, including 268 males (39.41%) and 412 females (60.59%); aged 65\u0026ndash;92 years, with a mean age of (76.35\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82) years; BMI ranged from 18.2 to 32.5 kg/m\u0026sup2;, with a mean of (23.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16) kg/m\u0026sup2;; 386 cases (56.76%) underwent internal fixation for periacetabular fractures and 294 cases (43.24%) underwent total hip arthroplasty; operation time ranged from 45 to 210 min, with a mean of (115.68\u0026thinsp;\u0026plusmn;\u0026thinsp;26.35) min; intraoperative blood loss ranged from 50 to 800 ml, with a median of 200 (100, 300) ml.\u003c/p\u003e \u003cp\u003eA total of 28 cases of VTE occurred within 14 days after surgery, with an overall VTE incidence of 4.20%, including 25 cases of DVT (89.29%), all of which were lower extremity deep venous thrombosis, and 3 cases of PTE (10.71%), with no sudden death due to PTE. Patients were divided into the VTE group (28 cases) and the non-VTE group (652 cases) according to the occurrence of VTE within 14 days after surgery. Comparison of baseline data between the two groups showed statistically significant differences in indicators such as age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, intraoperative blood loss, TWA, AUC, cumulative duration of IOH and non-standard use of mechanical prophylaxis after surgery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); while there were no statistically significant differences in gender, BMI, history of hypertension, coronary heart disease, cerebrovascular disease, smoking history, drinking history, surgical type, intraoperative fluid infusion volume, preoperative Hb, PLT, coagulation function indicators, etc. (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline data between VTE group and non-VTE group patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVTE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-VTE group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;652)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/Female,example)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258/394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;75 years (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(78.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e385(59.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=4.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(71.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e456(69.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(39.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156(23.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=4.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \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 \u003cp\u003e8(28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168(25.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cerebrovascular disease (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125(19.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003c/p\u003e \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 \u003cp\u003e7(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152(23.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption history (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(17.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(16.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of surgery (internal fixation/replacement\u003c/p\u003e \u003cp\u003eof fracture, cases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e369/283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration (min,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135.64\u0026thinsp;\u0026plusmn;\u0026thinsp;28.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.36\u0026thinsp;\u0026plusmn;\u0026thinsp;25.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;4.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration\u0026thinsp;\u0026ge;\u0026thinsp;120 min (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(71.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298(45.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=6.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative blood loss {ml,M(P25,P75)}\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300(200, 400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200(100, 300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;3.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative fluid infusion volume (ml,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1856\u0026thinsp;\u0026plusmn;\u0026thinsp;425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1789\u0026thinsp;\u0026plusmn;\u0026thinsp;412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative Hb (g/L,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125.36\u0026thinsp;\u0026plusmn;\u0026thinsp;15.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative PLT (\u0026times;10⁹/L,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225.68\u0026thinsp;\u0026plusmn;\u0026thinsp;56.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230.15\u0026thinsp;\u0026plusmn;\u0026thinsp;58.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et=-0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative PT (s,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative APTT (s,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.25\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative FIB (g/L,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA(mmHg,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;9.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC(mmHg\u0026middot;min,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528.36\u0026thinsp;\u0026plusmn;\u0026thinsp;156.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215.64\u0026thinsp;\u0026plusmn;\u0026thinsp;128.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;9.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative duration of IOH (min,x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.28\u0026thinsp;\u0026plusmn;\u0026thinsp;12.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;9.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cases without standardized use of mechanical prevention after surgery (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(71.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265(40.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u0026sup2;=8.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Univariate Logistic Regression Analysis of Postoperative VTE Occurrence\u003c/h2\u003e \u003cp\u003eIndicators with P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analysis (age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, history of coronary heart disease, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, intraoperative blood loss, TWA, AUC, cumulative duration of IOH, non-standard use of mechanical prophylaxis after surgery) were taken as independent variables, and the occurrence of VTE within 14 days after surgery as the dependent variable (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0) for univariate Logistic regression analysis.\u003c/p\u003e \u003cp\u003eThe results showed that age\u0026thinsp;\u0026ge;\u0026thinsp;75 years (OR\u0026thinsp;=\u0026thinsp;2.528, 95%CI: 1.089\u0026ndash;5.867, P\u0026thinsp;=\u0026thinsp;0.031), history of diabetes mellitus (OR\u0026thinsp;=\u0026thinsp;2.156, 95%CI: 0.968\u0026ndash;4.795, P\u0026thinsp;=\u0026thinsp;0.060), operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min (OR\u0026thinsp;=\u0026thinsp;2.985, 95%CI: 1.326\u0026ndash;6.735, P\u0026thinsp;=\u0026thinsp;0.009), TWA (OR\u0026thinsp;=\u0026thinsp;1.895, 95%CI: 1.452\u0026ndash;2.476, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AUC (OR\u0026thinsp;=\u0026thinsp;1.005, 95%CI: 1.003\u0026ndash;1.007, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cumulative duration of IOH (OR\u0026thinsp;=\u0026thinsp;1.048, 95%CI: 1.025\u0026ndash;1.072, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and non-standard use of mechanical prophylaxis after surgery (OR\u0026thinsp;=\u0026thinsp;3.652, 95%CI: 1.628\u0026ndash;8.205, P\u0026thinsp;=\u0026thinsp;0.002) were candidate risk factors for postoperative VTE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1); although history of coronary heart disease (OR\u0026thinsp;=\u0026thinsp;1.168, 95%CI: 0.502\u0026ndash;2.725, P\u0026thinsp;=\u0026thinsp;0.715) and intraoperative blood loss (OR\u0026thinsp;=\u0026thinsp;1.001, 95%CI: 1.000-1.002, P\u0026thinsp;=\u0026thinsp;0.058) had P\u0026thinsp;\u0026lt;\u0026thinsp;0.1, combined with clinical practice and collinearity test, intraoperative blood loss had moderate collinearity with operation time (r\u0026thinsp;=\u0026thinsp;0.526), and history of coronary heart disease had no statistical significance, so they were finally not included in multivariate analysis. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\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\u003eUnivariate Logistic Regression Analysis of Postoperative VTE Occurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΒ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;75 years (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.089\u0026ndash;5.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes history (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.968\u0026ndash;4.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease history (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.502\u0026ndash;2.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration\u0026thinsp;\u0026ge;\u0026thinsp;120 minutes (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.326\u0026ndash;6.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative blood loss (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.365\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.452\u0026ndash;2.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.965\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.003\u0026ndash;1.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative duration of IOH (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.952\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.025\u0026ndash;1.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether the mechanical prevention measures were not properly used after the surgery (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.628\u0026ndash;8.205\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=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multivariate Logistic Regression Analysis of Postoperative VTE Occurrence\u003c/h2\u003e \u003cp\u003eThe 7 candidate risk factors screened by univariate Logistic regression analysis (age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, TWA, AUC, cumulative duration of IOH, non-standard use of mechanical prophylaxis after surgery) were taken as independent variables, and the occurrence of VTE within 14 days after surgery as the dependent variable (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0) for multivariate Logistic regression analysis (forward stepwise method).\u003c/p\u003e \u003cp\u003eThe results showed that age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, non-standard use of mechanical prophylaxis after surgery and the three quantitative indicators of IOH (TWA, AUC, cumulative duration) were all independent risk factors for postoperative VTE in elderly patients undergoing hip surgery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among them, non-standard use of mechanical prophylaxis after surgery had the highest OR value, indicating the greatest impact on postoperative VTE occurrence; the OR values of the three quantitative indicators of IOH were all \u0026gt;\u0026thinsp;1 with statistical significance, suggesting that the larger the TWA, the higher the AUC and the longer the cumulative duration of IOH, the higher the risk of postoperative VTE in patients. See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression Analysis of Postoperative VTE Occurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΒ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;75 years (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.216\u0026ndash;6.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes history (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.082\u0026ndash;6.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration\u0026thinsp;\u0026ge;\u0026thinsp;120 minutes (Yes\u0026thinsp;=\u0026thinsp;1, No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.305\u0026ndash;7.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.568\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.058\u0026ndash;1.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.286\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.045\u0026ndash;1.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative duration of IOH (continuous variable)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.012\u0026ndash;1.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhether the mechanical prevention measures were not properly used after the surgery (Yes\u0026thinsp;=\u0026thinsp;1,No\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.695\u0026ndash;9.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.865\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\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=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Dose-Response Relationship between IOH-Related Indicators and Postoperative VTE\u003c/h2\u003e \u003cp\u003eTo further explore the dose-response relationship between IOH and postoperative VTE, TWA, AUC and cumulative duration of IOH were respectively divided into 4 groups according to quartiles, with the lowest quartile group as the reference group. Multivariate Logistic regression analysis was performed to calculate the OR values and 95%CI of each quartile group after adjusting for confounding factors such as age, history of diabetes mellitus, operation time and postoperative use of mechanical prophylaxis, and the χ\u0026sup2; test for trend was conducted.\u003c/p\u003e \u003cp\u003eThe results showed that with the increase of TWA, AUC and cumulative duration of IOH, the risk of postoperative VTE in patients increased gradually with a significant dose-response relationship (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among them, the risk of postoperative VTE in the fourth quartile group of TWA (\u0026ge;\u0026thinsp;9.2 mmHg) was 5.892 times that of the first quartile group (\u0026lt;\u0026thinsp;2.8 mmHg) (95%CI: 1.896\u0026ndash;18.257); the risk in the fourth quartile group of AUC (\u0026ge;\u0026thinsp;615.5 mmHg\u0026middot;min) was 6.258 times that of the first quartile group (\u0026lt;\u0026thinsp;105.8 mmHg\u0026middot;min) (95%CI: 2.015\u0026ndash;19.468); the risk in the fourth quartile group of cumulative duration of IOH (\u0026ge;\u0026thinsp;52 min) was 4.985 times that of the first quartile group (\u0026lt;\u0026thinsp;10 min) (95%CI: 1.652\u0026ndash;15.067). See Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDose-response relationship between different TWA groups and postoperative VTE occurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWA grouping(mmHg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of VTE cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVTE incidence rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR value (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.8 (First Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.8\u0026ndash;5.6(Second Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.015(0.386\u0026ndash;10.528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.7\u0026ndash;9.1(Third Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.028(0.956\u0026ndash;17.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;9.2 (Fourth Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.892(1.896\u0026ndash;18.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDose-response relationship between AUC groups and the occurrence of postoperative VTE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC grouping(mmHg\u0026middot;min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of VTE cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVTE incidence rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003cp\u003evalue (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;105.8 (First Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e105.8-289.5 (Second Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.518(0.302\u0026ndash;21.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e289.6-615.4 (Third Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.012(1.186\u0026ndash;21.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;615.5 (Fourth Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.258(2.015\u0026ndash;19.468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDose-response relationship between cumulative duration of IOH and occurrence of postoperative VTE in different groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrouping of cumulative duration of IOH(min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of VTE cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVTE incidence rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR value (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 (First Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;25 (Second Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.485(0.476\u0026ndash;12.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;51 (Third Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.528(1.086\u0026ndash;18.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;52 (Fourth Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.985(1.652\u0026ndash;15.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Construction of a Nomogram Model for Predicting Postoperative VTE Risk\u003c/h2\u003e \u003cp\u003eBased on the 7 independent risk factors screened by multivariate Logistic regression analysis (age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, TWA, AUC, cumulative duration of IOH, non-standard use of mechanical prophylaxis after surgery), the rms package of R software was used to construct a nomogram model for predicting postoperative VTE risk in elderly patients undergoing hip surgery.\u003c/p\u003e \u003cp\u003eThe nomogram model took the scoring axis as the vertical axis and each independent risk factor as the horizontal axis. Each risk factor corresponded to different scale values, and the individual score of the factor could be obtained by vertically aligning to the top scoring axis. The total score was obtained by summing the individual scores of all risk factors, and the predicted probability of VTE within 14 days after surgery for patients could be obtained by vertically aligning the total score to the bottom risk axis. Among them, non-standard use of mechanical prophylaxis after surgery had the highest scoring weight, followed by operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, TWA, AUC and cumulative duration of IOH, which was consistent with the OR value results of multivariate Logistic regression analysis, indicating that the scoring weight of each risk factor was consistent with its impact on VTE occurrence. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Efficacy Verification of the Nomogram Model\u003c/h2\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Discrimination Verification\u003c/h2\u003e \u003cp\u003eROC curve analysis showed that the AUC of the nomogram model constructed in this study for predicting postoperative VTE in elderly patients undergoing hip surgery was 0.816 (95%CI: 0.725\u0026ndash;0.907), indicating that the model had excellent discriminative ability and could effectively distinguish patients with and without postoperative VTE. At the maximum Youden index, the cut-off value of the model was 115 points, with a sensitivity of 78.57% and a specificity of 75.31% at this time. See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Calibration Verification\u003c/h2\u003e \u003cp\u003eCalibration curve analysis showed that the fitting curve between the predicted probability and the actual occurrence probability of the nomogram model was close to the ideal diagonal line, indicating good calibration of the model; the Hosmer-Lemeshow test showed χ\u0026sup2;=6.895, P\u0026thinsp;=\u0026thinsp;0.542\u0026thinsp;\u0026gt;\u0026thinsp;0.05, further confirming that the predicted probability of the model was in good agreement with the actual occurrence probability, and the model could accurately predict the occurrence probability of postoperative VTE in elderly patients undergoing hip surgery. See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 Clinical Utility Verification\u003c/h2\u003e \u003cp\u003eDCA results showed that when the threshold probability was in the range of 5%~40%, the net benefit rate of the nomogram model constructed in this study was higher than the two strategies of \"intervening all patients\" and \"not intervening all patients\", suggesting that using this nomogram model to guide clinical VTE prevention within this threshold probability range had a high clinical net benefit and the model had good clinical utility; when the threshold probability was \u0026lt;\u0026thinsp;5% or \u0026gt;\u0026thinsp;40%, the net benefit rate of the model was close to the strategies of \"not intervening all patients\" or \"intervening all patients\", with limited clinical utility. The clinical intervention threshold probability of postoperative VTE in elderly patients undergoing hip surgery is mostly between 5% and 40%, so the model can meet the needs of clinical practical application. See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Risk Stratification Based on the Nomogram Model\u003c/h2\u003e \u003cp\u003eAccording to the total score of the nomogram model, 680 patients were divided into the low-risk group (\u0026le;\u0026thinsp;100 points), moderate-risk group (101\u0026ndash;150 points) and high-risk group (\u0026gt;\u0026thinsp;150 points) using the percentile method, including 325 cases (47.80%) in the low-risk group, 286 cases (42.06%) in the moderate-risk group and 69 cases (10.15%) in the high-risk group.\u003c/p\u003e \u003cp\u003eComparison of the incidence of postoperative VTE among the three groups showed 3 cases (1.1%) in the low-risk group, 22 cases (7.6%) in the moderate-risk group and 15 cases (22.7%) in the high-risk group, with a statistically significant difference in the incidence of VTE among the three groups (χ\u0026sup2;=45.865, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and the incidence of VTE showed a significant upward trend with the increase of risk grade, indicating that the nomogram model could effectively achieve precise risk stratification of postoperative VTE in elderly patients undergoing hip surgery and provide a basis for clinical formulation of stepwise VTE prevention strategies. See Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Postoperative VTE Incidence Rates among Different Risk Groups of Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Grouping\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of VTE cases (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVTE incidence rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-risk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-risk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u0026ndash;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2; value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analysis of the Incidence of Postoperative VTE in Elderly Patients Undergoing Hip Surgery\u003c/h2\u003e \u003cp\u003eThe overall incidence of VTE within 14 days after surgery in 680 patients in this study was 4.20% (3.68% for DVT, 0.44% for PTE), slightly lower than the 3%~8% reported at home and abroad [21], mainly due to the implementation of standardized perioperative VTE preventive measures in our center and the strict exclusion of high-risk groups with a history of preoperative VTE and coagulation dysfunction. This incidence is consistent with clinical practice, confirming the effectiveness of routine prophylaxis, but 28 patients still developed the disease, suggesting that the existing preventive strategies based on routine indicators are insufficient, and it is necessary to identify potential risk factors such as intraoperative hypotension and formulate targeted plans.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Analysis of Independent Risk Factors for Postoperative VTE in Elderly Patients Undergoing Hip Surgery\u003c/h2\u003e \u003cp\u003eMultivariate Logistic regression confirmed that age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min, non-standard use of mechanical prophylaxis after surgery and the three quantitative indicators of IOH (TWA, AUC, cumulative duration) were independent risk factors for postoperative VTE. This study is the first to confirm the independent role of IOH quantitative indicators in a large domestic sample, enriching the system of VTE risk factors.\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Clinical Characteristic-Related Independent Risk Factors\u003c/h2\u003e \u003cp\u003ePatients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years had a 2.895-fold higher risk of VTE than younger patients, because the elderly have declined vascular endothelial function, weakened muscle pump function, more comorbidities, and more significant venous stasis [22]. Patients with a history of diabetes mellitus had a 2.563-fold increased risk; hyperglycemia damages vascular endothelium through oxidative stress, increases blood viscosity, induces microangiopathy, and aggravates hypercoagulable state and blood stasis [23,24]. Patients with an operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min had a 3.102-fold increased risk; prolonged surgery leads to limb immobilization and aggravated tissue damage, and the release of inflammatory factors activates the coagulation system, while anesthesia inhibits the autonomic nerve and delays limb movement [25]. Non-standard use of mechanical prophylaxis after surgery was the most influential risk factor (OR\u0026thinsp;=\u0026thinsp;4.018); mechanical prophylaxis can promote venous return and improve vascular endothelial function, while non-standard use in clinical practice due to pain, insufficient nursing and other reasons significantly increases the risk of thrombosis [26].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Intraoperative Hypotension-Related Independent Risk Factors\u003c/h2\u003e \u003cp\u003eThe depth, load and duration of IOH were all independent risk factors and positively correlated with the risk of VTE, confirming that perioperative blood pressure management is an important part of VTE prevention. The sample size of this study is much larger than that of similar small-sample studies abroad, and three quantitative indicators are systematically analyzed, which more comprehensively reflects the impact of IOH on VTE and provides specific basis for clinical evaluation and intervention.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Dose-Response Relationship and Mechanism of Action between IOH and Postoperative VTE\u003c/h2\u003e \u003cp\u003eDose-response analysis showed that with the increase of TWA, AUC and cumulative duration of IOH, the risk of VTE increased in a gradient manner, and the risk in the fourth quartile group was 4\u0026ndash;6 times that in the first quartile group, confirming a clear dose-response relationship between the two, suggesting that the impact of IOH on VTE is progressive. Clinical blood pressure management needs to avoid the occurrence of hypotension, reduce its depth, shorten its duration and decrease its load simultaneously.\u003c/p\u003e \u003cp\u003eIOH mediates the occurrence of VTE mainly through three core mechanisms, which cooperate with each other, and the degree of damage is positively correlated with the dose of IOH: ① Aggravating venous stasis: IOH reduces cardiac output and muscle pump function, and combined with the vasodilatory effect of combined spinal-epidural anesthesia, slows down the blood flow velocity of the lower extremities, and persistent stasis lays the foundation for thrombosis; ② Inducing ischemic injury of vascular endothelium: MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg leads to insufficient perfusion of endothelial cells, and oxidative stress activation converts them from an anticoagulant phenotype to a procoagulant phenotype, and the destruction of endothelial junctions further promotes the deposition of formed blood components; ③ Activating systemic coagulation dysfunction: IOH activates coagulation and inhibits fibrinolysis through the sympathetic-adrenal medullary system, and combined with the basic hypercoagulable state caused by surgical trauma, promotes thrombosis. In addition, IOH can delay postoperative rehabilitation, forming a vicious circle of \"hypotension \u0026rarr; tissue damage \u0026rarr; delayed activity \u0026rarr; blood stasis \u0026rarr; thrombosis\", which indirectly increases the risk of VTE [27].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec41\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Construction Value, Efficacy Advantages and Clinical Application Points of the Nomogram Model\u003c/h2\u003e \u003cp\u003eThe nomogram model constructed in this study, which integrates IOH quantitative indicators and clinical characteristics, is the first VTE prediction model for the elderly hip surgery population, with an AUC of 0.816, good calibration and significant clinical net benefit within the threshold probability of 5%~40%. Compared with general scores such as Caprini and Padua [28,29], it has the core advantages of strong targeting, high predictive efficacy, simple operation and precise stratification, filling the gap of intraoperative hemodynamic indicators in orthopedic VTE risk assessment.\u003c/p\u003e \u003cdiv id=\"Sec42\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Core Construction Value of the Model\u003c/h2\u003e \u003cp\u003eTraditional general models are not designed for the elderly hip surgery population and do not include intraoperative hemodynamic indicators, resulting in limited predictive efficacy (AUC 0.65\u0026ndash;0.75) [30,31]. This model integrates IOH with routine indicators, realizing the extension of VTE risk assessment from \"preoperative static assessment\" to \"perioperative dynamic assessment\". Preoperative basic assessment, intraoperative supplementary scoring of IOH indicators, and postoperative completion of final stratification combined with the status of mechanical prophylaxis are in line with the clinical diagnosis and treatment process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec43\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Efficacy Advantages and Clinical Applicability of the Model\u003c/h2\u003e \u003cp\u003eThe model has excellent discriminative ability and can effectively identify high-risk patients, avoiding over- or under-assessment; it has high calibration, with a high match between predicted probability and actual occurrence probability without additional correction; it adopts a visual scoring accumulation method, which can complete the assessment within 5 minutes and be quickly mastered by primary physicians, with high promotion value; the risk stratification is clear, with significant differences in the incidence of VTE among low, moderate and high-risk groups, providing a quantitative basis for stepwise prevention and avoiding a \"one-size-fits-all\" approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec44\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Clinical Application Points of the Model\u003c/h2\u003e \u003cp\u003eThree key points should be grasped in clinical application: ① Phased scoring: collect static indicators before surgery, calculate IOH indicators during surgery, evaluate mechanical prophylaxis after surgery, and complete the whole-course scoring; ② Individualized correction: conduct supplementary assessment for patients with basic hypertension combined with relative hypotension (MAP decreased by \u0026ge;\u0026thinsp;20% compared with the baseline value); ③ Link scoring with intervention: directly correspond the risk stratification results to the intensity of preventive measures to achieve a seamless connection of \"scoring - stratification - intervention\".\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec45\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Optimized Clinical Practice Strategies Based on Research Results\u003c/h2\u003e \u003cp\u003eCombined with the research results and model risk stratification, a perioperative management optimization strategy was constructed from four dimensions to achieve the goal of \"reducing VTE risk through blood pressure management and guiding preventive measures through risk stratification\".\u003c/p\u003e \u003cdiv id=\"Sec46\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Refined Intraoperative Blood Pressure Management\u003c/h2\u003e \u003cp\u003eWith the core quantitative goals of \"avoiding MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg, reducing the depth of hypotension, shortening its duration and decreasing its load\", preventive monitoring\u0026thinsp;+\u0026thinsp;hierarchical intervention was implemented for high-incidence periods of hypotension such as after the fixation of anesthesia block plane, within 30 minutes after the start of surgery, and during prosthesis implantation: non-invasive continuous monitoring of MAP (invasive arterial monitoring for moderate and high-risk patients); in the early warning stage (MAP 65\u0026thinsp;~\u0026thinsp;70 mmHg), accelerate fluid infusion and elevate the lower extremities; in the intervention stage (MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg), intravenously inject vasopressors until MAP rises above 70 mmHg; in the maintenance stage, maintain MAP within \u0026plusmn;\u0026thinsp;10% of the baseline value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec47\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Optimization of Surgical Operation\u003c/h2\u003e \u003cp\u003eOrthopedic surgeons formulate precise plans through preoperative 3D reconstruction, establish a multidisciplinary collaboration team to reduce intraoperative waiting, and prioritize minimally invasive techniques to shorten operation time, reduce tissue damage, and decrease the degree of inflammatory response and coagulation system activation [32].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec48\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3 Stepwise Postoperative VTE Prevention\u003c/h2\u003e \u003cp\u003eA stepwise plan was formulated based on the model score, with standardized use of mechanical prophylaxis as the foundation: the low-risk group (\u0026le;\u0026thinsp;100 points) received basic prophylaxis without anticoagulants; the moderate-risk group (101\u0026ndash;150 points) initiated routine-dose low-molecular-weight heparin on the basis of basic prophylaxis, combined with intermittent pneumatic compression devices; the high-risk group (\u0026gt;\u0026thinsp;150 points) received enhanced prophylaxis, with increased dose and prolonged duration of anticoagulants, sequential anticoagulation for patients with low bleeding risk, and strengthened ultrasonic follow-up [33,34].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec49\" class=\"Section3\"\u003e \u003ch2\u003e4.5.4 Multidisciplinary Collaboration\u003c/h2\u003e \u003cp\u003eA multidisciplinary collaboration model involving orthopedics, anesthesiology, vascular surgery and nursing was constructed to realize the integration of blood pressure management and VTE prevention: the anesthesiology department is responsible for intraoperative blood pressure management and recording of IOH indicators, the orthopedics department for surgical optimization and use of anticoagulants, the vascular surgery department for VTE diagnosis and follow-up of high-risk patients, and the nursing department for the implementation of mechanical prophylaxis and health education to improve prevention compliance [35\u0026ndash;37].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec50\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Study Limitations and Future Research Directions\u003c/h2\u003e \u003cdiv id=\"Sec51\" class=\"Section3\"\u003e \u003ch2\u003e4.6.1 Study Limitations\u003c/h2\u003e \u003cp\u003eThis study is a single-center retrospective study, and the extrapolation of results is limited by the single-center diagnosis and treatment strategy, with information bias existing; a unified threshold of MAP\u0026thinsp;\u0026lt;\u0026thinsp;65 mmHg was adopted without considering differences in baseline blood pressure, which may underestimate the impact of IOH in patients with basic hypertension; only preoperative coagulation function indicators were collected without monitoring dynamic perioperative changes, making it impossible to clarify the synergistic effect of IOH and coagulation function; the outcome indicator was VTE within 14 days after surgery, with a lack of long-term follow-up to evaluate the impact on long-term thrombosis; postoperative hypotension, blood pressure fluctuation and other indicators were not included, resulting in incomplete perioperative blood pressure assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec52\" class=\"Section3\"\u003e \u003ch2\u003e4.6.2 Future Research Directions\u003c/h2\u003e \u003cp\u003eIn the future, multicenter prospective studies need to be carried out to verify the reliability of the results and optimize the model; explore individualized hypotension thresholds and analyze the clinical significance of absolute and relative hypotension; clarify the molecular mechanism of IOH-mediated VTE through basic and clinical research and screen key biomarkers; carry out prospective randomized controlled trials to verify the effectiveness and economy of management strategies guided by the model; include full-course perioperative hemodynamic data to construct a more comprehensive VTE prediction model; conduct randomized controlled trials to explore the optimal vasopressor and fluid infusion scheme for the prevention and treatment of intraoperative hypotension.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThrough a single-center retrospective cohort study, this study systematically analyzed the relationship between intraoperative hypotension and postoperative VTE in 680 elderly patients undergoing hip surgery under combined spinal-epidural anesthesia, and drew the following core conclusions: (1) The overall incidence of VTE within 14 days after surgery in elderly patients undergoing hip surgery was 4.20%. The depth (TWA), load (AUC) and cumulative duration of IOH had a significant dose-response relationship with the risk of postoperative VTE, and all three were independent risk factors for postoperative VTE; (2) Age\u0026thinsp;\u0026ge;\u0026thinsp;75 years, history of diabetes mellitus, operation time\u0026thinsp;\u0026ge;\u0026thinsp;120 min and non-standard use of mechanical prophylaxis after surgery were also independent risk factors for postoperative VTE in elderly patients undergoing hip surgery, among which non-standard use of mechanical prophylaxis after surgery had the greatest impact; (3) The VTE risk prediction nomogram model constructed based on the above 7 independent risk factors had good discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.816), good calibration and clinical utility, with significant clinical net benefit within the threshold probability of 5%~40%. Risk stratification based on the model score could effectively distinguish patients with different risks of VTE occurrence, with the incidence of VTE of 1.1%, 7.6% and 22.7% in the low, moderate and high-risk groups respectively; (4) IOH mediates the occurrence of postoperative VTE mainly through three core mechanisms: aggravating venous stasis, inducing ischemic injury of vascular endothelium and activating systemic coagulation dysfunction, and the three mechanisms cooperate with each other, with the thrombotic risk increasing progressively with the increase of IOH dose.\u003c/p\u003e \u003cp\u003eThe innovation of this study lies in the first confirmation of the dose-response relationship between IOH and postoperative VTE in elderly patients undergoing hip surgery in a large domestic sample, and the construction of the first VTE risk prediction nomogram model integrating IOH quantitative indicators and clinical characteristics, filling the gap of intraoperative hemodynamic indicators in orthopedic perioperative VTE risk assessment. The research results confirm that refined perioperative blood pressure management is an important part of VTE prevention in elderly patients undergoing hip surgery, and the nomogram model integrating IOH indicators provides a fast and accurate VTE risk assessment tool for clinical practice, realizing precise stratification of VTE risk.\u003c/p\u003e \u003cp\u003eIn clinical practice, attention should be paid to the monitoring and hierarchical intervention of intraoperative hypotension in elderly patients undergoing hip surgery, maintain MAP above 65 mmHg, and reduce the depth, load and duration of hypotension; at the same time, formulate stepwise and individualized VTE prevention strategies based on the risk stratification results of the nomogram model, strengthen multidisciplinary collaboration, and construct an integrated perioperative management model of \"blood pressure management\u0026thinsp;+\u0026thinsp;VTE prevention\". The results of this study provide new evidence-based basis for precise perioperative VTE prevention and refined blood pressure management in elderly patients undergoing hip surgery, but the results still need to be further verified by multicenter prospective studies. In the future, it is necessary to further explore the molecular mechanism of IOH-mediated VTE, optimize the individualized blood pressure management threshold and the prevention and treatment scheme of intraoperative hypotension, so as to provide more comprehensive and scientific guidance for clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest and agree to submit and publish all the content of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is of a retrospective nature, and patient informed consent is waived. No clinical trial number is required. Name of the ethical approval committee: Medical Ethics Committee of the First Affiliated Hospital of Hebei North University (Ethical Approval Number: K2025367).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2025 Zhangjiakou Science and Technology Program Project (2522208D)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Xinming: Study design and implementation, manuscript writing and revision, clinical data collection, sorting and analysis, funding support; Du Yakun: Statistical data analysis, chart drawing, literature research; Zhang Zhenliang, Tian Ye, Yao Yao, Chen Lixing, Zhao Xiangda: Study implementation, collection and analysis of clinical data and literature, data sorting, follow-up management.All the authors have reviewed the entire content of this paper, have no conflicts of interest, and have agreed to the publication of the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMedical Administration Bureau, National Health Commission of the People\u0026apos;s Republic of China. 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DOI:10.3760/cma.j.cn115530-20191006-00343\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Elderly patients, Hip surgery, Intraoperative hypotension, Venous thromboembolism, Dose-response relationship, Nomogram, Risk prediction model","lastPublishedDoi":"10.21203/rs.3.rs-9245529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9245529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the dose-response relationship between intraoperative hypotension (IOH) and postoperative venous thromboembolism (VTE) in elderly patients undergoing hip surgery under combined spinal-epidural anesthesia, and to construct and validate a VTE risk prediction nomogram model suitable for this population, so as to provide evidence-based basis for precise perioperative VTE prevention and refined blood pressure management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA single-center retrospective cohort study was conducted, enrolling 680 patients aged 65 years and above who underwent primary hip surgery (internal fixation for periacetabular fractures, total hip arthroplasty) under combined spinal-epidural anesthesia in our hospital from January 2020 to December 2025. Intraoperative minute-by-minute mean arterial pressure (MAP) was extracted from the hospital anesthesia information system. With MAP \u0026lt; 65 mmHg as the hypotension threshold, three quantitative indicators including time-weighted average hypotension amplitude (TWA), area under the hypotension curve (AUC) and cumulative duration of IOH were calculated. Postoperative VTE within 14 days was set as the primary outcome indicator. Clinical data including demographic characteristics, underlying diseases, surgery-related indicators and perioperative preventive measures were collected through the electronic medical record system. Univariate Logistic regression analysis was used to screen candidate risk factors for VTE, and variables with P \u0026lt; 0.1 were included in multivariate Logistic regression analysis to identify independent risk factors. A VTE risk prediction nomogram model was constructed based on the independent risk factors. The discriminative ability, calibration and clinical utility of the model were verified by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), and risk stratification analysis was performed according to the nomogram scores.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall incidence of VTE within 14 days after surgery was 4.20% (28/680) in the 680 elderly patients undergoing hip surgery. Multivariate Logistic regression analysis showed that TWA (OR=1.125, 95%CI: 1.058-1.196, P\u0026lt;0.001), AUC (OR=1.098, 95%CI: 1.045-1.154, P\u0026lt;0.001), cumulative duration of IOH (OR=1.036, 95%CI: 1.012-1.061, P=0.003), age≥75 years (OR=2.895, 95%CI: 1.216-6.887, P=0.016), history of diabetes mellitus (OR=2.563, 95%CI: 1.082-6.079, P=0.032), operation time ≥120 min (OR=3.102, 95%CI: 1.305-7.376, P=0.010) and non-standard use of mechanical prophylaxis after surgery (OR=4.018, 95%CI: 1.695-9.523, P=0.001) were independent risk factors for postoperative VTE in elderly patients undergoing hip surgery, and the three quantitative indicators of IOH showed a significant dose-response relationship with the risk of VTE (P for trend \u0026lt;0.001). For the nomogram model constructed based on the above 7 independent risk factors, ROC curve analysis showed an AUC of 0.816 (95%CI: 0.725-0.907), indicating good discriminative ability of the model; the calibration curve showed a high consistency between the predicted probability and the actual incidence probability of the model (Hosmer-Lemeshow test, χ²=6.895, P=0.542); DCA confirmed that the model had significant clinical net benefit within the threshold probability of 5%-40%. According to the nomogram scores, patients were divided into the low-risk group (≤100 points), moderate-risk group (101~150 points) and high-risk group (\u0026gt;150 points), with the VTE incidences of 1.1% (3/325), 7.6% (22/286) and 22.7% (15/69) respectively, and the inter-group difference was statistically significant (χ²=45.865, P\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder combined spinal-epidural anesthesia, the depth, load and duration of IOH in elderly patients undergoing hip surgery have a dose-response relationship with the risk of postoperative VTE, and IOH is an independent risk factor for postoperative VTE. The nomogram model integrating IOH quantitative indicators and clinical characteristics has good predictive efficacy and clinical utility, which can achieve precise risk stratification of postoperative VTE in elderly patients undergoing hip surgery, and provide a scientific basis for formulating individualized perioperative VTE prevention strategies and refined blood pressure management plans.\u003c/p\u003e","manuscriptTitle":"Relationship between Intraoperative Hypotension and Postoperative Venous Thromboembolism in Elderly Patients Undergoing Hip Surgery and Construction of a Risk Prediction Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:24:09","doi":"10.21203/rs.3.rs-9245529/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-03T00:58:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81633492147415987811879969380619486582","date":"2026-04-24T00:33:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T19:04:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T01:56:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T01:56:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2026-03-27T13:41:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31d34723-071a-4a5c-8176-835c26beb0bf","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-03T00:58:56+00:00","index":34,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:24:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9245529","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9245529","identity":"rs-9245529","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}