Impact of a Pharmacist-led Anticoagulation Model Based on Early Active Consultation in Orthopedic Surgery: A Retrospective Cohort Study

Impact of a Pharmacist-led Anticoagulation Model Based on Early Active Consultation in Orthopedic Surgery: A Retrospective Cohort Study | 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 Impact of a Pharmacist-led Anticoagulation Model Based on Early Active Consultation in Orthopedic Surgery: A Retrospective Cohort Study Yalan Wang, Qian Du, Songqing Liu, Jun Xiao, Xuejiao Tang, Jun Feng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7058295/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Sep, 2025 Read the published version in International Journal of Clinical Pharmacy → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction Venous thromboembolism (VTE) is a common and preventable complication in orthopedic surgery, yet adherence to prophylaxis guidelines remains suboptimal. A pharmacist-led anticoagulation care model based on the Pharmacist Early Active Consultation (PEAC) framework may enhance the quality and safety of VTE prevention in surgical patients. Aim This study aimed to evaluate the impact of a pharmacist-led Venous Thromboembolism Clinical Pharmaceutical Care (VTE-CPC) model, derived from the PEAC framework, on VTE prevention and anticoagulation quality in orthopedic surgery patients. Method A retrospective cohort study was conducted at a tertiary hospital in China. Patients admitted between December 2023 and May 2024 received routine care (no VTE-CPC group), while those admitted between June and November 2024 received additional pharmacist-led interventions (VTE-CPC group). Multivariate logistic regression was used to identify independent risk factors for VTE. Propensity score matching (PSM) was performed to control baseline differences, resulting in a balanced cohort of 812 patients. Outcomes included VTE incidence, pharmacological prophylaxis practices, and safety endpoints. Results A total of 959 patients were included (no VTE-CPC: n = 531; VTE-CPC: n = 428). The incidence of VTE was significantly lower in the VTE-CPC group (3.74%) compared to the no VTE-CPC group (7.53%, p = 0.020). VTE-CPC remained an independent protective factor in multivariate analysis (OR = 0.45; 95% CI: 0.23–0.84; p = 0.015). In the PSM matched cohort, patients in the VTE-CPC group had higher rates of postoperative pharmacological prophylaxis (22.66% vs. 16.26%, p = 0.027) and improved dosage appropriateness across all perioperative phases (p < 0.05). No significant differences were observed between groups in rates of bleeding events, thrombocytopenia, or hepatic/renal dysfunction. Conclusion A pharmacist-led anticoagulation management model based on the PEAC framework significantly reduced perioperative VTE incidence without increasing adverse events. These findings support broader implementation of proactive, pharmacist-driven strategies to improve thromboprophylaxis quality in orthopedic surgery. Venous Thromboembolism Orthopedic Procedures Pharmaceutical Services Anticoagulants Clinical Pharmacy Services Patient Safety Propensity Score Matching Figures Figure 1 Figure 2 Impacts on practice Embedding clinical pharmacists into orthopedic care teams can enhance the quality and individualization of anticoagulation management, promoting safer and more effective VTE prevention strategies. The VTE-CPC model provides a scalable framework for implementing pharmacist-led interventions in surgical settings, particularly in healthcare systems seeking to improve adherence to thromboprophylaxis guidelines. Strengthening pharmacist involvement in perioperative care may bridge gaps in transitions from inpatient to outpatient anticoagulant use, supporting long-term continuity and patient education. Introduction Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common and potentially life-threatening complication of surgical procedures. It contributes to increased morbidity, mortality, and healthcare burdens [ 1 ]. Orthopedic surgery patients are particularly vulnerable to postoperative VTE due to factors such as tourniquet-induced ischemia, extended immobilization, surgical trauma, and the thrombogenic nature of the bone cement [ 2 ]. In Asian populations, the incidence of postoperative DVT in the absence of prophylaxis ranges from 31–58% [ 3 , 4 ]. Effective prophylactic strategies, including pharmacological and mechanical interventions, have demonstrated a significant reduction in the risk of VTE, lowering the incidence to approximately 6–12% in patients undergoing total joint arthroplasty [ 5 , 6 ]. Despite the availability of evidence-based guidelines, their use remains suboptimal. A multicenter survey across Asia revealed that only 37.6% of orthopedic care providers consistently adhered to recommended prophylaxis protocols, highlighting a gap between guidelines and real-world practice [ 7 ]. In this context, multidisciplinary anticoagulation management programs have emerged as promising approaches to enhance adherence, optimize therapy, and minimize adverse outcomes [ 8 – 10 ]. Pharmacists play a critical role in these programs by identifying and resolving drug-related problems, optimizing anticoagulant selection and dosing, and reducing thrombotic and bleeding events. However, current research predominantly focuses on pharmacist involvement in high-risk procedures such as total joint arthroplasty. The generalizability of these findings to other orthopedic surgeries remains unclear. Additionally, few studies have delineated a standardized pharmacist-led care model for VTE management that limits scalability and adoption in routine practice [ 11 ]. To address this gap, our team previously developed the Pharmacist Early Active Consultation (PEAC) model, which is a novel pharmacist service framework designed to enhance proactive and timely medication management. The PEAC model leverages hospital information systems (HIS) to systematically screen hospitalized patients for medication-related risks, enabling pharmacists to intervene early with targeted guideline-based recommendations [ 12 ].It has been successfully implemented in managing multidrug-resistant infections and drug-induced liver injury, improving patient outcomes and reducing healthcare costs [ 12 – 14 ]. Building on this foundation, we adapted the PEAC framework to create a specialized VTE management model for orthopedic patients, termed the Venous Thromboembolism Clinical Pharmaceutical Care (VTE-CPC) model. This pharmacist-led model integrates the proactive consultation structure of PEAC with guideline-directed VTE prophylaxis and a multidisciplinary collaboration. The VTE-CPC model covers a broad spectrum of orthopedic procedures, including joint replacement, spinal surgery, and trauma-related operations. Aim This study aimed to evaluate the impact of a pharmacist-led Venous Thromboembolism Clinical Pharmaceutical Care model, derived from the PEAC framework, on VTE prevention and anticoagulation quality in orthopedic surgery patients. Method Study Design and Patient Population This retrospective cohort study was conducted in the Orthopedics Department of a tertiary hospital in Chongqing, China. A pharmacist-led VTE-CPC model was implemented in June 2024. Patients who underwent elective orthopedic surgeries between December 2023 and May 2024 received routine care and formed the control group (no VTE-CPC group). Patients admitted between June and November 2024 received VTE-CPC services in addition to routine care and formed the intervention group (VTE-CPC group). Eligible participants were adult patients undergoing elective orthopedic procedures, including percutaneous vertebroplasty, cervical and lumbar decompression and/or internal fixation and/or fusion, lower limb fracture internal fixation, arthroscopic procedures of the hip or knee, and total knee or hip replacement. Patients were excluded if they: (1) had a pre-existing diagnosis of venous thrombosis or a positive Doppler ultrasound within 24 hours of admission; (2) discontinued treatment or died during hospitalization; (3) had communication barriers, including cognitive impairment or language/hearing difficulties; or (4) failed to adhere to medical or pharmaceutical recommendations. Pharmacist-Led VTE-CPC Model The VTE-CPC model was developed collaboratively by clinical pharmacists, orthopedic surgeons, cardiovascular surgeons, rehabilitation therapists, and hospital administrators. Building on the PEAC framework, this model integrates early pharmacist intervention, clinical decision support, and multidisciplinary collaboration into a standardized care pathway for perioperative VTE prevention. Its design was guided by major VTE prevention guidelines [ 15 – 18 ] and is detailed in the institutional clinical pathway ( Supplementary Fig. 1 ). Two intelligent electronic systems supported the VTE-CPC workflow. The PIP PASS Pharm Care system (version 1.1.202303.03, Medicom Software, Sichuan, China) enabled pharmacists to conduct patient screening, daily rounds, individualized pharmaceutical care, and patient education. The VTE Intelligent Decision Support System (VTE-IDSS) (version V3.0-20220932, Dr. Breath. Com, Beijing, China) provided real-time risk stratification for thrombosis and bleeding, with decision-support alerts and modifiable recommendations to assist clinicians. These systems were fully interoperable with HIS, allowing seamless integration of patient data and clinical documentation. The VTE-CPC model included the following pharmacist-led activities (Fig. 1 ): 1) verification of system-generated VTE and bleeding risk scores, 2) collaboration with physicians to select and optimize anticoagulation regimens, 3) delivery of patient-centered education on VTE prevention and anticoagulant use, 4) continuous monitoring of prophylaxis effectiveness and adverse drug reactions during hospitalization, and 5) optimization of sequential oral anticoagulant plans prior to discharge to ensure safe post-hospital management. Outcome Assessment The primary outcome was the incidence of VTE (DVT or PE) during hospitalization. Secondary outcomes included the rates of anticoagulant-related adverse events and indicators of care quality, such as adherence to guideline-recommended prophylaxis. Definitions VTE events were confirmed using imaging techniques and clinical evaluation. DVT was suspected based on symptoms such as edema, calf tenderness, or leg discomfort and confirmed via Doppler ultrasound or venography. PE suspicion was based on symptoms including dyspnea and tachypnea, and diagnosis required elevated D-dimer levels and computed tomography pulmonary angiography (CTPA). Prophylactic dosage appropriateness was defined according to current clinical guidelines [ 15 – 18 ]. Major and non-major bleeding events were classified using criteria from the International Society on Thrombosis and Haemostasis [ 19 ]. Data Collection Data were extracted from HIS, PIP PASS Pharm Care, and VTE-IDSS platforms. Variables included patient demographics (age and sex), clinical characteristics (body mass index [BMI], comorbidities, type of surgery, anesthesia duration, intraoperative blood loss and transfusion, drainage tube use), laboratory results (blood count, liver and kidney function, and coagulation profiles), and VTE prophylaxis details (mechanical and pharmacological measures, duration, and adherence). Statistical Analysis Statistical analyses were performed using R version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were analyzed using Student’s t-test or Mann–Whitney U test, as appropriate. Categorical variables were compared using chi-square or Fisher’s exact test. Univariate logistic regression identified potential predictors of VTE. Variables with p < 0.05 were entered into a multivariate logistic regression model to determine independent associations. To minimize confounding and selection bias, propensity score matching (PSM) was performed using 1:1 nearest-neighbor matching with a caliper of 0.2, based on baseline covariates. All tests were two-tailed, and a p-value < 0.05 was considered statistically significant. Ethics Approval This study received ethical approval from the Third Affiliated Hospital of Chongqing Medical University's Ethics Committee (approval No. 2025-18, January 18, 2025). Results Participant Characteristics A total of 1,090 patients were screened. After applying the exclusion criteria, 959 patients were included in the final analysis: 531 in the no-VTE-CPC group and 428 in the VTE-CPC group (Fig. 2 ). The baseline characteristics are summarized in Table 1 . A statistically significant difference was observed in age between the groups (p = 0.035), whereas other demographic and clinical variables were comparable. Table 1 Patient baseline and clinical characteristics Characteristic Overall No VTE-CPC service VTE-CPC service P (n = 959) (n = 533) (n = 426) Male 412 (42.96) 232 (43.53) 180 (42.25) 0.741 Age (median, years [IQR]) 64.00 [53.00, 74.00] 65.00 [54.00, 74.00] 61.00 [51.00, 74.00] 0.035* Age (≥ 70 years) 373 (38.89) 218 (40.90) 155 (36.38) 0.174 Preoperative hospital stays (≥ 7 days) 105 (10.95) 61 (11.44) 44 (10.33) 0.656 Disease history Cardiovascular disease 296 (30.87) 167 (31.33) 129 (30.28) 0.780 Chronic lung disease 51 (5.32) 30 (5.63) 21 (4.93) 0.738 Chronic kidney disease 15 (1.56) 8 (1.50) 7 (1.64) 0.999 Liver underlying disease 68 (7.09) 44 (8.26) 24 (5.63) 0.149 Gastrointestinal disease 16 (1.67) 9 (1.69) 7 (1.64) 0.999 Diabetes 118 (12.30) 62 (11.63) 56 (13.15) 0.542 Hyperlipidemia 17 (1.77) 10 (1.88) 7 (1.64) 0.980 Malignant tumor 14 (1.46) 11 (2.06) 3 (0.70) 0.141 Urinary system disease 22 (2.29) 11 (2.06) 11 (2.58) 0.752 BMI (≥ 25 kg/m 2 ) 344 (35.87) 190 (35.65) 154 (36.15) 0.925 Smoking 137 (14.29) 76 (14.26) 61 (14.32) 0.999 Admission laboratory test Platelet count (≥ 300×10 9 /L) 89 (9.28) 55 (10.32) 34 (7.98) 0.259 Hemoglobin (≥ 100 g/L) 926 (96.56) 514 (96.44) 412 (96.71) 0.955 Abnormal D-dimer values (≥ 5.0 µg/mL) 615 (64.13) 344 (64.54) 271 (63.62) 0.819 VTE risk assessment 0.194 Low risk 23 (2.40) 16 (3.00) 7 (1.64) Moderate risk 496 (51.72) 283 (53.10) 213 (50.00) High risk 440 (45.88) 234 (43.90) 206 (48.36) Bleeding risk assessment 0.402 Low risk 536 (55.89) 291 (54.60) 245 (57.51) High risk 423 (44.11) 242 (45.40) 181 (42.49) Surgery type 0.882 Arthroscopic surgery 132 (13.76) 72 (13.51) 60 (14.08) PVP 238 (24.82) 138 (25.89) 100 (23.47) Spinal surgery 269 (28.05) 148 (27.77) 121 (28.40) Internal fixation surgery 165 (17.21) 92 (17.26) 73 (17.14) THA 90 (9.38) 51 (9.57) 39 (9.15) TKA 65 (6.78) 32 (6.00) 33 (7.75) Anesthesia duration (≥ 180 min) 346 (36.08) 197 (36.96) 149 (34.98) 0.570 Intraoperative blood loss (≥ 50 mL) 445 (46.40) 243 (45.59) 202 (47.42) 0.618 Intraoperative blood transfusion 41 (4.28) 26 (4.88) 15 (3.52) 0.384 Catheter drainage 308 (32.12) 167 (31.33) 141 (33.10) 0.608 Data are presented as number (n) of patients (%) unless specified otherwise. *Results are statistically significant. VTE-CPC: venous thromboembolism clinical pharmaceutical care; IQR: interquartile range; BMI: body mass index; VTE: Venous thromboembolism; PVP: percutaneous vertebroplasty; THA: hip Arthroplasty; TKA: total knee arthroplasty. Incidence of Venous Thromboembolism The incidence of VTE was significantly lower in the VTE-CPC group compared with the no VTE-CPC group. Specifically, VTE occurred in 40 of 531 patients (7.53%) in the no VTE-CPC group and in 16 of 428 patients (3.74%) in the VTE-CPC group (p = 0.020), indicating a beneficial effect of the pharmacist-led intervention. Logistic Regression Analysis Univariate logistic regression identified 14 variables significantly associated with perioperative VTE (all p < 0.05): implementation of VTE-CPC, age ≥ 70 years, chronic lung disease, diabetes, malignant tumor, urinary system disease, hemoglobin ≥ 100 g/L, elevated D-dimer, high postoperative bleeding risk, surgery type, anesthesia duration ≥ 180 min, intraoperative blood loss > 50 mL, intraoperative blood transfusion, and catheter drainage (Table 2 ). Table 2 Univariate analysis and multivariate analysis of VTE-related risk factors in 959 patients with orthopedic surgery Univariate analysis Multivariate analysis OR(95%CI) P OR(95%CI) P VTE-CPC 0.48 (0.26–0.86) 0.016* 0.45 (0.23–0.84) 0.015* Male 1.09 (0.63–1.90) 0.768 - - Age (≥ 70 years) 2.03 (1.18–3.53) 0.010* 2.14 (1.07–4.28) 0.031* Preoperative hospital stays (≥ 7 days) 0.79 (0.27–1.84) 0.619 - - Disease history Cardiovascular disease 0.97 (0.53–1.72)) 0.932 - - Chronic lung disease 3.33 (1.39–7.15) 0.004* 2.52 (0.96–6.09) 0.048* Chronic kidney disease 2.54 (0.39–9.49) 0.228 - - Liver underlying disease 1.97 (0.79–4.28) 0.110 - - Gastrointestinal disease 1.08 (0.06–5.46) 0.944 - - Diabetes 2.30 (1.15–4.30) 0.013* 1.56 (0.72–3.19) 0.239 Hyperlipidemia 1.01(0.06–5.08) 0.994 - - Malignant tumor 4.59 (1.02–15.23) 0.022* 3.49 (0.67–14.03) 0.098 Urinary system disease 3.78 (1.06–10.58) 0.020* 2.44 (0.58–8.35) 0.182 BMI (≥ 25 kg/m 2 ) 0.84 (0.46–1.47) 0.549 - - Smoking 1.00 (0.43–2.05) 0.999 - - Admission Laboratory Examination Platelet count (≥ 300*10 9 /L) 0.54 (0.13–1.50) 0.305 - - Hemoglobin (≥ 100 g/L) 0.33 (0.13–0.99) 0.027* 0.53 (0.17–1.98) 0.307 Abnormal D-dimer values (≥ 5.0µg/mL) 2.70 (1.40–5.74) 0.005* 2.15 (1.00-4.98) 0.059 VTE risk assessment Low-risk Reference Moderate-risk 0.59 (0.11–10.99) 0.621 - - High-risk 2.32 (0.47–42.09) 0.416 - - Bleeding risk assessment Low-risk Reference High-risk 2.40 (1.38–4.28) 0.002* 0.36 (0.11–1.17) 0.084 Surgery type Arthroscopic surgery Reference PVP 1.11 (0.29–5.34) 0.882 0.46 (0.09–2.69) 0.353 Spinal surgery 4.02 (1.37–17.17) 0.026* 1.46 (0.24–10.01) 0.686 Internal fixation surgery 3.07 (0.94–13.79) 0.090 1.06 (0.23–5.91) 0.941 THA 6.62 (2.03–29.70) 0.004* 1.93 (0.29–14.62) 0.506 TKA 0.67 (0.03–5.36) 0.733 0.34 (0.01–3.80) 0.413 Anesthesia duration (≥ 180 min) 3.17 (1.83–5.63) <0.001* 2.63 (1.19–6.01) 0.019* Intraoperative bleeding (≥ 50 mL) 4.12 (2.25–8.08) <0.001* 2.35 (0.78–7.78) 0.143 Intraoperative blood transfusion 6.12 (2.71–12.87) <0.001* 1.46 (0.51–3.93) 0.466 Catheter drainage 2.40 (1.39–4.15) 0.002* 1.27 (0.47–3.74) 0.650 *Results are statistically significant. OR: odds ratio; CI: confidence interval; VTE-CPC: Venous thromboembolism clinical pharmaceutical care; BMI: body mass index; VTE: venous thromboembolism; PVP: percutaneous vertebroplasty; THA: total hip arthroplasty; TKA: total knee arthroplasty. In multivariate analysis, four variables remained independently associated with VTE risk. VTE-CPC was protective (odds ration [OR] = 0.45; 95% confidence interval [CI]: 0.23–0.84; p = 0.015), while age ≥ 70 years (OR = 2.14; 95% CI: 1.07–4.28; p = 0.031), chronic lung disease (OR = 2.52; 95% CI: 0.96–6.09; p = 0.048), and anesthesia duration ≥ 180 min (OR = 2.63; 95% CI: 1.19–6.01; p = 0.019) were associated with increased VTE risk. Propensity Score-Matched Cohort PSM was conducted to balance baseline covariates. After matching, 812 patients remained (406 in each group), and baseline characteristics were well balanced (Table 3 ). Covariate balance improvement is shown in Supplemental Fig. 2 . In the matched cohort, VTE incidence remained significantly lower in the VTE-CPC group, confirming the protective effect observed in multivariate analysis (OR = 0.40; 95% CI: 0.21–0.75; p = 0.006). Table 3 Patient baseline and characteristics in the propensity score-matched cohort. Characteristic Overall No VTE-CPC service VTE-CPC service P (n = 820) (n = 406) (n = 406) Male 337 (41.50) 168 (41.38) 169 (41.63) 0.999 Age (median, years [IQR]) 62.00 [52.00, 74.00] 64.00 [53.00, 73.75] 61.00 [51.00, 74.00] 0.306 Age (≥ 70 years) 305 (37.56) 152 (37.44) 153 (37.68) 0.999 Preoperative hospital stays (≥ 7 days) 77 (9.48) 34 (8.37) 43 (10.59) 0.338 Disease history Cardiovascular disease 242 (29.80) 122 (30.05) 120 (29.56) 0.939 Chronic lung disease 44 (5.42) 23 (5.67) 21 (5.17) 0.877 Chronic kidney disease 13 (1.60) 6 (1.48) 7 (1.72) 0.999 Liver underlying disease 52 (6.40) 28 (6.90) 24 (5.91) 0.667 Gastrointestinal disease 16 (1.97) 9 (2.22) 7 (1.72) 0.801 Diabetes 104 (12.81) 53 (13.05) 51 (12.56) 0.916 Hyperlipidemia 14 (1.72) 8 (1.97) 6 (1.48) 0.788 Malignant tumor 6 (0.74) 3 (0.74) 3 (0.74) 0.999 Urinary system disease 20 (2.46) 10 (2.46) 10 (2.46) 0.999 BMI (≥ 25 kg/m 2 ) 306 (37.68) 158 (38.92) 148 (36.45) 0.515 Smoking 120 (14.78) 61 (15.02) 59 (14.53) 0.921 Admission Laboratory Examination Platelet count (≥ 300*10 9 /L) 71 (8.74) 37 (9.11) 34 (8.37) 0.804 Hemoglobin (≥ 100 g/L) 787 (96.92) 393 (96.80) 394 (97.04) 0.999 Abnormal D-dimer values (≥ 5.0 µg/mL) 510 (62.81) 254 (62.56) 256 (63.05) 0.942 VTE risk assessment 0.914 Low-risk 14 (1.72) 7 (1.72) 7 (1.72) Moderate-risk 416 (51.23) 211 (51.97) 205 (50.49) High-risk 382 (47.04) 188 (46.31) 194 (47.78) Bleeding risk assessment 0.999 Low-risk 463 (57.02) 231 (56.90) 232 (57.14) High-risk 349 (42.98) 175 (43.10) 174 (42.86) Surgery type 0.998 Arthroscopic surgery 124 (15.27) 64 (15.76) 60 (14.78) PVP 193 (23.77) 95 (23.40) 98 (24.14) Spinal surgery 217 (26.72) 110 (27.09) 107 (26.35) Internal fixation surgery 140 (17.24) 69 (17.00) 71 (17.49) THA 77 (9.48) 38 (9.36) 39 (9.61) TKA 61 (7.51) 30 (7.39) 31 (7.64) Anesthesia duration (≥ 180 min) 287 (35.34) 147 (36.21) 140 (34.48) 0.660 Intraoperative blood loss (≥ 50 mL) 377 (46.43) 189 (46.55) 188 (46.31) 0.999 Intraoperative blood transfusion 26 (3.20) 11 (2.71) 15 (3.69) 0.550 Catheter drainage 259 (31.90) 130 (32.02) 129 (31.77) 0.999 Data are presented as number (n) of patients (%) unless specified otherwise. VTE-CPC: venous thromboembolism clinical pharmaceutical care; IQR: interquartile range; BMI: body mass index; VTE: venous thromboembolism; PVP: percutaneous vertebroplasty; THA: total hip arthroplasty; TKA,: total knee arthroplasty. Outcome Evaluation Table 4 presents the comparison of VTE prevention interventions in the matched cohort. While the rates of mechanical and overall pharmacological prophylaxis did not differ significantly between groups, the VTE-CPC group had a significantly higher rate of postoperative pharmacological prophylaxis compared to the no VTE-CPC group (22.66% vs 16.26%, p = 0.027). Table 4 Comparison of interventions in the propensity score-matched cohort. Overall (n = 812) No VTE-CPC service (n = 406) VTE-CPC service (n = 406) P Mechanical prophylaxis Prophylaxis rate (%) 274 (33.74) 135 (33.25) 139 (34.24) 0.824 Prophylaxis days (median, days, [IQR]) 5.00 [3.00, 8.00] 4.00 [3.00, 8.00] 5.00 [4.00, 8.00] 0.073 Pharmacological prophylaxis Prophylaxis rate (%) 193 (23.77) 86 (21.18) 107 (26.35) 0.099 Prophylaxis days (median, days, [IQR]) 6.00 [4.00, 10.70] 5.80 [4.00, 11.00] 6.20 [4.00, 9.95] 0.802 Dosage appropriateness (%) 183 (90.15) 76 (81.72) 107 (97.27) 0.001 * Preoperative pharmacological prophylaxis Prophylaxis rate (%) 96 (11.82) 42 (10.34) 54 (13.30) 0.232 Prophylaxis days (median, days, [IQR]) 4.00 [2.63, 5.90] 3.75 [2.55, 5.73] 4.25 [2.75, 5.98] 0.407 Dosage appropriateness (%) 90 (93.75) 36 (85.71) 54 (100.00) 0.015 * Postoperative pharmacological prophylaxis Prophylaxis rate (%) 158 (19.46) 66 (16.26) 92 (22.66) 0.027 * Prophylaxis days (median, days, [IQR]) 5.50 [4.00, 8.05] 5.95 [4.00, 8.10] 5.00 [3.90, 7.95] 0.160 Dosage appropriateness (%) 149 (94.30) 57 (86.36) 92 (100.00) 0.001 * Acceptance for any prophylaxis (%) 383 (47.17) 180 (44.33) 203 (50.00) 0.122 Sequential oral anticoagulants Rate of sequential oral anticoagulants (%) 106 (13.05) 34 (8.37) 72 (17.73) < 0.001 * Dosage appropriateness (%) 89 (83.96) 24 (70.59) 65 (90.28) 0.022 * *Results are statistically significant. VTE-CPC: Venous thromboembolism clinical pharmaceutical care; IQR: interquartile range. The VTE-CPC group demonstrated significantly greater appropriateness of anticoagulant dosing across all timepoints (preoperative, postoperative, overall, and sequential oral anticoagulants), as well as higher acceptance of sequential oral anticoagulant prescriptions (all p < 0.05). Although the duration of mechanical and pharmacological prophylaxis was slightly longer in the VTE-CPC group, these differences were not statistically significant. Adverse Events Among the 812 patients in the matched cohort, no significant differences were observed in the incidence of adverse events between groups ( Supplemental Table 3 ). Specifically, the VTE-CPC group showed no increased risk of bleeding (0.52% vs 0%, p > 0.05), leukocytosis (32.27% vs 36.70%), thrombocytopenia (1.97% vs 1.48%), anemia (40.89% vs 43.60%), elevated AST (12.56% vs 15.76%), elevated ALT (14.29% vs 16.01%), or elevated serum creatinine (11.58% vs 15.52%). These findings suggest that the pharmacist-led VTE-CPC model did not increase the risk of treatment-related adverse events. Discussion Principal Findings This study evaluated the effectiveness of a pharmacist-led anticoagulation management model, developed based on the PEAC framework, for reducing perioperative VTE in orthopedic surgery patients. Our findings demonstrate that implementation of the VTE-CPC model significantly reduced the incidence of hospital-acquired VTE. The VTE incidence in the VTE-CPC group was 3.74%, markedly lower than the 7.53% observed in the no VTE-CPC group. These results remained robust after adjustment for confounders using multivariate logistic regression and PSM, suggesting that the pharmacist-led model contributed meaningfully to improved clinical outcomes. Interpretation and Mechanisms Two potential explanations exist for the reduced VTE incidence in the VTE-CPC group. First, the observed benefit may directly result from enhanced quality of anticoagulation management through pharmacist involvement. Second, baseline imbalances due to the real-world study design could have contributed. For example, previous studies have shown substantial heterogeneity in VTE risk across orthopedic subspecialties, with incidence ranging from as low as 0.58% in percutaneous vertebroplasty [ 20 ] to as high as 60% in major joint arthroplasty [ 21 ]. Although some clinical characteristics differed between groups, logistic regression and PSM analyses were performed to control for such confounding, and the protective effect of VTE-CPC persisted, supporting its role in reducing VTE risk. Further analysis of the PSM cohort clarified how the VTE-CPC model achieved its effect. First, the VTE-CPC group demonstrated a significantly higher rate of postoperative pharmacological prophylaxis (22.66% vs. 16.26%, p = 0.027). The importance of pharmacologic prophylaxis is well-established; for instance, the incidence of DVT after total knee arthroplasty can exceed 60% without prophylaxis but drops to below 10% with appropriate anticoagulant use [ 22 ]. The integration of clinical pharmacists into multidisciplinary care teams enabled tailored anticoagulation strategies and patient-specific decision-making, improving prophylaxis rates. Second, appropriateness of anticoagulant dosing was significantly higher in the VTE-CPC group throughout the care continuum, including preoperative, postoperative, and discharge phases (all p < 0.05). This aligns with findings by Diana Yap et al. [ 23 ], who reported an increase in dosing appropriateness from 14.3–31.3% through pharmacist-led risk assessments. Additionally, acceptance of sequential oral anticoagulants doubled in the VTE-CPC group (17.73% vs. 8.37%, p < 0.001), with better dosing appropriateness (90.28% vs. 70.59%, p = 0.022), indicating improved transitions of care and patient education at discharge. These findings support the utility of the PEAC-based model in strengthening perioperative VTE prevention by addressing known implementation gaps in both prescribing behavior and care transitions. Safety Profile Importantly, the implementation of VTE-CPC did not lead to a higher incidence of adverse events. In the matched cohort, there were no significant differences between groups in the rates of bleeding, thrombocytopenia, anemia, leukocytosis, hepatic dysfunction, or renal impairment. Although a previous study reported an increased bleeding risk with anticoagulant use (Zheng et al., 2023), our findings did not show such an increase. This may be attributed to several safety mechanisms within the VTE-CPC model: (1) risk stratification tools embedded in the clinical pathway, (2) pharmacist-led review of contraindications, and (3) dynamic dose adjustments based on real-time monitoring. However, the low incidence of bleeding events may also reflect limited sample size and reduced power to detect small differences. Strengths and Limitations This study has several strengths. It is among the first to evaluate a pharmacist-led VTE management model across a broad range of orthopedic surgeries in a real-world setting. A key strength is the adaptation of the PEAC framework, which facilitates early pharmacist involvement through system-integrated patient identification and consultation, offering a replicable and scalable approach to clinical pharmacy services. The integration of advanced clinical decision support systems and structured care pathways provided a practical, technology-supported intervention that improved both clinical outcomes and care quality. However, several limitations should be noted. First, the retrospective design precludes full control of unmeasured confounding, despite statistical adjustments. Second, although the pharmacological prophylaxis rate in the VTE-CPC group (26.35%) was higher than that reported for surgical patients in China (11.80%) [ 24 ], it remained substantially lower than the global average (58.5%) [ 25 ]. This may be due to a higher proportion of patients at low to moderate VTE risk (52.96%) or high bleeding risk (42.98%), as well as limited pharmacist staffing. Third, our study was restricted to in-hospital outcomes. Given that the risk of VTE persists for 6–12 weeks postoperatively [ 26 ], long-term follow-up is essential to evaluate the sustained safety and effectiveness of VTE-CPC in out-of-hospital settings. Future Directions To strengthen the evidence base, future multicenter randomized controlled trials are warranted to confirm the generalizability of the VTE-CPC model. The PEAC model's flexibility and technology-driven structure also position it well for broader implementation in other clinical areas requiring proactive medication risk management. In addition, integration of telemedicine platforms could facilitate post-discharge anticoagulation monitoring, improving continuity of care. Extending follow-up to three months would allow for comprehensive assessment of both thrombotic recurrence and bleeding risks, offering valuable insights into the long-term benefit-risk profile of pharmacist-led anticoagulation services. Conclusion This study demonstrates that a pharmacist-led anticoagulation management model, based on the Pharmacist Early Active Consultation framework, significantly reduces perioperative VTE incidence in orthopedic surgery patients without increasing adverse events. These findings support the integration of clinical pharmacists into multidisciplinary surgical teams to enhance the quality of thromboprophylaxis. Declarations Competing Interests The authors declare no conflicts of interest. Ethics Approval This study received ethical approval from the Third Affiliated Hospital of Chongqing Medical University's Ethics Committee (approval No. 2025-18, January 18, 2025). Funding This study was supported by Technical foresight and project innovation project of the Science and Technology Bureau of Yuzhong District (20210102), the Clinical Pharmacy Priority Construction Project of the Chongqing Health Commission (SLCZDZK202501), the Chongqing Graduate Student Research Innovation Project of Chongqing Municipality Education Commission (No. CYS240329), the Incubation Program of The Third Affiliated Hospital of Chongqing Medical University (KY22056), and the Chongqing Young and Middle-aged Medical High-end Talents Project of the Chongqing Municipal Health Commission. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by J.D., Y.W., and Q.D.. The first draft of the manuscript was written by Y.W. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgement We extend our sincere appreciation to all the medical personnel of the Orthopedic Department at the Third Affiliated Hospital of Chongqing Medical University for their invaluable support throughout this project. We are grateful for the contributions of Professor Qiang Zhou of the Orthopedic Department. Additionally, we wish to acknowledge the efforts of all staff members who have offered their assisted with this study. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Lutsey PL, Zakai NA. Epidemiology and prevention of venous thromboembolism. Nat reviews Cardiol. 2023;20(4):248–. https://doi.org/10.1038/s41569-022-00787-6 . 62. Jones A, Al-Horani RA. Venous Thromboembolism Prophylaxis in Major Orthopedic Surgeries and Factor XIa Inhibitors. Medical sciences (Basel, Switzerland). 2023;11(3). https://doi.org/10.3390/medsci11030049 Piovella F, Wang CJ, Lu H, Lee K, Lee LH, Lee WC, et al. Deep-vein thrombosis rates after major orthopedic surgery in Asia. An epidemiological study based on postoperative screening with centrally adjudicated bilateral venography. J Thromb haemostasis: JTH. 2005;3(12):2664–70. https://doi.org/10.1111/j.1538-7836.2005.01621.x . Kanchanabat B, Stapanavatr W, Meknavin S, Soorapanth C, Sumanasrethakul C, Kanchanasuttirak P. Systematic review and meta-analysis on the rate of postoperative venous thromboembolism in orthopaedic surgery in Asian patients without thromboprophylaxis. Br J Surg. 2011;98(10):1356–64. https://doi.org/10.1002/bjs.7589 . Warren JA, Sundaram K, Anis HK, Kamath AF, Higuera CA, Piuzzi NS. Have Venous Thromboembolism Rates Decreased in Total Hip and Knee Arthroplasty? J Arthroplast. 2020;35(1):259–64. https://doi.org/10.1016/j.arth.2019.08.049 . Shang J, Ning W, Gong J, Su D, Jia X, Wang Y. Impact of clinical pharmacist services on anticoagulation management of total joint arthroplasty: A retrospective observational study. J Clin Pharm Ther. 2021;46(5):1301–7. https://doi.org/10.1111/jcpt.13428 . Wang H, Ye J, Wang L, Jin W. Risk Characteristics of Venous Thromboembolism in Chinese Patients. Clinical and applied thrombosis/hemostasis. official J Int Acad Clin Appl Thrombosis/Hemostasis. 2016;22(5):490–4. https://doi.org/10.1177/1076029615569272 . Wychowski MK, Ruscio CI, Kouides PA, Sham RL. The scope and value of an anticoagulation stewardship program at a community teaching hospital. J Thromb Thrombolysis. 2017;43(3):380–6. https://doi.org/10.1007/s11239-016-1455-z . Dreijer AR, Diepstraten J, Leebeek FWG, Kruip M, van den Bemt P. The effect of hospital-based antithrombotic stewardship on adherence to anticoagulant guidelines. Int J Clin Pharm. 2019;41(3):691–9. https://doi.org/10.1007/s11096-019-00834-2 . Koolian M, Wiseman D, Mantzanis H, Kampouris N, Kerzner RS, Kahn SR. Anticoagulation stewardship: Descriptive analysis of a novel approach to appropriate anticoagulant prescription. Res Pract Thromb haemostasis. 2022;6(6):e12758. https://doi.org/10.1002/rth2.12758 . Naseralallah L, Koraysh S, Alasmar M, Aboujabal B. Effect of pharmacist care on clinical outcomes and therapy optimization in perioperative settings: A systematic review. Am J health-system pharmacy: AJHP : official J Am Soc Health-System Pharmacists. 2024;82(1):44–73. https://doi.org/10.1093/ajhp/zxae177 . Du Q, Xi X, Dong J, Zhang T, Li D, Dong Y, et al. The impact of pharmacist early active consultation (PEAC) on multidrug resistance organism treatment outcomes: A prospective historically controlled study. Front Pharmacol. 2023;14:1128219. https://doi.org/10.3389/fphar.2023.1128219 . Li D, Dong J, Xi X, Huang G, Li W, Chen C, et al. Impact of pharmacist active consultation on clinical outcomes and quality of medical care in drug-induced liver injury inpatients in general hospital wards: A retrospective cohort study. Front Pharmacol. 2022;13:972800. https://doi.org/10.3389/fphar.2022.972800 . Shan X, Zheng X, Wang H, Kong L, Shan Y, Dong J, et al. Cost-benefit analysis of pharmacist early active consultation in patients with multidrug-resistant bacteria in China. Int J Clin Pharm. 2025. https://doi.org/10.1007/s11096-025-01889-0 . Falck-Ytter Y, Francis CW, Johanson NA, Curley C, Dahl OE, Schulman S, et al. Prevention of VTE in orthopedic surgery patients: Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2012;141(2 Suppl):eS278–e325. https://doi.org/10.1378/chest.11-2404 . S. Branch CMAO. Prevention of venous thromboembolism after major orthopaedic surgery. Chin J Orthop. 2016;36(2):65–71. Lin Q, Yang M, Hou ZJCJOT. Guidelines for prevention of perioperative venous thromboembolism in Chinese orthopedic trauma patients (2021). 2021;23(3):185–92. Uzel K, Azboy İ, Parvizi J. Venous thromboembolism in orthopedic surgery: Global guidelines. Acta orthopaedica et traumatologica turcica. 2023;57(5):192–203. https://doi.org/10.5152/j.aott.2023.23074 Franco L, Becattini C, Beyer-Westendorf J, Vanni S, Nitti C, Re R, et al. Definition of major bleeding: Prognostic classification. J Thromb haemostasis: JTH. 2020;18(11):2852–60. https://doi.org/10.1111/jth.15048 . Huang CH, Wang WH, Kor CT, Hsiao CH, Chang CC. Risk of venous thromboembolism in elderly patients with vertebral compression fracture: A population-based case-control study. Medicine. 2020;99(18):e20072. https://doi.org/10.1097/md.0000000000020072 . Geerts WH, Pineo GF, Heit JA, Bergqvist D, Lassen MR, Colwell CW et al. Prevention of venous thromboembolism: the Seventh ACCP Conference on Antithrombotic and Thrombolytic Therapy. Chest. 2004;126(3 Suppl):338s-400s. https://doi.org/10.1378/chest.126.3_suppl.338S Kahn SR, Shivakumar S. What's new in VTE risk and prevention in orthopedic surgery. Res Pract Thromb haemostasis. 2020;4(3):366–76. https://doi.org/10.1002/rth2.12323 . Diana Yap FS, Ng ZY, Wong CY, Muhamad Saifuzzaman MK, Yang LB. Appropriateness of deep vein thrombosis (DVT) prophylaxis use among medical inpatients: a DVT risk alert tool (DRAT) study. Med J Malay. 2019;74(1):45–50. Zhai Z, Kan Q, Li W, Qin X, Qu J, Shi Y, et al. VTE Risk Profiles and Prophylaxis in Medical and Surgical Inpatients: The Identification of Chinese Hospitalized Patients' Risk Profile for Venous Thromboembolism (DissolVE-2)-A Cross-sectional Study. Chest. 2019;155(1):114–22. https://doi.org/10.1016/j.chest.2018.09.020 . Cohen AT, Tapson VF, Bergmann JF, Goldhaber SZ, Kakkar AK, Deslandes B, et al. Venous thromboembolism risk and prophylaxis in the acute hospital care setting (ENDORSE study): a multinational cross-sectional study. Lancet (London England). 2008;371(9610):387–94. https://doi.org/10.1016/s0140-6736(08)60202-0 . Flevas DA, Megaloikonomos PD, Dimopoulos L, Mitsiokapa E, Koulouvaris P, Mavrogenis AF. Thromboembolism prophylaxis in orthopaedics: an update. EFORT open reviews. 2018;3(4):136–48. https://doi.org/10.1302/2058-5241.3.170018 . Additional Declarations No competing interests reported. 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14:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7058295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7058295/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11096-025-01997-x","type":"published","date":"2025-09-03T15:57:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86653742,"identity":"27310247-29f2-4ab4-88fa-f0d43883665e","added_by":"auto","created_at":"2025-07-14 10:03:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116195,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of the Venous Thromboembolism Clinical Pharmaceutical Care (VTE-CPC) model based on the Pharmacist Early Active Consultation (PEAC) framework.\u003c/p\u003e\n\u003cp\u003eADR, Adverse Drug Reaction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7058295/v1/2c668bc71a37b699c3628e69.png"},{"id":86653743,"identity":"de2439e2-913d-473d-8430-a5894a306bf7","added_by":"auto","created_at":"2025-07-14 10:03:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72400,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of patients included in the study.\u003c/p\u003e\n\u003cp\u003eVTE, Venous thrombus embolism; VTE-CPC, Venous thrombus embolism clinical pharmaceutical care.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7058295/v1/a3d7f6ff8980a8a000596036.png"},{"id":90827964,"identity":"56add477-b3c6-4bd5-b6b7-927af26a0605","added_by":"auto","created_at":"2025-09-08 16:04:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1448632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7058295/v1/86c26678-882e-467e-a8a7-772d8c0f4aaf.pdf"},{"id":86655210,"identity":"94537b0d-3324-46c1-83a3-fea392a7e670","added_by":"auto","created_at":"2025-07-14 10:11:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":475236,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7058295/v1/06e42d186272c3dc1a88a422.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of a Pharmacist-led Anticoagulation Model Based on Early Active Consultation in Orthopedic Surgery: A Retrospective Cohort Study","fulltext":[{"header":"Impacts on practice","content":"\u003cul\u003e\n \u003cli\u003eEmbedding clinical pharmacists into orthopedic care teams can enhance the quality and individualization of anticoagulation management, promoting safer and more effective VTE prevention strategies.\u003c/li\u003e\n \u003cli\u003eThe VTE-CPC model provides a scalable framework for implementing pharmacist-led interventions in surgical settings, particularly in healthcare systems seeking to improve adherence to thromboprophylaxis guidelines.\u003c/li\u003e\n \u003cli\u003eStrengthening pharmacist involvement in perioperative care may bridge gaps in transitions from inpatient to outpatient anticoagulant use, supporting long-term continuity and patient education.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eVenous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common and potentially life-threatening complication of surgical procedures. It contributes to increased morbidity, mortality, and healthcare burdens [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Orthopedic surgery patients are particularly vulnerable to postoperative VTE due to factors such as tourniquet-induced ischemia, extended immobilization, surgical trauma, and the thrombogenic nature of the bone cement [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Asian populations, the incidence of postoperative DVT in the absence of prophylaxis ranges from 31–58% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Effective prophylactic strategies, including pharmacological and mechanical interventions, have demonstrated a significant reduction in the risk of VTE, lowering the incidence to approximately 6–12% in patients undergoing total joint arthroplasty [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Despite the availability of evidence-based guidelines, their use remains suboptimal. A multicenter survey across Asia revealed that only 37.6% of orthopedic care providers consistently adhered to recommended prophylaxis protocols, highlighting a gap between guidelines and real-world practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, multidisciplinary anticoagulation management programs have emerged as promising approaches to enhance adherence, optimize therapy, and minimize adverse outcomes [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pharmacists play a critical role in these programs by identifying and resolving drug-related problems, optimizing anticoagulant selection and dosing, and reducing thrombotic and bleeding events. However, current research predominantly focuses on pharmacist involvement in high-risk procedures such as total joint arthroplasty. The generalizability of these findings to other orthopedic surgeries remains unclear. Additionally, few studies have delineated a standardized pharmacist-led care model for VTE management that limits scalability and adoption in routine practice [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo address this gap, our team previously developed the Pharmacist Early Active Consultation (PEAC) model, which is a novel pharmacist service framework designed to enhance proactive and timely medication management. The PEAC model leverages hospital information systems (HIS) to systematically screen hospitalized patients for medication-related risks, enabling pharmacists to intervene early with targeted guideline-based recommendations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].It has been successfully implemented in managing multidrug-resistant infections and drug-induced liver injury, improving patient outcomes and reducing healthcare costs [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Building on this foundation, we adapted the PEAC framework to create a specialized VTE management model for orthopedic patients, termed the Venous Thromboembolism Clinical Pharmaceutical Care (VTE-CPC) model. This pharmacist-led model integrates the proactive consultation structure of PEAC with guideline-directed VTE prophylaxis and a multidisciplinary collaboration. The VTE-CPC model covers a broad spectrum of orthopedic procedures, including joint replacement, spinal surgery, and trauma-related operations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Aim","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study aimed to evaluate the impact of a pharmacist-led Venous Thromboembolism Clinical Pharmaceutical Care model, derived from the PEAC framework, on VTE prevention and anticoagulation quality in orthopedic surgery patients.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Design and Patient Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective cohort study was conducted in the Orthopedics Department of a tertiary hospital in Chongqing, China. A pharmacist-led VTE-CPC model was implemented in June 2024. Patients who underwent elective orthopedic surgeries between December 2023 and May 2024 received routine care and formed the control group (no VTE-CPC group). Patients admitted between June and November 2024 received VTE-CPC services in addition to routine care and formed the intervention group (VTE-CPC group).\u003c/p\u003e\u003cp\u003eEligible participants were adult patients undergoing elective orthopedic procedures, including percutaneous vertebroplasty, cervical and lumbar decompression and/or internal fixation and/or fusion, lower limb fracture internal fixation, arthroscopic procedures of the hip or knee, and total knee or hip replacement. Patients were excluded if they: (1) had a pre-existing diagnosis of venous thrombosis or a positive Doppler ultrasound within 24 hours of admission; (2) discontinued treatment or died during hospitalization; (3) had communication barriers, including cognitive impairment or language/hearing difficulties; or (4) failed to adhere to medical or pharmaceutical recommendations.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePharmacist-Led VTE-CPC Model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe VTE-CPC model was developed collaboratively by clinical pharmacists, orthopedic surgeons, cardiovascular surgeons, rehabilitation therapists, and hospital administrators. Building on the PEAC framework, this model integrates early pharmacist intervention, clinical decision support, and multidisciplinary collaboration into a standardized care pathway for perioperative VTE prevention. Its design was guided by major VTE prevention guidelines [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and is detailed in the institutional clinical pathway (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTwo intelligent electronic systems supported the VTE-CPC workflow. The PIP PASS Pharm Care system (version 1.1.202303.03, Medicom Software, Sichuan, China) enabled pharmacists to conduct patient screening, daily rounds, individualized pharmaceutical care, and patient education. The VTE Intelligent Decision Support System (VTE-IDSS) (version V3.0-20220932, Dr. Breath. Com, Beijing, China) provided real-time risk stratification for thrombosis and bleeding, with decision-support alerts and modifiable recommendations to assist clinicians. These systems were fully interoperable with HIS, allowing seamless integration of patient data and clinical documentation.\u003c/p\u003e\u003cp\u003eThe VTE-CPC model included the following pharmacist-led activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): 1) verification of system-generated VTE and bleeding risk scores, 2) collaboration with physicians to select and optimize anticoagulation regimens, 3) delivery of patient-centered education on VTE prevention and anticoagulant use, 4) continuous monitoring of prophylaxis effectiveness and adverse drug reactions during hospitalization, and 5) optimization of sequential oral anticoagulant plans prior to discharge to ensure safe post-hospital management.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome Assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome was the incidence of VTE (DVT or PE) during hospitalization. Secondary outcomes included the rates of anticoagulant-related adverse events and indicators of care quality, such as adherence to guideline-recommended prophylaxis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDefinitions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eVTE events were confirmed using imaging techniques and clinical evaluation. DVT was suspected based on symptoms such as edema, calf tenderness, or leg discomfort and confirmed via Doppler ultrasound or venography. PE suspicion was based on symptoms including dyspnea and tachypnea, and diagnosis required elevated D-dimer levels and computed tomography pulmonary angiography (CTPA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eProphylactic dosage appropriateness was defined according to current clinical guidelines [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Major and non-major bleeding events were classified using criteria from the International Society on Thrombosis and Haemostasis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were extracted from HIS, PIP PASS Pharm Care, and VTE-IDSS platforms. Variables included patient demographics (age and sex), clinical characteristics (body mass index [BMI], comorbidities, type of surgery, anesthesia duration, intraoperative blood loss and transfusion, drainage tube use), laboratory results (blood count, liver and kidney function, and coagulation profiles), and VTE prophylaxis details (mechanical and pharmacological measures, duration, and adherence).\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using R version 4.3.3 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were analyzed using Student’s t-test or Mann–Whitney U test, as appropriate. Categorical variables were compared using chi-square or Fisher’s exact test. Univariate logistic regression identified potential predictors of VTE. Variables with p \u0026lt; 0.05 were entered into a multivariate logistic regression model to determine independent associations. To minimize confounding and selection bias, propensity score matching (PSM) was performed using 1:1 nearest-neighbor matching with a caliper of 0.2, based on baseline covariates. All tests were two-tailed, and a p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e This study received ethical approval from the Third Affiliated Hospital of Chongqing Medical University's Ethics Committee (approval No. 2025-18, January 18, 2025).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eParticipant Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 1,090 patients were screened. After applying the exclusion criteria, 959 patients were included in the final analysis: 531 in the no-VTE-CPC group and 428 in the VTE-CPC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The baseline characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A statistically significant difference was observed in age between the groups (p\u0026thinsp;=\u0026thinsp;0.035), whereas other demographic and clinical variables were comparable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient baseline and clinical characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo VTE-CPC service\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVTE-CPC service\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;959)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;533)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;426)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e412 (42.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232 (43.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180 (42.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (median, years [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.00 [53.00, 74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.00 [54.00, 74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.00 [51.00, 74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;70 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e373 (38.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e218 (40.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e155 (36.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative hospital stays (\u0026ge;\u0026thinsp;7 days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105 (10.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61 (11.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44 (10.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e296 (30.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167 (31.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e129 (30.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (5.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30 (5.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (4.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver underlying disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68 (7.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44 (8.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (5.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e118 (12.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62 (11.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (13.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17 (1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrinary system disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11 (2.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e344 (35.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e190 (35.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e154 (36.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e137 (14.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (14.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61 (14.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission laboratory test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026ge;\u0026thinsp;300\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89 (9.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55 (10.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34 (7.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (\u0026ge;\u0026thinsp;100 g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e926 (96.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e514 (96.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e412 (96.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal D-dimer values (\u0026ge;\u0026thinsp;5.0 \u0026micro;g/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e615 (64.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e344 (64.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e271 (63.62)\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\u003eVTE risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23 (2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e496 (51.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e283 (53.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e213 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e440 (45.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e234 (43.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e206 (48.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e536 (55.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e291 (54.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e245 (57.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e423 (44.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e242 (45.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e181 (42.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthroscopic surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132 (13.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72 (13.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60 (14.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e238 (24.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138 (25.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100 (23.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpinal surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e269 (28.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148 (27.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121 (28.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal fixation surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165 (17.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92 (17.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73 (17.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTHA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90 (9.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51 (9.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39 (9.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTKA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65 (6.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32 (6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnesthesia duration (\u0026ge;\u0026thinsp;180 min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e346 (36.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e197 (36.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e149 (34.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood loss (\u0026ge;\u0026thinsp;50 mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e445 (46.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e243 (45.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e202 (47.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood transfusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41 (4.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (4.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (3.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.384\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e308 (32.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167 (31.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e141 (33.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as number (n) of patients (%) unless specified otherwise. *Results are statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eVTE-CPC: venous thromboembolism clinical pharmaceutical care; IQR: interquartile range; BMI: body mass index; VTE: Venous thromboembolism; PVP: percutaneous vertebroplasty; THA: hip Arthroplasty; TKA: total knee arthroplasty.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncidence of Venous Thromboembolism\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe incidence of VTE was significantly lower in the VTE-CPC group compared with the no VTE-CPC group. Specifically, VTE occurred in 40 of 531 patients (7.53%) in the no VTE-CPC group and in 16 of 428 patients (3.74%) in the VTE-CPC group (p\u0026thinsp;=\u0026thinsp;0.020), indicating a beneficial effect of the pharmacist-led intervention.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLogistic Regression Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnivariate logistic regression identified 14 variables significantly associated with perioperative VTE (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): implementation of VTE-CPC, age\u0026thinsp;\u0026ge;\u0026thinsp;70 years, chronic lung disease, diabetes, malignant tumor, urinary system disease, hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;100 g/L, elevated D-dimer, high postoperative bleeding risk, surgery type, anesthesia duration\u0026thinsp;\u0026ge;\u0026thinsp;180 min, intraoperative blood loss\u0026thinsp;\u0026gt;\u0026thinsp;50 mL, intraoperative blood transfusion, and catheter drainage (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis and multivariate analysis of VTE-related risk factors in 959 patients with orthopedic surgery\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVTE-CPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48 (0.26\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.016*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45 (0.23\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09 (0.63\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;70 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.03 (1.18\u0026ndash;3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.14 (1.07\u0026ndash;4.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.031*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative hospital stays (\u0026ge;\u0026thinsp;7 days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79 (0.27\u0026ndash;1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97 (0.53\u0026ndash;1.72))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.33 (1.39\u0026ndash;7.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.52 (0.96\u0026ndash;6.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.048*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.54 (0.39\u0026ndash;9.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver underlying disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97 (0.79\u0026ndash;4.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08 (0.06\u0026ndash;5.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30 (1.15\u0026ndash;4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.56 (0.72\u0026ndash;3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01(0.06\u0026ndash;5.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.59 (1.02\u0026ndash;15.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.022*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.49 (0.67\u0026ndash;14.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrinary system disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.78 (1.06\u0026ndash;10.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.020*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.44 (0.58\u0026ndash;8.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.84 (0.46\u0026ndash;1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.43\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission Laboratory Examination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026ge;\u0026thinsp;300*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.54 (0.13\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (\u0026ge;\u0026thinsp;100 g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33 (0.13\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53 (0.17\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal D-dimer values (\u0026ge;\u0026thinsp;5.0\u0026micro;g/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.70 (1.40\u0026ndash;5.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.15 (1.00-4.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVTE risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59 (0.11\u0026ndash;10.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.32 (0.47\u0026ndash;42.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.40 (1.38\u0026ndash;4.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36 (0.11\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthroscopic surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11 (0.29\u0026ndash;5.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46 (0.09\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpinal surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.02 (1.37\u0026ndash;17.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.026*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46 (0.24\u0026ndash;10.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal fixation surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.07 (0.94\u0026ndash;13.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.23\u0026ndash;5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTHA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.62 (2.03\u0026ndash;29.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.004*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93 (0.29\u0026ndash;14.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTKA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 (0.03\u0026ndash;5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34 (0.01\u0026ndash;3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnesthesia duration (\u0026ge;\u0026thinsp;180 min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.17 (1.83\u0026ndash;5.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.63 (1.19\u0026ndash;6.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative bleeding (\u0026ge;\u0026thinsp;50 mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.12 (2.25\u0026ndash;8.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.35 (0.78\u0026ndash;7.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood transfusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.12 (2.71\u0026ndash;12.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46 (0.51\u0026ndash;3.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.466\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.40 (1.39\u0026ndash;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27 (0.47\u0026ndash;3.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Results are statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eOR: odds ratio; CI: confidence interval; VTE-CPC: Venous thromboembolism clinical pharmaceutical care; BMI: body mass index; VTE: venous thromboembolism; PVP: percutaneous vertebroplasty; THA: total hip arthroplasty; TKA: total knee arthroplasty.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn multivariate analysis, four variables remained independently associated with VTE risk. VTE-CPC was protective (odds ration [OR]\u0026thinsp;=\u0026thinsp;0.45; 95% confidence interval [CI]: 0.23\u0026ndash;0.84; p\u0026thinsp;=\u0026thinsp;0.015), while age\u0026thinsp;\u0026ge;\u0026thinsp;70 years (OR\u0026thinsp;=\u0026thinsp;2.14; 95% CI: 1.07\u0026ndash;4.28; p\u0026thinsp;=\u0026thinsp;0.031), chronic lung disease (OR\u0026thinsp;=\u0026thinsp;2.52; 95% CI: 0.96\u0026ndash;6.09; p\u0026thinsp;=\u0026thinsp;0.048), and anesthesia duration\u0026thinsp;\u0026ge;\u0026thinsp;180 min (OR\u0026thinsp;=\u0026thinsp;2.63; 95% CI: 1.19\u0026ndash;6.01; p\u0026thinsp;=\u0026thinsp;0.019) were associated with increased VTE risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePropensity Score-Matched Cohort\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePSM was conducted to balance baseline covariates. After matching, 812 patients remained (406 in each group), and baseline characteristics were well balanced (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Covariate balance improvement is shown in \u003cb\u003eSupplemental Fig.\u0026nbsp;2\u003c/b\u003e. In the matched cohort, VTE incidence remained significantly lower in the VTE-CPC group, confirming the protective effect observed in multivariate analysis (OR\u0026thinsp;=\u0026thinsp;0.40; 95% CI: 0.21\u0026ndash;0.75; p\u0026thinsp;=\u0026thinsp;0.006).\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\u003ePatient baseline and characteristics in the propensity score-matched cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo VTE-CPC service\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVTE-CPC service\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;820)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;406)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;406)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e337 (41.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168 (41.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e169 (41.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (median, years [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.00 [52.00, 74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.00 [53.00, 73.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.00 [51.00, 74.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.306\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026ge;\u0026thinsp;70 years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e305 (37.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152 (37.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e153 (37.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative hospital stays (\u0026ge;\u0026thinsp;7 days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77 (9.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (8.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43 (10.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e242 (29.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122 (30.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e120 (29.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic lung disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (5.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (5.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver underlying disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28 (6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104 (12.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53 (13.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51 (12.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignant tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrinary system disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10 (2.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e306 (37.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158 (38.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148 (36.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120 (14.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61 (15.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59 (14.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdmission Laboratory Examination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count (\u0026ge;\u0026thinsp;300*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71 (8.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37 (9.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34 (8.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (\u0026ge;\u0026thinsp;100 g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e787 (96.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e393 (96.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e394 (97.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal D-dimer values (\u0026ge;\u0026thinsp;5.0 \u0026micro;g/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e510 (62.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e254 (62.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e256 (63.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVTE risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7 (1.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416 (51.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e211 (51.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e205 (50.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e382 (47.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e188 (46.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e194 (47.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBleeding risk assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e463 (57.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e231 (56.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e232 (57.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e349 (42.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e175 (43.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e174 (42.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthroscopic surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e124 (15.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64 (15.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60 (14.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePVP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e193 (23.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95 (23.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98 (24.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpinal surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e217 (26.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110 (27.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107 (26.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal fixation surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e140 (17.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69 (17.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71 (17.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTHA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e77 (9.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38 (9.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39 (9.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTKA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (7.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30 (7.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31 (7.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnesthesia duration (\u0026ge;\u0026thinsp;180 min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e287 (35.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147 (36.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140 (34.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood loss (\u0026ge;\u0026thinsp;50 mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e377 (46.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e189 (46.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e188 (46.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntraoperative blood transfusion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26 (3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (2.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCatheter drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e259 (31.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (32.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e129 (31.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as number (n) of patients (%) unless specified otherwise.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eVTE-CPC: venous thromboembolism clinical pharmaceutical care; IQR: interquartile range; BMI: body mass index; VTE: venous thromboembolism; PVP: percutaneous vertebroplasty; THA: total hip arthroplasty; TKA,: total knee arthroplasty.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome Evaluation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the comparison of VTE prevention interventions in the matched cohort. While the rates of mechanical and overall pharmacological prophylaxis did not differ significantly between groups, the VTE-CPC group had a significantly higher rate of postoperative pharmacological prophylaxis compared to the no VTE-CPC group (22.66% vs 16.26%, p\u0026thinsp;=\u0026thinsp;0.027).\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\u003eComparison of interventions in the propensity score-matched cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;812)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo VTE-CPC service (n\u0026thinsp;=\u0026thinsp;406)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVTE-CPC service (n\u0026thinsp;=\u0026thinsp;406)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical prophylaxis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e274 (33.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135 (33.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e139 (34.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis days (median, days, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.00 [3.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.00 [3.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00 [4.00, 8.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePharmacological prophylaxis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e193 (23.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86 (21.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107 (26.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis days (median, days, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.00 [4.00, 10.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.80 [4.00, 11.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.20 [4.00, 9.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage appropriateness (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e183 (90.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (81.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e107 (97.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreoperative pharmacological prophylaxis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96 (11.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42 (10.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54 (13.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis days (median, days, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.00 [2.63, 5.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.75 [2.55, 5.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.25 [2.75, 5.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage appropriateness (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90 (93.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36 (85.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54 (100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative pharmacological prophylaxis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158 (19.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66 (16.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92 (22.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProphylaxis days (median, days, [IQR])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.50 [4.00, 8.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.95 [4.00, 8.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00 [3.90, 7.95]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage appropriateness (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e149 (94.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57 (86.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e92 (100.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAcceptance for any prophylaxis (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e383 (47.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180 (44.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e203 (50.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSequential oral anticoagulants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRate of sequential oral anticoagulants (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106 (13.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (8.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72 (17.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDosage appropriateness (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89 (83.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24 (70.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65 (90.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Results are statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eVTE-CPC: Venous thromboembolism clinical pharmaceutical care; IQR: interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe VTE-CPC group demonstrated significantly greater appropriateness of anticoagulant dosing across all timepoints (preoperative, postoperative, overall, and sequential oral anticoagulants), as well as higher acceptance of sequential oral anticoagulant prescriptions (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although the duration of mechanical and pharmacological prophylaxis was slightly longer in the VTE-CPC group, these differences were not statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAdverse Events\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the 812 patients in the matched cohort, no significant differences were observed in the incidence of adverse events between groups (\u003cb\u003eSupplemental Table\u0026nbsp;3\u003c/b\u003e). Specifically, the VTE-CPC group showed no increased risk of bleeding (0.52% vs 0%, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), leukocytosis (32.27% vs 36.70%), thrombocytopenia (1.97% vs 1.48%), anemia (40.89% vs 43.60%), elevated AST (12.56% vs 15.76%), elevated ALT (14.29% vs 16.01%), or elevated serum creatinine (11.58% vs 15.52%). These findings suggest that the pharmacist-led VTE-CPC model did not increase the risk of treatment-related adverse events.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003ePrincipal Findings\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study evaluated the effectiveness of a pharmacist-led anticoagulation management model, developed based on the PEAC framework, for reducing perioperative VTE in orthopedic surgery patients. Our findings demonstrate that implementation of the VTE-CPC model significantly reduced the incidence of hospital-acquired VTE. The VTE incidence in the VTE-CPC group was 3.74%, markedly lower than the 7.53% observed in the no VTE-CPC group. These results remained robust after adjustment for confounders using multivariate logistic regression and PSM, suggesting that the pharmacist-led model contributed meaningfully to improved clinical outcomes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInterpretation and Mechanisms\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTwo potential explanations exist for the reduced VTE incidence in the VTE-CPC group. First, the observed benefit may directly result from enhanced quality of anticoagulation management through pharmacist involvement. Second, baseline imbalances due to the real-world study design could have contributed. For example, previous studies have shown substantial heterogeneity in VTE risk across orthopedic subspecialties, with incidence ranging from as low as 0.58% in percutaneous vertebroplasty [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to as high as 60% in major joint arthroplasty [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although some clinical characteristics differed between groups, logistic regression and PSM analyses were performed to control for such confounding, and the protective effect of VTE-CPC persisted, supporting its role in reducing VTE risk.\u003c/p\u003e\u003cp\u003eFurther analysis of the PSM cohort clarified how the VTE-CPC model achieved its effect. First, the VTE-CPC group demonstrated a significantly higher rate of postoperative pharmacological prophylaxis (22.66% vs. 16.26%, p\u0026thinsp;=\u0026thinsp;0.027). The importance of pharmacologic prophylaxis is well-established; for instance, the incidence of DVT after total knee arthroplasty can exceed 60% without prophylaxis but drops to below 10% with appropriate anticoagulant use [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The integration of clinical pharmacists into multidisciplinary care teams enabled tailored anticoagulation strategies and patient-specific decision-making, improving prophylaxis rates.\u003c/p\u003e\u003cp\u003eSecond, appropriateness of anticoagulant dosing was significantly higher in the VTE-CPC group throughout the care continuum, including preoperative, postoperative, and discharge phases (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This aligns with findings by Diana Yap et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], who reported an increase in dosing appropriateness from 14.3\u0026ndash;31.3% through pharmacist-led risk assessments. Additionally, acceptance of sequential oral anticoagulants doubled in the VTE-CPC group (17.73% vs. 8.37%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with better dosing appropriateness (90.28% vs. 70.59%, p\u0026thinsp;=\u0026thinsp;0.022), indicating improved transitions of care and patient education at discharge. These findings support the utility of the PEAC-based model in strengthening perioperative VTE prevention by addressing known implementation gaps in both prescribing behavior and care transitions.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSafety Profile\u003c/em\u003e\u003c/p\u003e\u003cp\u003eImportantly, the implementation of VTE-CPC did not lead to a higher incidence of adverse events. In the matched cohort, there were no significant differences between groups in the rates of bleeding, thrombocytopenia, anemia, leukocytosis, hepatic dysfunction, or renal impairment. Although a previous study reported an increased bleeding risk with anticoagulant use (Zheng et al., 2023), our findings did not show such an increase. This may be attributed to several safety mechanisms within the VTE-CPC model: (1) risk stratification tools embedded in the clinical pathway, (2) pharmacist-led review of contraindications, and (3) dynamic dose adjustments based on real-time monitoring. However, the low incidence of bleeding events may also reflect limited sample size and reduced power to detect small differences.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStrengths and Limitations\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study has several strengths. It is among the first to evaluate a pharmacist-led VTE management model across a broad range of orthopedic surgeries in a real-world setting. A key strength is the adaptation of the PEAC framework, which facilitates early pharmacist involvement through system-integrated patient identification and consultation, offering a replicable and scalable approach to clinical pharmacy services. The integration of advanced clinical decision support systems and structured care pathways provided a practical, technology-supported intervention that improved both clinical outcomes and care quality.\u003c/p\u003e\u003cp\u003eHowever, several limitations should be noted. First, the retrospective design precludes full control of unmeasured confounding, despite statistical adjustments. Second, although the pharmacological prophylaxis rate in the VTE-CPC group (26.35%) was higher than that reported for surgical patients in China (11.80%) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], it remained substantially lower than the global average (58.5%) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This may be due to a higher proportion of patients at low to moderate VTE risk (52.96%) or high bleeding risk (42.98%), as well as limited pharmacist staffing. Third, our study was restricted to in-hospital outcomes. Given that the risk of VTE persists for 6\u0026ndash;12 weeks postoperatively [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], long-term follow-up is essential to evaluate the sustained safety and effectiveness of VTE-CPC in out-of-hospital settings.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFuture Directions\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo strengthen the evidence base, future multicenter randomized controlled trials are warranted to confirm the generalizability of the VTE-CPC model. The PEAC model's flexibility and technology-driven structure also position it well for broader implementation in other clinical areas requiring proactive medication risk management. In addition, integration of telemedicine platforms could facilitate post-discharge anticoagulation monitoring, improving continuity of care. Extending follow-up to three months would allow for comprehensive assessment of both thrombotic recurrence and bleeding risks, offering valuable insights into the long-term benefit-risk profile of pharmacist-led anticoagulation services.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that a pharmacist-led anticoagulation management model, based on the Pharmacist Early Active Consultation framework, significantly reduces perioperative VTE incidence in orthopedic surgery patients without increasing adverse events. These findings support the integration of clinical pharmacists into multidisciplinary surgical teams to enhance the quality of thromboprophylaxis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics Approval\u003c/h2\u003e\u003cp\u003eThis study received ethical approval from the Third Affiliated Hospital of Chongqing Medical University's Ethics Committee (approval No. 2025-18, January 18, 2025).\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by Technical foresight and project innovation project of the Science and Technology Bureau of Yuzhong District (20210102), the Clinical Pharmacy Priority Construction Project of the Chongqing Health Commission (SLCZDZK202501), the Chongqing Graduate Student Research Innovation Project of Chongqing Municipality Education Commission (No. CYS240329), the Incubation Program of The Third Affiliated Hospital of Chongqing Medical University (KY22056), and the Chongqing Young and Middle-aged Medical High-end Talents Project of the Chongqing Municipal Health Commission.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by J.D., Y.W., and Q.D.. The first draft of the manuscript was written by Y.W. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our sincere appreciation to all the medical personnel of the Orthopedic Department at the Third Affiliated Hospital of Chongqing Medical University for their invaluable support throughout this project. We are grateful for the contributions of Professor Qiang Zhou of the Orthopedic Department. Additionally, we wish to acknowledge the efforts of all staff members who have offered their assisted with this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLutsey PL, Zakai NA. Epidemiology and prevention of venous thromboembolism. Nat reviews Cardiol. 2023;20(4):248\u0026ndash;. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41569-022-00787-6\u003c/span\u003e\u003cspan address=\"10.1038/s41569-022-00787-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u0026thinsp;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones A, Al-Horani RA. Venous Thromboembolism Prophylaxis in Major Orthopedic Surgeries and Factor XIa Inhibitors. 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EFORT open reviews. 2018;3(4):136\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1302/2058-5241.3.170018\u003c/span\u003e\u003cspan address=\"10.1302/2058-5241.3.170018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-pharmacy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijcp","sideBox":"Learn more about [International Journal of Clinical Pharmacy](https://www.springer.com/journal/11096)","snPcode":"11096","submissionUrl":"https://submission.nature.com/new-submission/11096/3","title":"International Journal of Clinical Pharmacy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Venous Thromboembolism, Orthopedic Procedures, Pharmaceutical Services, Anticoagulants, Clinical Pharmacy Services, Patient Safety, Propensity Score Matching","lastPublishedDoi":"10.21203/rs.3.rs-7058295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7058295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVenous thromboembolism (VTE) is a common and preventable complication in orthopedic surgery, yet adherence to prophylaxis guidelines remains suboptimal. A pharmacist-led anticoagulation care model based on the Pharmacist Early Active Consultation (PEAC) framework may enhance the quality and safety of VTE prevention in surgical patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to evaluate the impact of a pharmacist-led Venous Thromboembolism Clinical Pharmaceutical Care (VTE-CPC) model, derived from the PEAC framework, on VTE prevention and anticoagulation quality in orthopedic surgery patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective cohort study was conducted at a tertiary hospital in China. Patients admitted between December 2023 and May 2024 received routine care (no VTE-CPC group), while those admitted between June and November 2024 received additional pharmacist-led interventions (VTE-CPC group). Multivariate logistic regression was used to identify independent risk factors for VTE. Propensity score matching (PSM) was performed to control baseline differences, resulting in a balanced cohort of 812 patients. Outcomes included VTE incidence, pharmacological prophylaxis practices, and safety endpoints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 959 patients were included (no VTE-CPC: n = 531; VTE-CPC: n = 428). The incidence of VTE was significantly lower in the VTE-CPC group (3.74%) compared to the no VTE-CPC group (7.53%, p = 0.020). VTE-CPC remained an independent protective factor in multivariate analysis (OR = 0.45; 95% CI: 0.23–0.84; p = 0.015). In the PSM matched cohort, patients in the VTE-CPC group had higher rates of postoperative pharmacological prophylaxis (22.66% vs. 16.26%, p = 0.027) and improved dosage appropriateness across all perioperative phases (p \u0026lt; 0.05). No significant differences were observed between groups in rates of bleeding events, thrombocytopenia, or hepatic/renal dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA pharmacist-led anticoagulation management model based on the PEAC framework significantly reduced perioperative VTE incidence without increasing adverse events. These findings support broader implementation of proactive, pharmacist-driven strategies to improve thromboprophylaxis quality in orthopedic surgery.\u003c/p\u003e","manuscriptTitle":"Impact of a Pharmacist-led Anticoagulation Model Based on Early Active Consultation in Orthopedic Surgery: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:03:22","doi":"10.21203/rs.3.rs-7058295/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-18T12:51:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T14:43:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T05:57:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118364154499877268739640193712642140342","date":"2025-07-11T12:19:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126165453561433552340337019580239278006","date":"2025-07-10T13:12:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-08T11:13:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-07T06:00:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-07T05:57:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Clinical Pharmacy","date":"2025-07-06T14:04:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-clinical-pharmacy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijcp","sideBox":"Learn more about [International Journal of Clinical Pharmacy](https://www.springer.com/journal/11096)","snPcode":"11096","submissionUrl":"https://submission.nature.com/new-submission/11096/3","title":"International Journal of Clinical Pharmacy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e1e5b544-8d20-477a-a566-61f8684107f2","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T16:00:55+00:00","versionOfRecord":{"articleIdentity":"rs-7058295","link":"https://doi.org/10.1007/s11096-025-01997-x","journal":{"identity":"international-journal-of-clinical-pharmacy","isVorOnly":false,"title":"International Journal of Clinical Pharmacy"},"publishedOn":"2025-09-03 15:57:46","publishedOnDateReadable":"September 3rd, 2025"},"versionCreatedAt":"2025-07-14 10:03:22","video":"","vorDoi":"10.1007/s11096-025-01997-x","vorDoiUrl":"https://doi.org/10.1007/s11096-025-01997-x","workflowStages":[]},"version":"v1","identity":"rs-7058295","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7058295","identity":"rs-7058295","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}