{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T08:04:35Z","timestamp":1789718675840,"version":"4.0.1"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Surg."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>A risk prediction model for venous thromboembolism (VTE) after orthopedic surgery was constructed and validated using machine learning algorithms, providing a reference for the early identification of high-risk individuals in clinical settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Patients who underwent orthopedic surgery were retrospectively collected from the Medical Information Mart for Intensive Care (MIMIC-IV) database as the modeling group. The patients were randomly divided into training and internal test sets in a 7:3 ratio. A total of 156 patients who underwent orthopedic surgery in our hospital from January 2022 to July 2025 were selected for the external validation set. Predictive variables were selected using the least absolute shrinkage and selection operator (LASSO) regression. Six machine learning models were constructed: logistic regression (LR), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and naive Bayes (NBM). The model performance was evaluated by using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, and G-mean. The interpretability of the optimal model was analyzed using the calibration curves, decision curve analysis (DCA), and SHAP methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The MIMIC-IV database included 461 patients (VTE incidence rate, 15.8%). LASSO regression identified two predictive variables: fibrinogen and D-dimer levels. The AUC of the LGBM model in the internal test set was 0.893 (95% CI: 0.820\u20130.966), and in the external validation, the AUC of the LGBM model was 0.788. The predictive performance of this model was superior to that of the other five models tested. The calibration curve indicated that the model was well calibrated, and the decision curve suggested a clinical net benefit within the 0.1\u20130.6 threshold range. SHAP analysis showed that elevated fibrinogen and D-dimer levels were positively correlated with the risk of VTE.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The LGBM model constructed based on the MIMIC-IV database has excellent predictive efficacy for VTE after orthopedic surgery and certain external generalization abilities. Fibrinogen and D-dimer levels are key predictive factors. This model can serve as a preliminary reference tool for risk stratification of VTE after orthopedic surgery.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fsurg.2026.1884949","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:57:43Z","timestamp":1789718263000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based prediction of postoperative venous thromboembolism in orthopedic surgery using the MIMIC-IV database and external validation"],"prefix":"10.3389","volume":"13","author":[{"given":"Genfa","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Orthopedic Surgery, Yingtan 184 Hospital, China RongTong Medical Healthcare Group Co. Ltd.","place":["Yingtan, Jiangxi Province, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gengjiao","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Yingtan 184 Hospital, China RongTong Medical Healthcare Group Co. Ltd.","place":["Yingtan, Jiangxi Province, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Yingtan 184 Hospital, China RongTong Medical Healthcare Group Co. Ltd.","place":["Yingtan, Jiangxi Province, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Yingtan 184 Hospital, China RongTong Medical Healthcare Group Co. Ltd.","place":["Yingtan, Jiangxi Province, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxing","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, Yingtan 184 Hospital, China RongTong Medical Healthcare Group Co. Ltd.","place":["Yingtan, Jiangxi Province, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"B1","first-page":"189","article-title":"Venous thromboembolism following major orthopedic surgery","volume":"8","author":"Cionac Florescu","year":"2013","journal-title":"Maedica (Bucur)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e52652","DOI":"10.7759\/cureus.52652","article-title":"Association between venous thromboembolism prophylaxis and the incidence of thromboembolism following orthopedic procedures: a cross-sectional study","volume":"16","author":"Ghulam","year":"2024","journal-title":"Cureus"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3317","DOI":"10.1007\/s00167-014-3141-z","article-title":"Less deep vein thrombosis due to transcutaneous fibular nerve stimulation in total knee arthroplasty: a randomized controlled trial","volume":"23","author":"Izumi","year":"2015","journal-title":"Knee Surg Sports Traumatol Arthrosc"},{"key":"B4","doi-asserted-by":"publisher","first-page":"142","DOI":"10.11817\/j.issn.1672-7347.2021.190722","article-title":"Construction and efficiency analysis of prediction model for venous thromboembolism risk in the elderly after hip fracture","volume":"46","author":"Peng","year":"2021","journal-title":"Zhong Nan Da Xue Xue Bao Yi Xue Ban"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1398614","DOI":"10.3389\/fcvm.2024.1398614","article-title":"The association between lactate dehydrogenase to serum albumin ratio and in-hospital mortality in patients with pulmonary embolism: a retrospective analysis of the MIMIC-IV database","volume":"11","author":"Hu","year":"2024","journal-title":"Front Cardiovasc Med"},{"key":"B6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41597-023-01945-2","article-title":"Author correction: MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1431150","DOI":"10.3389\/fimmu.2024.1431150","article-title":"Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression","volume":"15","author":"Guo","year":"2024","journal-title":"Front Immunol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.jpurol.2023.10.030","article-title":"Quantification of vesicoureteral reflux using machine learning","volume":"20","author":"Kabir","year":"2024","journal-title":"J Pediatr Urol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"100691","DOI":"10.1016\/j.apjon.2025.100691","article-title":"Development, validation, and clinical utility of risk prediction models for cancer-associated venous thromboembolism: a retrospective and prospective cohort study","volume":"12","author":"Jin","year":"2025","journal-title":"Asia Pac J Oncol Nurs"},{"key":"B10","first-page":"964","article-title":"Research progress of prevention of venous thromboembolism in patients with foot and ankle surgery","volume":"13","author":"Chen","year":"2020","journal-title":"Chin J Bone Joint Surg"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1186\/s12891-024-08256-6","article-title":"The incidence of venous thromboembolism is low when risk stratification-based thromboprophylaxis is used after fast-track hip and knee arthroplasty","volume":"25","author":"Moisander","year":"2024","journal-title":"BMC Musculoskelet Disord"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e850","DOI":"10.5435\/JAAOS-D-24-00232","article-title":"Venous thromboembolism after knee arthroscopy: incidence, risk factors, prophylaxis, and management","volume":"32","author":"Wollenman","year":"2024","journal-title":"J Am Acad Orthop Surg"},{"key":"B13","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1186\/s12891-020-3100-4","article-title":"Venous thromboembolism rates after hip and knee arthroplasty and hip fractures","volume":"21","author":"Mula","year":"2020","journal-title":"BMC Musculoskelet Disord"},{"key":"B14","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1002\/rth2.12292","article-title":"Machine learning to predict venous thrombosis in acutely ill medical patients","volume":"4","author":"Nafee","year":"2020","journal-title":"Res Pract Thromb Haemost"},{"key":"B15","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/bjh.15780","article-title":"The use of artificial neural network analysis can improve the risk-stratification of patients presenting with suspected deep vein thrombosis","volume":"185","author":"Willan","year":"2019","journal-title":"Br J Haematol"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.3390\/jcm9051582","article-title":"Predicting the risk of recurrent venous thromboembolism: current challenges and future opportunities","volume":"9","author":"Stevens","year":"2020","journal-title":"J Clin Med"},{"key":"B17","first-page":"2327","article-title":"Development of risk prediction model for venous thromboembolism in elderly critically ill patients based on machine learning","volume":"39","author":"Jin","year":"2025","journal-title":"Chin Nurs Res"},{"key":"B18","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.annemergmed.2017.08.005","article-title":"Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index","volume":"71","author":"Levin","year":"2018","journal-title":"Ann Emerg Med"},{"key":"B19","doi-asserted-by":"publisher","first-page":"106","DOI":"10.3870\/j.issn.1001-4152.2022.16.106","article-title":"Application of information technology in nursing care of venous thromboembolism:a literature review","volume":"37","author":"Feng","year":"2022","journal-title":"J Nurs Sci"},{"key":"B20","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1161\/ATVBAHA.122.317164","article-title":"Fibrinogen and factor XIII in venous thrombosis and thrombus stability","volume":"42","author":"Wolberg","year":"2022","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"B21","first-page":"88","article-title":"Prediction value of wells score combined with serum fibrinogen and D-dimer in deep venous thrombosis after hip arthroplasty under general anesthesia","volume":"22","author":"Di","year":"2025","journal-title":"Health Med Res Pract"},{"key":"B22","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1186\/s13018-023-04407-y","article-title":"Coagulation parameters correlate to venous thromboembolism occurrence during the perioperative period in patients with spinal fractures","volume":"18","author":"Jiao","year":"2023","journal-title":"J Orthop Surg Res"},{"key":"B23","doi-asserted-by":"publisher","first-page":"2128","DOI":"10.1016\/j.hrthm.2021.08.027","article-title":"Diagnostic accuracy of D-dimer to detect left atrial thrombus in patients with atrial fibrillation: a systematic review and meta-analysis","volume":"18","author":"Diaz-Arocutipa","year":"2021","journal-title":"Heart Rhythm"},{"key":"B24","doi-asserted-by":"publisher","first-page":"288","DOI":"10.19367\/j.cnki.2096-8388.2026.02.016","article-title":"Development of a nomogram model based on multiple factors for predicting risk of venous thromboembolism in patients with traumatic lower extremity fractures","volume":"51","author":"Dong","year":"2026","journal-title":"J Guizhou Med Univ"},{"key":"B25","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1186\/s13018-024-05074-3","article-title":"Identifying high-risk groups for deep vein thrombosis after primary total knee arthroplasty using preoperative Caprini scores and D-dimer levels","volume":"19","author":"Qiao","year":"2024","journal-title":"J Orthop Surg Res"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.3390\/diagnostics15141787","article-title":"Machine learning-based prediction of postoperative deep vein thrombosis following tibial fracture surgery","volume":"15","author":"Baki","year":"2025","journal-title":"Diagnostics (Basel)"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1574546","DOI":"10.3389\/fmed.2025.1574546","article-title":"A study on the risk prediction model for venous thromboembolism in orthopedic inpatients based on machine learning","volume":"12","author":"Zhang","year":"2025","journal-title":"Front Med (Lausanne)"}],"container-title":["Frontiers in Surgery"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fsurg.2026.1884949\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:57:48Z","timestamp":1789718268000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fsurg.2026.1884949\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"references-count":27,"alternative-id":["10.3389\/fsurg.2026.1884949"],"URL":"https:\/\/doi.org\/10.3389\/fsurg.2026.1884949","relation":{},"ISSN":["2296-875X"],"issn-type":[{"value":"2296-875X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]},"article-number":"1884949"}}