Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study
BackgroundPerioperative monitoring thrombosis has become more crucial due to the rising demand for arthroplasty and shorter hospital stays. We aimed to comprehensively explore immune-inflammatory and hypercoagulable states during perioperative periods patients undergoing arthroplasty to identify the...
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Frontiers Media S.A.
2025-05-01
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| author | Wenming Yang Wenming Yang Qitai Lin Qitai Lin Zehao Li Zehao Li Chuanjie Shan Chuanjie Shan Xiaoyu Cheng Yugang Xing Yugang Xing Yongsheng Ma Yongsheng Ma Yang Liu Yang Liu Meiming Li Meiming Li Ruifeng Liang Wangping Duan Wangping Duan Wangping Duan Pengcui Li Pengcui Li Xiaochun Wei Xiaochun Wei |
| author_facet | Wenming Yang Wenming Yang Qitai Lin Qitai Lin Zehao Li Zehao Li Chuanjie Shan Chuanjie Shan Xiaoyu Cheng Yugang Xing Yugang Xing Yongsheng Ma Yongsheng Ma Yang Liu Yang Liu Meiming Li Meiming Li Ruifeng Liang Wangping Duan Wangping Duan Wangping Duan Pengcui Li Pengcui Li Xiaochun Wei Xiaochun Wei |
| author_sort | Wenming Yang |
| collection | DOAJ |
| description | BackgroundPerioperative monitoring thrombosis has become more crucial due to the rising demand for arthroplasty and shorter hospital stays. We aimed to comprehensively explore immune-inflammatory and hypercoagulable states during perioperative periods patients undergoing arthroplasty to identify the risk factors for early postoperative deep vein thrombosis (DVT) and construct a nomogram prediction model for postoperative DVT.MethodsElectronic medical records of 841 patients who underwent primary arthroplasty at a single institution were retrospectively reviewed. Patients’ demographic and perioperative laboratory data were collected and divided into training (73.8%) and validation sets (26.2%) based on order of procedure date. Variables were screened from the training set using the Least Absolute Shrinkage and Selection Operator (LASSO) regression; a nomogram was constructed after multivariate logistic regression. The validation set was used to evaluate its discriminatory capacity and efficacy. The model’s performance was evaluated through the Brier score, receiver operating characteristic curves, area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsWe found an asymptomatic DVT incidence of 27.5% (231/841) on postoperative day three and identified seven predictors: age, chronic heart failure, stroke, tourniquet, postoperative monocyte-to-lymphocyte ratio, and postoperative alpha and D-dimer levels. The predictive model yielded an AUC of 0.737 (95% CI, 0.6933–0.7785), with an external validation AUC of 0.683 (95% CI, 0.6139–0.7716). The Brier score was 0.176, indicating the model’s strong robustness in predicting perioperative DVT incidence in arthroplasty. Clinical impact and decision curve analysis revealed that using the proposed nomogram for prediction yielded a net benefit for threshold probabilities of 10–70%.ConclusionOur risk prediction model demonstrated reasonable discriminative capacity for predicting perioperative DVT risk in arthroplasty. This model may help increase the clinical benefits for patients by promptly identifying high-risk individuals early postoperatively. |
| format | Article |
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| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-ac60671ba3eb48769bef8c9422af1e322025-08-20T03:13:08ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15281541528154Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective studyWenming Yang0Wenming Yang1Qitai Lin2Qitai Lin3Zehao Li4Zehao Li5Chuanjie Shan6Chuanjie Shan7Xiaoyu Cheng8Yugang Xing9Yugang Xing10Yongsheng Ma11Yongsheng Ma12Yang Liu13Yang Liu14Meiming Li15Meiming Li16Ruifeng Liang17Wangping Duan18Wangping Duan19Wangping Duan20Pengcui Li21Pengcui Li22Xiaochun Wei23Xiaochun Wei24Academy of Medical Sciences, Shanxi Medical University, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaAcademy of Medical Sciences, Shanxi Medical University, Taiyuan, ChinaDepartment of Environmental Health, School of Public Health, Shanxi Medical University, Taiyuan, ChinaDepartment of Environmental Health, School of Public Health, Shanxi Medical University, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Environmental Health, School of Public Health, Shanxi Medical University, Taiyuan, ChinaAcademy of Medical Sciences, Shanxi Medical University, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaDepartment of Orthopaedics, Second Hospital of Shanxi Medical University, Taiyuan, ChinaShanxi Key Laboratory of Bone and Soft Tissue Injury Repair, Taiyuan, ChinaBackgroundPerioperative monitoring thrombosis has become more crucial due to the rising demand for arthroplasty and shorter hospital stays. We aimed to comprehensively explore immune-inflammatory and hypercoagulable states during perioperative periods patients undergoing arthroplasty to identify the risk factors for early postoperative deep vein thrombosis (DVT) and construct a nomogram prediction model for postoperative DVT.MethodsElectronic medical records of 841 patients who underwent primary arthroplasty at a single institution were retrospectively reviewed. Patients’ demographic and perioperative laboratory data were collected and divided into training (73.8%) and validation sets (26.2%) based on order of procedure date. Variables were screened from the training set using the Least Absolute Shrinkage and Selection Operator (LASSO) regression; a nomogram was constructed after multivariate logistic regression. The validation set was used to evaluate its discriminatory capacity and efficacy. The model’s performance was evaluated through the Brier score, receiver operating characteristic curves, area under the curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsWe found an asymptomatic DVT incidence of 27.5% (231/841) on postoperative day three and identified seven predictors: age, chronic heart failure, stroke, tourniquet, postoperative monocyte-to-lymphocyte ratio, and postoperative alpha and D-dimer levels. The predictive model yielded an AUC of 0.737 (95% CI, 0.6933–0.7785), with an external validation AUC of 0.683 (95% CI, 0.6139–0.7716). The Brier score was 0.176, indicating the model’s strong robustness in predicting perioperative DVT incidence in arthroplasty. Clinical impact and decision curve analysis revealed that using the proposed nomogram for prediction yielded a net benefit for threshold probabilities of 10–70%.ConclusionOur risk prediction model demonstrated reasonable discriminative capacity for predicting perioperative DVT risk in arthroplasty. This model may help increase the clinical benefits for patients by promptly identifying high-risk individuals early postoperatively.https://www.frontiersin.org/articles/10.3389/fmed.2025.1528154/fullarthroplastydeep vein thrombosisrisk factorthromboelastographynomogram |
| spellingShingle | Wenming Yang Wenming Yang Qitai Lin Qitai Lin Zehao Li Zehao Li Chuanjie Shan Chuanjie Shan Xiaoyu Cheng Yugang Xing Yugang Xing Yongsheng Ma Yongsheng Ma Yang Liu Yang Liu Meiming Li Meiming Li Ruifeng Liang Wangping Duan Wangping Duan Wangping Duan Pengcui Li Pengcui Li Xiaochun Wei Xiaochun Wei Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study Frontiers in Medicine arthroplasty deep vein thrombosis risk factor thromboelastography nomogram |
| title | Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study |
| title_full | Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study |
| title_fullStr | Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study |
| title_full_unstemmed | Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study |
| title_short | Development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty: a retrospective study |
| title_sort | development and validation of a nomogram prediction model for perioperative deep vein thrombosis risk in arthroplasty a retrospective study |
| topic | arthroplasty deep vein thrombosis risk factor thromboelastography nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1528154/full |
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