Study on the risk of preoperative deep vein thrombosis after lower limb fracture based on grey relational analysis and BP neural network_West China Medical Journal

Authors：

YE Jiahui ¹ , WANG Zhicong ² , MA Mingzhi ³ ,  CHEN Xi ²

1. School of Medicine and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P. R. China;
2. Department of Orthopedics, Deyang People’s Hospital, Deyang, Sichuan 618000, P. R. China;
3. Clinical Medical Department, North Sichuan Medical College, Nanchong, Sichuan 637000, P. R. China;

Corresponding?author：

CHEN Xi, Email: 529590157@qq.com

Keywords：

Fracture of lower limb; artificial intelligence; deep vein thrombosis

DOI：

10.7507/1002-0179.202308152

Video：

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Objective To explore the efficiency of artificial intelligence algorithm model using preoperative blood indexes on the prediction of deep vein thrombosis (DVT) in patients with lower limb fracture before operation. Methods Patients with lower limb fracture treated in the Department of Orthopedics of Deyang People’s Hospital between January 2018 and December 2022 were retrospectively selected. Their basic and clinical data such as age, gender, height and weight, and laboratory examination indicators at admission were collected, then the neutrophi to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), and platelet to lymphocyte ratio (PLR) were calculated. According to color Doppler ultrasound indication of DVT in lower extremities at admission, the patients were divided into DVT group and non-DVT group. After data preprocessing, grey relational analysis (GRA) was used to screen the combination model of important predictive features of DVT, and BP neural network prediction model was established using the selected features. Finally, the accuracy of BP neural network prediction model was evaluated, and was compared with those of different models in clinical prediction of DVT. Results A total of 4033 patients with lower limb fracture were enrolled, including 3127 cases in the DVT group and 906 cases in the non-DVT group. GRA selected seven important predictive features: absolute lymphocyte value, NLR, MLR, PLR, plasma D-dimer, direct bilirubin, and total bilirubin. The accuracies of logistic regression analysis, random forest, decision tree, BP neural network and GRA-BP neural network combination model were 74%, 76%, 75%, 84% and 87%, respectively. The GRA-BP neural network combination model had the highest accuracy. Conclusion The GRA-BP neural network selected in this paper has the highest accuracy in preoperative DVT risk prediction in patients with lower limb fracture, which can provide a reference for the formulation of DVT prevention strategies.

Citation： YE Jiahui, WANG Zhicong, MA Mingzhi, CHEN Xi. Study on the risk of preoperative deep vein thrombosis after lower limb fracture based on grey relational analysis and BP neural network. West China Medical Journal, 2023, 38(10): 1485-1489. doi: 10.7507/1002-0179.202308152 Copy

1.	孫健平, 張堃, 王鵬飛, 等. 骨折患者近端深靜脈血栓形成臨床特征及危險因素. 中華創傷雜志, 2019, 35(7): 625-630.
2.	劉海龍, 王志聰, 陳曦, 等. 老年髖部骨折患者受傷至入院時間與下肢深靜脈血栓形成的關系. 山東醫藥, 2022, 62(17): 69-72.
3.	夏芊, 向小容, 謝仟, 等. 機器學習在靜脈血栓栓塞疾病中的研究進展. 四川醫學, 2023, 44(1): 78-81.
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6.	Liu H, Yuan H, Wang Y, et al. Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients. Sci Rep, 2021, 11(1): 12868.
7.	孫林凱, 金家善, 耿俊豹. 基于修正鄧氏灰色關聯度的設備費用影響因素分析. 數學的實踐與認識, 2012, 42(8): 140-145.
8.	袁晨杰, 田侃. 基于灰色關聯度分析法的四川省人均衛生費用影響因素研究. 中國醫療管理科學, 2022, 12(1): 23-28.
9.	果爽, 程偉, 劉鑫鑫, 等. 基于 BP 神經網絡的醫療衛生服務質量滿意度研究. 中國醫學物理學雜志, 2023, 40(6): 788-792.
10.	Diao S, Li J, Zhao J, et al. Risk factors and new inflammatory indicators of deep vein thrombosis after adult patella fractures. Front Surg, 2022, 9: 1028542.
11.	Dou C, Li T, Yang S, et al. Epidemiological status and risk factors of deep vein thrombosis in patients with femoral neck fracture. J Orthop Surg Res, 2022, 17(1): 41.
12.	Niu S, Pei Y, Hu X, et al. Relationship between the neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio and deep venous thrombosis (DVT) following femoral neck fractures in the elderly. Front Surg, 2022, 9: 1001432.
13.	Melinte RM, Arb?na?i EM, Blesneac A, et al. Inflammatory biomarkers as prognostic factors of acute deep vein thrombosis following the total knee arthroplasty. Medicina (Kaunas), 2022, 58(10): 1502.
14.	張巧云, 李音音, 蒙杰, 等. 中性粒細胞相關參數在下肢深靜脈血栓形成的臨床意義. 中國實驗診斷學, 2021, 25(6): 833-836.
15.	Wang G, Zhao W, Zhao Z, et al. Leukocyte as an independent predictor of lower-extremity deep venous thrombosis in elderly patients with primary intracerebral hemorrhage. Front Neurol, 2022, 13: 899849.
16.	Wang P, Wang Y, Yuan Z, et al. Venous thromboembolism risk assessment of surgical patients in Southwest China using real-world data: establishment and evaluation of an improved venous thromboembolism risk model. BMC Med Inform Decis Mak, 2022, 22(1): 59.
17.	徐松, 葉哲偉. 人工智能在骨科的應用現狀及前景. 中國醫刊, 2019, 54(2): 117-119.
18.	Xiang YF, Zhao LQ, Liu ZZ, et al. Implementation of artificial intelligence in medicine: status analysis and development suggestions. Artif Intell Med, 2020, 102: 101780.
19.	Martins TD, Annichino-Bizzacchi JM, Romano AVC, et al. Artificial neural networks for prediction of recurrent venous thromboembolism. Int J Med Inform, 2020, 141: 104221.
20.	高遠, 潘曉英, 李建濤, 等. 人工智能算法模型在創傷患者下肢靜脈血栓栓塞癥診斷中的預測價值. 中華創傷雜志, 2021, 37(10): 932-937.
21.	周武, 曹發奇, 曾睿寅, 等. 創傷骨科患者圍術期下肢靜脈血栓形成診斷及防治專家共識(2022 年). 中華創傷雜志, 2022, 38(1): 23-31.
22.	王藝燕, 何凌霄, 歐陽朝威, 等. 創傷患者靜脈血栓栓塞癥風險評估工具的研究進展. 軍事護理, 2023, 40(2): 95-97.

1. 孫健平, 張堃, 王鵬飛, 等. 骨折患者近端深靜脈血栓形成臨床特征及危險因素. 中華創傷雜志, 2019, 35(7): 625-630.
2. 劉海龍, 王志聰, 陳曦, 等. 老年髖部骨折患者受傷至入院時間與下肢深靜脈血栓形成的關系. 山東醫藥, 2022, 62(17): 69-72.
3. 夏芊, 向小容, 謝仟, 等. 機器學習在靜脈血栓栓塞疾病中的研究進展. 四川醫學, 2023, 44(1): 78-81.
4. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet, 2020, 395(10236): 1579-1586.
5. Matuchansky C. Deep medicine, artificial intelligence, and the practising clinician. Lancet, 2019, 394(10200): 736.
6. Liu H, Yuan H, Wang Y, et al. Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients. Sci Rep, 2021, 11(1): 12868.
7. 孫林凱, 金家善, 耿俊豹. 基于修正鄧氏灰色關聯度的設備費用影響因素分析. 數學的實踐與認識, 2012, 42(8): 140-145.
8. 袁晨杰, 田侃. 基于灰色關聯度分析法的四川省人均衛生費用影響因素研究. 中國醫療管理科學, 2022, 12(1): 23-28.
9. 果爽, 程偉, 劉鑫鑫, 等. 基于 BP 神經網絡的醫療衛生服務質量滿意度研究. 中國醫學物理學雜志, 2023, 40(6): 788-792.
10. Diao S, Li J, Zhao J, et al. Risk factors and new inflammatory indicators of deep vein thrombosis after adult patella fractures. Front Surg, 2022, 9: 1028542.
11. Dou C, Li T, Yang S, et al. Epidemiological status and risk factors of deep vein thrombosis in patients with femoral neck fracture. J Orthop Surg Res, 2022, 17(1): 41.
12. Niu S, Pei Y, Hu X, et al. Relationship between the neutrophil-to-lymphocyte ratio or platelet-to-lymphocyte ratio and deep venous thrombosis (DVT) following femoral neck fractures in the elderly. Front Surg, 2022, 9: 1001432.
13. Melinte RM, Arb?na?i EM, Blesneac A, et al. Inflammatory biomarkers as prognostic factors of acute deep vein thrombosis following the total knee arthroplasty. Medicina (Kaunas), 2022, 58(10): 1502.
14. 張巧云, 李音音, 蒙杰, 等. 中性粒細胞相關參數在下肢深靜脈血栓形成的臨床意義. 中國實驗診斷學, 2021, 25(6): 833-836.
15. Wang G, Zhao W, Zhao Z, et al. Leukocyte as an independent predictor of lower-extremity deep venous thrombosis in elderly patients with primary intracerebral hemorrhage. Front Neurol, 2022, 13: 899849.
16. Wang P, Wang Y, Yuan Z, et al. Venous thromboembolism risk assessment of surgical patients in Southwest China using real-world data: establishment and evaluation of an improved venous thromboembolism risk model. BMC Med Inform Decis Mak, 2022, 22(1): 59.
17. 徐松, 葉哲偉. 人工智能在骨科的應用現狀及前景. 中國醫刊, 2019, 54(2): 117-119.
18. Xiang YF, Zhao LQ, Liu ZZ, et al. Implementation of artificial intelligence in medicine: status analysis and development suggestions. Artif Intell Med, 2020, 102: 101780.
19. Martins TD, Annichino-Bizzacchi JM, Romano AVC, et al. Artificial neural networks for prediction of recurrent venous thromboembolism. Int J Med Inform, 2020, 141: 104221.
20. 高遠, 潘曉英, 李建濤, 等. 人工智能算法模型在創傷患者下肢靜脈血栓栓塞癥診斷中的預測價值. 中華創傷雜志, 2021, 37(10): 932-937.
21. 周武, 曹發奇, 曾睿寅, 等. 創傷骨科患者圍術期下肢靜脈血栓形成診斷及防治專家共識(2022 年). 中華創傷雜志, 2022, 38(1): 23-31.
22. 王藝燕, 何凌霄, 歐陽朝威, 等. 創傷患者靜脈血栓栓塞癥風險評估工具的研究進展. 軍事護理, 2023, 40(2): 95-97.

West China Medical Journal

Study on the risk of preoperative deep vein thrombosis after lower limb fracture based on grey relational analysis and BP neural network

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