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

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13.	Melinte RM, Arbănași 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.	张巧云, 李音音, 蒙杰, 等. 中性粒细胞相关参数在下肢深静脉血栓形成的临床意义. 中国实验诊断学, 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.
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17.	徐松, 叶哲伟. 人工智能在骨科的应用现状及前景. 中国医刊, 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.	高远, 潘晓英, 李建涛, 等. 人工智能算法模型在创伤患者下肢静脉血栓栓塞症诊断中的预测价值. 中华创伤杂志, 2021, 37(10): 932-937.
21.	周武, 曹发奇, 曾睿寅, 等. 创伤骨科患者围术期下肢静脉血栓形成诊断及防治专家共识(2022 年). 中华创伤杂志, 2022, 38(1): 23-31.
22.	王艺燕, 何凌霄, 欧阳朝威, 等. 创伤患者静脉血栓栓塞症风险评估工具的研究进展. 军事护理, 2023, 40(2): 95-97.

1. 孙健平, 张堃, 王鹏飞, 等. 骨折患者近端深静脉血栓形成临床特征及危险因素. 中华创伤杂志, 2019, 35(7): 625-630.
2. 刘海龙, 王志聪, 陈曦, 等. 老年髋部骨折患者受伤至入院时间与下肢深静脉血栓形成的关系. 山东医药, 2022, 62(17): 69-72.
3. 夏芊, 向小容, 谢仟, 等. 机器学习在静脉血栓栓塞疾病中的研究进展. 四川医学, 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. 孙林凯, 金家善, 耿俊豹. 基于修正邓氏灰色关联度的设备费用影响因素分析. 数学的实践与认识, 2012, 42(8): 140-145.
8. 袁晨杰, 田侃. 基于灰色关联度分析法的四川省人均卫生费用影响因素研究. 中国医疗管理科学, 2022, 12(1): 23-28.
9. 果爽, 程伟, 刘鑫鑫, 等. 基于 BP 神经网络的医疗卫生服务质量满意度研究. 中国医学物理学杂志, 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, Arbănași 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. 张巧云, 李音音, 蒙杰, 等. 中性粒细胞相关参数在下肢深静脉血栓形成的临床意义. 中国实验诊断学, 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, 叶哲伟. 人工智能在骨科的应用现状及前景. 中国医刊, 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. 高远, 潘晓英, 李建涛, 等. 人工智能算法模型在创伤患者下肢静脉血栓栓塞症诊断中的预测价值. 中华创伤杂志, 2021, 37(10): 932-937.
21. 周武, 曹发奇, 曾睿寅, 等. 创伤骨科患者围术期下肢静脉血栓形成诊断及防治专家共识(2022 年). 中华创伤杂志, 2022, 38(1): 23-31.
22. 王艺燕, 何凌霄, 欧阳朝威, 等. 创伤患者静脉血栓栓塞症风险评估工具的研究进展. 军事护理, 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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