基于机器学习与SHAP的全髋关节置换术患者下肢深静脉血栓可解释性预测模型构建研究

doi:10.3969/j.issn.1006-5253.2024.01.003

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (6533 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS) Supporting Info

Guide

Abstract Objective To construct a machine learning model to predict the risk of deep venous thrombosis (DVT) in patients undergoing total hip arthroplasty (THA), and to identify key risk factors influencing DVT using shapley additive explanations (SHAP) method.Methods We retrospectively analyzed data from 416 patients who underwent THA in Wenzhou People′s Hospital from January 1, 2017 to July 31, 2022, and randomly divided them into a training set and a test set in a 4∶〖KG-*2/3〗1 ratio. Recursive feature elimination and five-fold cross-validation were used to select the best features. Six machine learning algorithms were utilized to develop predictive models, and various performance metrics were employed to evaluate them. The SHAP method was used to analyze the interpretability of the optimal model.Results Four hundred and sixteen patients were included in the final study, including 333 in the training set and 83 in the test set. The XGBoost model was the most accurate on the test dataset, achieving a sensitivity of 0.817, specificity of 0.783, F1 score of 0.860, ROC-AUC of 0.800, and a Brier score of 0.106. SHAP summary plots showed that age, cholesterol, postoperative bed time, fibrinogen, and preoperative plasma D-dimer levels were the top five determinants for post-THA DVT. SHAP values feature dependence plots revealed complex non-linear effects of these factors on DVT risk, with age, bed rest, and fibrinogen showing an inverted U-shaped relationship, and cholesterol displaying a positive correlation. Individual SHAP values offered insights into each predictor′s role in DVT risk.Conclusion This study developed an efficient and interpretable machine learning model to predict DVT risk in THA patients, which is helpful for clinical health professionals in identifying high-risk patients and providing personalized intervention.

Key words： total hip arthroplasty deep venous thrombosis machine learning predictive model model interpretation

Received: 08 September 2023

PACS:

Fund:

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Xu Qing
	Yu Bing
	Zhou Peimin
	Dai Shenjie
	Dong Xiaomin

Cite this article:

Xu Qing,Yu Bing,Zhou Peimin, et al. Construction of interpretable prediction model for lower limb deep vein thrombosis in patients undergoing total hip arthroplasty based on machine learning and SHAP[J]. journal1, 2024, 31(1): 11-18.

URL:

http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2024.01.003 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2024/V31/I1/11

［1］SCOTT C E H, CLEMENT N D, DAVIS E T, et al. Modern total hip arthroplasty: Peak of perfection or room for improvement?［J］. Bone Joint J, 2022, 104-B(2):189-192. DOI: 10.1302/0301-620X.104B2.BJJ-2022-0007.

［2］楼小华，章炳文，景道远．机械通气患者深静脉血栓的发病率和危险因素分析［J］．中国医院统计，2022，29(1)：18-24．DOI: 10.3969/j.issn.1006-5253.2022.01.004.

［3］BAWA H, WEICK J W, DIRSCHL D R, et al. Trends in deep vein thrombosis prophylaxis and deep vein thrombosis rates after total hip and knee arthroplasty［J］. J Am Acad Orthop Surg, 2018, 26(19):698-705. DOI: 10.5435/JAAOS-D-17-00235.

［4］周玥杉．医院静脉血栓栓塞症综合防治管理体系建设及实践研究［D］．遵义：遵义医科大学，2021．

［5］KLEN J, HORVAT G, BLINC A. Perioperative prevention of venous thromboembolism in abdominal surgery patients based on the caprini or the Padua risk score-a single centre prospective observational study［J］. Life (Basel), 2022, 12(11):1843. DOI: 10.3390/life12111843.

［6］GREENER J G, KANDATHIL S M, MOFFAT L, et al. A guide to machine learning for biologists［J］. Nat Rev Mol Cell Biol, 2022, 23(1):40-55. DOI: 10.1038/s41580-021-00407-0.

［7］HAUG C J, DRAZEN J M. Artificial intelligence and machine learning in clinical medicine, 2023［J］. N Engl J Med, 2023, 388(13):1201-1208. DOI: 10.1056/NEJMra2302038.

［8］THE LANCET RESPIRATORY MEDICINE. Opening the black box of machine learning［J］. Lancet Respir Med, 2018, 6(11):801. DOI: 10.1016/s2213-2600(18)30425-9.

［9］CHOWDHURY S U, SAYEED S, RASHID I, et al. Shapley-additive-explanations-based factor analysis for dengue severity prediction using machine learning［J］. J Imaging, 2022, 8(9):229. DOI: 10.3390/jimaging8090229.

［10］中华医学会外科学分会血管外科学组．深静脉血栓形成的诊断和治疗指南：第二版［J］．中国血管外科杂志(电子版)，2013，5(1)：23-26． DOI: 10.3969/j.issn.1674-7429.2013.01.009.

［11］LINGYU, LI, . Robust biomarker screening from gene expression data by stable machine learning-recursive feature elimination methods［J］. Comput Biol Chem, 2022, 100:107747. DOI: 10.1016/j.compbiolchem.2022.107747.

［13］HEIT J A, SPENCER F A, WHITE R H. The epidemiology of venous thromboembolism［J］. J Thromb Thrombolysis, 2016, 41(1):3-14. DOI: 10.1007/s11239-015-1311-6.

［14］ZHANG Z, SONG K, YAO Y, et al. Incidence and risk factors for post-thrombotic syndrome in patients with deep vein thrombosis following total knee and hip arthroplasty［J］. J Arthroplasty, 2019, 34(3):560-563. DOI: 10.1016/j.arth.2018.10.013.

［15］孙丹丹．基于德尔菲法的髋关节置换术患者下肢深静脉血栓风险预警模型的构建［D］．呼和浩特：内蒙古医科大学，2020．

［16］KARASAVVIDIS T, BOURIS V, XIANG W, et al. Prophylaxis for venous thromboembolic events in elective total hip and total knee arthroplasty［J］. Curr Pharm Des, 2022, 28(10):771-777. DOI: 10.2174/1381612828666220418090928.

［17］王宇，张攀，韩文锋，等．快速康复理念在髋关节置换围手术期中的应用［J］．实用骨科杂志，2017，23(2)：110-113．

［18］YANG B S, ZHU Y K, LU X, et al. A novel composite indicator of predicting mortality risk for heart failure patients with diabetes admitted to intensive care unit based on machine learning［J］. Front Endocrinol (Lausanne), 2022, 13:917838. DOI: 10.3389/fendo.2022.917838.

［19］NOHARA Y, MATSUMOTO K, SOEJIMA H, et al. Explanation of machine learning models using shapley additive explanation and application for real data in hospital［J］. Comput Methods Programs Biomed, 2022, 214:106584. DOI: 10.1016/j.cmpb.2021.106584.