中国血液净化

中国血液净化 ›› 2024, Vol. 23 ›› Issue (03): 209-213.doi: 10.3969/j.issn.1671-4091.2024.03.012

• 血管通路 • 上一篇    下一篇

维持性血液透析患者自体动静脉内瘘血栓形成风险预测模型的构建

金晓瑜    李京淑    吴风如    刘露凝    范宇莹   

  1. 150001 哈尔滨，1哈尔滨医科大学附属第二医院血液透析中心
    150081 哈尔滨，2哈尔滨医科大学护理学院
  • 收稿日期:2023-09-21 修回日期:2023-12-18 出版日期:2024-03-12 发布日期:2024-03-12
  • 通讯作者: 范宇莹 E-mail:fanyuying2008@126.com
  • 基金资助:
    国家自然科学基金(72174048)；黑龙江省省属高等学校基本科研业务费科研项目(31041220049)；
    中央支持地方高校改革发展资金人才培养项目(31021220009)

Construction of risk prediction model for thrombosis in autogenous arteriovenous fistula in maintenance hemodialysis patients

JIN Xiao-yu, LI Jing-shu, WU Feng-ru, LIU Lu-ning, FAN Yu-ying   

  1. Hemodialysis Center, The Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China; 2School of Nursing, Harbin Medical University, Harbin 150081, China
  • Received:2023-09-21 Revised:2023-12-18 Online:2024-03-12 Published:2024-03-12
  • Contact: 150081 哈尔滨，2哈尔滨医科大学护理学院 E-mail:fanyuying2008@126.com

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摘要： 目的 基于机器学习方法构建自体动静脉内瘘血栓形成风险预测模型并进行模型验证。  方法 以2020年3月—2021年12月在哈尔滨医科大学附属第二医院血液净化中心行维持性血液透析的患者为研究对象，应用逻辑回归(logistic regression，Logistic)和随机森林(random forest，RF)构建模型。绘制各模型受试者工作特征曲线(receiver operating characteristic curve，ROC)和受试者工作特征曲线下面积(area under the curve，AUC),并用准确率、特异度、灵敏度和F1度量评价模型性能。 结果 270例MHD患者中，AVF血栓形成组105例(38.89%)，非AVF血栓形成组165例(61.11%)，最终纳入吸烟史(OR=2.992，95% CI：1.306～6.854，P=0.010)、高血压史(OR=12.376，95% CI：3.432～44.624，P＜0.001)、糖尿病史(OR=7.477，95% CI：2.887～19.360，P＜0.001)、高血脂史(OR=6.947，95% CI：2.733～17.659，P＜0.001)、冠心病史(OR=12.894，95% CI：4.827～34.439，P＜0.001)、穿刺点压迫时间(OR=1.132，95% CI：1.053～1.217，P=0.010)、三酰甘油(OR=1.322，95% CI：1.005～1.741，P=0.046)等7个因素构建风险预测模型。RF预测模型的AUC为0.944，Logistic模型的AUC为0.895(Z=1.688，P=0.092)。 结论 吸烟史、高血压史、糖尿病史、高血脂史、冠心病史、穿刺点压迫时间和三酰甘油是MHD患者发生AVF血栓形成的高危因素，基于上述危险因素构建的2种预测模型性能良好，可互相补充。

关键词: 维持性血液透析, 动静脉内瘘, 预测模型, 机器学习, 血栓形成

Abstract: Objective  To construct a risk prediction model for thrombosis in autogenous arteriovenous fistula (AVF) based on machine learning, and to verify the model.  Methods  A total of 270 patients undergoing maintenance hemodialysis (MHD) in Hemodialysis Center, The Second Affiliated Hospital of Harbin Medical University from March 2020 to December 2021 were recruited as the study subjects. Logistic regression and random forest were used to construct the models. Receiver operating characteristic curve and area under the curve (AUC) were plotted for each model. AUC, accuracy, specificity, sensitivity and F1-score were used to evaluate the models.  Results  Among 270 MHD patients, 105 cases (38.89%) were in AVF thrombosis group and 165 cases (61.11%) in non-AVF thrombosis group. The seven risk factors of smoking history (OR=2.992, 95% CI: 1.306～6.854, P=0.010), hypertension history (OR=12.376, 95% CI: 3.432～44.624, P<0.001), diabetes history (OR=7.477, 95% CI: 2.887～19.360, P<0.001), hyperlipidemia history (OR=6.947, 95% CI: 2.733～17.659, P<0.001), coronary heart disease history (OR=12.894, 95% CI: 4.827～34.439, P<0.001), puncture point compression time (OR=1.132, 95% CI: 1.053～1.217, P=0.010), and triglyceride (OR=1.322, 95% CI: 1.005～1.741, P=0.046) were used to construct risk prediction models. The area under the curve of random forest prediction model was 0.944, and that of logistic regression model was 0.895 (Z=1.688, P=0.092).  Conclusion  Smoking history, hypertension history, diabetes history, hyperlipidemia history, coronary heart disease history, puncture point compression time and triacylglycerol are high risk factors for thrombosis in AVF in MHD patients. The two models based on the seven risk factors have good predictive performance and can be complementary each other.

Key words: Maintenance hemodialysis, Autogenous arteriovenous fistula, Prediction model, Machine learning, Thrombosis

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