中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (01): 27-30,53.doi: 10.3969/j.issn.1671-4091.2025.01.005

• 临床研究 • 上一篇    下一篇

维持性血液透析患者腹主动脉钙化风险预测模型构建

邵 枫    王小琪    袁 丹    刘聪慧    连晓英    李忠心   

  1. 101149 北京，1首都医科大学附属北京潞河医院肾病中心
  • 收稿日期:2024-06-11 修回日期:2024-09-18 出版日期:2025-01-12 发布日期:2025-01-12
  • 通讯作者: 李忠心 E-mail:lhyy6806@ccmu.edu.cn
  • 基金资助:
    生物标志物与透析患者认知功能和血管钙化的相关性及其对预后的影响

Construction of a risk prediction model for abdominal aortic calcification in maintenance hemodialysis patients

SHAO Feng, WANG Xiao-qi, YUAN Dan, LIU Cong-hui, LIAN Xiao-ying, LI Zhong-xin   

  1. Department of Nephrology, Beijing Luhe Hospital, Capital Medical University, Beijing 101149, China
  • Received:2024-06-11 Revised:2024-09-18 Online:2025-01-12 Published:2025-01-12
  • Contact: 101149 北京，1首都医科大学附属北京潞河医院肾病中心 E-mail:lhyy6806@ccmu.edu.cn
  • Supported by:
    The correlation between biomarkers and cognitive function and vascular calcification in dialysis patients and their impact on prognosis

PDF

12

摘要： 目的 研究维持性血液透析(maintenance hemodialysis，MHD)患者血浆致动脉硬化指数(atherogenic index of plasma,AIP)、淋巴细胞与单核细胞比值(lymphocyte/monocyte ratio,LMR)与腹主动脉钙化(abdominal aortic calcification,AAC)的相关性，并构建风险预测模型。 方法 选取2023年5月1日─2024年4月30日在首都医科大学附属北京潞河医院郎府院区血液净化中心接受MHD 治疗的患者。通过单因素及多因素Logistic回归分析来确定ACC的危险因素，建立列线图，并进行内部验证。结果 共纳入158例MHD患者，分为AAC组(n=106)和非AAC组(n=52)；AAC组的年龄(F=1.325，P＜0.001)、校正钙(F=0.343，P=0.016)、AIP(F=8.726，P=0.003)、合并糖尿病(F=9.287，P=0.002)高于非AAC组；透析时长(F=3.681，P=0.007)、血白蛋白(F=3.287，P=0.002)、血磷(F=0.344，P=0.018)、LMR(F=1.824，P=0.022)低于非AAC组。多因素Logistic回归分析发现高龄(OR=1.071,95% CI:1.034～1.108,P＜0.001)、合并糖尿病(OR=3.346,95% CI:1.428～7.843,P=0.005)、高AIP(OR=1.176,95% CI:1.041～1.33,P=0.009)、低LMR(OR=0.777,95% CI:0.614～0.983,P=0.036)是MHD患者发生AAC的独立危险因素。绘制列线图，计算C-index为0.834(95% CI:0.769～0.899)，说明该列线图模型的区分能力较好。绘制校准曲线，模拟曲线和实际曲线绝对误差为0.029，说明模型具有较强的一致性。 结论 对于MHD患者来说，高龄、合并糖尿病、高AIP、低LMR是MHD患者发生AAC的独立危险因素。依据年龄、糖尿病、AIP、LMR构建的风险预测模型有很好的预测效能。

关键词: 血浆致动脉硬化指数, 淋巴细胞与单核细胞比值, 维持性血液透析, 腹主动脉钙化, 风险预测模型

Abstract: Objective To study the correlation between the values of plasma atherosclerotic index (AIP) and lymphocyte/monocyte ratio (LMR) and the presence of abdominal aortic calcification (AAC) in maintenance hemodialysis (MHD) patients, from which a risk prediction model of AAC was then constructed.  Methods  This was a cross-sectional study. Clinical data of the MHD patients treated in our hospital from May 1, 2023 to April 30, 2024 were retrospectively analyzed. Univariate and multivariate logistic regression analyses were applied to determine the risk factors for ACC, from which a column chart was constructed and internally validated.  Results A total of 158 MHD patients were enrolled in this study. They were divided into AAC group (n=106) and non-AAC group (n=52). Age (F=1.325, P＜0.001), corrected serum calcium (F=0.343, P=0.016), AIP (F=8.726, P=0.003) and rate of diabetes history (F=9.287, P=0.002) were higher in AAC group than in non-AAC group; dialysis age (F=3.681, P=0.007), blood albumin (F=3.287, P=0.002), blood phosphorus (F=0.344, P=0.018), and LMR (F=1.824, P=0.022) were lower in AAC group than in non-AAC group. Multivariate logistic regression found that older age (OR=1.071, 95% CI: 1.034～1.108, P＜0.001), diabetes (OR=3.346, 95% CI: 1.428～7.843, P=0.005), high AIP (OR=1.176, 95% CI: 1.041～1.33, P=0.009) and low LMR (OR=0.777, 95% CI: 0.614～0.983, P=0.036) were the independent risk factors for AAC in MHD patients. A column chart was then constructed and the C-index of 0.834 (95% CI: 0.769～0.899) was calculated, indicating a better discriminative ability of the column chart model. A calibration curve was drawn, and the absolute error between simulated curve and actual curve was 0.029, indicating a strong consistency of the model.  Conclusions  Older age, diabetes, higher AIP and lower LMR are the independent risk factors for AAC in MHD patients. The risk prediction model based on age, diabetes, AIP and LMR has a better prediction efficiency.

Key words: Plasma atherosclerotic index, Lymphocyte/monocyte ratio, Maintenance hemodialysis, Abdominal aortic calcification, Risk prediction model

中图分类号: