中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (04): 278-282.doi: 10.3969/j.issn.1671-4091.2025.04.004

• 临床研究 • 上一篇    下一篇

构建评估连续性肾脏替代治疗抗凝失败风险的列线图模型

刘 倩   张 明   孙邵婷   刘一坤   

  1. 061000 沧州，1沧州市人民医院肾病内科
  • 收稿日期:2024-10-31 修回日期:2025-01-23 出版日期:2025-04-12 发布日期:2025-04-12
  • 通讯作者: 张明 E-mail:zm20052220@163.com
  • 基金资助:
    2022年沧州市科技计划项目(222106116)

Constructing nomogram model to evaluate the risk of anticoagulation failure in continuous renal replacement therapy

LIU Qian, ZHANG Ming, SUN Shao-ting, LIU Yi-kun   

  1. Department of Nephrology, Cangzhou People's Hospital, Cangzhou 061000, China
  • Received:2024-10-31 Revised:2025-01-23 Online:2025-04-12 Published:2025-04-12
  • Contact: 061000 沧州，1沧州市人民医院肾病内科 E-mail:zm20052220@163.com

摘要： 目的  探讨基于LASSO-Logistic回归分析构建列线图模型评估连续性肾脏替代治疗(continuous renal replacement therapy,CRRT)抗凝失败的风险。 方法  采用便利抽样法，回顾性收集2021年3月—2024年3月在沧州市人民医院行CRRT患者的临床资料，依据患者24小时内是否发生体外循环凝血分为凝血组和非凝血组。应用单因素分析危险因素变量，再使用LASSO回归筛选全部变量，合并共同变量；影响因素应用Logistic回归分析，再构建列线图模型，利用ROC曲线、校准曲线、Bootstrap法内部验证列线图模型性能；应用决策曲线评估临床效用。 结果  共纳入168例患者，其中凝血组50例，非凝血组118例。Logistic回归结果显示高血小板计数(PLT)、高红细胞比容(HCT)、活化部分凝血酶原时间(APTT)缩短、跨膜压(transmembrane pressure,TMP)增加、血流速度减慢、血泵停泵是CRRT体外循环24小时内凝血的危险因素(OR=2.672、3.040、0.435、2.568、0.471、2.662，95% CI：1.436～4.974、1.507～6.133、0.220～0.861、1.288～5.119、0.253～0.874、1.284～5.519，P=0.002、0.002、0.017、0.007、0.017、0.008)。构建CRRT体外循环24小时内凝血风险的列线图模型，Bootstrap法、ROC曲线验证显示模型有较好的区分度，一致性指数(C-index)为0.936(95% CI：0.844～0.963)，AUC为0.978(95% CI：0.958～0.997)；Hosmer-Lemeshoe拟合优度检验结果显示模型具有良好的拟合效果和精准度(χ2=2.922，P=0.939)，校正曲线接近于理想曲线。 结论  CRRT体外循环24小时内凝血与PLT、HCT、APTT、TMP、血流速度减慢、血泵停泵等因素密切相关。本研究构建的列线图模型具有较好的预测价值和临床效益。

关键词: 连续性肾脏替代治疗, 凝血, 列线图模型, LASSO-Logistic回归分析

Abstract: Objective  To explore the nomogram model based on LASSO-logistic regression analysis to evaluate the risk of anticoagulation failure in continuous renal replacement therapy (CRRT).  Methods  The clinical data of 168 patients who underwent CRRT in our hospital from March 2021 to March 2024 were retrospectively collected by convenient sampling method. According to whether the patients had coagulation during cardiopulmonary bypass within 24 hours, they were divided into coagulation group and non-coagulation group. The risk factors were selected by single factor analysis, then all variables were screened by LASSO regression, and the common variables were combined. Logistic regression analysis was used to analyze the influencing factors, and then nomogram model was constructed, and the performance of nomogram model is verified by using receiver operating characteristic (ROC) curve, calibration curve and Bootstrap method. The decision curve was used to evaluate its clinical utility.  Results  50 patients with CRRT had cardiopulmonary bypass coagulation within 24 hours, and the incidence rate was 29.76%. Logistic regression results showed that high platelet count (PLT), high hematocrit (HCT), shortened activated partial prothrombin time (APTT), increased transmembrane pressure (TMP), slow blood flow and pump failure with blood were the risk factors for coagulation during 24h of CRRT cardiopulmonary bypass (OR=2.672, 3.040, 0.435, 2.568, 0.471, 2.662, 95% CI: 1.436～4.974, 1.507～6.133, 0.220～0.861, 1.288～5.119, 0.253～0.874, 1.284～5.519, P=0.002, 0.002, 0.017, 0.007, 0.017, 0.008，respectively). To construct a nomogram model of the risk of coagulation during 24h of CRRT cardiopulmonary bypass. The results of Bootstrap method and ROC curve show that the model has a good discrimination, and the consistency index (C-index) was 0.936 (95% CI: 0.844～0.963), and the area under the curve (AUC) was 0.978 (95% CI: 0.958～0.997). Hosmer-Lemeshoe goodness-of-fit test results show that the model has good fitting effect and accuracy (χ2=2.922, P=0.939), and the calibration curve was close to the ideal curve.  Conclusions  Coagulation during 24h of CRRT cardiopulmonary bypass is closely related to PLT, HCT, APTT, TMP, slow blood flow and pump stop, and the nomogram model has good predictive value and clinical benefits. 

Key words: Continuous renal replacement therapy, Coagulation, Nomogram model, LASSO-Logistic regression analysis

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