中国血液净化

中国血液净化 ›› 2022, Vol. 21 ›› Issue (05): 350-355.doi: 10.3969/j.issn.1671-4091.2022.05.011

• 临床研究 • 上一篇    下一篇

列线图模型对3906名血液透析患者透析中低血压发生风险的预测及评估

何鑫1,2詹亚1,2张红1,2洪大情1,2,3李贵森   

  1. 1西南医科大学临床医学院 2四川省医学科学院·四川省人民医院 3电子科技大学医学院
  • 收稿日期:2021-11-09 修回日期:2021-12-11 出版日期:2022-05-12 发布日期:2022-05-12
  • 通讯作者: 李贵森 E-mail:guisenli@163.com
  • 基金资助:
    四川省重点研发项目(2019YFS0538)

Prediction and evaluation of intradialytic hypotension risk in 3,906 hemodialysis patients by a nomogram model 

HE Xin1,2, ZHAN Ya1,2, ZHANG Hong1,2, HONG Da-qing1,2,3, LI Gui-sen1,   

  1. 1School of Clinical Medicine, Southwest Medical University, Luzhou 646000, China;  2Department of Nephrology, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu 610072, China; 3School of Medicine, University of Electronic Science and Technology, Chengdu 610072, China
  • Received:2021-11-09 Revised:2021-12-11 Online:2022-05-12 Published:2022-05-12

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摘要： 目的透析中低血压(indradialytichypotension,IDH)是血液透析最常见的并发症之一。本研究的目的是通过构建列线图来预测血液透析患者IDH的发生风险。方法本研究纳入2014年～2020年于四川省人民医院接受血液透析的患者，采集患者的基本信息、实验室检查和透析治疗记录，设计了多变量逻辑回归模型，应用R语言程序构建列线图模型以预测IDH发生风险，使用Bootstrap法进行验证，通过绘制受试者操作特征(ROC)曲线，计算曲线下面积和内部验证C-指数以评估模型的预测性能。预测模型的一致性通过绘制预测结果与实际结果的校正曲线来判断。结果共纳入3906例患者，314534条透析治疗记录。经回归分析，年龄(OR：1.016，95%CI：1.015～1.017，P＜0.001)、性别(男性OR：1.280，95%CI：1.268～1.282，P＜0.001)、透析前收缩压[＜90mmHg(1mmHg=0.133kPa)OR：7.242，95%CI：6.376～8.156，P＜0.001；＞140mmHgOR：2.656，95%CI：2.345～2.996，P＜0.001]、透析前舒张压(＜60mmHgOR：3.772，95%CI：3.596～3.883，P＜0.001；＞90mmHgOR：1.741，95%CI：1.707～1.783，P＜0.001)、心率(＜60次/minOR：2.314，95%CI：2.173～2.461，P＜0.001；＞100次/minOR：1.456，95%CI：1.397～1.514，P＜0.001)、初步超滤量(OR：1.350，95%CI：1.336～1.362，P＜0.001)、血红蛋白(OR：1.001，95%CI：1.001～1.002，P＜0.001)和血钙(＜2.25mmol/LOR：1.270，95%CI：1.211～1.339，P＜0.001；＞2.75mmol/LOR：1.087，95%CI：1.068～1.102，P＜0.001)被识别并输入列线图。列线图显示出较好的辨别力，ROC曲线下面积为0.729(95%CI：0.727～0.732)，内部验证C-指数0.730，测试集ROC曲线下面积为0.727(95%CI0.724～0.731)。IDH概率的校准曲线显示了列线图预测的概率与实际概率之间的一致性。结论本研究构建的列线图可用于甄别IDH的高风险患者并指导IDH患者个性化的诊疗干预措施，表明列线图可能具有临床效用。该模型有可能帮助临床医生提出治疗建议。

关键词: 血液透析, 透析中低血压, 列线图

Abstract: Objective Intradialytic hypotension (IDH) is one of the most common complications in hemodialysis. The purpose of this study was to predict the risk of IDH in hemodialysis patients by constructing a nomogram model.  Methods  The hemodialysis patients in Sichuan Provincial People's Hospital from 2014 to 2020 was included in this study. Their basic information, laboratory examinations and dialysis treatment records were collected. A multivariate logistic regression model was designed, and the nomogram model was constructed using R language program to predict the risk of IDH. The receiver operating characteristic curve (ROC) and calibration curves were used to investigate and evaluate the discrimination and calibration of the model, while the decision curve analysis was used to evaluate its clinical validity.  Results  A total of 3,906 patients and 314,534 dialysis records were included. After regression analysis, age (OR=1.016, 95% CI 1.015~1.017, P＜0.001), gender (male OR=1.280, 95% CI：1.268~1.282, P＜0.001), pre-dialysis systolic blood pressure (＜90mmHg OR=7.242, 95% CI：6.376~8.156, P＜0.001;>140mmHg OR=2.656, 95% CI：2.345~2.996, P＜0.001), pre-dialysis diastolic blood pressure (＜60mmHg OR=3.772, 95% CI：3.596~3.883, P＜0.001;    ＞90mmHg OR=1.741, 95% CI：1.707~1.783, P＜0.001), heart rate (＜60/min OR=2.314, 95% CI：2.173~2.461, P＜0.001; >100/min OR=1.456, 95% CI：1.397~1.514, P＜0.001), preliminary ultrafiltration volume (OR=1.350, 95% CI：1.336~1.362, P＜0.001), hemoglobin (OR=1.001, 95% CI：1.001~1.002, P＜0.001) and blood calcium (＜2.25mmol/L OR=1.270, 95% CI：1.211~1.339, P＜0.001; ＞2.75mmol/L OR=1.087, 95% CI 1.068~1.102, P＜0.001) were identified and input into the nomogram. The nomogram shows better discrimination. The area under the receiver operating characteristic (ROC) curve of the training set is 0.729 (95% CI 0.727~0.732), and the internal verification C-index is 0.730; the area under the ROC curve of the test set is 0.727 (95% CI 0.724~0.731). The calibration curve of IDH probability shows the consistency between the predicted probability of nomogram and the actual probability.  Conclusion  The nomogram model constructed in this study can be used to identify the patients with high-risk of IDH and to help clinicians make personalized diagnosis and treatment interventions for IDH, indicating that this nomogram model may have clinical utilities.

Key words: emodialysis, Intradialytic hypotension, Nomogram

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