中国血液净化

中国血液净化 ›› 2023, Vol. 22 ›› Issue (07): 488-492.doi: 10.3969/j.issn.1671-4091.2023.07.003

• 临床研究 • 上一篇    下一篇

基于维持性血液透析患者失衡综合征的高危因素构建预测模型与验证

王以旺   王 磊   崔 颖   

  1. 100078 北京，1首都医科大学附属北京友谊医院肾内科(血液净化中心)
  • 收稿日期:2023-03-28 修回日期:2023-04-24 出版日期:2023-07-12 发布日期:2023-07-12
  • 通讯作者: 王以旺 E-mail:thasmey25854@21cn.com
  • 基金资助:
    北京市卫生科技发展专项(2019-2-612)

Based on the high risk factors of imbalance syndrome in maintenance hemodialysis patients, a prediction model was constructed and verified

WANG Yi-Wang, WANG Lei, CUI Ying   

  1. Department of Nephrology (Blood Purification Center), Beijing Friendship Hospital, Capital Medical University, Beijing 100078, China
  • Received:2023-03-28 Revised:2023-04-24 Online:2023-07-12 Published:2023-07-12
  • Contact: 100078 北京，首都医科大学附属北京友谊医院肾内科(血液净化中心) E-mail:thasmey25854@21cn.com

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摘要： 目的  探讨维持性血液透析(maintenance hemodialysis，MHD)患者发生失衡综合征(dialysis disequilibrium syndrome，DDS)的高危因素，并构建预测模型。 方法  选取2019年8月～2022年8月首都医科大学附属北京友谊医院321例MHD患者，按照7：3比例随机分为训练组(n=225)和内部验证组(n=96)，根据是否发生DDS分为DDS亚组、非DDS亚组，统计2组失衡综合征发生率、人口学特征、生化指标等，基于训练组数据构建Logistic模型及随机森林模型，并行内外部验证。 结果 单因素分析显示：训练组和内部验证组中，DDS亚组年龄(t=32.154、24.618，均P＜0.001)、每周透析次数(t=10.632、8.211，均P＜0.001)、癫痫(χ2=4.647、7.248，P=0.031、0.007)、血红蛋白(t=21.366、15.476，均P＜0.001)、认知障碍(χ2=4.644、5.403，P=0.031、0.020)、尿素氮(t=21.284、13.058，均P＜0.001)、白蛋白(t=13.094、9.018，均P＜0.001)与非DDS亚组比较，差异有统计学意义；Logistic分析显示：每周透析次数(OR=6.360，95%CI：1.968～20.554，P＜0.001)、认知障碍(OR=8.404，95% CI：2.446～28.877，P＜0.001)、血红蛋白(OR=4.889，95% CI：1.436～16.645，P＜0.001)、白蛋白(OR=0.596，95%CI：0.447～0.794，P＜0.001)、尿素氮(OR=4.429，95% CI：1.879～10.441，P＜0.001)是患者发生DDS的影响因素；DDS发生的前5位影响因素依次为尿素氮、认知障碍、血红蛋白、白蛋白、每周透析次数；基于以上因素构建患者发生DDS的Logistic模型及随机森林模型，内部验证显示2种模型预测患者DDS的AUC无明显差异，外部验证显示2种预测模型与实际结果比较无明显差异。 结论  MHD患者发生DDS影响因素依次为尿素氮、认知障碍、血红蛋白、白蛋白、每周透析次数，基于以上因素构建的预测模型价值可靠。

关键词: 维持性血液透析, 失衡综合征, 高危因素

Abstract: Objective  To investigate the risk factors for the development of dialysis disequilibrium syndrome (DDS) in patients on maintenance hemodialysis (MHD) and to construct a predictive model. Methods  A total of 321 patients with MHD in our hospital from August 2019 to August 2022 were selected and patients were randomly divided into the training group (n=225) and the internal verification group (n=96) according to the proportion of 7:3. The two groups were divided into the DDS subgroup and the non-DDS subgroup according to the occurrence of DDS. The incidence of imbalance syndrome, demographic characteristics and biochemical indicators of the two groups were analyzed. The logistic regression model and random forest model were constructed based on the data of the training group. Then Parallel internal and external validation was performed in the groups.  Results Univariate analysis showed that in the training and internal validation groups, age (t=32.154, 24.618, both P＜0.001), number of dialysis sessions per week (t=10.632, 8.211, both P＜0.001), epilepsy (χ2=4.647, 7.248, P=0.031, 0.007), hemoglobin (t= 21.366, 15.476, all P＜0.001), cognitive impairment (χ2=4.644, 5.403, P=0.031, 0.020), urea nitrogen (t=21.284, 13.058, all P＜0.001), and albumin (t=13.094, 9.018, all P＜0.001) between the DDS subgroups and the non-DDS subgroup were significant differences (P＜0.05). Logistic regression analysis showed that the number of dialysis sessions per week (OR=6.360, 95% CI: 1.968 to 20.554, P＜0.001), cognitive impairment (OR=8.404, 95% CI: 2.446 to 28.877, P＜0.001), hemoglobin (OR=4.889, 95% CI: 1.436 to 16.645, P＜0.001), albumin (OR=0.596, 95% CI: 0.447 to 0.794, P＜0.001), and urea nitrogen (OR=4.429, 95% CI: 1.879 to 10.441, P＜0.001) were factors influencing the occurrence of DDS in patients (P＜0.05). The top 5 influencing factors for the occurrence of DDS were obtained in the following order: urea nitrogen, cognitive impairment, hemoglobin, albumin, and number of dialysis sessions per week. Based on the above factors, logistic regression models and random forest models for the occurrence of DDS in patients were constructed, and internal validation showed that there was no significant difference between the two models in predicting the AUC of DDS in patients, and external validation showed that there was no significant difference between the two models and the actual results.  Conclusion  The influence of DDS in MHD patients is due to urea nitrogen, cognitive impairment, hemoglobin, albumin, and weekly dialysis times. The prediction model built based on the above factors is reliable and provides a certain reference for clinical treatment identification of DDS.

Key words: Maintenance hemodialysis, Imbalance syndrome, High risk factors

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