中国血液净化

中国血液净化 ›› 2025, Vol. 24 ›› Issue (11): 935-937.doi: 10.3969/j.issn.1671-4091.2025.11.012

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血液透析与腹膜透析患者全因死亡率预测模型研究进展

朱文博   崔金朝   熊玉香   张 硕   周 跃   秦 岩  夏京华   

  1. 100730 北京，1中国医学科学院北京协和医院肾内科
    100005 北京，2中国医学科学院北京协和医学院群医学及公共卫生学院
  • 收稿日期:2025-03-19 修回日期:2025-04-11 出版日期:2025-11-12 发布日期:2025-11-12
  • 通讯作者: 夏京华 E-mail:xjhsdxt@163.com
  • 基金资助:
    中央高水平医院临床科研专项(2022-PUMCH-B-130)；北京协和医院护理科研基金(XHHLKY202203)

Research progress of prediction models for all-cause mortality in hemodialysis and peritoneal dialysis patients

ZHU Wen-bo, CUI Jin-zhao, XIONG Yu-xiang, ZHANG Shuo, ZHOU Yue, QIN Yan, XIA Jing-hua   

  1. Department of Nephrology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100730, China; 2School of Population Medicine and Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100005, China
  • Received:2025-03-19 Revised:2025-04-11 Online:2025-11-12 Published:2025-11-12
  • Contact: 100730 北京，1中国医学科学院北京协和医院肾内科 E-mail:xjhsdxt@163.com

摘要： 血液透析(hemodialysis,HD)与腹膜透析(peritoneal dialysis,PD)患者全因死亡率受透析模式特异性生物学机制影响，现有风险预测模型存在跨模态验证效能衰减及治疗时序参数缺失等问题。通过整合动态治疗参数与多维度临床数据，构建透析模式适配的机器学习预测体系，可突破传统模型变量选择局限性，为精准制定透析方案提供决策支持。

关键词: 血液透析, 腹膜透析, 全因死亡率, 预测模型

Abstract: All-cause mortality in hemodialysis and peritoneal dialysis patients exhibits modality-specific biological mechanisms, while current risk prediction models face challenges in cross-modality validation and temporal treatment parameter integration. Developing machine learning frameworks that dynamically incorporate multidimensional clinical data and dialysis-specific variables could overcome limitations in traditional model construction, ultimately supporting precision decision-making for end-stage renal disease management.

Key words: Hemodialysis, Peritoneal dialysis, All-cause mortality, Predict model

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