中国血液净化

中国血液净化 ›› 2026, Vol. 25 ›› Issue (03): 238-241.doi: 10.3969/j.issn.1671-4091.2026.03.013

• 综述 • 上一篇    下一篇

慢性肾脏病患者高钾血症影响因素及风险预测模型的研究进展

佟 韩    王筱怡    周 丹   

  1. 116000 大连，1大连医科大学附属第一医院护理部
  • 收稿日期:2025-08-04 修回日期:2025-11-04 出版日期:2026-03-12 发布日期:2026-03-12
  • 通讯作者: 周丹 E-mail:zhoudanwin@163.com
  • 基金资助:
    辽宁省自然基金指导计划(2019-ZD-0924)

Research advances regarding the risk factors and prediction models of hyperkalemia in chronic kidney disease patients

ONG Han, WANG Xiao-yi, ZHOU Dan   

  1. Department of Nursing, First Affiliated Hospital of Dalian Medical University, Dalian 116000, China
  • Received:2025-08-04 Revised:2025-11-04 Online:2026-03-12 Published:2026-03-12
  • Contact: 116000 大连，1大连医科大学附属第一医院护理部 E-mail:zhoudanwin@163.com

摘要： 慢性肾脏病患者的电解质紊乱是肾脏病学领域持续关注的重点问题。其中，高钾血症是最致命的并发症之一。本文综述了慢性肾脏病患者发生高钾血症的危险因素，通过对比传统统计学方法与机器学习方法在预测模型构建中的应用差异，重点分析不同风险预测模型的预测效能，旨在为开发更精准的风险预测工具并推动其临床转化提供理论依据。

关键词: 慢性肾脏病, 高钾血症, 机器学习, 预测模型

Abstract: Electrolyte disorders in chronic kidney disease patients are critical issues in nephrology research, with hyperkalemia being one of the most life-threatening electrolyte disturbances. This review examines the risk factors for hyperkalemia in chronic kidney disease patients, compares the differences in predictive models constructed by traditional statistical methods and machine learning approaches, and analyzes the predictive performance of various models in depth, aiming to provide a theoretical basis for the development of more precise risk prediction tools for clinical applications.

Key words: Chronic kidney disease, Hyperkalemia, Machine learning, Prediction model

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