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机器学习启用β-Iactam诱导的电解质失衡的早期风险分层

Inho Ryu¹, Da Hoon Lee², Hyeonwoo Cho¹

¹College of Pharmacy, Kangwon National University, Chuncheon-Si, The Republic of Korea.

Personalized medicine

|February 27, 2026

概括

电解质失衡常见于β-乳酸/β-乳酸酶抑制剂药物. 使用常规临床数据的预测模型可以帮助识别患有这些失衡风险的患者.

科学领域:

药理学药理学是指药理学的学科.
临床医学临床医学
医疗信息学医疗信息学

背景情况:

广泛使用的是β-乳酸/β-乳酸酶抑制剂 (BLBLI) 抗生素.
电解质失衡是BLBLI治疗的潜在不良影响.
早期识别患有电解质干扰风险的患者对于患者的安全至关重要.

研究的目的:

为了确定与BLBLI使用相关的电解质失衡的发生率.
开发和验证一个预测模型,用于早期识别患有BLBLI相关电解质失衡风险的患者.

主要方法:

对240名住院成年患者进行了回顾性分析,这些患者接受了皮佩拉-塔扎巴克坦或安培-苏尔巴克坦的治疗.
电解质不平衡,定义为治疗后的,,化物,或酸盐的异常.
后勤回归和机器学习用于预测器选择和模型开发.

主要成果:

电解质失衡发生在29.6%的患者中.
呼吸系统疾病是一个显著的风险因素 (AOR 2.616).
一个六个预测模型 (包括年龄,心血管疾病,呼吸系统疾病) 显示出良好的表现 (AUROC 0.68),具有高负预测值 (~0.80).

结论:

电解质失衡是BLBLI治疗的常见并发症.
使用常规临床变量可解释的预测模型可以帮助风险分层.
建议对开发的模型进行外部验证,以确认其可通用性.

关键词:

皮佩拉西林是一种扎巴巴克坦.氨基素硫巴克坦胺是一种抗微生物药物管理管理低血症是指低血症.低血糖症 (hypokalemia) 是一种疾病,低酸盐血症是什么意思预测模型预测模型有关风险因素的风险因素.

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