基于FP生长频繁模式挖矿的抗微生物诱导性心律失常的药物关联分析
Shuang Chai1, Li Deng2, Jie Dong1
1Department of Pharmacy, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Naunyn-Schmiedeberg's archives of pharmacology
|February 11, 2026
概括
与抗生素相关的心律失常是一个临床挑战. 这项研究使用FP增长来链接抗生素类别,患者个人资料和QT延长和过早收缩等特定心律失常,揭示了不同的风险模式.
科学领域:
- 药物监督 药物监督 药物监督
- 心脏病学 心脏病学
- 数据挖掘 数据挖掘
背景情况:
- 与抗生素相关的心律失常是一种严重的临床问题,不完全了解风险因素.
- 确定特定的抗生素类别和与心律失常相关的患者个人资料对于患者的安全至关重要.
研究的目的:
- 应用FP增长算法来发现抗生素类和特定心律失常之间的关联.
- 为了确定与抗生素诱导的心律失常相关的高风险患者概况.
- 阐明与抗生素使用有关的心律失常发作的时间模式.
主要方法:
- 在246例与抗生素相关的心律失常病例中使用FP-增长关联规则算法.
- 分析了抗生素类别,年龄,性别和发病时间作为先例,心律失常类型作为后果.
- 设置支持 (≥1.0%),信心 (≥50.0%) 和提升 (>1.8) 的最低门.
主要成果:
- 鼻高心率 (36.18%),过早收缩 (25.20%) 和QT延长 (16.26%) 是最常见的心律失常.
- 抗结核药物和类药物与QT延长有关;宏类药物与早发性心跳有关.
- 观察到明显的发病时间:在抗结核治疗5天后或长期使用类药物后,QT延长;在药物输注过程中出现过早收缩和鼻动心.
结论:
- 该FP增长算法有效地识别了复杂的抗生素-心律失常风险模式.
- 结果突出了特定的关联,例如女性患有抗结核药物诱导的QT延长和类药物相关的过早收缩.
- 结果支持在抗生素治疗期间基于药物类别,患者人口统计和潜在心律失常事件的时间进行有针对性的监测策略.
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