基于深度学习的tigecycline的毒性和副作用的预测和分析
Yin Xiong1, Guoxin Liu2, Xin Tang3
1Department of Intervention, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Frontiers in microbiology
|January 1, 2025
概括
这项研究开发了一种深度学习模型,用于预测用广谱抗生素tigecycline治疗的患者住院的几天. 该模型根据临床因素和潜在的肝毒性准确预测住院时间,有助于安全使用tigecycline.
科学领域:
- 药理学 药理学是指药理学的学科.
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
背景情况:
- 蒂格环素是一种用于复杂感染的广谱糖环素抗生素.
- 它的有效性与药理动力学 (AUC0-24/MIC) 有关,但它具有恶心,吐和氨基转移酶水平升高等风险.
- 需要进一步调查患者的安全性,特别是在重症患者中.
研究的目的:
- 在接受tigecycline的患者中开发住院时间的预测模型.
- 探索tigecycline的毒性副作用与住院时间的长度之间的相关性.
- 建立一个深度学习模型,使用临床数据和模拟药物度来预测住院日数.
主要方法:
- 对263名肺部感染患者的临床数据进行了回顾性分析.
- 开发一种肝毒性预测模型.
- 住院日数与临床实验室指标,模拟血液药物度,年龄和APACHE II分数之间的相关性分析.
- 深度学习模型用于医院住院日预测的应用.
主要成果:
- 异常的肝功能与AST,GGT,MCHC和住院几天有显著的相关性.
- 多种临床参数,剂量,年龄和APACHE II评分与住院时间有关.
- 模拟的血液药物度与住院相关,在24-48小时之间达到峰值.
- 肝功能预测模型的AUC达到0.90.
- 住院时间的中位数随着肝损伤的严重程度 (20,23,30天) 增加.
- 深度学习模型预测了停留时间的长度,误差为<1天.
结论:
- 一个深度学习模型准确地预测了用tigecycline治疗的感染患者住院的几天.
- 住院时间与临床参数,肝毒性和剂量有关.
- 结果为临床tigecycline应用提供了关键的见解,强调需要监测其不良影响.
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