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在中国南部的一个省份,基于大数据对抗线化物耐药性Enterococcus faecalis的风险探索和预测模型的构建

Peijun Liu¹, Bangwei Zeng², Xiaoyan Wu¹

¹Administration Department of Nosocomial Infection, Fujian Medical University Union Hospital, Fuzhou, 350001, Fujian Province, China.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology

|November 30, 2023

概括

耐莱涅佐利德 (Linezolid) 的菌 (Enterococcus faecalis) 是一个日益严重的威胁. 预测因素包括患者人口统计学,临床因素和医院特征,为有针对性的预防策略提供信息.

科学领域:

传染性疾病传染性疾病
临床微生物学临床微生物学
医疗保健服务研究医疗服务研究

背景情况:

Enterococcus faecalis是医疗保健相关感染的重要原因之一.
对抗万科米辛耐药性肠球菌的关键抗生素linezolid的耐药性日益增加,构成了重大的临床挑战.

研究的目的:

确定对线化物耐药E. faecalis (LREFs) 的独立预测因子.
开发和比较人工神经网络 (ANN) 和后勤回归 (LR) 模型,用于预测线化物耐药性.

主要方法:

从26个医疗机构 (2018-2020年) 分析了12,089个E. faecalis分离物.
单变量和多变量逻辑回归用于预测器识别.
开发和评估对线化物耐药性的ANN和LR预测模型.

主要成果:

6.25%的E. faecalis分离物是对线化物耐药的.
抗线索利德的耐药性在抗万科米辛菌株 (24.44%) 中显著高 (24.44%).
关键预测因素包括性别,年龄,样本类型,停留时间,季节,地区,GDP,床位数和医院水平.
ANN和LR模型显示出良好的预测性能 (AUC为0.754和0.741).

结论:

根据地理区域,经济因素和医疗机构的特点,E. faecalis中的linezolid耐药率有所不同.
建议对社区获得的LREF进行密切监测.
开发的预测模型可以指导临床决策,并为公共卫生干预提供信息.

关键词:

人工神经网络的人工神经网络菌 (Enterococcus faecalis) 是一种菌.线化物对线化物具有抗性.后勤回归的逻辑回归风险因素风险因素风险因素

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