意大利的道式抗菌素耐药性监测:FUN-IT研究
Simone Milanesi1, Marta Colaneri2,3,4, Sara Laura Ferrari2,4
1Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
Scientific reports
|November 18, 2025
概括
统计过程控制 (SPC) 方法揭示了意大利显著的抗菌素耐药性 (AMR) 趋势. SPC强调了特定耐药细菌的区域变异性和异常值,有助于针对性的公共卫生干预.
科学领域:
- 公共卫生 公共卫生
- 传染性疾病 传染性疾病
- 生物统计学 生物统计学
背景情况:
- 意大利面临高抗菌素耐药性 (AMR) 率,需要加强监测策略.
- 有效的抗菌耐药性监测需要区分自然变异性和重大趋势.
研究的目的:
- 将统计过程控制 (SPC) 方法应用于意大利AMR监测数据 (AR-ISS, 2015-2023).
- 分析关键耐药病原体的区域和时间趋势,包括耐卡巴尼姆的Klebsiella pneumoniae (CRKP) 等.
- 为了确定AMR模式的显著偏差和区域间差异.
主要方法:
- 使用定制的SPC方法:漏斗图,Z-score图表和千平方控制图表.
- 分析了CRKP,3GCephRE,CRAS,CRPA,VRE-faecium和MRSA的血液中感染数据.
- 专注于从2015年到2023年的区域和时间趋势分析.
主要成果:
- 抗万科素的Enterococcus faecium (VRE-faecium) 在全国范围内持续增加.
- 耐卡巴胺的阿西尼托巴克特菌 spp. (CRAS) 和CRKP表现出显著的异常值,在2020-2021年达到峰值.
- 千平方图表显示,AMR趋势的区域间差异正在扩大,这可能与COVID-19大流行期间不均的制努力和增加的抗生素使用有关.
结论:
- 产品简介方法有效地突出了意大利显著的AMR偏差和区域间差异.
- 通过SPC识别异常者补充了传统的监视,标记模式用于进一步调查.
- 这些工具支持有针对性的公共卫生干预,特别是解决AMR的明显区域差异.
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