贝叶斯模型基于局部表型抵抗数据,以告知实证抗生素升级决策的贝叶斯模型
Ranjeet Bamber1, Brian Sullivan1, Léo Gorman2
1Department of Population Health Sciences, Bristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK.
Infectious diseases and therapy
|July 18, 2024
概括
临床医生应使用升级抗生素图 (EA) 来指导抗生素选择,因为耐药性模式在不同患者群体之间有很大差异. 我们的贝叶斯模型量化了这种不确定性,改善了经验性的抗生素切换,以获得更好的患者结果.
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
- 生物统计学 生物统计学
背景情况:
- 由于临床进展不佳,临床医生经常在没有微生物学指导的情况下升级经验性抗生素治疗.
- 升级决策应考虑对初始抗生素的耐药性如何影响对后续选择的耐药性,这一概念称为升级抗生素图 (EA).
- 了解特定患者亚组EA结果的不确定性对于临床应用至关重要.
研究的目的:
- 开发和应用贝叶斯模型来估计抗生素耐药率和不确定性.
- 计算各种患者群体的升级抗生素图,包括重症监护室 (ICU),血液瘤学和儿科患者.
- 评估全医院EA对特定患者子组的适用性.
主要方法:
- 一个贝叶斯模型被开发用于估计抗生素耐药性率在格拉姆阴性血流感染使用表型耐药性数据.
- 该模型生成可靠的曲线以适应阻力数据,并结合了适应性平滑的集成惩罚术语.
- 使用可信的间隔来说明抗性率估计的不确定性,特别是对于较小的患者子组.
主要成果:
- 对实证和升级抗生素的耐药性率计算了10486例血液感染,涉及一般医院人口和特定群体 (ICU,血液瘤学,儿科).
- 在患者子组之间观察到耐药性率的显著差异;例如,在ICU患者中,皮佩拉西林/塔扎巴克坦耐药性为27.3%,而在一般人群中为13.4%.
- 该模型估计了抗生素之间的劣势概率,并突出了患者组之间最佳升级选择的差异.
结论:
- 贝叶斯知情升级抗生素图 (EA) 分析对于指导经验性抗生素切换是有价值的,通过估计局部耐药率和比较选项与不确定性措施.
- 由于耐药性模式的显著变化,不能对特定的患者群体可靠地应用全医院的EA.
- 该模型提供了一种定量方法,用于管理抗生素耐药性数据中的不确定性,以改善临床决策.
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