Rational Antibiotic Escalation Applied to Specific Patient Groups
Rational Antibiotic Escalation Applied to Specific Patient Groups
复制标题
针对特定患者群体合理使用抗生素
DOI：
10.1101/2023.11.03.23298025
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Bhamber R
中科院分区：
文献类型：
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作者：
Bhamber R
背景临床医生通常由于临床进展不佳而在没有微生物学指导的情况下增加经验性抗生素治疗。当逐步升级时，他们应该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的可能性。术语Escalation Antibiogram（EA）已经被创造来描述这个概念。应用EA的概念，临床实践中的一个困难是理解结果的不确定性，以及这种变化如何为特定的patient subgroup.MethodsA贝叶斯模型的开发，以估计抗生素耐药率革兰氏阴性血流感染的基础上表型耐药数据。它提供了一个预期值（后验平均值），具有95%的可信区间，以说明不确定性，基于患者亚组的大小，并估计两种抗生素之间的劣效性概率。该模型可以应用于特定的患者群体的耐药率和潜在的微生物学可能会有所不同，从整个医院population.ResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResultsResults在特定的患者groupsnoted.ConclusionsEA分析告知我们的贝叶斯模型之间的最佳升级抗生素选择的差异是一个有用的工具，以支持经验性抗生素开关，提供估计的局部耐药率，并在数据中的不确定性的措施的抗生素选择的比较。我们证明，不能假设为整个人群计算的EA适用于特定的患者群体。
BackgroundClinicians commonly escalate empiric antibiotic therapy due to poor clinical progress, without microbiology guidance. When escalating, they should take account of how resistance to an initial antibiotic affects the probability of resistance to subsequent options. The term Escalation Antibiogram (EA) has been coined to describe this concept. One difficulty when applying the EA concept to clinical practice is understanding the uncertainty in results and how this changes for specific patient subgroups.MethodsA Bayesian model was developed to estimate antibiotic resistance rates in Gram-negative bloodstream infections based on phenotypic resistance data. It provides an expected value (posterior mean) with 95% credible interval to illustrate uncertainty, based on the size of the patient subgroup, and estimates probability of inferiority between two antibiotics. This model can be applied to specific patient groups where resistance rates and underlying microbiology may differ from the whole hospital population.ResultsRates of resistance to empiric first choice and potential escalation antibiotics were calculated for the whole hospitalised population based on 10,486 individual bloodstream infections, and for a range of specific patient groups, including ICU, haematology-oncology, and paediatric patients. Differences in optimal escalation antibiotic options between specific patient groups were noted.ConclusionsEA analysis informed by our Bayesian model is a useful tool to support empiric antibiotic switches, providing an estimate of local resistances rates, and a comparison of antibiotic options with a measure of the uncertainty in the data. We demonstrate that EAs calculated for the whole population cannot be assumed to apply to specific patient groups.