知情和不知情的经验治疗政策。

IF 0.8 4区 数学 Q4 BIOLOGY Mathematical Medicine and Biology-A Journal of the Ima Pub Date : 2020-09-10 DOI:10.1093/imammb/dqz015
Nicolas Houy, Julien Flaig
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引用次数: 1

摘要

我们认为,在制定经验治疗政策时,必须对知情和不知情的决策做出适当的区分,因为这允许人们估计收集更多关于病原体及其传播的信息的价值,从而确定研究重点。我们依靠区隔模型的随机版本来描述卫生保健设施中感染生物体的传播以及对两种药物的耐药性的出现和传播。我们关注的是这个模型参数的信息和不确定性。我们考虑一系列适应性经验治疗政策。在不知情的情况下,与联合治疗相比,最佳适应性政策允许将2年内的平均累计感染天数减少39.3%(95%置信区间(CI), 30.3-48.1%)。在知道确切参数值的情况下选择经验性治疗策略,可使累计感染患者天数平均进一步减少3.9% (95% CI, 2.1-5.8%)。在我们的环境中,完美信息的好处可能会被增加的毒品消费所抵消。
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Informed and uninformed empirical therapy policies.

We argue that a proper distinction must be made between informed and uninformed decision making when setting empirical therapy policies, as this allows one to estimate the value of gathering more information about the pathogens and their transmission and thus to set research priorities. We rely on the stochastic version of a compartmental model to describe the spread of an infecting organism in a health care facility and the emergence and spread of resistance to two drugs. We focus on information and uncertainty regarding the parameters of this model. We consider a family of adaptive empirical therapy policies. In the uninformed setting, the best adaptive policy allowsone to reduce the average cumulative infected patient days over 2 years by 39.3% (95% confidence interval (CI), 30.3-48.1%) compared to the combination therapy. Choosing empirical therapy policies while knowing the exact parameter values allows one to further decrease the cumulative infected patient days by 3.9% (95% CI, 2.1-5.8%) on average. In our setting, the benefit of perfect information might be offset by increased drug consumption.

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来源期刊
CiteScore
2.20
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Formerly the IMA Journal of Mathematics Applied in Medicine and Biology. Mathematical Medicine and Biology publishes original articles with a significant mathematical content addressing topics in medicine and biology. Papers exploiting modern developments in applied mathematics are particularly welcome. The biomedical relevance of mathematical models should be demonstrated clearly and validation by comparison against experiment is strongly encouraged. The journal welcomes contributions relevant to any area of the life sciences including: -biomechanics- biophysics- cell biology- developmental biology- ecology and the environment- epidemiology- immunology- infectious diseases- neuroscience- pharmacology- physiology- population biology
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