Early treatment gains for antibiotic administration and within human host time series data.

IF 0.8 4区 数学 Q4 BIOLOGY Mathematical Medicine and Biology-A Journal of the Ima Pub Date : 2018-06-13 DOI:10.1093/imammb/dqw025
Todd R Young, Erik M Boczko
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引用次数: 1

Abstract

As technological improvements continue to infiltrate and impact medical practice, it has become possible to non-invasively collect dense physiological time series data from individual patients in real time. These advances continue to improve physicians' ability to detect and to treat infections early. One important benefit of early detection and treatment of nascent infections is that it leads to earlier resolution. In response to current and anticipated advances in data capture, we introduce the Early Treatment Gain (ETG) as a measure to quantify this benefit. Roughly, we define the gain to be the limiting ratio: ETG=differential change in time of resolutiondifferential change in treatment time.We study the gain using standard dynamical models and demonstrate its use with time series data from Surgical Intensive Care Unit (SICU) patients facing ventilator associated pneumonia. The main conclusion from the mathematical modelling is that the ETG is always greater than one unless there is an effective immune response, in which case the ETG can be less than one. Using real patient time series data, we observe that the formula derived for a linear model can be applied and that this produces a ETG greater than one.

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早期治疗获得抗生素管理和人类宿主时间序列数据。
随着技术进步不断渗透和影响医疗实践,非侵入性地实时收集个体患者的密集生理时间序列数据已经成为可能。这些进步继续提高医生早期发现和治疗感染的能力。早期发现和治疗新生感染的一个重要好处是,它可以导致早期解决。为了响应当前和预期的数据捕获进展,我们引入早期治疗增益(ETG)作为量化这一益处的措施。粗略地,我们将增益定义为极限比:ETG=分辨率时间的微分变化,处理时间的微分变化。我们使用标准动态模型研究增益,并演示了其与外科重症监护病房(SICU)患者面临呼吸机相关性肺炎的时间序列数据的使用。数学模型的主要结论是,除非存在有效的免疫反应,否则ETG总是大于1,在这种情况下,ETG可以小于1。使用真实的患者时间序列数据,我们观察到线性模型的公式可以应用,并且产生大于1的ETG。
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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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