Aerts Jean-Marie, Haddad Wassim M, An Gary, Vodovotz Yoram
Division Measure, Model & Manage Bioresponses (M3-BIORES), Department of Biosystems, KU Leuven, Leuven, Belgium B-3001.
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150.
J Crit Care. 2014 Aug;29(4):604-10. doi: 10.1016/j.jcrc.2014.03.018. Epub 2014 Mar 29.
The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches to address this unmet need. Two main paths of development have characterized the society's approach: (i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiologic signals or multivariate analyses of molecular and genetic data and (ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience and the impact that merging these modeling approaches can have on general anesthesia.
急性危重病中生理和炎症反应的复杂性阻碍了准确诊断和治疗方法的开发。复杂急性病学会于十年前成立，目标是利用多种复杂系统方法来满足这一未被满足的需求。该学会的发展主要有两条路径：（i）数据模式分析，即确定生理信号复杂性指标的诊断/预后效用或对分子和遗传数据进行多变量分析，以及（ii）机制性数学和计算建模，所有这些都以明确的转化目标进行。在此，我们总结了迄今为止每种方法取得的进展，以及单独使用每种方法所固有的缺陷。我们认为，未来十年有可能将这些方法融合起来，通过基于机制的动态系统建模和反馈控制将患者诊断与治疗联系起来，并允许从生理信号推断生物标志物和新型候选药物。作为一个典型例子，我们重点关注数据驱动模型和机制模型在神经科学中的作用，以及融合这些建模方法对全身麻醉可能产生的影响。
