Centre for Experimental and Molecular Medicine (CEMM), Amsterdam University Medical Centres - Location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands.
Crit Care. 2023 Mar 11;27(1):102. doi: 10.1186/s13054-023-04374-0.
Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.
脓毒症涉及病原体、宿主反应、器官系统衰竭、医疗干预以及无数其他因素之间的动态相互作用。这些因素共同导致了一种复杂、动态和失调的状态，迄今为止一直无法控制。虽然人们普遍认为脓毒症确实非常复杂，但理解这种复杂性所需的概念、方法和手段仍未得到充分认识。在这个观点中，我们通过复杂性理论的视角来看待脓毒症。我们描述了支持将脓毒症视为高度复杂、非线性和时空动态系统状态的概念。我们认为，复杂系统领域的方法对于更全面地理解脓毒症至关重要，并且强调了在这方面过去几十年所取得的进展。尽管取得了这些相当大的进展，但像计算建模和基于网络的分析等方法仍然在一般科学雷达之下。我们讨论了促成这种脱节的障碍是什么，以及我们可以采取什么措施来接受与测量、研究方法和临床应用有关的复杂性。具体来说，我们提倡在脓毒症中关注纵向、更连续的生物数据收集。理解脓毒症的复杂性将需要巨大的多学科努力，其中源自复杂系统科学的计算方法必须得到生物数据的支持，并与生物数据集成。这种集成可以优化计算模型，指导验证实验，并确定可以靶向的关键途径，以有利于宿主来调节系统。我们提供了一个免疫预测建模的例子，它可以为可以在疾病过程中进行调整的敏捷试验提供信息。总的来说，我们认为我们应该扩展我们目前对脓毒症的思维框架，接受基于系统的非线性思维，以推动该领域的发展。
