Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery
Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery
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DOI：
10.1109/lra.2022.3183253
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发表时间：
2022-07-01
影响因子：
5.2
通讯作者：
Gajic, Ognjen
中科院分区：
文献类型：
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作者：
Trevena, William;Lal, Amos;Gajic, Ognjen
COVID-19大流行暴露了全球医院在重症护理知识和实践方面的长期缺陷。需要新的方法和战略，以促进及时和准确的干预措施。重症患者的虚拟对应物（数字孪生）将允许床边提供者可视化器官系统如何相互作用以产生临床效果，为他们提供机会，在将实际患者暴露于潜在伤害之前评估特定干预对虚拟患者的影响。这项工作的目的是开发一种数字模拟，对重症患者的临床路径进行建模。在多专业临床专家的支持下，使用混合方法，我们首先确定器官系统，医疗条件，临床标志物和干预措施之间的因果关系和关联关系。我们将这些关系记录为结构化专家规则，以有向无环图（DAG）格式描述它们，并将它们存储在图形数据库（Neo4j）中。这些结构化的专家规则随后用于驱动模拟应用程序，该模拟应用程序使用户能够模拟危重患者在给定模拟时间段内的状态轨迹，以测试不同干预措施对患者结局的影响。该模拟模型将成为驱动未来数字孪生原型的引擎，该原型将用作医学生的教育工具，并作为床边决策支持工具，使临床医生能够做出更快，更明智的治疗决策。
The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside providers to visualize how the organ systems interact to cause a clinical effect, offering them the opportunity to evaluate the effect of a specific intervention on a virtual patient before exposing an actual patient to potential harm. This work aims at developing a digital simulation that models the clinical pathway of critically ill patients. Using the mixed-methods approach with the support of multiprofessional clinical experts, we first identify the causal and associative relationships between organ systems, medical conditions, clinical markers, and interventions. We record these relationships as structured expert rules, depict them in a directed acyclic graph (DAG) format, and store them in a graph database (Neo4j). These structured expert rules are subsequently utilized to drive a simulation application that enables users to simulate the state trajectory of critically ill patients over a given simulated time period to test the impact of different interventions on patient outcomes. This simulation model will be the engine driving a future digital twin prototype, which will be used as an educational tool for medical students, and as a bedside decision support tool to enable clinicians to make faster and more informed treatment decisions.