Denaï Mouloud A, Mahfouf Mahdi, Ross Jonathan J
Department of Automatic Control & Systems Engineering, University of Sheffield, Mappin Street, Sheffield, United Kingdom.
Artif Intell Med. 2009 Jan;45(1):35-52. doi: 10.1016/j.artmed.2008.11.009. Epub 2008 Dec 27.
To develop a clinical decision support system (CDSS) that models the different levels of the clinician's decision-making strategies when controlling post cardiac surgery patients weaned from cardio pulmonary bypass.
A clinical trial was conducted to define and elucidate an expert anesthetists' decision pathway utilised in controlling this patient population. This data and derived knowledge were used to elicit a decision-making model. The structural framework of the decision-making model is hierarchical, clearly defined, and dynamic. The decision levels are linked to five important components of the cardiovascular physiology in turn, i.e. the systolic blood pressure (SBP), central venous pressure (CVP), systemic vascular resistance (SVR), cardiac output (CO), and heart rate (HR). Progress down the hierarchy is dependent upon the normalisation of each physiological parameter to a value pre-selected by the clinician via fluid, chronotropes or inotropes. Since interventions at each and every level cause changes and disturbances in the other components, the proposed decision support model continuously refers back decision outcomes back to the SBP which is considered to be the overriding supervisory safety component in this hierarchical decision structure. The decision model was then translated into a computerised decision support system prototype and comprehensively tested on a physiological model of the human cardiovascular system. This model was able to reproduce conditions experienced by post-operative cardiac surgery patients including hypertension, hypovolemia, vasodilation and the systemic inflammatory response syndrome (SIRS).
In all the simulated patients scenarios considered the CDSS was able to initiate similar therapeutic interventions to that of the expert, and as a result, was also able to control the hemodynamic parameters to the prescribed target values.
开发一种临床决策支持系统（CDSS），该系统可对心脏手术后脱离体外循环的患者进行管理时，模拟临床医生不同水平的决策策略。
进行了一项临床试验，以定义和阐明专家麻醉师在管理这类患者群体时所采用的决策路径。这些数据和衍生知识被用于构建一个决策模型。该决策模型的结构框架是分层的、定义明确且动态的。决策水平依次与心血管生理学的五个重要组成部分相关联，即收缩压（SBP）、中心静脉压（CVP）、全身血管阻力（SVR）、心输出量（CO）和心率（HR）。沿着层次结构向下推进取决于每个生理参数通过液体、变时药物或变力药物归一化为临床医生预先选择的值。由于在每个水平上的干预都会导致其他组成部分发生变化和干扰，因此所提出的决策支持模型会不断将决策结果反馈回SBP，SBP被认为是这种分层决策结构中首要的监督安全组成部分。然后将决策模型转化为计算机化的决策支持系统原型，并在人体心血管系统的生理模型上进行全面测试。该模型能够再现心脏手术后患者所经历的情况，包括高血压、低血容量、血管舒张和全身炎症反应综合征（SIRS）。
在所有考虑的模拟患者场景中，CDSS能够启动与专家相似的治疗干预措施，因此也能够将血流动力学参数控制在规定的目标值。
