Quantitative, Model-based Medical Decision support by Bayesian Inference

描述（由申请人提供）：由于医学监测和成像技术的进步，临床医生在床边可用的定量数据量，特别是在数据丰富的环境中，如重症监护病房（ICU），已经大大增加。然而，这些进步在很大程度上未能改善结果，并显著影响床边临床医生的医疗决策，特别是在急性护理环境中。基于定量的、机械的生理学数学模型的计算机化决策支持技术可能有助于缓解这种情况。然而，在解决与个体患者的观察结果最相容的模型参数和状态的反问题时遇到的基本困难阻碍了它们的应用。我们最近在一个简化的模拟设置中展示了贝叶斯推理如何使用这些模型来定量解释临床测量和观察结果，并最终预测急性干预的结果并优化个体患者的治疗。而不是试图找到一个单一的，最好的参数向量，我们计算完整的后验概率分布的参数和状态的机械模型的条件下可用的数据。这些分布整合了测量误差引起的不确定性和基本参数/状态估计问题解决方案的基本非唯一性，并转化为生理上有意义的概率，但临床测量的定量解释。我们已经表明，多模态结构之间的直接映射的推断分布表示估计的病人病情和临床概念的鉴别诊断可能存在。本提案中概述的项目总体目标是探索这种新方法在危重患者队列中的实际用途。拟议的研究计划将侧重于心血管病理生理学，其目标是：(1)使用数学模型提供患者病情的定量估计，其中包括临床医生无法处理的常规获得的高密度数据；(2)正式验证这些估计的准确性和预测能力。因此，它将提供这种方法作为一个定量的，基于生理学的决策支持工具在重症监护医学的潜在有用性的第一个定量评估。

DESCRIPTION (provided by applicant): The amount of quantitative data available to the clinician at the bedside, in particular in data-rich environments such as the intensive care unit (ICU), has grown tremendously due to advances in medical monitoring and imaging technology. Yet, these advances have largely failed to improve outcomes and significantly impact medical decision making by bedside clinicians, particularly in the acute care setting. Computerized decision support technologies based on quantitative, mechanistic mathematical models of physiology might help alleviate this situation. Their application, however, has been hindered by the fundamental difficulties encountered when solving the inverse problem of finding model parameters and states best compatible with the observations in individual patients. We have recently shown in a simplified simulation setting how Bayesian inference may use such models to quantitatively interpret clinical measurements and observations, and eventually predict the result of acute interventions and optimize therapy in individual patients. Rather than attempting to find a single, best parameter vector, we compute the full posterior probability distributions of parameters and states of a mechanistic model conditional on the available data. These distributions integrate uncertainty arising from measurement error and the fundamental non- uniqueness of the solution of the underlying parameter/state estimation problem, and translate into physiologically meaningful probabilistic, yet quantitative interpretations of clinical measurements. We have shown that a direct mapping between the multimodal structure of the inferred distributions representing estimated patient condition and the clinical concept of differential diagnoses may exist. The overarching goal of the program outlined in this proposal is to explore the practical usefulness of this novel approach in a cohort of critically ill patients. The proposed research plan will focus on cardiovascular pathophysiology, with the objectives of (1) using mathematical models to provide quantitative estimates of patient condition that incorporate more of the routinely acquired high density data than can be processed by the clinician, (2) formally validating the accuracy and predictive power of these estimates. It will thus provide a first quantitative evaluation of the potential usefulness of this approach as a quantitative, physiology based decision support tool in critical care medicine. PUBLIC HEALTH RELEVANCE: Clinical decision making and clinical outcomes have not benefitted from the large increase in availability of quantitative data collected at the bedside and in the laboratory. This proposal suggests the development of an entirely novel bedside computerized decision support technology that interprets data flow based on quantitative, mechanistic mathematical models of physiology. Such a tool might be of great assistance to clinicians in integrating this dynamic flow of information in the formulation of diagnoses and optimally informed therapeutic strategies.