Improving monitoring data utility in acute care medicine using mechanistic mathematical models of physiological processes

在过去的几十年里，医疗监测技术取得了显著的进步。在数据特别丰富的麻醉学和重症监护环境中，这些进步在很大程度上未能转化为改善的结果。这可能至少部分归因于人类无法完全吸收、定量解释和利用多模态时间序列生物信号及其相互作用中包含的定量信息进行患者状态评估、预测和治疗优化，这表明计算机决策支持的作用。本提案中概述的研究计划将测试临床数据解释、预测和治疗优化的假设，该假设基于定量描述高度非线性的生理机制数学模型的概率反演。产生观察结果的耦合生理过程可以帮助缓解这种情况，并为麻醉科和重症监护的医生提供计算机化的决策支持工具，该工具集成了来自所有可用监测模式的临床数据，并提供易于获得的生理和临床解释的输出。有可能实现一种完全个性化的急性护理方法，但仍可通过验证满足循证医学的标准。拟议的项目将侧重于模型和方法的开发和验证，以评估和预测血液动力学状态为目标的纯观察性研究，为未来对所开发方法的临床有效性进行介入评估奠定基础。

Medical monitoring technology has advanced significantly over the past decades. In the particularly data rich environments of anaesthesiology and intensive care, these advancements have largely failed to translate into improved outcomes. This may at least partially be attributable to the human inability to fully assimilate, quantitatively interpret, and utilize for patient status assessment, prediction, and therapy optimization the quantitative information contained in the multimodal time-series of biosignals and their interactions, suggesting a role for computerized decision support. The research plan outlined in this proposal will test the hypothesis that clinical data interpretation, prediction, and therapeutic optimization based on probabilistic inversion of mechanistic mathematical models of physiology describing quantitatively the highly nonlinear, coupled physiological processes generating the observations can help to alleviate this situation and provide physicians in anaesthesiology and intensive care with a computerized decision support tool that integrates clinical data from all available monitoring modalities and provides output that is readily accessible to physiological and clinical interpretation, with the potential to realize a fully personalized approach to acute care that is nevertheless accessible to validation satisfying the criteria of Evidence Based Medicine. The proposed project will focus on model and methods development and validation in purely observational studies targeting assessment and prediction of hemodynamic status to set the stage for future interventional evaluations of the clinical usefulness of the developed methodologies.