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

使用生理过程的机械数学模型提高急症护理医学中的监测数据效用

• 批准号: 192127833
• 负责人: Privatdozent Dr. Sven Zenker
• 金额: --
• 依托单位: Klinik und Poliklinik für Anästhesiologie und Operative Intensivmedizin
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/192127833?language=en
• 关键词: Improving; monitoring; data; utility; acute

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.

在过去的几十年里，医疗监测技术有了很大的进步。在麻醉学和重症监护的数据特别丰富的环境中，这些进展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不能完全吸收、定量解释和利用包含在多模式生物信号时间序列及其相互作用中的定量信息来进行患者状态评估、预测和治疗优化，这暗示了计算机化决策支持的作用。这项建议中概述的研究计划将测试这样一种假设，即基于定量描述产生观察的高度非线性、耦合的生理过程的生理学机械数学模型的概率倒置的临床数据解释、预测和治疗优化有助于缓解这种情况，并为麻醉学和重症监护医生提供计算机化的决策支持工具，该工具集成了所有可用的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，有可能实现完全个性化的急性护理方法，但仍可获得符合循证医学标准的验证。拟议的项目将侧重于纯观察性研究中模型和方法的开发和验证，目标是评估和预测血流动力学状态，为未来对所开发方法的临床有效性进行介入性评估奠定基础。