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

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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.