This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Monitoring patients in an ICU setting involves collection and analysis of huge amounts of data. Due to the vulnerable state of the patients, it is of utmost importance to correctly interpret this data and modify treatment procedures accordingly. ICUs today are equipped with monitoring systems that sound an alert when some quantity exceeds a predefined fixed value. However, such systems generate high rates of false alarms and are incapable of evaluating the physiological state of the patients. This project uses computational models of the CV system to interpret the observed data in terms of the underlying physiology. We begin with simple models to investigate the relationship between model parameters and measurable quantities (e.g., ABP, CVP, PAP) to estimate cardiovascular parameters over time. Such a technique is more likely to be successful in a reduced order model that can be used to track patient state on an appropriate time scale. Being able to track model parameters through time would not only help in accurately determining patient state, but will also permit early recognition of impending deterioration and the development of 'intelligent' alarms.
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