If one could accurately predict who, when and why patients develop cardiorespiratory instability (CRI), then effective preemptive treatments could be given to improve outcome and better use care resources. However, CRI is often unrecognized until it is well established and patients are more refractory to treatment, or progressed to organ injury. We have shown that an integrated monitoring system alert obtained from continuous noninvasively acquired monitoring parameters and coupled to a care algorithm improved step-down unit (SDU) patient outcomes. We also showed that advanced HR variability analysis (sample entropy) identified SDU patients at CRI risk within 2 minutes, and if monitored for 5 minutes differentiated between patients who would develop CRI or remain stable over the next 48 hours. We also applied machine learning (ML) modeling to our clinically-relevant porcine model of hemorrhagic shock to characterize responses to hypovolemia, hemorrhage, and resuscitation, predict which animals would or would not collapse during hypovolemia, and identify occult bleeding 5 minutes earlier than with traditional monitoring. We now propose to apply our work to vulnerable and invasively monitored ICU patients. We will develop multivariable models through ML data-driven classification techniques such as regression, Fourier and principal component analysis, artificial neural networks, random forest classification, etc. as well as more novel approaches (temporal rule learning developed by our team; Bayesian Aggregation) to predict CRI in ICU patients. We will first use our existing annotated high fidelity waveform MIMIC II clinical data set (4200 patients) to develop predictive models and differential signatures for various CRI drivers. We will also use our high-density data collection and processing platform (Bernoulli) to prospectively collect data from ICUs in three institutions: Univ. Pittsburgh (PITT), Univ. California (UC) Irvine and UC San Diego (initial algorithm development conducted at PITT and validated in the UC systems). We will identify the number and type of independent measures, sampling frequency, and lead time necessary to create robust algorithms to: 1) predict impending CRI, 2) select the most effective treatments, 3) monitor treatment response, and 4) determine when treatment has restored physiologic stability and can be stopped. We will also determine the smallest number and types of parameters coupled to the longest CRI lead time to achieve the above four targets with the best sensitivity and specificity (a concept we call Monitoring Parsimony).We will simultaneously iteratively design and test a graphical user interface (GUI) and clinical decision support system (CDSS) driven by these parsimoniously derived predictive smart alerts and functional hemodynamic monitoring treatment approaches in two human simulation environments (PITT & UC Irvine).We envision a basic monitoring surveillance that identifies patients most likely to develop CRI to apply focused clinician attention and targeted treatments to deliver highly personalized medical care.
If one could accurately predict who, when and why patients develop shock then effective preemptive treatments could be given to improve outcome and more effectively use healthcare resources. But signs of shock often occur late once organ injury is already present. The purpose of this study is to first develop multivariable models through data-driven classification techniques to parsimoniously predict cardiovascular insufficiency, etiology and response to treatment. We will do this first in our existing MIMIC II clinical data sets of 4200 ICU patients as to timing and types of instability. Then we will prospectively collect real time high- density data on patients admitted to our trauma intensive care units of University of Pittsburgh, UC Irvine and UC San Diego. We will create and test in simulators of ICU care bedside user interfaces to drive recognition and treatment algorithms based on these models in all three medical centers.
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