Sepsis Physiomarkers for Appropriate Risk Knowledge of monitored patients in the ICU (SPARK-ICU)

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Project Summary Critically ill patients admitted to the ICU who develop secondary infection and sepsis, can face up to a five-fold increase in the risk for death when compared to non-sepsis patients. The majority of patients who developed secondary infections are more critically ill at admission and therefore require significantly greater resources. Traditional machine learning algorithms for predicting sepsis has been largely focused and relied on the use of structured data from the electronic medical record (EMR), however the EMR was developed largely as a billing mechanism and an audit log for clinical workflow. Hence, much of the structure and availability of data are often time-delayed, prone to errors from manual entry, biases from various institutional, personal and training biases, and finally contain a significant amount of missing data. In this proposal, we seek to discover novel `physiomarkers' extracted from continuous physiological data streams, generated from non-human derived data sources, that predict the onset of sepsis in this critical population. Using such routinely collected data, along with common clinical indicators extracted from the EMR, we propose to generate robust machine learning algorithms that can be more generalized, reproducible and removed from the biases and pitfalls of manual data entry. We propose that such classes of models not only may alert clinicians to acute and critically ill patients at risk for developing sepsis in real-time, but also investigate intervention effectiveness, such as volume responsiveness and support the discovery of novel sub-types of sepsis. Secondly, much of the existing literature on predictive models for sepsis focus on hospitalized patients in the general ward, however, models that predict the onset of sepsis among patients who developed secondary infections after admission to the ICU is limited. In our previous work, we have demonstrated that markers discovered from continuous numeric data streams can inform earlier prediction of sepsis in children and adults. However, those analysis did not use high-fidelity data from the waveforms, which encapsulate rich characteristics of physiology. Therefore, by emphasizing the discovery of such novel markers and through the application of data-driven learning algorithms, we expect to develop algorithms and tools that improve our understanding of the changing physiologic dynamics of sepsis in critically ill patient. In this proposed program, we will integrate knowledge across a number of distinctive expertise that spans signal processing, mathematics, computer science and medicine to develop sophisticated tools that can analyze such data to reveal meaningful insight. In short, we will contribute significant knowledge about the role and utility of complex physiological interactions that are at present abundantly available in clinical practice but seldom used for clinical decision making.