Abstract
Pediatric sepsis is a heterogeneous disease with varying physiological dynamics associated with recovery, disability, and mortality. Using risk scores generated from a sepsis prediction model to define illness states, we used Markov chain modeling to describe disease dynamics over time by describing how children transition among illness states. We analyzed 18,666 illness state transitions over 157 pediatric intensive care unit admissions in the 3 days following blood cultures for suspected sepsis. We used Shannon entropy to quantify the differences in transition matrices stratified by clinical characteristics. The population-based transition matrix based on the sepsis illness severity scores in the days following a sepsis diagnosis can describe a sepsis illness trajectory. Using the entropy based on Markov chain transition matrices, we found a different structure of dynamic transitions based on ventilator use but not age group. Stochastic modeling of transitions in sepsis illness severity scores can be useful in describing the variation in transitions made by patient and clinical characteristics.
Highlights
The hallmark of sepsis, organ dysfunction resulting from a dysregulated host response to infection, often requires ICU-level interventions for physiologic organ support [1]
We studied the trajectories of illness severity indices in a cohort of children admitted to the Pediatric Intensive Care Units (PICUs)
Differences in trajectories can be seen in the differences between the transition probabilities when stratifying by ventilator status or age or other clinical factors to understand the differences in temporal dynamics of illness severity between these clinical factors
Summary
The hallmark of sepsis, organ dysfunction resulting from a dysregulated host response to infection, often requires ICU-level interventions for physiologic organ support [1]. Predictive models exist that were developed as time series measures of changing risk based on clinical variables that detect physiological changes with illness [5,6,7,8,9] The utility of such continuous predictive analytics is intuitive: novel monitoring to alert busy clinicians to a change. While risk scores from predictive analytic models have been used to provide early warning to clinicians, less research has focused on the use of this innovative derivation of complex physiologic data to characterize illness states [10]. We use Markov chain modeling to evaluate the dynamic transitions in illness states following sepsis in PICU patients in order to quantify the early illness trajectory. We examined the sequence of transitions among illness states to determine how much time was required to reach a target illness state, given an initial illness state, in a probabilistic fashion
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