Abstract
Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.
Highlights
IntroductionAs a science, aims to understand the mechanical, physical, and biochemical functions of humans; because human dynamics transpire both on multiple spatial scales, ranging from molecular (e.g., genetics), to cell (e.g., metabolism), to organ (e.g., the heart [1]), to collections of organs (e.g., the circulatory system) and on multiple time scales ranging from fractions of a second to decades, it is likely that complete models of human functioning will consist of highly complex models whose scales interact in complex ways (e.g., via nonlinear resonance) [2]
As a science, aims to understand the mechanical, physical, and biochemical functions of humans; because human dynamics transpire both on multiple spatial scales, ranging from molecular, to cell, to organ, to collections of organs and on multiple time scales ranging from fractions of a second to decades, it is likely that complete models of human functioning will consist of highly complex models whose scales interact in complex ways [2]
Note that the peaks and length of time over which the glucose response exists depends on the magnitude of the calories in the meal — one way of conceptualizing this system is as a forced oscillator with damping that depends on caloric input and metabolism
Summary
As a science, aims to understand the mechanical, physical, and biochemical functions of humans; because human dynamics transpire both on multiple spatial scales, ranging from molecular (e.g., genetics), to cell (e.g., metabolism), to organ (e.g., the heart [1]), to collections of organs (e.g., the circulatory system) and on multiple time scales ranging from fractions of a second to decades, it is likely that complete models of human functioning will consist of highly complex models whose scales interact in complex ways (e.g., via nonlinear resonance) [2] In this context, population physiology aims to understand medium to long time scales of human physiology and pathophysiology where a population of humans is required to construct or discover a signal (metaphorically, population physiology is to physiology as climatology is to weather). Here we will employ diverse populations in an attempt to verify that an EHR-data-derived signal can be used to resolve first-order physiologic dynamics
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