Identifying outcome-discriminative dynamics in multivariate physiological cohort time series

Background Physiological control systems typically involve multiple interacting variables operating in feedback loops that enhance an organism's ability to self-regulate and respond to internal and external disturbances. The resulting multivariate time series often exhibit rich dynamical patterns that are altered under pathological conditions, and are therefore informative of health and disease (Ivanov et al. 1996; Costa et al. 2002; Stein et al. 2005; Nemati et al. 2011). Previous studies using nonlinear (Ivanov et al. 1996; Costa et al. 2002) indices of heart rate (HR) variability (i.e., beat-to-beat fluctuations in HR) have shown that subtle changes to the dynamics of HR may act as an early sign of adverse cardiovascular outcomes (e.g., mortality after myocardial infarction (Stein et al. 2005)) in large patient cohort. However, these studies fall short of assessing the multivariate dynamics of the vital signs (such as HR, blood pressure, respiration, etc.), and do not yield any mechanistic hypotheses for the observed deteriorations of normal variability. This shortcoming is in part due to the inherent difficulty of parameter estimation in physiological time series, where one is confronted by nonlinearities (including rapid regime changes), measurement artifacts, and/or missing data, which are particularly prominent in ambulatory recordings (due to patient movements) and bedside monitoring (due to equipment malfunction). In Chapter 11, a framework has been described for unsupervised discovery of shared dynamics in multivariate physiological time series from large patient cohorts. A central premise of our approach was that even within heterogeneous cohorts (with respect to demographics, genetic factors, etc.) there are common phenotypic dynamics that a patient's vital signs may exhibit, reflecting underlying pathologies (e.g., detraction of the baroreflex system) or temporary physiological state changes (e.g., postural changes or sleep/wake related changes in physiology). We used a switching state space model (SSM), or in particular, a switching vector autoregressive (VAR) model, to automatically segment the time series into regions with similar dynamics, i.e., time-dependent rules describing the evolution of the system state. The state space modeling approach allows for incorporation of physiologically constrained linear models (e.g., via linearization of the nonlinear dynamics around equilibrium points of interest) to derive mechanistic explanations of the observed dynamical patterns, for instance, in terms of directional influences among the interacting variables (e.g., baroreflex gain or chemoreflex sensitivity).