Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series

Physiologic systems generate complex dynamics in their output signals that reflect the changing state of the underlying control systems. In this work, we used a switching vector autoregressive (switching VAR) framework to systematically learn and identify a collection of vital sign dynamics, which can possibly be recurrent within the same patient and shared across the entire cohort. We show that these dynamical behaviors can be used to characterize and elucidate the progression of patients' states of health over time. Using the mean arterial blood pressure time series of 337 ICU patients during the first 24 hours of their ICU stays, we demonstrated that the learned dynamics from as early as the first 8 hours of patients' ICU stays can achieve similar hospital mortality prediction performance as the well-known SAPS-I acuity scores, suggesting that the discovered latent dynamics structure may yield more timely insights into the progression of a patient's state of health than the traditional snapshot-based acuity scores.National Institutes of Health (U.S.) (NIH grant R01-EB001659)National Institute for Biomedical Imaging and Bioengineering (U.S.) (grant U01-EB008577)National Institutes of Health (U.S.) (NIH R01 HL090897)National Institutes of Health (U.S.) (AHA 0840159N)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award N66001-12-1-4219)James S. McDonnell Foundation (Postdoctoral grant