Insights From Multi-Dimensional Physiological Signals to Predict and Prevent Cardiac Arrests.

Pediatric Critical Care Medicine www.pccmjournal. org 81 In recent years, we have seen an explosion in the amount of physiological data collected by multiple sensors from patients in adult and PICUs an accompanying surge in interest in Big Data in healthcare and its promise (1). Currently, however, most of these data are not utilized by bedside clinicians and usually not even stored, even though several large databases (or repository systems) have been established (2, 3), mainly for the adult population with the pediatric counterpart still lagging in that extent (4). Even though it has been known for decades that statistics derived from time-varying signals such as measures of heart rate variability can predict adverse events (5–7), these have not entered into common practice. Current-day alarm systems and monitors even do not take into account the trajectory of the patient’s vital signs or utilize the information embedded in the correlations between different physiological signals. Moreover, even though there have been a multitude of published tools that are based on physiological multivariate data (8–11), usually these do not use time-series analysis techniques (12) that are in common use in other disciplines in order predict events (13), Thus, in general, this ability to collect and store large amounts of physiological data is possible but is yet to translate to routinely preventing cardiac arrests by predicting patient trajectory or a physiological state. The research approaches based on large database analysis to improve estimation of patient status, response to treatment and trajectory prediction can be divided into data-pattern analyses using statistical and machine learning techniques, or to more mechanistic modelling (14). An elegant example of a data driven pattern analysis approach to the prediction of cardiac arrest in children can be found in the November 2015 issue of Pediatric Critical Care Medicine. In the article, “Using Time Series Analysis to Predict Cardiac Arrest in a PICU” (15), the authors report a very important investigation that tests different prediction models of cardiac arrest. The motivation of the study is to determine if different modelling approaches regularly applied in the field of engineering science could improve the prediction of cardiac arrests in critically-ill children monitored in an intensive care setting. The researchers used medical record information to select their cardiac arrest cases systematically, and used the information from bedside monitoring systems used in the clinical setting with additional features that can be purchased to record and export data from these monitors. This investigation is important because cardiac arrest—in an individual child hospitalized in an intensive care unit should be predictable and intensive care therapy should attempt to prevent it—and because it illustrates the feasibility of applying the power of computational science to achieve this goal. This study illustrates the use of time series data analysis and other approaches, such as machine learning algorithms. These enable the relative contribution of different time-series and physiological data characteristics to improve the accuracy of predictions. The application of these data-analytic approaches are needed as the number of patients in data sets becomes larger and the number of variables considered for each patient increases. Major downsides of data driven approaches (14) are that without a mechanistic model they lack a “physiological sense”, which in turn may make clinicians wary of their use. Clinicians may not be as confident in interpreting Copyright © 2016 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies DOI: 10.1097/PCC.0000000000000627 Danny Eytan, MD, PhD Department of Critical Care Medicine Neurosciences and Mental Health Program Research Institute The Hospital for Sick Children University of Toronto Toronto, Ontario, Canada