Time Series Analysis as input for Predictive Modeling: Predicting Cardiac Arrest in a Pediatric Intensive Care Unit

Author: Curtis E. Kennedy, MD, MS(2010)

Primary Advisor: James P. Turley, PhD, RN

Committee Members: Jack W. Smith MD, PhD; Noriaki Aoki, MD, PhD;  M. Michele Mariscalco, MD

PhD Thesis, The University of Texas School of Biomedical Informatics at Houston.

Abstract: 

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for acute care units. Current tools are based on a multivariate approach that does not characterize deterioration, which often precedes cardiac arrests. Deterioration requires a time series approach in order to characterize. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities.