• Curtis E Kennedy, Noriaki Aoki, Michele Mariscalco, and James P Turley.
• 1Section of Critical Care Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, TX. 2Department of Pediatrics, University of Illinois College of Medicine, Urbana, IL. 3The University of Texas School of Biomedical Informatics, Houston, TX.
• Pediatr Crit Care Me. 2015 Nov 1; 16 (9): e332-9.

ObjectivesTo build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data.DesignRetrospective cohort study.SettingThirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients.SubjectsPatients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes.InterventionsNone.Measurements And Main ResultsOne hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve.ConclusionsCardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

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