Prediction of periventricular leukomalacia. Part I: Selection of hemodynamic features using logistic regression and decision tree algorithms

  • Authors:

  • Biswanath Samanta;Geoffrey L. Bird;Marijn Kuijpers;Robert A. Zimmerman;Gail P. Jarvik;Gil Wernovsky;Robert R. Clancy;Daniel J. Licht;J. William Gaynor;Chandrasekhar Nataraj

  • Affiliations:

  • Department of Mechanical Engineering, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USA;Division of Critical Care Medicine and Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;Academic Medical Center, University of Amsterdam, Department of Anesthesiology, Amsterdam, Netherlands;Division of Neuroradiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;Department of Medicine (Medical Genetics), University of Washington, Seattle, WA 98195, USA;Division of Critical Care Medicine and Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;Division of Cardiothoracic Surgery, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA;Department of Mechanical Engineering, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USA

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2009

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Abstract

Objective: Periventricular leukomalacia (PVL) is part of a spectrum of cerebral white matter injury which is associated with adverse neurodevelopmental outcome in preterm infants. While PVL is common in neonates with cardiac disease, both before and after surgery, it is less common in older infants with cardiac disease. Pre-, intra-, and postoperative risk factors for the occurrence of PVL are poorly understood. The main objective of the present work is to identify potential hemodynamic risk factors for PVL occurrence in neonates with complex heart disease using logistic regression analysis and decision tree algorithms. Methods: The postoperative hemodynamic and arterial blood gas data (monitoring variables) collected in the cardiac intensive care unit of Children's Hospital of Philadelphia were used for predicting the occurrence of PVL. Three categories of datasets for 103 infants and neonates were used-(1) original data without any preprocessing, (2) partial data keeping the admission, the maximum and the minimum values of the monitoring variables, and (3) extracted dataset of statistical features. The datasets were used as inputs for forward stepwise logistic regression to select the most significant variables as predictors. The selected features were then used as inputs to the decision tree induction algorithm for generating easily interpretable rules for prediction of PVL. Results: Three sets of data were analyzed in SPSS for identifying statistically significant predictors (p