Predicting Long-term Neurocognitive Outcome after Pediatric Intensive Care Unit Admission - Exploring the Potential of Machine Learning

Abstract

Abstract Purpose: For successful prevention and intervention, it is important to unravel the complex constellation of factors that affect neurocognitive functioning after Pediatric Intensive Care Unit (PICU) admission. This study aims (1) to elucidate the potential relevance of patient and PICU-related characteristics for long-term neurocognitive outcome after PICU admission; and (2) to determine the potential of machine learning to improve outcome prediction. Methods: In this single-center cohort study we investigated 65 children aged 6-12 years with previous PICU admission for bronchiolitis (age ≤1 year). Patient and PICU-related characteristics used for the prediction models were: demographic characteristics, perinatal and disease parameters, laboratory results and intervention characteristics, including hourly validated mechanical ventilation parameters. Neurocognitive outcome was measured by intelligence and computerized neurocognitive testing. Prediction models were developed for each of the neurocognitive outcomes using Regression Trees, k-Nearest Neighbors and conventional Linear Regression analysis. Results: Lower intelligence was predicted by lower birth weight and lower socioeconomic status (R2 = 25.9%). Poorer performance on the Speed and Attention domain was predicted by younger age at follow-up (R2 = 53.5%). Poorer verbal memory was predicted by lower birth weight, younger age at follow-up, and greater exposure to acidotic events (R2 = 50.6%). The machine learning models did not reveal added value in terms of model performance as compared to Linear Regression. Conclusions: The findings of this study suggest that in children with previous PICU admission for bronchiolitis: (1) lower birth weight and lower socioeconomic status are associated to poorer neurocognitive outcome; and (2) greater exposure to acidotic events during PICU admission is associated with poorer verbal memory outcome. Findings of this study provide no evidence for added value of machine learning models as compared to linear regression analysis in the prediction of long-term neurocognitive outcome in a relatively small sample of children.