Novel Pediatric Mortality Risk Prediction Score Based on Nonlinear Feature Transformations
Existing patient risk scores such as PRISM III are widely used in pediatric intensive care units (PICU) and have been extensively validated in various settings. While simple enough to allow for fast manual evaluation, PRISM III dichotomizes predictive variables to form prediction scores, which may lead to critical failtures. In this work, WNCG Prof. Haris Vikalo and students Natalia Arzeno, Karla Lawson and Sarah Duzinski seek to develop a risk prediction score that preserves clinical knowledge embedded in the features and structure of PRISM III while addressing limitations caused by variable dichotomization. The novel method transforms predictive variables using nonlinear logistic functions that allow for a fine differentiation between critical and normal values of the variables. Optimal parameters of the logistic functions are inferred for a given patient population and standards of care evolve.
The WNCG team tested the proposed technique on brain trauma patients admitted to the PICU of the Dell Children's Medical Center of Central Texas between 2007 and 2012. The prediction power of the score is evaluated using area under ROC curve (AUC), Youden's index J, and precision-recall balance in a leave-one-out-cross-validation study. The results demonstrate that the new score significantly outperforms PRISM III in terms of all three criteria.
Paper: Novel Pediatric Mortality Risk Prediction Score Based on Nonlinear Feature Transformations