Neonatal mortality prediction using real-time medical measurements

Abstract

Current neonatal illness scoring systems are not designed to predict outcomes for individual patients, but rather can provide an overview of a population of patients for objective comparison when reporting outcomes. Having more patient-specific predictions may help physicians make better treatment decisions in a Neonatal Intensive Care Unit (NICU) environment. We developed neonatal mortality prediction models using C5.0 decision tree software that met criteria for clinically useful results (>;50-60% sensitivity, >;90% specificity) for individual patients using data from real-time medical measurement devices. The models were evaluated to identify: (1) the model with the best performance based on minimizing false positives, and (2) the attributes used most often in the best clinically useful models. Performance results showed that the mortality model using summary data during the first 48 hours after NICU admission provided, on average, the highest sensitivity and specificity with the least number of false positives (sensitivity=63%, specificity=94%, positive predictive value=38%), exceeding the performance criteria requested by our clinical partners. The attributes used most often in the best models for predicting mortality with our data were: mean blood pressure, serum pH, immature/total neutrophil ratio, serum sodium, serum glucose, respiratory rate, heart rate, and pO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> blood oxygen level.