Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks

Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using twenty-five maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician’s diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well relative to the state-of-the-art methods using similar maternal/neonatal variables. The top ten factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight. In addition, we provide an online neonatal sepsis diagnosis calculator applicable for newborns 25-40 weeks' gestation including maternal/neonatal risk factors, clinical symptoms and comorbidities, to assist the bedside clinician with simple clinical criteria that identify patients with sepsis the day of clinical suspicion.