Predictive model for bacterial late-onset neonatal sepsis in a tertiary care hospital in Thailand

Abstract

© 2020 The Author(s). Background: Early diagnosis of neonatal sepsis is essential to prevent severe complications and avoid unnecessary use of antibiotics. The mortality of neonatal sepsis is over 18%in many countries. This study aimed to develop a predictive model for the diagnosis of bacterial late-onset neonatal sepsis. Methods: A case-control study was conducted at Queen Sirikit National Institute of Child Health, Bangkok, Thailand. Data were derived from the medical records of 52 sepsis cases and 156 non-sepsis controls. Only proven bacterial neonatal sepsis cases were included in the sepsis group. The non-sepsis group consisted of neonates without any infection. Potential predictors consisted of risk factors, clinical conditions, laboratory data, and treatment modalities. The model was developed based on multiple logistic regression analysis. Results: The incidence of late proven neonatal sepsis was 1.46%. The model had 6 significant variables: poor feeding, abnormal heart rate (outside the range 100-180 x/min), abnormal temperature (outside the range 36o-37.9 °C), abnormal oxygen saturation, abnormal leucocytes (according to Manroe's criteria by age), and abnormal pH (outside the range 7.27-7.45). The area below the Receiver Operating Characteristics (ROC) curve was 95.5%. The score had a sensitivity of 88.5% and specificity of 90.4%. Conclusion: A predictive model and a scoring system were developed for proven bacterial late-onset neonatal sepsis. This simpler tool is expected to somewhat replace microbiological culture, especially in resource-limited settings.