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