Neonatal sepsis is a significant cause of morbidity and mortality, particularly among preterm infants, and remains a pressing global health concern. Early-onset neonatal sepsis is particularly challenging to diagnose due to its nonspecific clinical presentation, necessitating effective and timely diagnostic tools to reduce adverse outcomes. Traditional methods, such as microbial cultures, are slow and often unavailable in resource-limited settings. This study aimed to develop a robust multivariable risk prediction model tailored to improve early detection of Early Onset Sepsis (EOS) among preterm infants in the Philippines.

We conducted a retrospective analysis at a tertiary hospital in the Philippines using data from 1,354 preterm infants admitted between January 2019 and June 2024. Logistic regression models were employed, and predictors were selected through reverse stepwise elimination. Two scoring methods were developed: one based on beta coefficients divided by standard errors and another standardized to a total score of 100. The models were validated using Receiver Operator Characteristic curve analysis.

Version 1 of the scoring model demonstrated an Area Under the Curve (AUC) of 0.991, with a sensitivity of 90.91% and a specificity of 98.10%. Version 2 achieved an AUC of 0.999, with a sensitivity of 96.4% and a specificity of 92.44%.

The developed models provide a reliable, region specific tool for early detection of neonatal sepsis. Further validation across diverse populations and the integration of emerging diagnostic technologies, such as biomarkers and artificial intelligence, are warranted to enhance their applicability and accuracy.