Abstract
BACKGROUND AND OBJECTIVES: Early diagnosis of neonatal infection is essential to prevent serious complications and to avoid unnecessary use of antibiotics. The prevalence of healthcare-associated infections (HAIs) among very low birthweight (VLBW; <1500 g) infants is 20%; and the mortality in low-resource settings can be as high as 70%. This study aimed to develop an Infection Prediction Score to diagnose bacterial HAIs. METHODS: A retrospective cohort of VLBW infants investigated for HAI was randomised into two unmatched cohorts. The first cohort was used for development of the score, and the second cohort was used for the internal validation thereof. Potential predictors included risk factors, clinical features, interventions, and laboratory data. The model was developed based on logistic regression analysis. RESULTS: The study population of 655 VLBW infants with 1116 episodes of clinically suspected HAIs was used to develop the model. The model had five significant variables: capillary refill time >3 s, lethargy, abdominal distention, presence of a central venous catheter in the previous 48 hours and a C reactive protein ≥10 mg/L. The area below the receiver operating characteristic curve was 0.868. A score of ≥2 had a sensitivity of 54.2% and a specificity of 96.4%. CONCLUSION: A novel Infection Prediction Score for HAIs among VLBW infants may be an important tool for healthcare providers working in low-resource settings but external validation needs to be performed before widespread use can be recommended.