Multicentre external validation of the Neonatal Healthcare-associated infectiOn Prediction (NeoHoP) score: a retrospective case–control study

Abstract

Background and objectivesNeonatal mortality due to severe bacterial infections is a pressing global issue, especially in low-middle-income countries (LMICs) with constrained healthcare resources. This study aims to validate the Neonatal Healthcare-associated infectiOn Prediction (NeoHoP) score, designed for LMICs, across diverse neonatal populations.MethodsProspective data from three South African neonatal units in the Neonatal Sepsis Observational (NeoOBS) study were analysed. The NeoHoP score, initially developed and validated internally in a South African hospital, was assessed using an external cohort of 573 sepsis episodes in 346 infants, focusing on different birth weight categories. Diagnostic metrics were evaluated, including sensitivity, specificity, positive predictive value and area under the receiver operating characteristic curve.ResultsThe external validation cohort displayed higher median birth weight and gestational age compared with the internal validation cohort. A significant proportion were born before reaching healthcare facilities, resulting in increased sepsis evaluation, and diagnosed healthcare-associated infections (HAIs). Gram-negative infections predominated, with fungal infections more common in the external validation cohort.The NeoHoP score demonstrated robust diagnostic performance, with 92% specificity, 65% sensitivity and a positive likelihood ratio of 7.73. Subgroup analysis for very low birth weight infants produced similar results. The score’s generalisability across diverse neonatal populations was evident, showing comparable performance across different birth weight categories.ConclusionThis multicentre validation confirms the NeoHoP score as a reliable 'rule-in' test for HAI in neonates, regardless of birth weight. Its potential as a valuable diagnostic tool in LMIC neonatal units addresses a critical gap in neonatal care in low-resource settings.