ArticleArchives of disease in childhood2023
Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model.
Article in Archives of disease in childhood, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- Construction and validation of neonatal sepsis diagnostic nomograms: a single-site retrospective study.Frontiers in pediatrics · 2026Article
- Development and Validation of a Diagnostic Prediction Model for Neonatal Sepsis in a Low-Resource Setting, Ethiopia.Journal of epidemiology and global health · 2025Article
- Predicting risk of early-onset sepsis in low-resource neonatal units using routine healthcare data: development and evaluation of multivariable statistical and machine learning models.BMJ paediatrics open · 2025Article
- Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries: a scoping review.BMJ global health · 2025Article
- A practical predictive model to predict 30-day mortality in neonatal sepsis.Revista da Associacao Medica Brasileira (1992) · 2024Article
- Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings.Wellcome open research · 2022Article
- Maternal perinatal risk factors and full-term neonatal early-onset sepsis: A novel prediction model based on machine learning.Digital healthArticle
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveTo develop a clinical prediction model to diagnose neonatal sepsis in low-resource settings.
designSecondary analysis of data collected by the Neotree digital health system from 1 February 2019 to 31 March 2020. We used multivariable logistic regression with candidate predictors identified from expert opinion and literature review. Missing data were imputed using multivariate imputation and model performance was evaluated in the derivation cohort.
settingA tertiary neonatal unit at Sally Mugabe Central Hospital, Zimbabwe. PATIENTS: We included 2628 neonates aged <72 hours, gestation ≥32
interventionsParticipants received standard care as no specific interventions were dictated by the study protocol.
main outcome measuresClinical early-onset neonatal sepsis (within the first 72 hours of life), defined by the treating consultant neonatologist.
resultsClinical early-onset sepsis was diagnosed in 297 neonates (11%). The optimal model included eight predictors: maternal fever, offensive liquor, prolonged rupture of membranes, neonatal temperature, respiratory rate, activity, chest retractions and grunting. Receiver operating characteristic analysis gave an area under the curve of 0.74 (95% CI 0.70-0.77). For a sensitivity of 95% (92%-97%), corresponding specificity was 11% (10%-13%), positive predictive value 12% (11%-13%), negative predictive value 95% (92%-97%), positive likelihood ratio 1.1 (95% CI 1.0-1.1) and negative likelihood ratio 0.4 (95% CI 0.3-0.6).
conclusionsOur clinical prediction model achieved high sensitivity with low specificity, suggesting it may be suited to excluding early-onset sepsis. Future work will validate and update this model before considering implementation within the Neotree.
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