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Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model

Neal, Samuel R; Fitzgerald, Felicity; Chimhuya, Simba; Heys, Michelle; Cortina-Borja, Mario; Chimhini, Gwendoline; (2023) Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model. Archives of Disease in Childchood 10.1136/archdischild-2022-325158. (In press). Green open access

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Abstract

OBJECTIVE: To develop a clinical prediction model to diagnose neonatal sepsis in low-resource settings. DESIGN: Secondary 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. SETTING: A tertiary neonatal unit at Sally Mugabe Central Hospital, Zimbabwe. PATIENTS: We included 2628 neonates aged <72 hours, gestation ≥32+0 weeks and birth weight ≥1500 g. INTERVENTIONS: Participants received standard care as no specific interventions were dictated by the study protocol. MAIN OUTCOME MEASURES: Clinical early-onset neonatal sepsis (within the first 72 hours of life), defined by the treating consultant neonatologist. RESULTS: Clinical 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). CONCLUSIONS: Our 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.

Type: Article
Title: Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1136/archdischild-2022-325158
Publisher version: http://dx.doi.org/10.1136/archdischild-2022-325158
Language: English
Additional information: © Author(s) (or their employer(s)) 2023. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: Global Health, Infectious Disease Medicine, Intensive Care Units, Neonatal, Neonatology, Sepsis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Infection, Immunity and Inflammation Dept
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept
URI: https://discovery.ucl.ac.uk/id/eprint/10169189
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