RT Journal Article
SR Electronic
T1 Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model
JF Archives of Disease in Childhood
JO Arch Dis Child
FD BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health
SP 608
OP 615
DO 10.1136/archdischild-2022-325158
VO 108
IS 8
A1 Samuel R Neal
A1 Felicity Fitzgerald
A1 Simba Chimhuya
A1 Michelle Heys
A1 Mario Cortina-Borja
A1 Gwendoline Chimhini
YR 2023
UL http://adc.bmj.com/content/108/8/608.abstract
AB 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.Data are available upon reasonable request. An open-source, anonymised research database is planned as part of the wider Neotree project. Currently, sharing of deidentified individual participant data will be considered on a case-by-case basis.