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

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 01/02/2019 to 31/03/2020. 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 grams. 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 (11.3%). 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 0.736 (95% confidence interval 0.701-0.772). 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 modest specificity, suggesting it may be suited to excluding early-onset sepsis. Future work will validate and refine this model before considering it for clinical use within the Neotree. What is already known on this topic Various clinical prediction models exist to diagnose neonatal sepsis. However, there is a paucity of literature on models in low-resource settings, particularly sub-Saharan Africa. What this study adds We developed a clinical prediction model to diagnose clinical early-onset neonatal sepsis with over 2,500 neonates in a lower middle-income, low-resource neonatal unit. Our model is easy to implement, does not require laboratory tests and achieved high sensitivity with modest specificity in the derivation cohort. How this study might affect research, practice or policy Our model could support less experienced healthcare professionals avoid unnecessary antibiotic therapy in the absence of immediate local senior support. Before implementation, this model must be externally validated and its impact on sepsis-related neonatal morbidity and mortality must be assessed in future studies.