Diagnosing early-onset neonatal sepsis in low-resource settings: development of a multivariable prediction model
Samuel R. Neal, Felicity Fitzgerald, Simbarashe Chimhuya, Michelle Heys, Mario Cortina‐Borja, Gwendoline Chimhini
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: 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.