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.

Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings

“…So far, the following diagnostic algorithms have been developed and are implemented as part of daily clinical care at SMCH, CPH and KCH: resuscitation and stabilisation, thermoregulation, convulsions, low birth weight, prematurity, hypoglycaemia, HIV, respiratory distress, neonatal encephalopathy, sepsis, syphilis, jaundice and some congenital abnormalities (cleft lip/palate, congenital dislocation of the hip, talipes, gastroschisis and omphalocele, and spina bifida). Ongoing analysis of the clinical database is underway to optimise these algorithms ( e.g., to create a data driven predictive model for neonatal sepsis ( Neal et al, 2022 )).…”

Neonatal Sepsis Due to Multidrug-resistant Bacteria at a Tertiary Teaching Hospital in Ethiopia

“…Additionally, judicious and targeted use of antibiotics, early diagnosis and proper sanitation in neonatal units can help to reduce the risk of these infections. 13,14…”

“…Additionally, judicious and targeted use of antibiotics, early diagnosis and proper sanitation in neonatal units can help to reduce the risk of these infections. 13,14 In neonates, bacterial infections with extended-spectrum β-lactamases (ESBL)-producing Enterobacterales, 9,15,16 methicillinresistant Staphylococcus aureus (MRSA) 9,17 and extremely drugresistant Acinetobacter baumannii 18 are more common and frequently fatal. 7,16,19 The problem is even worse, particularly, in low-income countries where there are inadequate diagnostic facilities, limited antimicrobial susceptibility tests, lack of reliable diagnostic markers, a shortage of skilled health workers and limited antibiotic options to treat these infections.…”