A machine learning model for prediction of early-onset neonatal sepsis in low-income and middle-income countries: development and validation study

Objective Early-onset sepsis (EOS), which occurs within the first 72 hours of life, can often be fatal for neonates. Machine learning (ML) models demonstrate promise for timely diagnosis. However, current ML models primarily rely on data from high-income countries, which reduces their applicability to low-income and middle-income countries (LMICs) that have a higher burden and different disease profiles. We developed an ML model for the timely prediction of culture-proven EOS in LMICs. Methods We conducted a secondary analysis of the Delhi Neonatal Infection Study (2011–2014) carried out in three level-3 neonatal units in India. Data from inborn neonates suspected of having EOS were extracted, and cases of culture-negative sepsis were excluded. By implementing a dynamic 80:20 (train:test) data split, two feature selection methods were employed—Boruta and Lasso—across 64 variables, and five ML techniques were applied. The aim was to achieve 90% sensitivity to identify the optimal model based on performance metrics. The developed model was integrated into a web application and validated in an external cohort of neonates born between 2015 and 2021. Results Of 2924 neonates, 548 (18.7%) had culture-proven sepsis. The mean gestation and birth weight were 35.3 (±3.8) weeks and 2112 (±754) g, respectively. The Boruta and random forest classifier yielded the best model, which included 28 perinatal–neonatal variables. The sensitivity and specificity of the model were 90.3% and 40.6%, respectively. In external validation (n=147; 26 culture-proven sepsis cases), the model’s sensitivity, specificity, positive predictive value and negative predictive value were 92.3%, 37.2%, 24.0% and 95.7%, respectively. The sensitivity was 100% in asymptomatic neonates with only perinatal risk factors for EOS. The use of the model could have reduced antibiotic usage from 74.8% to 55.7% (risk difference: −19.1%; 95% CI −8.3 to −29.7). Conclusions The ML model demonstrated high sensitivity and acceptable specificity in predicting EOS. This prediction model has the potential to assist in the timely and reliable identification of culture-positive sepsis and may serve as a bedside decision support tool in LMICs.