“Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review.”

Abstract

About 2.9 million neonates die every year worldwide, and most of these deaths occur in low resource settings where it causes about 30–50 % of the total neonatal deaths annually. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, timely diagnosis is critical. The gold standard test for diagnosing neonatal sepsis is blood culture, which takes at least 72 h. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality.

Matching articles were identified by searching PubMed, IEEE, and Cochrane bibliography databases. Full-text articles with the following criteria were included for analysis based on 1) the subject population are neonates. 2) the study provided a clear definition of neonatal sepsis. 3) the study provides neonatal sepsis onset definition (i.e., time of onset). 4) the study clearly described the predictor variables used. 5) the study clearly described machine learning models used or evaluated any of the consolidated screening parameters. 6) the study must have provided diagnostic performance results. Thirty-one studies met full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests.

A combination of predictor variables has shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.