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. 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, a timely diagnosis is critical. The standard gold test for diagnosing neonatal sepsis is blood culture, which takes at least 72 hours. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality. The matching articles were identified by searching the PubMed, IEEE, and Cochrane bibliography databases. For the inclusion of articles, the abstract and titles were first screened based on some predetermined criteria and then, the full-text articles were screened. Thirty-one studies met the 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. The main limitation is the variation in the performance measures used in the studies, which made it difficult to perform a quantitative assessment. A combination of predictor variables has been 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.