Early detection of neonatal sepsis and reduction of overall antibiotic exposure: Towards precision medicine

Abstract

Infections claim the lives of over half a million newborns annually and expose survivors to the risk of lifelong disability. The challenge to clinicians is to identify newborns with invasive infections rapidly, promptly initiate antimicrobial treatment, and take measures to prevent and treat organ dysfunction. Moreover, excessive antibiotic use is a global public health problem. Despite considerable research on clinical and laboratory markers of neonatal sepsis, the effective translation into clinical practice remains limited. There is no single clinical or laboratory marker, nor any combination of markers that definitively confirms or rules out neonatal sepsis. The interpretation of these markers should take into account their diagnostic value for a given patient, along with their added value to the clinical decision-making process. The digitalization of health care systems, combined with increased computational power and advances in machine learning, offers the possibility of developing accurate predictive algorithms for early detection of neonatal sepsis.

Keywords: Artificial intelligence; Biomarkers; Infection; Machine learning; Newborn; Sepsis.