Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data - Qin, Yidi; Caldino Bohn, Rebecca I.; Sriram, Aditya; Kernan, Kate F.; Carcillo, Joseph A.; Kim, Soyeon; Park, Hyun Jung (Frontiers in Pediatrics, 2023)
Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid ‘one-size-fits-all’ approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.