PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

ABSTRACT:

Abstract

Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteria from the definition of pediatric sepsis. The objective of this study is to derive and validate machine learning models predicting in-hospital mortality for children with suspected or confirmed infection or who met the Phoenix sepsis criteria for sepsis and septic shock. Materials and Methods: Retrospective cohort analysis of 63,824 patients with suspected or confirmed infection admission diagnosis in two pediatric intensive care units (PICUs) in Atlanta, Georgia, from January 1, 2010, through May 10, 2022. The Phoenix Sepsis

SUBMITTER: Moore R 

PROVIDER: S-EPMC12084116 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature