Prediction of Late-Onset Sepsis in Preterm Infants Using Monitoring Signals and Machine Learning.

ABSTRACT:

Objectives

Prediction of late-onset sepsis (onset beyond day 3 of life) in preterm infants, based on multiple patient monitoring signals 24 hours before onset.

Design

Continuous high-resolution electrocardiogram and respiration (chest impedance) data from the monitoring signals were extracted and used to create time-interval features representing heart rate variability, respiration, and body motion. For each infant with a blood culture-proven late-onset sepsis, a Cultures, Resuscitation, and Antibiotics Started Here moment was defined. The Cultures, Resuscitation, and Antibiotics Started Here moment served as an anchor point for the prediction analysis. In the group with controls (C), an "equivalent crash moment" was calculated as anchor point, based on comparable gestationa

SUBMITTER: Cabrera-Quiros L 

PROVIDER: S-EPMC7846455 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature