<i>Clostridioides difficile</i> infection surveillance in intensive care units and oncology wards using machine learning.

ABSTRACT:

Objective

Screening individuals admitted to the hospital for Clostridioides difficile presents opportunities to limit transmission and hospital-onset C. difficile infection (HO-CDI). However, detection from rectal swabs is resource intensive. In contrast, machine learning (ML) models may accurately assess patient risk without significant resource usage. In this study, we compared the effectiveness of swab surveillance to daily risk estimates produced by an ML model to identify patients who will likely develop HO-CDI in the intensive care unit (ICU) setting.

Design

A prospective cohort study was conducted with patient carriage of toxigenic C. difficile identified by rectal swabs analyzed by anaerobic culture and polymerase chain reaction (PCR). A previous

SUBMITTER: Otles E 

PROVIDER: S-EPMC10665879 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature