Transportability of bacterial infection prediction models for critically ill patients.

ABSTRACT:

Objective

Bacterial infections (BIs) are common, costly, and potentially life-threatening in critically ill patients. Patients with suspected BIs may require empiric multidrug antibiotic regimens and therefore potentially be exposed to prolonged and unnecessary antibiotics. We previously developed a BI risk model to augment practices and help shorten the duration of unnecessary antibiotics to improve patient outcomes. Here, we have performed a transportability assessment of this BI risk model in 2 tertiary intensive care unit (ICU) settings and a community ICU setting. We additionally explored how simple multisite learning techniques impacted model transportability.

Methods

Patients suspected of having a community-acquired BI were identified in 3 datasets: Medical Informatio

SUBMITTER: Eickelberg G 

PROVIDER: S-EPMC10746321 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature