ICU readmission and mortality risk prediction: Generalizability of a multi-hospital model.

ABSTRACT:

Background

Inadvertent intensive care unit (ICU) readmission is associated with longer length of stay and increased mortality. Conversely, delayed ICU discharge may represent inefficient use of resources. To better inform discharge timing, several hospitals have implemented machine learning models to predict readmission risk following discharge. However, these models are typically created locally and may not generalize well to other hospitals or patient populations. A single multi-hospital-based model might provide more accurate predictions and insight into features that are applicable across diverse clinical settings.

Methods

This study involved a retrospective multi-center cohort from one academic hospital (Amsterdam University Medical Center [AUMC]) and two large teaching

SUBMITTER: Dam TA 

PROVIDER: S-EPMC12572862 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature