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OBJECTIVES:

Many machine learning (ML) models have been developed for application in the ICU, but few models have been subjected to external validation. The performance of these models in new settings therefore remains unknown. The objective of this study was to assess the performance of an existing decision support tool based on a ML model predicting readmission or death within 7 days after ICU discharge before, during, and after retraining and recalibration.

DESIGN:

A gradient boosted ML model was developed and validated on electronic health record data from 2004 to 2021. We performed an independent validation of this model on electronic health record data from 2011 to 2019 from a different tertiary care center.

SETTING:

Two ICUs in tertiary care centers in The Netherlands.

PATIENTS:

Adult patients who were admitted to the ICU and stayed for longer than 12 hours.

INTERVENTIONS:

None …