Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.

ABSTRACT:

Objective

To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19).

Method

Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission.

Results

Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.7

SUBMITTER: Castro VM 

PROVIDER: S-EPMC8562039 | biostudies-literature | 2022 Jan-Feb

REPOSITORIES: biostudies-literature