  In the United States, more than 5 million patients are admitted annually to
ICUs, with ICU mortality of 10%-29% and costs over $82 billion. Acute brain
dysfunction status, delirium, is often underdiagnosed or undervalued. This
study's objective was to develop automated computable phenotypes for acute
brain dysfunction states and describe transitions among brain dysfunction
states to illustrate the clinical trajectories of ICU patients. We created two
single-center, longitudinal EHR datasets for 48,817 adult patients admitted to
an ICU at UFH Gainesville (GNV) and Jacksonville (JAX). We developed algorithms
to quantify acute brain dysfunction status including coma, delirium, normal, or
death at 12-hour intervals of each ICU admission and to identify acute brain
dysfunction phenotypes using continuous acute brain dysfunction status and
k-means clustering approach. There were 49,770 admissions for 37,835 patients
in UFH GNV dataset and 18,472 admissions for 10,982 patients in UFH JAX
dataset. In total, 18% of patients had coma as the worst brain dysfunction
status; every 12 hours, around 4%-7% would transit to delirium, 22%-25% would
recover, 3%-4% would expire, and 67%-68% would remain in a coma in the ICU.
Additionally, 7% of patients had delirium as the worst brain dysfunction
status; around 6%-7% would transit to coma, 40%-42% would be no delirium, 1%
would expire, and 51%-52% would remain delirium in the ICU. There were three
phenotypes: persistent coma/delirium, persistently normal, and transition from
coma/delirium to normal almost exclusively in first 48 hours after ICU
admission. We developed phenotyping scoring algorithms that determined acute
brain dysfunction status every 12 hours while admitted to the ICU. This
approach may be useful in developing prognostic and decision-support tools to
aid patients and clinicians in decision-making on resource use and escalation
of care.
