  Continuous monitoring and patient acuity assessments are key aspects of
Intensive Care Unit (ICU) practice, but both are limited by time constraints
imposed on healthcare providers. Moreover, anticipating clinical trajectories
remains imprecise. The objectives of this study are to (1) develop an
electronic phenotype of acuity using automated variable retrieval within the
electronic health records and (2) describe transitions between acuity states
that illustrate the clinical trajectories of ICU patients. We gathered two
single-center, longitudinal electronic health record datasets for 51,372 adult
ICU patients admitted to the University of Florida Health (UFH) Gainesville
(GNV) and Jacksonville (JAX). We developed algorithms to quantify acuity status
at four-hour intervals for each ICU admission and identify acuity phenotypes
using continuous acuity status and k-means clustering approach. 51,073
admissions for 38,749 patients in the UFH GNV dataset and 22,219 admissions for
12,623 patients in the UFH JAX dataset had at least one ICU stay lasting more
than four hours. There were three phenotypes: persistently stable, persistently
unstable, and transitioning from unstable to stable. For stable patients,
approximately 0.7%-1.7% would transition to unstable, 0.02%-0.1% would expire,
1.2%-3.4% would be discharged, and the remaining 96%-97% would remain stable in
the ICU every four hours. For unstable patients, approximately 6%-10% would
transition to stable, 0.4%-0.5% would expire, and the remaining 89%-93% would
remain unstable in the ICU in the next four hours. We developed phenotyping
algorithms for patient acuity status every four hours while admitted to the
ICU. This approach may be useful in developing prognostic and clinical
decision-support tools to aid patients, caregivers, and providers in shared
decision-making processes regarding escalation of care and patient values.
