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Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit.

Yuanfang Ren, Tyler J Loftus, Ziyuan Guan

    Arxiv
    |March 22, 2023
    PubMed
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    Automated algorithms identify acute brain dysfunction, including coma and delirium, in ICU patients. This helps understand patient trajectories and aids clinical decision-making for better resource allocation.

    Area of Science:

    • Critical Care Medicine
    • Neuroscience
    • Health Informatics

    Background:

    • Acute brain dysfunction, such as delirium, is common in ICUs but often underdiagnosed.
    • ICU patients face significant mortality and healthcare costs, highlighting the need for better monitoring.
    • Understanding clinical trajectories of acute brain dysfunction is crucial for patient care.

    Approach:

    • Developed and validated algorithms to quantify acute brain dysfunction status (coma, delirium, normal, death) at 12-hour intervals.
    • Utilized longitudinal EHR data from two single-center ICUs, encompassing over 48,000 adult patients.
    • Employed k-means clustering on continuous acute brain dysfunction status to identify distinct patient phenotypes.

    Key Points:

    • Phenotyping algorithms identified three main trajectories: persistent coma/delirium, persistently normal, and transitions to normal within 48 hours.

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  • Analysis revealed transitions between states: e.g., 4-7% moved from coma to delirium every 12 hours.
  • 18% of patients experienced coma, and 7% experienced delirium as their worst brain dysfunction state.

    • Conclusions:

    • Automated phenotyping provides a computable method to assess acute brain dysfunction in ICU patients.
    • This approach can support the development of prognostic tools and clinical decision support systems.
    • Improved understanding of brain dysfunction trajectories can aid in resource utilization and care escalation decisions.