Delirium Dynamics: Understanding Causes and Effects
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Project Summary / Abstract Delirium is an acute disturbance of attention and awareness that afflicts >20% of hospitalized older adults and costs up to $152 billion each year due to associations with prolonged hospitalization, cognitive decline, and an accelerated course of dementia and Alzheimer’s disease. Delirium is especially challenging to evaluate and treat because it is dynamic: patients fluctuate between severe symptoms one hour to minimal the next, exposing a knowledge gap: we do not know which factors mediate those fluctuations. Many delirium risk factors, such as age, are irreversible and do not explain delirium’s dynamic course. In contrast, sleep-wake activity and circadian rhythms are highly dynamic and potentially modifiable. While the incidences of sleep disturbance and delirium are generally associated, whether and how they interact remains unclear, and clinical trials targeting sleep do not consistently improve delirium. There is a critical need to determine the sequence of events through which specific disturbances of sleep-wake activity impact delirium’s trajectory. These efforts have been limited by a lack of tools to measure delirium and sleep-wake activity continuously. The long-term goal of this project is to identify new delirium treatments by understanding the characteristics, causes, and effects of delirium fluctuations. The current objective is to apply novel continuous monitoring to test the central hypothesis that specific sleep-wake and circadian features predict acute delirium fluctuations, which in turn forecast long-term cognitive decline. The rationale for the proposed work is that continuous monitoring of delirium and sleep-wake activity will identify specific disturbances in sleep-wake and circadian activity that impact delirium’s trajectory, sharpening focus on those specific disturbances to manage delirium acutely. This project utilizes an innovative approach that integrates recently developed measures of physiologic delirium severity, sleep-wake activity, and circadian rhythms with artificial intelligence (AI) algorithms to quantify the course of delirium rigorously. These measures will be used to study the acute course and long-term trajectory of delirium in 400 hospitalized, non-intubated adults aged 65 years or older, admitted to the hospital with pneumonia, one of the most common causes of delirium in older adults. Aim 1 will establish the temporal characteristics of delirium using EEG in hospitalized older adults. Aim 2 will determine which features of sleep- wake activity affect the subsequent course of delirium. Aim 3 will identify the features of acute delirium trajectories that predict long-term cognitive impairment. The proposed research is significant because it will lead to the following expected outcomes: (1) new methods to quantify and track delirium severity at the bedside, (2) identification of specific sleep, circadian biology, and environmental factors as therapeutic targets to improve delirium, and (3) improved ability to identify patients with elevated risk of poor cognitive outcome during the therapeutic window. These outcomes will establish a firm physiologic foundation, objective biomarkers, and critical targets to understand and ameliorate delirium’s acute and long-term impacts.
