Considering Causes for Hypoactive Delirium

Research in Nursing & Health

Albanese

Kim

et al. 2022

Delirium occurs in as many as 80% of critically ill older adults and is associated with increased long‐term cognitive impairment, institutionalization, and mortality. Less than half of delirium cases are identified using currently available subjective assessment tools. Electroencephalogram (EEG) has been identified as a reliable objective measure but has not been feasible. This study was a prospective pilot proof‐of‐concept study, to examine the use of machine learning methods evaluating the use of gamma band to predict delirium from EEG data derived from a limited lead rapid response handheld device. Data from 13 critically ill participants aged 50 or older requiring mechanical ventilation for more than 12 h were enrolled. Across the three models, accuracy of predicting delirium was 70 or greater. Stepwise discriminant analysis provided the best overall method. While additional research is needed to determine the best cut points and efficacy, use of a handheld limited lead rapid response EEG device capable of monitoring all five cerebral lobes of the brain for predicting delirium hold promise.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Delirium detection using GAMMA wave and machine learning: A pilot study

Research in Nursing & Health

Albanese

Kim

et al. 2022

“…Hyperactive delirium gains attention as a result of interruption in care and clinician workload. However, a majority of patients, especially older adults, develop the more severe hypoactive subtype otherwise known as “quiet delirium” (Inouye et al, ; Mulkey, Hardin, Olson, Munro, & Everhart, ). Identifying quiet delirium poses additional challenges because many tools are not as sensitive for this delirium subtype, further delaying detection (Mulkey, Roberson, Everhart, & Hardin, ).…”

Section: Introductionmentioning

confidence: 99%

Methods of identifying delirium: A research protocol

Research in Nursing & Health

Hardin

Munro

et al. 2019

Delirium is an acute disorder affecting up to 80% of intensive care unit (ICU) patients. It is associated with a 10‐fold increase in cognitive impairment, triples the rate of in‐hospital mortality, and costs $164 billion annually. Delirium acutely affects attention and global cognitive function with fluctuating symptoms caused by underlying organic etiologies. Early detection is crucial because the longer a patient experiences delirium the worse it becomes and the harder it is to treat. Currently, identification is through intermittent clinical assessment using standardized tools, like the Confusion Assessment Method for ICU. Such tools work well in clinical research but do not translate well into clinical practice because they are subjective, intermittent and have low sensitivity. As such, healthcare providers using these tools fail to recognize delirium symptoms as much as 80% of the time. Delirium‐related biochemical derangement leads to electrical changes in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms. However, continuous EEG monitoring is not feasible due to cost and need for skilled interpretation. Studies using limited‐lead EEG show large differences between patients with and without delirium while discriminating delirium from other causes. The Ceribell is a limited‐lead device that analyzes EEG. If it is capable of detecting delirium, it would provide an objective physiological monitor to identify delirium before symptom onset. This pilot study was designed to explore relationships between Ceribell and delirium status. Completion of this study will provide a foundation for further research regarding delirium status using the Ceribell data.

“…10,11 Even when identified, delirium is often dismissed as dementia or due to another cause. 12 Therefore, an objective physiologic monitor capable of early accurate detection is desperately needed.…”

mentioning

confidence: 99%

Rapid Handheld Continuous Electroencephalogram (EEG) Has the Potential to Detect Delirium in Older Adults

Gantt

Hardin

et al. 2022

Dimens Crit Care Nurs

Background: Delirium-related biochemical derangements lead to electrical changes that can be detected in electroencephalographic (EEG) patterns followed by behavioral signs and symptoms.Studies using limited lead EEG show a large difference between patients with and without delirium while discriminating delirium from other causes. Handheld rapid EEG devices may be capable of detecting delirium before symptom onset, thus providing an objective physiological method to detect delirium when it is most amenable to interventions.Objective: The aim of this study was to explore the potential for rapid EEG to detect waveform pattern changes consistent with delirium status.Methods: This prospective exploratory pilot study used a correlational design and mixed models to explore the relationships between handheld portable EEG data and delirium status.Results: While being under powered minimized opportunities to detect statistical differences in EEGderived ratios using spectral density analysis, sleep-to-wake ratios tended to be higher in patients with delirium.Conclusions: Limited lead EEG may be useful in predicting adverse outcomes and risk for delirium in older critically ill patients. Although this population is at the highest risk for mortality, delirium is not easily identified by current clinical assessments. Therefore, further investigation of limited lead EEG for delirium detection is warranted.