• Malissa A Mulkey, Daniel Erik Everhart, Sunghan Kim, DaiWai M Olson, and Sonya R Hardin.
• Malissa A. Mulkey, MSN, APRN, CCNS, CCRN, CNRN, is a neuroscience clinical nurse specialist at Duke University Hospital, Durham, North Carolina; and PhD candidate at East Carolina University, Greenville, North Carolina. Daniel Erik Everhart, PhD, ABPP, is a professor at East Carolina University, Greenville, North Carolina. Sunghan Kim, PhD, is from East Carolina University, Greenville, North Carolina. DaiWai M. Olson, PhD, RN, CCRN, FNCS, is a professor at the University of Texas Southwestern, Dallas. Sonya R. Hardin, PhD, CCRN, ACNS-BC, NP-C, is a dean and professor at University of Louisville, Kentucky.
• Dimens Crit Care Nurs. 2019 Sep 1; 38 (5): 241-247.

AbstractFor the past 2500 years, delirium has been described based on the presence of behavioral symptoms. Each year, as many as 1 in 5 acute care and 80% of critically ill patients develop delirium. The United States spends approximately $164 million annually to combat the associated consequences of delirium. There are no laboratory tools available to assist with diagnosis and ongoing monitoring of delirium; therefore, current national guidelines for psychiatry, geriatrics, and critical care strongly recommend routine bedside screening. Despite the significance, health care teams fail to accurately identify approximately 80% of delirium episodes.The utility of conventional electroencephalogram (EEG) in the diagnosis and monitoring of delirium has been well established. Neurochemical and the associated neuroelectrical changes occur in response to overwhelming stress before behavioral symptoms; therefore, using EEG will improve early delirium identification. Adding EEG analysis to the current routine clinical assessment significantly increases the accuracy of detection. Using newer EEG technology with a limited number of leads that is capable of processing EEG may provide a viable option by reducing the cost and need for expert interpretation. Because EEG monitoring with automatic processing has become technically feasible, it could increase delirium recognition. Electroencephalogram monitoring may also provide identification before symptom onset when nursing interventions would be more effective, likely reducing the long-term ramifications. Having an objective method that nurses can easily use to detect delirium could change the standard of care and provide earlier identification.

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