Background: Delirium is a prevalent and dangerous state of confusion that affects millions >65 years of age (1-3). Delirium is underdiagnosed and undertreated due to lack of effective screening methods (4, 5). Undetected delirium in hospitalized elderly patients substantially increases mortality, length of stay, and post-discharge institutionalization rates (1-3). Beyond being a very dangerous condition for patients, the financial cost associated with delirium is immense. Annual financial losses due to delirium are estimated to be over $150 billion in the U.S. alone (3). This study aims to prove the feasibility of developing an innovative, noninvasive, user-friendly, accurate/objective point-of-care (POC) device with bispectral electroencephalography (“BSEEG”) for mass screening of delirium.
Methods: Study subjects from two cohorts, 1) general medicine patients who are 70 year or older, and 2) orthopedic hip fracture repair patients who are 60 year or older, were recruited from the University of Iowa upon admission, and assessed for the presence of delirium with Confusion Assessment Method for ICU (CAM-ICU), and Delirium Rating Scare (DRS). For confirmed cases of delirium and controls, we obtained 10 minutes EEG recording with limited channels from a hand held EEG device twice a day during their hospital stay. We analyzed those raw EEG data by converting to digital signals with spectral density analysis and additional algorithm to differentiate the two groups with and without delirium.
Results: 142 subjects were recruited. Average age was 74 yo with SD 13.5, and female was 58 %. The preliminary data has demonstrated that a simplified EEG can provide good-quality brain wave signals that compare well to the signals obtained by a traditional EEG. The data has also shown that preliminary analysis of the spectral density of brain waves from bispectral EEG (BSEEG) can clearly distinguish delirious patients from normal controls, as well as a delirious state from a recovered state for a single individual. The performance metrics were as follows. Accuracy: 87.5%, Sensitivity: 80.0%, Specificity: 87.7%, Positive Predictive Value: 0.153846, Negative Predictive Value: 0.993671.
Conclusions: Our preliminary data showed the feasibility of this technology and approach for mass screening and detection of delirium among general medicine inpatients, who are at high risk for delirium without using traditional EEG or other conventional questionnaire style screening instruments. Further data collection to improve the performance of algorithm is required before making this strategy useful in clinical settings.
References:
1. Fong TG, Tulebaev SR, Inouye SK. Delirium in elderly adults: diagnosis, prevention and treatment. Nature reviews Neurology. 2009;5(4):210-20.
2. Inouye SK. Delirium in older persons. The New England journal of medicine. 2006;354(11):1157-65.
3. Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014;383(9920):911-22.
4. Ely EW, Stephens RK, Jackson JC, Thomason JW, Truman B, Gordon S, et al. Current opinions regarding the importance, diagnosis, and management of delirium in the intensive care unit: a survey of 912 healthcare professionals. Critical care medicine. 2004;32(1):106-12.
5. Spronk PE, Riekerk B, Hofhuis J, Rommes JH. Occurrence of delirium is severely underestimated in the ICU during daily care. Intensive care medicine. 2009;35(7):1276-80.