Abstract
Current methods for screening and detecting delirium are not practical in clinical settings. We previously showed that a simplified EEG with bispectral electroencephalography (BSEEG) algorithm can detect delirium in elderly inpatients. In this study, we performed a post-hoc BSEEG data analysis using larger sample size and performed topological data analysis to improve the BSEEG method. Data from 274 subjects included in the previous study were analyzed as a 1st cohort. Subjects were enrolled at the University of Iowa Hospitals and Clinics (UIHC) between January 30, 2016, and October 30, 2017. A second cohort with 265 subjects was recruited between January 16, 2019, and August 19, 2019. The BSEEG score was calculated as a power ratio between low frequency to high frequency using our newly developed algorithm. Additionally, Topological data analysis (TDA) score was calculated by applying TDA to our EEG data. The BSEEG score and TDA score were compared between those patients with delirium and without delirium. Among the 274 subjects from the first cohort, 102 were categorized as delirious. Among the 206 subjects from the second cohort, 42 were categorized as delirious. The areas under the curve (AUCs) based on BSEEG score were 0.72 (1st cohort, Fp1-A1), 0.76 (1st cohort, Fp2-A2), and 0.67 (2nd cohort). AUCs from TDA were much higher at 0.82 (1st cohort, Fp1-A1), 0.84 (1st cohort, Fp2-A2), and 0.78 (2nd cohort). When sensitivity was set to be 0.80, the TDA drastically improved specificity to 0.66 (1st cohort, Fp1-A1), 0.72 (1st cohort, Fp2-A2), and 0.62 (2nd cohort), compared to 0.48 (1st cohort, Fp1-A1), 0.54 (1st cohort, Fp2-A2), and 0.46 (2nd cohort) with BSEEG. BSEEG has the potential to detect delirium, and TDA is helpful to improve the performance.
Highlights
Delirium among elderly inpatients is very common, expensive, and dangerous[1,2,3]
Our published data on bispectral EEG (BSEEG) and other literature about EEG support the notion that such approaches are useful for detecting delirium[29,30,31,32], challenges existed in accuracy of performance, and no large-scale study has been conducted to see whether improvement of performance through advanced signal analysis technology in detecting delirium using the BSEEG method is possible
The Clinical Dementia Rating (CDR) score for the 2nd cohort was significantly higher in the delirious group than in the control group (Table 1)
Summary
Delirium among elderly inpatients is very common, expensive, and dangerous[1,2,3]. Delirium is difficult to be diagnosed and less likely to be treated[4,5,6]. Instruments, including the Confusion Assessment Method for Intensive Care Unit (CAM-ICU)[19,20] and the Delirium Rating Scale-Revised-98 (DRS)[21] Despite these instruments being effective when rigorously implemented, delirium remains seriously underdiagnosed and undertreated[4,5,6], in part because these methods are often challenging to apply in a hospital setting because they involve extensive questionnaires administered multiple times daily by busy hospital personnel. A well-trained technician must work for a significant amount of time to correctly position the numerous EEG electrodes on a patient’s scalp, and a neurologist specialized in electrophysiology must interpret the data and report the findings in the record This results in significant delay in starting treatment for patients with abnormal brain signals suggestive of delirium. Our published data on BSEEG and other literature about EEG support the notion that such approaches are useful for detecting delirium[29,30,31,32], challenges existed in accuracy of performance, and no large-scale study has been conducted to see whether improvement of performance through advanced signal analysis technology in detecting delirium using the BSEEG method is possible
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