Diagnostic Performance and Utility of Quantitative EEG Analyses in Delirium: Confirmatory Results From a Large Retrospective Case-Control Study

Abstract

Background. The lack of objective disease markers is a major cause of misdiagnosis and nonstandardized approaches in delirium. Recent studies conducted in well-selected patients and confined study environments suggest that quantitative electroencephalography (qEEG) can provide such markers. We hypothesize that qEEG helps remedy diagnostic uncertainty not only in well-defined study cohorts but also in a heterogeneous hospital population. Methods. In this retrospective case-control study, EEG power spectra of delirious patients and age-/gender-matched controls (n = 31 and n = 345, respectively) were fitted in a linear model to test their performance as binary classifiers. We subsequently evaluated the diagnostic performance of the best classifiers in control samples with normal EEGs (n = 534) and real-world samples including pathologic findings (n = 4294). Test reliability was estimated through split-half analyses. Results. We found that the combination of spectral power at F3-P4 at 2 Hz (area under the curve [AUC] = .994) and C3-O1 at 19 Hz (AUC = .993) provided a sensitivity of 100% and a specificity of 99% to identify delirious patients among normal controls. These classifiers also yielded a false positive rate as low as 5% and increased the pretest probability of being delirious by 57% in an unselected real-world sample. Split-half reliabilities were .98 and .99, respectively. Conclusion. This retrospective study yielded preliminary evidence that qEEG provides excellent diagnostic performance to identify delirious patients even outside confined study environments. It furthermore revealed reduced beta power as a novel specific finding in delirium and that a normal EEG excludes delirium. Prospective studies including parameters of pretest probability and delirium severity are required to elaborate on these promising findings.