Electrographic Seizure Detection by Neuroscience Intensive Care Unit Nurses via Bedside Real-Time Quantitative EEG

Neurol Clin Pract. 2021 Oct;11(5):420-428. doi: 10.1212/CPJ.0000000000001107.

Abstract

Objective: Our primary objective was to determine the performance of real-time neuroscience intensive care unit (neuro-ICU) nurse interpretation of quantitative EEG (qEEG) at the bedside for seizure detection. Secondary objectives included determining nurse time to seizure detection and assessing factors that influenced nurse accuracy.

Methods: Nurses caring for neuro-ICU patients undergoing continuous EEG (cEEG) were trained using a 1-hour qEEG panel (rhythmicity spectrogram and amplitude-integrated EEG) bedside display. Nurses' hourly interpretations were compared with post hoc cEEG review by 2 neurophysiologists as the gold standard. Diagnostic performance, time to seizure detection compared with standard of care (SOC), and effects of other factors on nurse accuracy were calculated.

Results: A total of 109 patients and 65 nurses were studied. Eight patients had seizures during the study period (7%). Nurse sensitivity and specificity for the detection of seizures were 74% and 92%, respectively. Mean nurse time to seizure detection was significantly shorter than SOC by 132 minutes (Cox proportional hazard ratio 6.96). Inaccurate nurse interpretation was associated with increased hours monitored and presence of brief rhythmic discharges.

Conclusions: This prospective study of real-time nurse interpretation of qEEG for seizure detection in neuro-ICU patients showed clinically adequate sensitivity and specificity. Time to seizure detection was less than that of SOC.

Trial registration information: Clinical trial registration number NCT02082873.

Classification of evidence: This study provides Class I evidence that neuro-ICU nurse interpretation of qEEG detects seizures in adults with a sensitivity of 74% and a specificity of 92% compared with traditional cEEG review.

Associated data

  • ClinicalTrials.gov/NCT02082873