Nurses The Missing Link in Continuous EEG Monitoring?

Article Clinical Neurology

Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset

Mark L. Scheuer et al.

Summary: In comparing the seizure detection performance of expert humans and computer algorithms in a large set of epilepsy monitoring unit EEG recordings, Persyst 14 algorithm was found to be comparable with human experts, meeting statistical noninferiority criteria. The study showed a modest level of agreement among human experts in seizure marking and low false-positive rates, with Persyst 14 algorithm performing similarly to the experts for the first time.

JOURNAL OF CLINICAL NEUROPHYSIOLOGY (2021)

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Article Critical Care Medicine

Teaching Important Basic EEG Patterns of Bedside Electroencephalography to Critical Care Staffs: A Prospective Multicenter Study

Stephane Legriel et al.

Summary: A training program consisting of face-to-face EEG courses and e-learning was effective in helping critical care staff accurately recognize important basic EEG patterns. The majority of participants achieved a success rate of at least 80% correct answers at day 90, showing a significant increase in knowledge and skills over time. Challenges still exist in correctly identifying some specific EEG patterns, indicating areas for potential improvement in training strategies.

NEUROCRITICAL CARE (2021)

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Article Clinical Neurology

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

Safa Kaleem et al.

Summary: This study demonstrates that real-time nurse interpretation of qEEG for seizure detection in neuro-ICU patients has clinically adequate sensitivity and specificity. The time to seizure detection by nurses is shorter than that of standard of care. Factors affecting nurse accuracy include increased hours monitored and the presence of brief rhythmic discharges.

NEUROLOGY-CLINICAL PRACTICE (2021)

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Article Critical Care Medicine