- Adult patients admitted to the Duke Neuro ICU and started on cEEG as a part of usual clinical care between September 2018 and January 2020 were identified prospectively and included based on study team availability.
Digital cEEG recordings (Natus Medical, Pleasanton, CA) were obtained with electrodes placed according to the International 10/20 electrode placement system. All cEEG recordings were processed through Persyst 12 (Persyst Inc., Prescott, AZ) to yield 1-hour qEEG trends displayed in real time at the patient bedside adjacent to a cEEG display. The qEEG panels consisted of rhythmicity spectrogram (Persyst Inc., displayed for both the left and right hemispheres; range 1-25Hz, 3s epochs with 1s step size, EEG time constant = 0.16s, high-frequency filter = 35Hz, rhythmicity sampling rate = 128 Hz, epoch duration = 3s, epoch step = 2s, y-axis range 1-25Hz, y-axis scaling type = square root, z-axis scaling using uV/Hz, z-axis range 0-4uV/Hz z-axis color palette) and amplitude integrated EEG (aEEG, displayed for the left and right hemispheres; time constant of 0.5s with 1s epochs; downsampled to a rate of 64 samples per second, then filtered using a 60 Hz notch filter and an asymmetrical filter). These trends also underwent automated artifact reduction through the Persyst 12 software, which removes electrode and physiological artifact.
Patients were selected based on the following inclusion criteria: 18 years of age or older, cEEG initiation within the past 12 hours, absence of seizures at the time of study initiation, and indication for cEEG any reason except hypoxic brain injury. The same patient could be studied more than once if cEEG was discontinued then restarted and no seizures were appreciated during the previous recording.
Nurses were consented, then administered a 5-10 minute, standardized, in-person qEEG training session by a member of the research team via printed PowerPoint slide set. The training included background information on qEEG and example qEEG panel printouts. The research team member then inspected the bedside display to ensure the correct trends were on-screen and allowed the nurses to ask any questions about the bedside display. For the remainder of their 12-hour shift, nurses were instructed to log the number of seizures seen on the bedside display based on their interpretation. Nurses were able to defer the hour if the patient was disconnected from EEG for CT for example, or if nurses had other prioritized clinical responsibilities. Seizures were logged in the following bins: no seizures, 1-2 seizures, 3-5 seizures, 6-10 seizures, and >10 seizures. The training slides were available to nurses for the duration of the shift. Information about the number of years of experience each nurse had in the Neuro ICU was also collected.
Patient demographics and diagnosis information were collected post-hoc using the electronic medical record. The corresponding cEEG recordings from each patient were deidentified and reviewed by study authors and board-certified neurophysiologists CBS and CEH post-hoc to identify each seizure and describe their characteristics. For any discrepancies, consensus was reached through deliberation by the two cEEG readers. The seizure spatial extent (focal, hemispheric, or generalized/secondary generalized), duration (10-30s, 31-60s, 61s-5min, or >5min), amplitude (low [20-49uV], medium [50-199uV], or high [>200uV]), and background (brief rhythmic discharges [BRD], periodic discharges [PD], rhythmic delta activity [RDA], or spike-and-wave [SW]) were determined by study author CBS.
At our institution, standard of care (SOC) detection is the time of clinical seizure detection by neurophysiology fellows. This is followed by post-hoc verification by a board-certified neurophysiologist. ... [Read More]
- Total Size
- 6 files (43.8 KB)
- Data Citation
- Kaleem, S. & Swisher, C. B. (2020). Data from: Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG. Duke Research Data Repository. https://doi.org/10.7924/r4mp51700
- Publication Date
- May 24, 2020
- Collection Dates
- 2018-09-01 - 2020-31-01
- Related Materials
- Funding Agency
- The role of the first author in this study was supported in part by a Pfizer Foundation grant and the Duke Translational Science Institute (CTSI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Pfizer Foundation of the Duke CTSI.
- Safa Kaleem, 0000-0001-5834-5958, email@example.com
- Data from: Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG
|demographics and characteristics.csv||2020-05-24||Public||Download|
|nurse accuracy v GS.csv||2020-05-24||Public||Download|
|seizure log and consensus.csv||2020-05-24||Public||Download|