Can critical care providers be trained to reliably detect seizures using quantitative electroencephalography in the intensive care unit?
CCCF ePoster library. Lalgudi Ganesan S. Oct 2, 2017; 198208
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Dr. Saptharishi Lalgudi Ganesan
Dr. Saptharishi Lalgudi Ganesan
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Can critical care providers be trained to reliably detect seizures using quantitative electroencephalography in the intensive care unit?

Lalgudi Ganesan, Saptharishi1; Stewart, Craig2; Atenafu, Eshetu3; Sharma, Rohit2; Guerguerian, Anne-Marie1; Hutchison, Jamie1; Hahn, Cecil2

1 Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, Canada

2 Division of Neurology, Department of Paediatrics, The Hospital for Sick Children, Toronto, Canada

3 Department of Bio-statistics, University Health Network, Toronto, Canada

Introduction: Electroencephalography (EEG) alone can detect non-convulsive seizures (NCS). Although EEG is acquired and recorded continuously in the critical care unit (CCU), seizure-detection is delayed as most hospitals lack ‘round-the-clock’ analysis by neurophysiologists. If critical care providers could be trained to recognize seizures on simplified, time-compressed quantitative EEG tools at the bedside, it could lead to more timely detection, diagnosis and therapy of NCS.

1. To evaluate the ability of critical care providers to identify seizures reliably using quantitative EEG (QEEG) tools like color density spectral array (CDSA) and amplitude-integrated EEG (aEEG), after a 2-hour educational intervention
2. To identify the determinants of performance when healthcare providers use QEEG tools
Methods: Continuous EEG recordings from 22 critically ill children admitted in a multi-specialty tertiary-care pediatric intensive care unit in Toronto were transformed to quantitative EEG displays. We provided a 2-hour interactive training session on the use of quantitative EEG tools for seizure detection to four groups of healthcare provider groups - ICU Fellows, ICU nurses, Neurophysiologists and EEG Technologists. This educational intervention was followed by supervised individual review of 27 aEEG and CDSA displays with the task of identifying
Results: The 12 participants reviewed 27 cEEGs comprising 487 hours of recording and containing 553 seizures. Performance was compared among groups using mixed- and nested-model analysis with appropriate adjustment for inter-tester variability within groups and collinearity. Using CDSA, sensitivity was comparable among fellows (82.4%), nurses (88.2%), neurophysiologists (83.3%), and technologists (73.3%) (P=0.09). Using aEEG, fellows (83.8%), nurses (73.1%), and neurophysiologists (81.5%) had comparable sensitivities, but technologists had a lower value (66.7%) (P=0.002). Positive predictive values were better for technologists (100 CDSA, 100 aEEG) and neurophysiologists (96.7, 96.6) than fellows (87.5, 90.5) and nurses (80, 87.5) with CDSA (p=0.01) but not for aEEG (p=0.09). Daily false positive rates were comparable among fellows (7.7 CDSA, 2.8 aEEG), nurses (7.1, 4.2), neurophysiologists (1.5, 1.2) and technologists (0, 0) (p=0.13 CDSA, p=0.41 aEEG,). Sensitivities and false positive rates varied greatly across individual EEG recordings. Sensitivity with CDSA was determined equally by professional background (43.2%) and recording characteristics (43.1%), while with aEEG, 70% was determined by professional background and 24% by recording characteristics.

Conclusions: Critical care providers can identify as many seizures as neurophysiologists using CDSA and aEEG. While using aEEG, sensitivity, false positive rate and positive predictive values for critical care providers (fellows and nurses) were comparable with that of neurophysiologists. With CDSA, sensitivity was comparable among groups. Although false positive rates were not significantly different among groups, positive predictive values for seizure identification among critical care providers were lower. Further 'feedback-based educational sessions' for critical care providers could reduce the false positive rates and improve the positive predictive values.

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