Predictive Value of the Bispectral Index for Burst Suppression on Diagnostic Electroencephalogram During Drug-Induced Coma

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Abstract

Study Purpose: To determine correlation and predictive value between data obtained with the bispectral index (BIS) and diagnostic electroencephalogram (EEG) in determining degree of burst suppression during drug-induced coma. This study seeks to answer the question: “To what degree can EEG suppression and burst count as measured by diagnostic EEG during drug-induced coma be predicted from data obtained from the BIS such as BIS value, suppression ratio (SR), and burst count?” Background/Significance: During drug-induced coma, cortical EEG is the gold standard for real-time monitoring and drug titration. Diagnostic EEG is, from setup through data analysis, labor intensive, costly, and difficult to maintain uniform clinician competency. BIS monitoring is less expensive, less labor-intensive, and easier to interpret data and establish/maintain competency. Validating BIS data versus diagnostic EEG facilitates effective brain monitoring during drug-induced coma at lower cost with similar outcomes. Method: This is a prospective, observational cohort study. Four consecutive patients receiving drug-induced coma/EEG monitoring were enrolled. BIS was initiated after informed consent. Variables recorded per minute included presence or absence of EEG burst suppression, burst count, BIS value over time, burst count, and SR. Pearson’s product–moment and Spearman rank coefficient for BIS value and SR versus burst count were performed. Regression analysis was utilized to plot BIS values versus bursts/minute on EEG as well as SR versus burst count on EEG. EEG/BIS data were collected from digital data files and transcribed onto data sheets for corresponding time indices. Results: Four patients yielded 1,972 data sets over 33 hours of EEG/BIS monitoring. Regression coefficient of 0.6673 shows robust predictive value between EEG burst count and BIS SR. Spearman rank coefficient of −0.8727 indicates strong inverse correlation between EEG burst count and BIS SR. Pearson’s correlation coefficient between EEG versus BIS burst count was .8256 indicating strong positive correlation. Spearman’s rank coefficient of 0.8810 and Pearson’s correlation coefficient of .6819 showed strong correlation between BIS value versus EEG burst count. Number of patients (4) limits available statistics and ability to generalize results. Graphs and statistics show strong correlation/predictive value for BIS parameters to EEG suppression. Conclusions: This study is the first to measure correlation and predictive value between BIS monitoring and diagnostic EEG for degree of EEG suppression and burst count in the adult population. Available statistic tests and graphing of variables from BIS and diagnostic EEG show strong correlation and predictive value between both monitoring technologies during drug-induced coma. These support using BIS value, SR, and burst count to predict degree of EEG suppression in real time for titrating metabolic suppression therapy.

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