3 Scopus citations

Abstract

BACKGROUND: Electroencephalographic (EEG) brain monitoring during general anesthesia provides information on hypnotic depth. We hypothesized that anesthesia clinicians could be trained rapidly to recognize typical EEG waveforms occurring with volatile-based general anesthesia. METHODS: This was a substudy of a trial testing the hypothesis that EEG-guided anesthesia prevents postoperative delirium. The intervention was a 35-minute training session, summarizing typical EEG changes with volatile-based anesthesia. Participants completed a preeducational test, underwent training, and completed a posteducational test. For each question, participants indicated whether the EEG was consistent with (1) wakefulness, (2) non–slow-wave anesthesia, (3) slow-wave anesthesia, or (4) burst suppression. They also indicated whether the processed EEG (pEEG) index was discordant with the EEG waveforms. Four clinicians, experienced in intraoperative EEG interpretation, independently evaluated the EEG waveforms, resolved disagreements, and provided reference answers. Ten questions were assessed in the preeducational test and 9 in the posteducational test. RESULTS: There were 71 participants; 13 had previous anesthetic-associated EEG interpretation training. After training, the 58 participants without prior training improved at identifying dominant EEG waveforms (median 60% with interquartile range [IQR], 50%–70% vs 78% with IQR, 67%–89%; difference: 18%; 95% confidence interval [CI], 8–27; P < .001). In contrast, there was no significant improvement following the training for the 13 participants who reported previous training (median 70% with IQR, 60%–80% vs 67% with IQR, 67%–78%; difference: −3%; 95% CI, −18 to 11; P = .88). The difference in the change between the pre- and posteducational session for the previously untrained versus previously trained was statistically significant (difference in medians: 21%; 95% CI, 2–28; P = .005). Clinicians without prior training also improved in identifying discordance between the pEEG index and the EEG waveform (median 60% with IQR, 40%–60% vs median 100% with IQR, 75%–100%; difference: 40%; 95% CI, 30–50; P < .001). Clinicians with prior training showed no significant improvement (median 60% with IQR, 60%–80% vs 75% with IQR, 75%–100%; difference: 15%; 95% CI, −16 to 46; P = .16). Regarding the identification of discordance, the difference in the change between the pre- and posteducational session for the previously untrained versus previously trained was statistically significant (difference in medians: 25%; 95% CI, 5–45; P = .012). CONCLUSIONS: A brief training session was associated with improvements in clinicians without prior EEG training in (1) identifying EEG waveforms corresponding to different hypnotic depths and (2) recognizing when the hypnotic depth suggested by the EEG was discordant with the pEEG index.

Original languageEnglish
Pages (from-to)777-786
Number of pages10
JournalAnesthesia and analgesia
DOIs
StateAccepted/In press - 2020

Fingerprint

Dive into the research topics of 'Practical training of anesthesia clinicians in electroencephalogram-based determination of hypnotic depth of general anesthesia'. Together they form a unique fingerprint.

Cite this