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- Title
The Alaris auditory evoked potential monitor as an indicator of seizure inducibility and duration during electroconvulsive therapy: an observational study.
- Authors
Hsing-Hao Huang; Chun-Yu Wu; Feng-Sheng Lin; Yi-Ping Wang; Wei-Zen Sun; Chih-Peng Lin; Shou-Zen Fan
- Abstract
Background Precise control of anesthetic depth during electroconvulsive therapy (ECT) is crucial because most intravenous anesthetics have anticonvulsant effects. In this study, we investigated the association between anesthetic depth measured by the Alaris auditory evoked potential index (AAI) and seizure inducibility and seizure duration during ECT. Methods Sixty-four ECTs were evaluated in 12 consecutive patients. General anesthesia was performed with a thiopental-based method. The relationship between the pre-ictal AAI, seizure activity and seizure duration was analyzed, and a possible threshold pre-ictal AAI to induce a seizure duration of at least 25 seconds was calculated. Results Forty-one of the 64 ECT stimuli successfully induced seizure activity that lasted longer than 25 seconds. Pre-ictal AAI was significantly correlated to seizure duration (r = 0.54, p < 0.001) and the threshold pre-ictal AAI value was calculated to be 26 (area under curve: 0.76, sensitivity: 70.3% and specificity: 73.9%, p < 0.001). ECT with a pre- ictal AAI ≥ 26 had a higher incidence of successful seizure activity (p < 0.001) and a longer seizure duration (55 ± 35 v.s. 21 ± 27 seconds, p < 0.001). Conclusion Maintenance of a pre-ictal AAI value ≥ 26 was associated with an increased incidence of successful seizure activities and a longer seizure duration. This is the first report to investigate Alaris AEP monitoring during ECT.
- Subjects
TAIWAN; MENTAL depression; THERAPEUTICS; SCHIZOPHRENIA treatment; BIPOLAR disorder; ANESTHESIA; ANESTHETICS; AUDITORY evoked response; SEIZURES (Medicine); STATISTICAL correlation; ELECTROCONVULSIVE therapy; FISHER exact test; PROBABILITY theory; STATISTICAL sampling; SPASMS; STATISTICS; DATA analysis; RECEIVER operating characteristic curves; DATA analysis software
- Publication
BMC Anesthesiology, 2014, Vol 14, Issue 1, p1
- ISSN
1471-2253
- Publication type
Article
- DOI
10.1186/1471-2253-14-34