Analysis on the training effect of criteria and practical guidance for determination of brain death: electroencephalogram

Abstract

Objective To analyze the training results of electroencephalogram (EEG) for brain death determination and to improve the training program. Methods A total of 114 trainees received theoretical training, simulation skills training, bedside skills training and test analysis. The composition of the trainees and the results of EEG tests were analyzed. The error rates of 5 knowledge points of EEG tests were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional category, professional qualification and hospital level on the error rates. Results All of 114 trainees came from 72 hospitals. Among them, 91 trainees (79.82%) were between 30-49 years old, 108 trainees (94.74%) came from third grade, grade A hospitals, and most of them were from Department of Neurology (57.89% , 66/114) and Electrophysiology (19.30% , 22/114). There were 98 clinicians (85.96% ) and 52 trainees (45.61% ) had intermediate certificate. Of the 5 knowledge points, the total error rate was 9.19% (204/2221). Among them, the error rate of parameter setting was the highest (11.40% , 26/228), followed by those of result determination (10.44%, 80/766), recording techniques (10.25%, 69/673), environmental requirements (7.46%, 17/228) and pitfalls (3.68%, 12/326). The error rate of trainees who were older than 50 was significantly higher than that in other ages (P = 0.000, for all). The error rate of technicians was higher than that of clinicians (P = 0.039). Univariate and multivariate Logistic regression analyses showed that age was independent risk factor associated with high error rates (OR = 1.382, 95%CI: 1.156-1.652; P = 0.000). Conclusions Among the trainees, degree of mastering the knowledge points is different. The training program should be optimized according to the trainees. More attention should be paid to the difference of EEG between brain death determination and routine check to improve the quality of determination for brain death by EEG. DOI: 10.3969/j.issn.1672-6731.2015.12.008

Keywords

• Brain death
• Electroencephalography
• Reference standards
• Training (not in MeSH)