摘要：

目的 临床判定是脑死亡判定标准中的核心内容，对培训效果的分析可以发现培训模式存在的问题，以利于进一步改进和完善。方法 采用理论培训、模拟技能培训、床旁技能培训和考核后培训的方式对461 名学员进行临床判定标准的培训，通过单因素和多因素后退法Logistic 回归分析评价学员性别、年龄、专科类别、专业技术职称和医院级别等项因素对各知识点考核错误率的影响。结果 461 名学员中30 ~ 49 岁占77.87%（359/461），主要来自三级甲等医院（88.29%，407/461）的神经内科（43.39%，200/461）、神经外科（23.64%，109/461）和重症医学科（19.09%，88/461），其中高级职称占66.59%（307/461）。6 项13 个知识点考核总错误率为5.81%（1054/18 128），其中角膜反射（24.64%，104/422）、深昏迷判断（11.59%，365/3149）、头眼反射（9.48%，40/422）、脑死亡判定步骤与次数（7.48%，138/1844）、瞳孔对光反射（5.10%，90/1766）错误率均> 5%。单因素和多因素Logistic 回归分析显示，年龄（OR = 1.558，95%CI：1.435 ~ 1.693；P = 0.000）、专科类别（OR = 1.080，95%CI：1.021 ~ 1.143；P = 0.007）和医院级别（OR = 1.395，95%CI：1.174 ~ 1.659；P = 0.000）为导致试卷考核错误率高的独立危险因素。结论 应进一步改进临床判定标准的培训模式和方法，特别是进行以“强”带“弱”的针对性培训，以提高培训质量。

关键词: 脑死亡, 参考标准, 培训（非MeSH 词）

Abstract:

Objective  Clinical diagnosis is the most predominant in the criteria for determination of brain death. This paper aims to analyze the training results of clinical diagnosis for brain death determination and to improve the training program. Methods  A total of 461 trainees received theoretical training, simulation skills training, bedside skills training and test analysis. The composition of trainees was analyzed and the error rates of knowledge points were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional qualification and hospital level, on the error rates. Results  Four hundred and sixty-one trainees came from 161 hospitals. Among them, trainees of 30-49 years old occupied 77.87% (359/461), and most of them came from third grade, grade A hospitals (88.29%, 407/461). There were 200 trainees (43.39% ) from Department of Neurology, 109 trainees (23.64% ) from Department of Neurosurgery, and 88 trainees (19.09%) from Intensive Care Unit. Most of them (66.59%, 307/461) had senior certificate. Total error rate of 13 knowledge points was 5.81% (1054/18 128). The error rate of corneal reflex was the highest (24.64% , 104/422), followed by deep coma (11.59% , 365/3149), oculocephalogyric reflex (9.48%, 40/422), step and time of determination (7.48%, 138/1844), and pupillary light reflex (5.10% , 90/1766). Univariate and multivariate Logistic regression analyses showed that age (OR = 1.558, 95%CI: 1.435-1.693; P = 0.000), specialty (OR = 1.080, 95%CI: 1.021-1.143; P = 0.007) and hospital level (OR = 1.395, 95%CI: 1.174-1.659; P = 0.000) were independent risk factors associated with high error rates. Conclusions  The training patterns and methods of clinical diagnosis for brain death determination should be further improved, especially the individual training, to rise the training quality.

Key words: Brain death, Reference standards, Training (not in MeSH)