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Objective To determine the optimal timing of electroencephalogram (EEG) in predicting the outcome in patients with massive cerebral hemispheric infarction (MCHI) by evaluating and comparing the EEG changes during different acute stages after onset. Methods A total of 72 MCHI patients were selected to be monitored by bedside EEG at two stages: 1-3 and 4-7 d after onset, respectively. The outcome after 3 months was assessed as good [Glasgow Outcome Scale (GOS) score 3-5] or poor (GOS score 1-2). Then the predictive accuracy of EEG patterns and gradings (the Synek scale) were calculated and compared between two stages to confirm the optimal timing of prediction. Results In 72 cases, 62.50% (45/72) had good and 37.50% (27/72) had poor outcome 3 months after onset. Benign EEG patterns were protective factors for the outcome of MCHI (1-3 d: RR = 0.357, 95%CI: 0.153-0.834, Mantel-Haenszel χ2 = 6.147, P = 0.013; 4-7 d: RR = 0.240, 95%CI: 0.102-0.564, Mantel-Haenszel χ2 = 13.601, P = 0.000) and they could predict good outcome with incidence rate of 82.14% (23/28)-85.71% (30/35) and the accuracy of 62.50%-72.22% . Malignant EEG patterns at 4-7 d were risk factors for the outcome of MCHI (RR = 2.909, 95%CI: 1.611-5.253, Mantel-Haenszel χ2 = 11.110, P = 0.001), and they could predict poor outcome with incidence rate of 66.67% (16/24) and the accuracy of 73.63%. There was a significant negative correlation between the Synek scale and GOS score both during 1-3 d (rs = - 0.354, P = 0.002) and 4-7 d (rs = - 0.417, P = 0.000) after onset, indicating the higher the Synek scale was, the worse the outcome would be. The accuracy (83.33% vs 70.78%; χ2 = 4.000, P = 0.039), consistency (Kappa test: κ = 0.639, 95%CI: 0.522-0.746 vs κ = 0.406, 95%CI: 0.353-0.459; P = 0.001) and area under curves (0.86 ± 0.05, 95%CI: 0.761-0.958 vs 0.69 ± 0.07, 95%CI: 0.554-0.822; P = 0.002) of the Synek scale for prediction of long-term outcome during 4-7 d after onset were significantly higher than those during 1-3 d after onset. Conclusions EEG patterns and the Synek scale during 1-3 d after MCHI onset could be used to evaluate the severity of brain injury and to further guide medical treatment, while the Synek scale during 4-7 d after onset could be used to evaluate the long-term outcome and treatment with a high accuracy. DOI: 10.3969/j.issn.1672-6731.2015.12.005

Objective To analyze the training effects of transcranial Doppler (TCD) for brain death determination conducted by Brain Injury Evaluation Quality Control Centre of National Health and Family Planning Commission to optimize the training program and improve the training effects. Methods A total of 106 trainees received theoretical training, simulation skill training, bedside skill training and test analysis on TCD confirmatory test for brain death determination. The composition of trainees was analyzed and the error rates of 6 knowledge points 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 The trainees including 42 males and 64 females, came from 69 hospitals. Trainees of 30-49 years old occupied 77.36% (82/106). In the trainees, 96.23% (102/106) were from third grade, grade A hospitals, and most of them were from Department of Neurology (64.15% , 68/106) and Ultrasound (19.81% , 21/106). There were 82 clinicians (77.36%). Thirty four (32.08%) trainees had senior certificate and 49 (46.23%) had intermediate certificate. Total error rate of 6 knowledge points was 7.26% (149/2052). Of the 6 knowledge points, the error rate of parameter setting was the highest (9.43%, 10/106), followed by checking position (8.73%, 37/424), artery recognition (8.67%, 43/496), result determination (7.41%, 55/742), equipment (1.89%, 2/106) and pitfalls (1.12%, 2/178). Univariate and multivariate Logistic regression analyses showed that specialty (OR = 1.313, 95% CI: 1.072-1.610; P = 0.009) and hospital level (OR = 2.943, 95% CI: 1.623-5.338; P = 0.000) were independent risk factors associated with high error rates. Conclusions Among the trainees, degree of mastering the knowledge points is different, and the characteristics of trainees influence the training effect. The training program should be optimized according to key and difficult points to make more and more trainees qualifying for TCD confirmatory test in brain death determination. DOI: 10.3969/j.issn.1672-6731.2015.12.009

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

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. DOI: 10.3969/j.issn.1672-6731.2015.12.006

Objective To analyze the training results of short-latency somatosensory-evoked potential (SLSEP) for brain death determination and to improve the training program. Methods A total of 101 trainees received theoretical training, simulation skills training, bedside skills training and test analysis for SLSEP in brain death determination. The composition of trainees was analyzed and the error rates of 6 knowledge points 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 Among them, trainees of 30-49 years old occupied 76.24% (77/101), most of them were from third grade, grade A hospitals (98.02%, 99/101), and 78 trainees (77.23%) were from Department of Neurology. There were 82 clinicians (81.19%), 31 (30.69%) had senior certificate and 42 (41.58%) had intermediate certificate. Total error rate of 6 knowledge points was 4.50% (91/2020). Of the 6 knowledge points, the error rate of pitfalls was the highest (9.41%, 19/202), followed by result determination (5.94% , 12/202), recording techniques (4.75% , 24/505), procedures (3.96%, 32/808), sequence of confirmatory tests (1.98%, 2/101) and environmental conditions (0.99%, 2/202). Univariate and multivariate Logistic regression analyses showed that age (OR = 1.566, 95% CI: 1.116-2.197; P = 0.009) and professional qualification (OR = 1.669, 95% CI: 1.163-2.397; P = 0.005) were independent risk factors associated with high error rates. Conclusions The differences between brain death determination and routine check of SLSEP should be paid more attention to improve the quality of determination for brain death by SLSEP. DOI: 10.3969/j.issn.1672-6731.2015.12.007

Objective To analyze the related factors associated with immediate treatment failure of using anesthetics in refractory status epilepticus (RSE). Methods Thirty patients derived from Neurocritical Care Unit of Xuanwu Hospital from January 2004 to December 2013 were divided into 2 groups (acute treatment failure group and acute treatment success group) based on the treatment outcome 6 h after intravenous injection of anesthetics. Univariate and multivariate forward Logistic regression analyses were used to analyze and screen the risk factors associated with immediate treatment failure, and calculate the failure rate of final outcome. Results According to the results of univariate and multivariate Logistic regression analyses, among influencing factors such as sex, age, etiology, Acute Physiology and Chronic Health EvaluationⅡ (APACHEⅡ), type of status epilepticus (SE), type of antiepileptic drugs (AEDs), total duration of primary treatment, duration of prehospital primary treatment, duration of posthospital primary treatment, type of RSE and primary choice of anesthetics, only total duration of primary treatment was the independent risk factor for immediate treatment failure (OR = 1.007, 95%CI: 1.000-1.014; P = 0.047). The rate of immediate treatment failure of RSE by using anesthetics was 50% (15/30), and the rate of final treatment failure was 43.33% (13/30). The ratio of final treatment failure was much higher in acute treatment failure group than that in acute treatment success group (10/15 vs 3/15, P = 0.025). Conclusions The acute treatment result of RSE depends on the total duration of primary treatment, and determinates the final result of treatment. On the basis of treating primary disease, the therapy to terminate SE or RSE should be started as early as possible. DOI: 10.3969/j.issn.1672-6731.2015.11.008