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P300预警精神分裂症患者暴力行为中的应用评价 被引量:8

Application evaluation of P300 in early warning of violence in patients with schizophrenia
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摘要 目的:探究精神分裂症患者不同暴力风险等级和有无暴力行为中事件相关电位P300的变化特点,并分析P300在预测暴力行为方面的作用。方法:收集2019年1月-2020年8月就诊于石河子绿洲医院的精神分裂症患者,在3天内完成听觉P300的检测,在P300检测之后严密观察3天,按是否发生暴力行为分为暴力组和非暴力组。结果:暴力组与非暴力组相比,潜伏期和波幅差异均有统计学意义(F=4.82,5.44;P<0.01);P300潜伏期的ROC曲线下面积AUC为0.742、波幅的AUC为0.810,P300联合潜伏期和波幅的AUC为0.89,P300联合作用下预测效果优于单独的潜伏期和波幅。结论:P300的潜伏期和波幅可以作为评估判断精神分裂症患者暴力风险的特异性神经电生理指标,对于预测精神分裂症患者的暴力行为有重要的临床应用价值,可以在今后的评估中推广使用。 Objective:To explore the characteristics of event-related potential P300 in schizophrenia patients with different violence risk levels and with or without violence,and to analyze the role of P300 in predicting violence.Methods:Schizophrenic patients who were treated in Shihezi oasis hospital from January 2019 to August 2020 were collected.The auditory P300 test was completed within 3 days.After the P300 test,they were closely observed for 3 days.They were divided into patients with violence and patients without violence according to whether they had violent behavior.Results:There were significant differences in latency and amplitude between group with violence and group without violence(F=4.82,5.44,P<0.01);AUC of area under ROC curve of P300 latency was 0.742,AUC of amplitude was 0.810,AUC of P300 combined latency and amplitude was 0.89,and the prediction effect of P300 combined latency and amplitude was better than that of single latency and amplitude(P<0.05).Conclusion:The latency and amplitude of P300 can be used to evaluate the specific neuroelectrophysiological indicators of violence risk in patients with schizophrenia.It has important clinical application value for predicting violence in patients with schizophrenia,and can be used in the future evaluation.
作者 李丹玉 陈玲 刘超猛 肖兰 张桂青 LI Danyu;CHEN Ling;LIU Chaomeng(Shihezi University,Shihezi 832000,China)
出处 《中国健康心理学杂志》 北大核心 2022年第3期335-339,共5页 China Journal of Health Psychology
基金 新疆生产建设兵团科技攻关项目(编号:2018AB201)。
关键词 P300 精神分裂症 暴力行为 预警 评价 P300 Schizophrenia Violent behavior Early warning Evaluation
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