文章摘要
张蕾洁,金娜,王琪,张晓曙,朱莞琪,焦永卓,袁艳,李娟生,孟蕾.Bayes综合判别对流行性乙型脑炎临床分型的鉴别[J].中华流行病学杂志,2019,40(9):1164-1167
Bayes综合判别对流行性乙型脑炎临床分型的鉴别
Study on clinical classification of Japanese encephalitis by Bayes discriminant analysis
投稿时间:2019-04-02  
DOI:10.3760/cma.j.issn.0254-6450.2019.09.028
中文关键词: 流行性乙型脑炎;Bayes判别分析;临床分型
英文关键词: Japanese encephalitis;Bayes discriminant analysis;Clinical classification
基金项目:甘肃省自然科学基金(18JR3RA040);国家科技重大专项(2017ZX10103006)
作者单位E-mail
张蕾洁 兰州大学公共卫生学院流行病与卫生统计学研究所 730000  
金娜 甘肃省疾病预防控制中心传染病预防控制所, 兰州 730000  
王琪 兰州大学公共卫生学院流行病与卫生统计学研究所 730000  
张晓曙 甘肃省疾病预防控制中心传染病预防控制所, 兰州 730000  
朱莞琪 兰州大学公共卫生学院流行病与卫生统计学研究所 730000  
焦永卓 甘肃省疾病预防控制中心传染病预防控制所, 兰州 730000  
袁艳 兰州大学公共卫生学院流行病与卫生统计学研究所 730000  
李娟生 兰州大学公共卫生学院流行病与卫生统计学研究所 730000 lijsh@lzu.edu.cn 
孟蕾 甘肃省疾病预防控制中心传染病预防控制所, 兰州 730000 mleicdc@163.com 
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中文摘要:
      目的 构建普通型和重型流行性乙型脑炎(乙脑)临床分型的Bayes判别函数,采用量化指标鉴别乙脑病例。方法 选取2005-2017年甘肃省CDC疫情监测系统报告的普通型和重型乙脑病例为研究对象,应用非条件logistic回归和Bayes逐步判别分析筛选有意义的临床指标,构建Bayes判别函数并进行评价。结果 普通型病例256例;重型病例257例。两组病例在性别、年龄和职业分布差异无统计学意义(P>0.05),病死率差异有统计学意义(P<0.05)。联合非条件logistic回归与Bayes逐步判别分析,再结合相关文献筛选11个临床指标建立Bayes判别函数,交互验证法显示普通型和重型乙脑病例的判别函数灵敏度为71.48%(95% CI:65.53%~76.93%)、特异度为73.93%(95% CI:68.11%~79.19%)、ROC曲线下面积为0.761(95% CI:0.720~0.803),总体准确率为72.71%。结论 通过构建Bayes判别函数可以较为准确地鉴别普通型和重型乙脑病例,有利于病例合理救治。
英文摘要:
      Objective To construct Bayes discriminant function for clinical classification of common and severe Japanese encephalitis (JE) cases, and to identify cases accurately with quantitative indicators. Methods Samples of confirmed common and severe JE cases reported by the epidemic surveillance system of Gansu Provincial Center for Disease Control and Prevention from 2005 to 2017 were collected. Non-conditional logistic regression analysis and Bayes stepwise discriminant analysis were used to screen meaningful clinical indicators, so as to construct and evaluate Bayes discriminant function. Results There were 256 common JE cases and 257 severe JE cases. There were no significant differences in sex, age and occupation distributions between the two groups (P>0.05) and there was significant difference in case fatality rate (P<0.05). Non-conditional logistic regression analysis and Bayes stepwise discriminant analysis, combined with using related literature, to screen 11 clinical indicators for the construction of Bayes discriminant function. Interactive validation showed that the sensitivity of discriminant function was 71.48% (95%CI:65.53%-76.93%) and the specificity was 73.93% (95%CI:68.11%-79.19%). The area under ROC curve was 0.761 (95%CI:0.720-0.803) and the total accuracy rate was 72.71%. Conclusion Bayes discriminant function can be used to identify common and severe JE cases more accurately, which is helpful for the reasonable treatment and good prognosis of JE patients.
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