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结核病项目地区涂阴肺结核诊断预测工具研究
引用本文:史素红,王伟炳,葛兆发,王标,王健,徐飚.结核病项目地区涂阴肺结核诊断预测工具研究[J].中华流行病学杂志,2007,28(10):984-987.
作者姓名:史素红  王伟炳  葛兆发  王标  王健  徐飚
作者单位:1. 江苏省建湖县疾病预防控制中心,224700
2. 复旦大学公共卫生学院
基金项目:江苏省卫生厅预防医学基金课题资助项目(Y200407)
摘    要:目的建立和评估痰涂片检查阴性(涂阴)结核菌培养阳性(菌阳)肺结核病例预告因子的Classification Tree模型,为临床判断涂阴病例中的菌阳患者提供决策依据。方法连续调查苏北地区结核病控制项目县(建湖县)2004年9月1日至2005年8月31日在建湖县疾病预防控制中心结核病防治门诊就诊的结核病症状疑似病例,并进行临床和痰涂片检查及痰培养。结果胸部X线片(胸片)浸润组菌阳的比例显著高于非浸润组(P〈0.0001),浸润组菌阳的比例高达77.0%,而非浸润组菌阳的比例仅4.4%;在浸润阴性的对象中,胸片单侧病变对象的菌阳诊断比例显著高于双侧病变对象(1.6% VS 19.4%;P〈0.0001);在浸润阴性、单侧病变的对象中,结核菌素皮试阴性对象的菌阳诊断比例显著低于皮试阳性对象(0.0%VS 2.6%;P=0.014)。结论胸片浸润、病变部位双侧以及结核菌素试验阳性是涂阴病例菌阳诊断的重要预告因子。该模型在农村人群中的应用具有良好的效果。

关 键 词:结核病  痰涂片检查阴性  树模型
收稿时间:2007/4/8 0:00:00
修稿时间:2007-04-08

Study on the prediction of smear negative pulmonary tuberculosis with classification trees
Shi Suhong,Wang Weibing,Ge Zhaof,Wang Biao,Wang Jian and Xu Biao.Study on the prediction of smear negative pulmonary tuberculosis with classification trees[J].Chinese Journal of Epidemiology,2007,28(10):984-987.
Authors:Shi Suhong  Wang Weibing  Ge Zhaof  Wang Biao  Wang Jian and Xu Biao
Institution:Jianhu Center for Disease Control and Prevention, Jianhu 224700 ,China
Abstract:OBJECTIVE: To improve the respiratory isolation policy for patients with suspected pulmonary tuberculosis (TB). METHODS: All consecutive patients with suspicion of having pulmonary TB when seeking health care at the County TB dispensary of the Center of Disease Control and Prevention received face to face interview. RESULTS: A Classification model was constructed with a sensitivity of 90.9% and specificity of 90.2%, while predictive factors of culture-proven pulmonary TB among smear negativecases were soakage in Chest X-ray exam (77.0% vs. 4.4%; P<0.0001), bilateral lung's abnormal (1.6% vs. 19.4%; P<0.0001) and reaction of tuberculin skin testing (0.0% vs. 2.6%; P=0.014). CONCLUSION: Soakage, bilateral lung's abnormal and positive reaction of tuberculin skin testing were important predictors to prognosticate culture positive diagnosis. The model had been proved to have promising sensitivity and specificity in the rural population covered by NTP-DOTs.
Keywords:Tuberculosis  Smear negative  Classification tree model
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