Objective: To build a predicting model of X-ray-positive community-acquired pneumonia based on clinical symptoms, signs and hemogram. Methods: A total of 1 080 suspicious CAP cases from outpatients of our hospital are studied. 15 clinical symptoms, signs, hemogram and chest X ray representation of the cases are analyzed in a method of logistic regression. Results: 11 variables were observed, including respiratory rate, cough severity, fever duration, rales, rhonchi,wheezes, neutrophil percentage,lymphocyte percentage, monocyte percentage, oxyphile leukocytes percentage, C- reaction protein. Their regression coefficients were respectively 1. 477,2. 147,0. 623,1. 568, - 0. 870,1. 422, - 0. 627, - 0. 691, - 0. 621, - 0. 936,1. 122, with the constant of 52. 103. General condition, cough duration, fever spike, white cell count were excluded. Conclusion: Physicians need to revaluate some important variables according to their experiences in order to make an individualized diagnosis of CAP. Aided by the prediction model, they could predict the probability of positive resuhs of chest x- ray, thus avoid some of the patients to the exposure of diagnostic medical radiation.%目的:试图以患儿临床症状、体征及血象表现建立一种可以预报社区获得性肺炎X线阳性发现的数学模型.方法:收集2010年2月至5月间我院门诊1 080例拟诊小儿社区获得性肺炎(CAP)病例的15个与诊断高度关联的临床表现及实验室检查因素以及其胸部X线表现,通过logistic回归分析,得到一个"X线阳性CAP"的数学预测模型.结果:呼吸频率、咳嗽程度、发热持续时间、湿啰音、干啰音、喘鸣音、中性粒细胞比例、淋巴细胞比例、单核细胞比例、嗜酸粒细胞比例、C反应蛋白入选,回归系数依次为1.477、2.147、0.623、1.568、-0.870、1.422、-0.627、-0.691、-0.621、-0.936、1.122,常数为52.103,一般情况、咳嗽持续时间、热峰以及白细胞总数被剔除.结论:在呼吸科医生对CAP作出客观的诊断时,根据以往经验有重要价值的因素可能需要重新评价.根据患儿的上述11个临床及血象表现,通过预测模型,可以预知其X线阳性的概率,并使大部分患儿免于X线照射.
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