CAJ | 학술논문

目的建立判别麻疹临床症状的logistic回归模型,比较麻疹与其他发热出疹性疾病临床症状的差异.方法收集2011年3月至2012年2月四川省4个县级以上医院报告的所有麻疹、风疹、猩红热、幼儿急疹等病例,以实验室确诊麻疹为反应变量,各临床症状为自变量建立logistic回归方程,并绘制ROC曲线,选择最佳截断点.结果共收集551名病例,麻疹和风疹临床诊断与实验室诊断一致性均较差,Kappa值=0.349;根据两水平logistic回归分析,与麻疹确诊病例有关的症状有咳嗽(OR=5.75)、结膜炎(OR=3.00)、柯氏斑(OR=7.52)、淋巴结肿大(OR=0.07)、热退疹出(OR=0.07);回归方程ROC曲线的曲线下面积为0.97,最佳截断点为0.249.结论通过临床症状建立了预测效果较好的logistic回归模型,但应开展更大范围的调查和实验室检测,明确与麻疹相关的主要发热出疹性疾病类型及构成.
목적 건립판별마진림상증상적logistic회귀모형,비교마진여기타발열출진성질병림상증상적차이.방법 수집2011년3월지2012년2월사천성4개현급이상의원보고적소유마진、풍진、성홍열、유인급진등병례,이실험실학진마진위반응변량,각림상증상위자변량건립logistic회귀방정,병회제ROC곡선,선택최가절단점.결과 공수집551명병례,마진화풍진림상진단여실험실진단일치성균교차,Kappa치=0.349;근거량수평logistic회귀분석,여마진학진병례유관적증상유해수(OR=5.75)、결막염(OR=3.00)、가씨반(OR=7.52)、림파결종대(OR=0.07)、열퇴진출(OR=0.07);회귀방정ROC곡선적곡선하면적위0.97,최가절단점위0.249.결론 통과림상증상건립료예측효과교호적logistic회귀모형,단응개전경대범위적조사화실험실검측,명학여마진상관적주요발열출진성질병류형급구성.
Objective To study the prevalence rates of Rash and Febrile Illnesses (RFIs) including measles,rubella,scarlet fever,exanthema subitum and the differences among measles and other RFIs to tentatively formulate the logistic regression model through clinical manifestation.Methods All the suspected cases of measles,rubella,scarlet fever,exanthema subitum reported by the county/prefecture lever hospitals at four counties were collected during March 2011 to February 2012.When setting laboratory confirmed measles as dependent variable and existed symptoms as independent variable,a logistic regression model was formulated and optimal operational point (OOP) chosen,according to the ROC curve.Results A total number of 551 cases were collected but the consistency of measles diagnosis between clinical and laboratory was not satisfied,with Kappa value=0.349,same to the diagnosis of rubella.As for the result from the two-lever logistic regression model,symptoms that related to the confirmation of measles would include cough (OR=5.75),conjunctivitis (OR=3.00),Koplik spot (OR =7.52),lymphadenectasis (OR =0.07),rash after fever (OR=0.07).The area under ROC curve was 0.97 and the optimal operational point was 0.249.Conclusion A logistic regression model was formulated using the clinical symptoms which was resulted in better performance on prediction.As the sample size of this survey was small,the expansion on the scale of investigation and laboratory testings were needed before the types and components of measles-related RFIs be clarified.