摘要
目的探讨影响非艾滋病相关的中枢神经系统隐球菌感染预后的相关因素,建立有效合理的预测模型。方法回顾性分析100例非艾滋病相关性中枢神经系统隐球菌感染患者的临床资料,利用多元Logistic回归对影响预后的因素进行统计建模分析。结果基础疾病(x_(10))、脑实质受累(x_4)、脑脊液糖与血糖比值(x_7)、首次脑脊液隐球菌培养阳性率(x_(13))、首次脑脊液隐球菌墨汁染色涂片阳性率(x_(12))与预后影响(y)有紧密联系,线性回归方程为:y=1/[1+exp(-z)],其中,z=0.781x_(10)+2.287x4+0.707x7+1.134x_(13)-1.837x_(12)-4.420。通过Hosmer-Lemeshow等混合检验发现所得模型是合理的,通过样本检测发现平均准确率高达84%。结论非艾滋病相关的中枢神经系统隐球菌感染的预后与宿主存在的基础疾病、脑实质有无受累、脑脊液糖与血糖比值、首次脑脊液隐球菌墨汁染色图片阳性率、首次脑脊液真菌培养阳性率相关程度较大。
Objective To discuss the relevant factors affecting the prognosis of cryptococcal infections of central nervous systeminnon-acquired immune deficiency syndrome(AIDS)patients and to establish effective and reasonable forecast models.Methods With retrospective analysis of 100 cases with cryptococcal infections of the central nervous system in non-AIDS patients,multivariate logical regression analysis was used to conduct statistical analyses of the data.Results The underlying diseases(x10),parenchymal involvement(x4),the glucose ratio of cerebrospinal fluid(CSF)to the blood(x7),CSF cryptococcus cultivate positive rate(x13),and the cryptococcus ink staining smear positive rate(x12)on the initially collected CSF,were closely correlated with the outcome(y).And thelinear regression equation was: y=1/[1+exp(-z)],of which z=0.781x10+2.287x4+0.707x7+1.134x13-1.837x12-4.420.By means of the hybrid tests,e.g.the Hosmer-Lemeshow test,it was found that the model was reasonable and that the average accuracy was as high as 84%based on sample tests.Conclusion This study shows that the prognosis of the cryptococcal infections of the central nervous system innon-AIDS patients is largely correlated with the host underlying conditions,parenchymal involvement,cerebrospinal fluid glucose drops,positive rate of cryptococcus culture,and ink staining smear on the initially collected CSF.
出处
《福建医科大学学报》
北大核心
2017年第1期40-43,共4页
Journal of Fujian Medical University
基金
福建省卫生厅青年科研课题B类项目(2013-2-42)
关键词
隐球菌属
脑膜炎
隐球菌性
中枢神经系统感染
预后
回归分析
Cryptococcus
meningitis, cryptococcal
central nervous system infections
prognosis
regression analysis