摘要
目的：探讨在多囊卵巢综合征（PCOS）诊断中纳入代谢指标的必要性。方法以鹿特丹诊断为金标准，选择51例 PCOS 患者作为病例组，同期就诊的非 PCOS 患者47例为对照组，利用单因素分析筛选的有意义变量，使用分类树 C5．0算法建立联合生殖和代谢指标的诊断模型，进行 ROC 曲线分析、评价其真实性和可靠性，并与金标准进行一致性和差异性分析。结果单因素分析筛选出13个有意义的变量。联合诊断模型的 ROC 曲线下面积（AUC）为0．930（P ＝0．000），0．190为最佳诊断界值，此时对应的灵敏度、特异度和符合率分别为94．10％、91．50％和92．86％，可见联合诊断模型具有较高的真实性和可靠性。两种诊断方法具极好的一致性（Kappa ＝0．857，P ＝0．000），差异无统计学意义（P ＝1．000）。结论在 PCOS 诊断中是否纳入代谢指标对 PCOS 的诊断没有影响，站在这个角度，可以认为没有必要在 PCOS 诊断中纳入代谢指标。
Objective To discuss whether it is necessary to integrate metabolic indices into diagnosis of polycystic ovary syndrome（PCOS）.Methods Taking ESHRE /ASRM diagnosis standard as gold standard,the case group composed of 51 women with PCOS and the control group composed of 47 women without PCOS were selected.Bynbsp;using classification tree C5.0,significant variables chosen by single factor analysis were used to establish a new diagnostic model which combined reproductive indices and metabolic indices.The validity and reliability of the new diagnostic model by using ROC curve analysis were evaluated.Finally,the consistence and difference between the new diagnostic model and the gold standard were analyzed.Results Single factor analysis chose 13 significant variables. ROC analysis showed an area under the curve of 0.930（P =0.000）and the optimal cut -off point was 0.190 with a sensitivity of 94.10%,a specificity of 91.50% and a consistency of 92.86%,which told us the new diagnostic model had superior validity and reliability.The two diagnostic methods had excellent consistence （Kappa =0.857,P =0.000）and there was no statistically significant difference （P =1.000）.Conclusion Considering that whether metabolic indices are integrated into diagnosis of PCOS doesn′t change the diagnosis result,and it is unnecessary to integrate metabolic indices into diagnosis of PCOS.
作者
邱梅
蒋婷婷
李晓毅
潘景
苏莹
Jiang Tingting Li Xiaoyi Pan Jing Su Ying(Department of Gynecology, the Second Affiliated Hospital of Kunming Medical University, Kunming , Yunnan 650101, China)
出处
《中国基层医药》
CAS
2016年第21期3225-3229,共5页
Chinese Journal of Primary Medicine and Pharmacy
基金
云南省科技厅-昆明医科大学应用基础研究联合专项课题（2013FZ272）
