多囊卵巢综合征诊断的潜在新指标--抗苗勒管激素:一项Meta分析

A potential diagnostic marker for patients with polycystic ovary syndrome-anti-Müllerian hormone:a Meta analysis

目的探讨抗苗勒管激素(AMH)对多囊卵巢综合征(PCOS)患者的诊断价值。方法系统检索1950年1月—2017年5月期间国内外电子文献数据库关于血清AMH水平评价PCOS诊断效能的相关数据。按照纳入标准及排除标准收集文献,根据QUADAS标准评价纳入文献的质量,并采用Meta-Disc 1.4软件拟合汇总受试者工作特征(SROC)曲线,合并诊断效应量,对AMH在PCOS患者中的诊断效能进行Meta分析。结果本研究共纳入28篇文献,其中24篇为研究AMH作为单独指标的诊断效力,4篇为研究AMH替代2003年鹿特丹诊断标准中"多囊卵巢"指标的诊断效力。结果显示,AMH作为单独指标及取代"多囊卵巢"诊断生育年龄PCOS的敏感度分别为0.77和0.85,特异度为0.84和0.98,其诊断比值比为14.97和343.18。此外,SROC曲线图显示,AMH单独诊断及联合诊断PCOS的曲线下面积分别为0.8640和0.9858,Q指数为0.7946和0.9488,两者比较具有统计学差异(Z=4.21,P<0.01)。结论血清AMH水平对PCOS的诊断具有良好的临床应用价值,AMH取代鹿特丹诊断标准中"多囊卵巢"指标与其余指标联合具有较高的诊断效能,值得临床推广应用。

Objective To evaluate the diagnostic value of serum anti-M（u｜¨）llerian hormone（AMH） in patients with polycystic ovary syndrome（PCOS）. Methods All literatures in English and Chinese for differential diagnosis of PCOS by serum AMH were collected from January 1950 to May 2017. Data were extracted and assessed according to established criteria and QUADAS standard. Sensitivity, specificity and diagnostic odds ratio were pooled with Meta-Disc1.4 software. Summary receiver operating characteristics（SROC） curve analysis was used to evaluate the diagnostic value of serum AMH. Results Twenty-eight studies were included in the Meta-analysis. The sensitivity,specificity and diagnostic odds ratio of serum AMH alone in the diagnosis of PCOS were 0.77, 0.84 and 14.97. As the AMH was substitution of polycystic ovary morphology（PCOM） and combined with either oligo/amenorrhea or hyperandrogenism data, the sensitivity, specificity and diagnostic odds ratio of serum AMH were 0.85.0.98 and 343.18,respectively. Moreover,concerning the area under curve（AUC） of AMH level by SROC,the AUC was 0.8640 in single serum AMH and 0.9858 in AMH combined with aforementioned clinical features, which was significantly different（Z=4.21,P〈0.01）. Conclusion AMH might be a potential diagnostic value for PCOS patients. Moreover,AMH replaced PCOM and combined with aforementioned clinical features was a more accurate diagnostic marker.