Jeve Yadava Bapurao
Department Obstetrics and Gynaecology, University Hospitals of Leicester, Leicester, UK.
J Hum Reprod Sci. 2013 Oct;6(4):259-62. doi: 10.4103/0974-1208.126298.
Reduced ovarian response to stimulation represents one of the most intractable problems in infertility treatment. As failed cycle can cause considerable amount of emotional and economical loss, there are various attempts made to predict ovarian response.
To evaluate different factors influencing outcome of assisted reproduction in women with predicted reduced response (antimullerian hormone between 1 and 5 pmol/L) and to develop a model using of AMH and age to predict the number of oocytes in poor responders.
Retrospective study in a teaching hospital.
We analyzed 85 cycles (57 women) with predicted reduced response with serum AMH value between 1 and 5 pmol/L. Standard ovarian stimulation protocol was used. Primary outcome measures were clinical pregnancy rates and oocytes retrieved.
Data were analyzed using Microsoft excel and MetlabR software.
Clinical pregnancy rate/ET was 20.33%, in this group. AMH and age was analyzed using linear regression model which produced an equation to give predicted oocyte count if AMH and age are known. (Oocytes = age × (-ß) + Serum AMH × α) (Constant ß=0.0102 and α = 1.0407).
Combined use of serum AMH and age to predict ovarian response within reduced responder group should be further evaluated. For first time, we suggested combining both factors to predict ovarian response using a simple equation which allow developing tailored strategy.
卵巢对刺激的反应降低是不孕症治疗中最棘手的问题之一。由于周期失败会导致相当大的情感和经济损失,人们进行了各种尝试来预测卵巢反应。
评估影响预测反应降低(抗苗勒管激素在1至5 pmol/L之间)的女性辅助生殖结局的不同因素,并建立一个使用抗苗勒管激素(AMH)和年龄来预测反应不良者卵母细胞数量的模型。
在一家教学医院进行的回顾性研究。
我们分析了85个周期(57名女性),这些女性预测反应降低,血清AMH值在1至5 pmol/L之间。采用标准的卵巢刺激方案。主要结局指标为临床妊娠率和获取的卵母细胞数量。
使用Microsoft excel和MetlabR软件对数据进行分析。
该组临床妊娠率/胚胎移植率为20.33%。使用线性回归模型分析AMH和年龄,得出一个方程,若已知AMH和年龄,可给出预测的卵母细胞数量。(卵母细胞数量 = 年龄×(-ß)+血清AMH×α)(常数ß = 0.0102,α = 1.0407)。
血清AMH和年龄联合使用以预测反应降低组内的卵巢反应应进一步评估。我们首次建议结合这两个因素,使用一个简单方程来预测卵巢反应,这有助于制定个性化策略。