La Marca Antonio, Capuzzo Martina, Donno Valeria, Mignini Renzini Mario, Del Giovane Cinzia, D'Amico Roberto, Sunkara Sesh Kamal
Department of Medical and Surgical Sciences of the Mother, Children and Adults, University of Modena and Reggio Emilia, Policlinico, Via del Pozzo 71, 41124 Modena, Italy; Clinica EUGIN, Via Nobili 188/F, 41126, Modena, Italy.
Department of Medical and Surgical Sciences of the Mother, Children and Adults, University of Modena and Reggio Emilia, Policlinico, Via del Pozzo 71, 41124 Modena, Italy.
J Gynecol Obstet Hum Reprod. 2021 Mar;50(3):101878. doi: 10.1016/j.jogoh.2020.101878. Epub 2020 Aug 1.
How much the variability in patients' response during in vitro fertilization (IVF) may add to the initial predicted prognosis based only on patients' basal characteristics?
Anonymous data were obtained from the Human Fertilization and Embryology Authority (HFEA). Data involving 114,882 stimulated fresh IVF cycles were retrospectively analyzed. Logistic regression was used to develop the models.
Prediction of live birth was feasible with moderate accuracy in all of the three models; discrimination of the model based only on basal patients' characteristics (AUROC 0.61) was markedly improved adding information of number of embryos (AUROC 0.65) and, mostly, number of oocytes (AUROC 0.66).
The addition to prediction models of parameters such as the number of embryos obtained and especially the number of oocytes retrieved can statistically significantly improve the overall prediction of live birth probabilities when based on only basal patients' characteristics. This seems to be particularly true for women after the first IVF cycle. Since ovarian response affects the probability of live birth in IVF, it is highly recommended to add markers of ovarian response to models based on basal characteristics to increase their predictive ability.
在体外受精（IVF）过程中，患者反应的变异性相较于仅基于患者基础特征的初始预测预后，会增加多少？
从人类受精与胚胎学管理局（HFEA）获取匿名数据。对涉及114,882个刺激新鲜IVF周期的数据进行回顾性分析。使用逻辑回归建立模型。
在所有三个模型中，预测活产具有中等准确性；仅基于患者基础特征的模型（曲线下面积[AUROC]为0.61），加入胚胎数量信息（AUROC为0.65）后，区分能力有显著改善，加入卵母细胞数量信息（AUROC为0.66）后改善更为明显。
在仅基于患者基础特征的预测模型中加入所获胚胎数量尤其是所取卵母细胞数量等参数，在统计学上可显著提高活产概率的总体预测。对于首次IVF周期后的女性而言似乎尤其如此。由于卵巢反应会影响IVF中的活产概率，强烈建议在基于基础特征的模型中加入卵巢反应标志物，以提高其预测能力。
