目的:是否可以预测先前患有非整倍体妊娠丢失(PAPL)并接受非整倍体植入前遗传学检测(PGT-A)的患者的囊胚非整倍体?
结论:使用四个确定的因素建立了多变量逻辑回归模型来预测囊胚非整倍体的高风险,呈现良好的预测性能。
背景:非整倍体是导致妊娠丢失的最常见的胚胎染色体异常。一些研究表明,PAPL患者的胚胎非整倍性率较高,这表明PGT-A对PAPL患者有改善妊娠结局的作用.然而,最近的研究未能证明PGT-A对PAPL患者的疗效.提高疗效的一种可能方法是预测胚泡非整倍性风险,以确定可能受益于PGT-A的特定PAPL群体。
方法:对2014年1月至2020年6月在大学附属教学医院三个生殖医学中心接受PGT-A治疗的1119例患者进行了多中心回顾性队列研究。有1至3个PAPL的550名患者被纳入PAPL组。此外,569例单基因疾病无妊娠丢失患者作为非PAPL组。
方法:使用单核苷酸多态性微阵列和下一代测序进行PGT-A。计算每位患者第5天囊胚的非整倍体率,并将高风险非整倍体率定义为≥50%。使用Akaike信息标准选择高风险非整倍体的候选危险因素,随后将其纳入多变量逻辑回归模型。使用混淆矩阵评估总体预测准确性,通过接收器工作特性曲线(AUC)下的面积进行区分,并通过绘制预测概率与观察到的概率进行校准。P<0.05时具有统计学意义。
结果:PAPL和非PAPL组的囊胚非整倍体率分别为30±25%和21±19%,分别。产妇年龄(比值比(OR)=1.31,95%CI1.24-1.39,P<0.001),PAPL数(OR=1.40,95%CI1.05-1.86,P=0.02),排卵触发日的雌二醇水平(OR=0.47,95%CI0.30-0.73,P<0.001),囊胚形成率(OR=0.13,95%CI0.03-0.50,P=0.003)与囊胚非整倍体的高风险相关。基于上述四个变量的预测模型,使用训练数据集得到的AUC为0.80,使用测试数据集得到的AUC为0.83,训练数据集的平均和最大差异为2.89%和12.76%,测试数据集的0.98%和5.49%,分别。
结论:我们的结论可能与那些小于4个活检囊胚和卵巢储备减少的患者不一致。因为所有纳入的患者都有4个或更多的活检囊胚,并且表现出良好的卵巢储备。
结论:开发的预测模型对于在PGT-A之前咨询PAPL患者至关重要,PAPLs的数量,在排卵触发日的雌二醇水平,和胚泡形成率。该预测模型实现了良好的风险分层,因此可能有助于识别可能具有较高的胚泡非整倍体风险的PAPL患者,因此可以通过PGT-A获得更好的妊娠结局。
背景:这项工作得到了国家自然科学基金(81871159)的资助。研究中不存在竞争兴趣。
背景:不适用。
OBJECTIVE: Can blastocyst aneuploidy be predicted for patients with previous aneuploid pregnancy loss (PAPL) and receiving preimplantation genetic testing for aneuploidy (PGT-A)?
CONCLUSIONS: Multivariable logistic regression models were established to predict high risk of blastocyst aneuploidy using four identified factors, presenting good predictive performance.
BACKGROUND: Aneuploidy is the most common embryonic chromosomal abnormality leading to pregnancy loss. Several studies have demonstrated a higher embryo aneuploidy rate in patients with PAPL, which has suggested that PGT-A should have benefits in PAPL patients intending to improve their pregnancy outcomes. However, recent studies have failed to demonstrate the efficacy of PGT-A for PAPL patients. One possible way to improve the efficacy is to predict the risk of blastocyst aneuploidy risk in order to identify the specific PAPL population who may benefit from PGT-A.
METHODS: We conducted a multicenter retrospective cohort study based on data analysis of 1119 patients receiving PGT-A in three reproductive medical centers of university affiliated teaching hospitals during January 2014 to June 2020. A cohort of 550 patients who had one to three PAPL(s) were included in the PAPL group. In addition, 569 patients with monogenic diseases without pregnancy loss were taken as the non-PAPL group.
METHODS: PGT-A was conducted using single nucleotide polymorphism microarrays and next-generation sequencing. Aneuploidy rates in Day 5 blastocysts of each patient were calculated and high-risk aneuploidy was defined as a rate of ≥50%. Candidate risk factors for high-risk aneuploidy were selected using the Akaike information criterion and were subsequently included in multivariable logistic regression models. Overall predictive accuracy was assessed using the confusion matrix, discrimination by area under the receiver operating characteristic curve (AUC), and calibration by plotting the predicted probabilities versus the observed probabilities. Statistical significance was set at P < 0.05.
RESULTS: Blastocyst aneuploidy rates were 30 ± 25% and 21 ± 19% for PAPL and non-PAPL groups, respectively. Maternal age (odds ratio (OR) = 1.31, 95% CI 1.24-1.39, P < 0.001), number of PAPLs (OR = 1.40, 95% CI 1.05-1.86, P = 0.02), estradiol level on the ovulation trigger day (OR = 0.47, 95% CI 0.30-0.73, P < 0.001), and blastocyst formation rate (OR = 0.13, 95% CI 0.03-0.50, P = 0.003) were associated with high-risk of blastocyst aneuploidy. The predictive model based on the above four variables yielded AUCs of 0.80 using the training dataset and 0.83 using the test dataset, with average and maximal discrepancies of 2.89% and 12.76% for the training dataset, and 0.98% and 5.49% for the test dataset, respectively.
CONCLUSIONS: Our conclusions might not be compatible with those having fewer than four biopsied blastocysts and diminished ovarian reserves, since all of the included patients had four or more biopsied blastocysts and had exhibited good ovarian reserves.
CONCLUSIONS: The developed predictive model is critical for counseling PAPL patients before PGT-A by considering maternal age, number of PAPLs, estradiol levels on the ovulation trigger day, and the blastocyst formation rate. This prediction model achieves good risk stratification and so may be useful for identifying PAPL patients who may have higher risk of blastocyst aneuploidy and can therefore acquire better pregnancy outcomes by PGT-A.
BACKGROUND: This work was supported by the National Natural Science Foundation of
China under Grant (81871159). No competing interest existed in the study.
BACKGROUND: N/A.