Department of Obstetrics and Gynecology, Reproductive Medicine Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Reproductive Medicine, Guangzhou, China.
Hum Reprod. 2023 Dec 4;38(12):2382-2390. doi: 10.1093/humrep/dead202.
Can blastocyst aneuploidy be predicted for patients with previous aneuploid pregnancy loss (PAPL) and receiving preimplantation genetic testing for aneuploidy (PGT-A)?
Multivariable logistic regression models were established to predict high risk of blastocyst aneuploidy using four identified factors, presenting good predictive performance.
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.
STUDY DESIGN, SIZE, DURATION: 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.
PARTICIPANTS/MATERIALS, SETTING, 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.
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.
LIMITATIONS, REASONS FOR CAUTION: 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.
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.
STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the National Natural Science Foundation of China under Grant (81871159). No competing interest existed in the study.
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对于有过胚胎非整倍体妊娠丢失（PAPL）病史且接受过胚胎植入前遗传学检测（PGT-A）的患者，是否可以预测囊胚的非整倍体率？
建立了多变量逻辑回归模型，使用四个已确定的因素来预测囊胚非整倍体率高的风险，具有良好的预测性能。
非整倍体是导致妊娠丢失的最常见胚胎染色体异常。几项研究表明，PAPL 患者的胚胎非整倍体率更高，这表明 PGT-A 应该有助于改善 PAPL 患者的妊娠结局。然而，最近的研究未能证明 PGT-A 对 PAPL 患者有效。提高疗效的一种可能方法是预测囊胚非整倍体风险，以确定可能受益于 PGT-A 的特定 PAPL 人群。
研究设计、规模、持续时间：我们进行了一项基于数据分析的多中心回顾性队列研究，纳入了三家大学附属医院生殖医学中心在 2014 年 1 月至 2020 年 6 月期间接受 PGT-A 的 1119 名患者。其中 550 名患者有 1 至 3 次 PAPL，被纳入 PAPL 组。此外，569 名患有单基因疾病且无妊娠丢失的患者作为非 PAPL 组。
参与者/材料、设置、方法：PGT-A 使用单核苷酸多态性微阵列和下一代测序进行。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定义为≥50%。使用赤池信息量准则选择候选风险因素，并将其纳入多变量逻辑回归模型。使用混淆矩阵评估整体预测准确性，通过绘制接受者操作特征曲线下的面积（AUC）进行区分，通过绘制预测概率与观察概率来进行校准。统计学意义设为 P < 0.05。
PAPL 和非 PAPL 组的囊胚非整倍体率分别为 30±25%和 21±19%。母亲年龄（优势比（OR）=1.31，95%CI 1.24-1.39，P < 0.001）、PAPL 数量（OR=1.40，95%CI 1.05-1.86，P = 0.02）、促排卵日雌激素水平（OR=0.47，95%CI 0.30-0.73，P < 0.001）和囊胚形成率（OR=0.13，95%CI 0.03-0.50，P = 0.003）与囊胚非整倍体高风险相关。基于上述四个变量的预测模型在训练数据集和测试数据集上的 AUC 分别为 0.80 和 0.83，训练数据集的平均和最大差异分别为 2.89%和 12.76%，测试数据集的平均和最大差异分别为 0.98%和 5.49%。
局限性、谨慎原因：我们的结论可能与活检囊胚数量少于四个和卵巢储备功能减退的患者不兼容，因为所有纳入的患者都有四个或更多活检囊胚，并且卵巢储备功能良好。
所开发的预测模型对于在 PGT-A 前通过考虑母亲年龄、PAPL 数量、促排卵日雌激素水平和囊胚形成率来为 PAPL 患者提供咨询至关重要。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更高囊胚非整倍体风险的 PAPL 患者，并通过 PGT-A 获得更好的妊娠结局。
研究资金/利益冲突：本工作得到国家自然科学基金资助（81871159）。研究中不存在竞争利益。
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