Seli Emre, Kalafat Erkan, Reig Andres, Whitehead Christine, Ata Baris, Garcia-Velasco Juan
IVIRMA Global Research Alliance, IVIRMA New Jersey, Basking Ridge, NJ, USA.
Department of Obstetrics, Gynecology, and Reproductive Sciences, Yale School of Medicine, New Haven, CT, USA.
Hum Reprod. 2025 Sep 1. doi: 10.1093/humrep/deaf163.
Can a prediction model classify IVF patients into distinct prognostic groups based on their expected yield of euploid blastocysts?
Five distinct prognostic groups were identified, with chance of obtaining at least one euploid blastocyst ranging from <1% to 2% in very poor to ∼95% in very good prognosis groups.
Euploid blastocyst yield is a critical determinant of IVF success. While female age strongly influences embryo euploidy, other factors like ovarian reserve markers, partner age, and BMI may also contribute. Current approaches rely on basic and somewhat arbitrary classification of ovarian reserve markers and patient age, which are unable to represent the granular multidimensional relationship between them. A systematic approach to classify patients based on their expected euploid yield would enable better treatment planning and patient counseling.
STUDY DESIGN, SIZE, DURATION: A retrospective analysis of 10 774 IVF cycles from 8256 couples undergoing pre-implantation genetic testing for aneuploidy (PGT-A) of all available blastocysts from years 2020 to 2023 and a temporal validation cohort of 2089 cycles of 2089 patients from year 2024. The prediction model was developed using generalized additive models for location, scale, and shape using a development cohort from 2020 to 2023, and cross-validated through exhaustive 5-fold cross-validation. Temporal validation was performed using an entirely separate cohort from 2024. Model performance was assessed through calibration plots and discrimination metrics.
PARTICIPANTS/MATERIALS, SETTING, METHODS: Couples undergoing IVF with PGT-A of all their available blastocysts were included. Model included female age, partner age, anti-Müllerian hormone (AMH), antral follicle count, and BMI as predictors. Non-linear associations were captured using neural networks and restricted cubic splines. Missing data were handled using multivariate imputation by chained equations.
The median female age was 36.3 years (IQR: 33.3-39.5) and AMH was 2.0 ng/ml (IQR: 1.0-3.8). Models for predicting ≥1, ≥2, and ≥3 euploid blastocysts yield achieved very good discrimination performance in 5-fold cross-validation samples with mean AUCs of 0.834, 0.849, and 0.861, respectively. Models showed negligible shrinkage (<1%) between training and cross-validation sets with near-perfect calibration slopes (mean: 1.00, IQR: 0.99-1.01) and intercepts (mean: 0.015, IQR: 0.00-0.03). Using predicted absolute counts of euploid blastocysts, five distinct prognostic groups were created based on predicted euploid blastocyst yield. Patients in the very poor prognosis group had 98.3% probability of obtaining no euploid blastocysts after stimulation while the probabilities were 80.2%, 47.5%, 15.8%, and 4.7% in poor, borderline, good, and very good prognosis groups. The chances of obtaining ≥3 euploid blastocysts were 79.8%, 43.7%, 8.9%, 0.2%, and 0% in very good, good, borderline, poor, and very poor prognosis groups. In the temporal validation set (n = 2089), which constituted the first cycles of patients that were treated, the rates of no euploid blastocysts obtained at the end of stimulation were 100.0%, 82.6%, 47.4%, 13.1%, and 4.4% in the very poor, poor, borderline, good, and very good prognosis groups. The rates of three or more euploid blastocysts obtained at the end of stimulation in the temporal validation set were 83.5%, 51.0%, 10.7%, 0.6%, and 0.0% for very good, good, borderline, poor, and very poor prognosis groups. The FORTUNE (Forecasting Outcomes of Reproductive Treatments Using artificial Networks) model is available for use and further validation at https://epsilonkappa-analytics.shinyapps.io/FORTUNE and App Store for iOS mobile devices https://apps.apple.com/en/app/fortune-ivf/id6747190429.
LIMITATIONS, REASONS FOR CAUTION: Single-center study design may limit generalizability. The model does not account for laboratory-specific factors or stimulation protocols.
This novel classification system provides objective, personalized counseling for IVF patients regarding expected euploid yield, enabling better-informed decision-making about treatment options and number of planned stimulation cycles.
STUDY FUNDING/COMPETING INTEREST(S): There was no funding needed for this study. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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预测模型能否根据整倍体囊胚的预期产出将体外受精（IVF）患者分为不同的预后组？
确定了五个不同的预后组，获得至少一个整倍体囊胚的概率在预后非常差的组中小于1%至2%，而在预后非常好的组中约为95%。
整倍体囊胚产出是IVF成功的关键决定因素。虽然女性年龄对胚胎整倍体有很大影响，但其他因素如卵巢储备标志物、伴侣年龄和体重指数（BMI）也可能起作用。目前的方法依赖于对卵巢储备标志物和患者年龄的基本且有些随意的分类，无法体现它们之间细致的多维关系。基于预期整倍体产出对患者进行系统分类的方法将有助于更好地进行治疗规划和患者咨询。
研究设计、规模、持续时间：对2020年至2023年8256对接受所有可用囊胚非整倍体植入前基因检测（PGT - A）的夫妇的10774个IVF周期进行回顾性分析，并对2024年2089名患者的2089个周期进行时间验证队列研究。使用广义相加模型对位置、尺度和形状进行预测模型开发，使用2020年至2023年的开发队列，并通过详尽的五折交叉验证进行交叉验证。使用2024年完全独立的队列进行时间验证。通过校准图和区分度指标评估模型性能。
参与者/材料、设置、方法：纳入对所有可用囊胚进行PGT - A的IVF夫妇。模型将女性年龄、伴侣年龄、抗苗勒管激素（AMH）、窦卵泡计数和BMI作为预测因子。使用神经网络和受限立方样条捕捉非线性关联。使用链式方程的多变量插补处理缺失数据。
女性年龄中位数为36.3岁（四分位间距：33.3 - 39.5），AMH为2.0 ng/ml（四分位间距：1.0 - 3.8）。预测≥1、≥2和≥3个整倍体囊胚产出的模型在五折交叉验证样本中具有非常好的区分性能，平均曲线下面积（AUC）分别为0.834、0.849和0.861。模型在训练集和交叉验证集之间的收缩可忽略不计（<1%），校准斜率接近完美（均值：1.00，四分位间距：0.99 - 1.01），截距（均值：0.015，四分位间距：0.00 - 0.03）。根据预测的整倍体囊胚绝对计数，基于预测的整倍体囊胚产出创建了五个不同的预后组。预后非常差的组中患者在刺激后获得无整倍体囊胚的概率为98.3%，而在预后差、临界、好和非常好的组中概率分别为8
