背景:卵巢低反应(POR)与临床妊娠率下降有关,强调开发临床预测模型的必要性。这样的模型可以提高预后的准确性,个性化医疗干预,并最终提高POR患者的活产率。
目的:本研究旨在开发并验证预测接受体外受精/卵胞浆内单精子注射(IVF/ICSI)周期的POR患者临床妊娠结局的预测模型。
方法:纳入福建省妇幼保健院生殖中心2018年1月至2022年1月969例接受新鲜胚胎移植周期的POR患者的回顾性队列。该队列以7:3的比例随机分为模型组(n=678)和验证组(n=291)。对模型组进行单因素分析,找出影响临床妊娠的变量。使用LASSO回归选择最优变量,采用多因素logistic回归分析构建临床预测模型。使用受试者工作特性(ROC)和校准曲线评估模型的校准和鉴别,而使用决策曲线分析评估临床效用。
结果:多变量逻辑回归分析显示,女性的年龄(优势比[OR]0.936,95%置信区间[CI]0.898-0.976,P=0.002),体重指数(BMI)≤24(OR2.748,95%CI1.724-4.492,P<0.001),窦卵泡计数(AFC)(OR1.232,95%CI1.073-1.416,P=0.003),抗苗勒管激素(AMH)(OR1.67,95%CI1.178-2.376,P=0.004),成熟卵母细胞数(OR1.227,95%CI1.075-1.403,P=0.003),移植胚胎数(OR1.692,95%CI1.132-2.545,P=0.011),优质胚胎移植(OR3.452,95%CI1.548~8.842,P=0.005)是POR患者临床妊娠的独立预测因子。根据接收机工作特性(ROC)分析,预测模型的曲线下面积(AUC)在模型组中为0.752(0.714,0.789),在验证组中为0.765(0.708,0.821).临床决策曲线表明,当临床妊娠的阈值概率范围为6-81%至12-82%时,该模型在两个队列中都具有最大的临床效用。分别。
结论:接受IVF/ICSI治疗的POR患者的临床妊娠结局受几个独立因素的影响。包括女性的年龄,BMI,AFC,AMH,成熟卵母细胞的数量,移植的胚胎数量,和转移高质量的胚胎。基于这些因素的临床预测模型具有良好的临床预测和应用价值。因此,该模型可以作为临床预后的有价值的工具,干预,促进个性化医疗。
BACKGROUND: Poor ovarian response (POR) is associated with decreased clinical pregnancy rates, emphasizing the need for developing clinical prediction models. Such models can improve prognostic accuracy, personalize medical interventions, and ultimately enhance live birth rates among patients with POR.
OBJECTIVE: This study aims to develop and validate a prognostic model for predicting clinical pregnancy outcomes in individuals with POR undergoing in vitro fertilization/ intracytoplasmic sperm injection (
IVF/ICSI) cycles.
METHODS: A retrospective cohort of 969 patients with POR undergoing fresh embryo transfer cycles at the Reproductive Center of Fujian Maternal and Child Health Center from January 2018 to January 2022 was included. The cohort was randomly divided into model (n = 678) and validation (n = 291) groups in a 7:3 ratio. A single-factor analysis was performed on the model group to identify variables influencing clinical pregnancy. Optimal variables were selected using LASSO regression, and a clinical prediction model was constructed using multivariate logistic regression analysis. The model\'s calibration and discrimination were assessed using receiver operating characteristic (ROC) and calibration curves, while the clinical utility was evaluated using decision curve analysis.
RESULTS: Multivariate logistic regression analysis revealed that the age of the women (odds ratio [OR] 0.936, 95% confidence interval [CI] 0.898-0.976, P = 0.002), body mass index (BMI) ≤ 24 (OR 2.748, 95% CI 1.724-4.492, P < 0.001), antral follicle count (AFC) (OR 1.232, 95% CI 1.073-1.416, P = 0.003), anti-Müllerian hormone (AMH) (OR 1.67, 95% CI 1.178-2.376, P = 0.004), number of mature oocytes (OR 1.227, 95% CI 1.075-1.403, P = 0.003), number of embryos transferred (OR 1.692, 95% CI 1.132-2.545, P = 0.011), and transfer of high-quality embryos (OR 3.452, 95% CI 1.548-8.842, P = 0.005) were independent predictors of clinical pregnancy in patients with POR. According to the receiver operating characteristic (ROC) analysis, the prediction model exhibited an area under the curve (AUC) of 0.752 (0.714, 0.789) in the model group and 0.765 (0.708, 0.821) in the validation group. The clinical decision curve demonstrated that the model held maximum clinical utility in both cohorts when the threshold probability of clinical pregnancy ranged from 6-81% to 12-82%, respectively.
CONCLUSIONS: Clinical pregnancy outcomes in patients with POR who underwent
IVF/ICSI treatment were influenced by several independent factors, including the age of the women, BMI, AFC, AMH, number of mature oocytes, number of embryos transferred, and transfer of high-quality embryos. A clinical prediction model based on these factors exhibited favorable clinical predictive and applicative value. Therefore, this model can serve as a valuable tool for clinical prognosis, intervention, and facilitating personalized medical treatment.