PDF(Pubmed)
Abstract：
BACKGROUND: The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations.
METHODS: This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation.
RESULTS: Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05.
CONCLUSIONS: This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.
METHODS: This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation.
RESULTS: Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05.
CONCLUSIONS: This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.
摘要：
背景：累积活产率（CLBR）已被视为完整治疗周期后体外受精（IVF）成功的关键指标。接受试管婴儿的妇女面临着巨大的心理压力和经济负担。临床实践中需要一种预测模型来估计CLBR，以进行患者咨询和塑造期望。
方法：这项回顾性研究包括从2014年至2020年在中国一所大学附属生育中心接受IVF治疗的29,023对夫妇的32,306个完整周期。基于完整周期的三个阶段开发了三种CLBR预测模型：预处理，刺激后,和后处理。非线性关系用受限三次样条处理。2014年至2018年的受试者以7:3的比例随机分为训练集和测试集进行模型推导和内部验证,而2019年至2020年的受试者用于时间验证。
结果：治疗前模型的预测因素包括女性年龄（非线性关系），窦卵泡计数（非线性关系），身体质量指数,先前试管婴儿的尝试次数，先前胚胎移植失败的数量，不孕症的类型，输卵管因素,男性因素,还有子宫疤痕.刺激后模型的预测因素包括女性年龄(非线性关系)，检索到的卵母细胞数（非线性关系），先前试管婴儿的尝试次数，先前胚胎移植失败的数量，不孕症的类型，瘢痕子宫,刺激方案，以及子宫内膜厚度，触发日的孕酮和黄体生成素。治疗后模型的预测因素包括女性年龄(非线性关系)，检索到的卵母细胞数（非线性关系），累积第3天胚胎活产能力(非线性关系)，先前试管婴儿的尝试次数，瘢痕子宫,刺激方案，以及子宫内膜厚度，触发日的孕酮和黄体生成素。三个模型的C指数分别为0.7559、0.7744和0.8270。所有模型均经过良好校准（p=0.687，p=0.468，p=0.549）。在内部验证中，3个模型的C指数分别为0.7422、0.7722、0.8234,校正P值均大于0.05。在时间验证中，C指数分别为0.7430、0.7722、0.8234；然而，校正P值均小于0.05.
结论：本研究根据不同治疗阶段的信息提供了三种IVF模型来预测CLBR，这些模型已经被转换成一个在线计算器(https://h5。eheren.com/hcyc/pc/index.html#/home）。内部验证和时间验证验证了预测模型的良好区分度。然而,时间验证表明预测模型的准确性较低，这可能归因于与时间相关的IVF实践的改善。
方法：这项回顾性研究包括从2014年至2020年在中国一所大学附属生育中心接受IVF治疗的29,023对夫妇的32,306个完整周期。基于完整周期的三个阶段开发了三种CLBR预测模型：预处理，刺激后,和后处理。非线性关系用受限三次样条处理。2014年至2018年的受试者以7:3的比例随机分为训练集和测试集进行模型推导和内部验证,而2019年至2020年的受试者用于时间验证。
结果：治疗前模型的预测因素包括女性年龄（非线性关系），窦卵泡计数（非线性关系），身体质量指数,先前试管婴儿的尝试次数，先前胚胎移植失败的数量，不孕症的类型，输卵管因素,男性因素,还有子宫疤痕.刺激后模型的预测因素包括女性年龄(非线性关系)，检索到的卵母细胞数（非线性关系），先前试管婴儿的尝试次数，先前胚胎移植失败的数量，不孕症的类型，瘢痕子宫,刺激方案，以及子宫内膜厚度，触发日的孕酮和黄体生成素。治疗后模型的预测因素包括女性年龄(非线性关系)，检索到的卵母细胞数（非线性关系），累积第3天胚胎活产能力(非线性关系)，先前试管婴儿的尝试次数，瘢痕子宫,刺激方案，以及子宫内膜厚度，触发日的孕酮和黄体生成素。三个模型的C指数分别为0.7559、0.7744和0.8270。所有模型均经过良好校准（p=0.687，p=0.468，p=0.549）。在内部验证中，3个模型的C指数分别为0.7422、0.7722、0.8234,校正P值均大于0.05。在时间验证中，C指数分别为0.7430、0.7722、0.8234；然而，校正P值均小于0.05.
结论：本研究根据不同治疗阶段的信息提供了三种IVF模型来预测CLBR，这些模型已经被转换成一个在线计算器(https://h5。eheren.com/hcyc/pc/index.html#/home）。内部验证和时间验证验证了预测模型的良好区分度。然而,时间验证表明预测模型的准确性较低，这可能归因于与时间相关的IVF实践的改善。
