摘要
背景:降低异常受精率是提高体外受精应用效能与降低患者经济压力的有效手段。然而,现阶段对异常受精的研究主要集中于探讨原核类型及其产生机制,以及对异常受精所形成胚胎、染色体倍性与利用价值的分析,缺乏基于回顾性研究而建立的异常受精临床预测模型。目的:构建常规体外受精中基于女方因素发生异常受精的临床预测模型列线图。方法:回顾性分析2017年3月至2022年3月于广西壮族自治区南溪山医院接受常规体外受精助孕治疗患者共5075例,以匹配容差为0.02按1∶1倾向评分校准男方混杂因素,匹配成功1672例,根据维也纳共识,以正常受精能力值≥60%的患者纳入正常受精组(836例),<60%的患者纳入异常受精组(836例),通过模型组∶验证组=7∶3随机抽样获得模型组与验证组;采用单因素分析筛选模型组发生异常受精的影响因素,并采用套索算法(LASSO)挑选出最佳匹配因素,将其纳入多因素向前逐步Logistic回归,找出其独立影响因素并绘制列线图;最后采用受试者工作曲线、校准曲线、临床决策曲线、临床影响曲线对该预测模型进行区分度与准确度及临床应用效能验证。结果与结论:①单因素分析发生异常受精的影响因素为年龄、控制性促排卵方案、助孕次数、不孕年限、不孕因素、抗苗勒管激素、窦状卵泡数、基础促黄体生成素、人绒毛膜促性腺激素注射日促黄体生成素、人绒毛膜促性腺激素注射日雌二醇(P<0.05);②LASSO回归进一步筛选出的最佳匹配因素为年龄、微刺激方案、助孕次数、不孕年限、抗苗勒管激素、人绒毛膜促性腺激素注射日促黄体生成素、人绒毛膜促性腺激素注射日雌二醇(P<0.05);③多因素向前逐步Logistic回归结果显示发生异常受精的独立影响因素为年龄、微刺激方案、助孕次数、不孕年限、抗苗勒管激素、人绒毛膜促性腺激素注射日雌二醇;④受试者工作曲线显示模型组曲线下面积为0.761(0.746,0.777),验证组曲线下面积为0.767(0.733,0.801),说明该模型具有较好的区分度;校准曲线平均绝对误差0.044,Hosmer-Lemeshow检验表明该模型预测异常受精的概率与实际异常受精的概率无统计学差异(P>0.05),具有较好的一致性与准确性;临床决策曲线与临床影响曲线显示,模型组和验证组分别在阈概率值为0.00-0.52与0.00-0.48时具有临床最大净获益,且在该阈概率范围内具有较好的临床应用效能;⑤结果表明,基于年龄、微刺激方案、助孕次数、不孕年限、抗苗勒管激素、人绒毛膜促性腺激素注射日雌二醇构建女方常规体外受精发生异常受精的预测模型列线图,具有较好的区分度与准确度以及临床应用效能。
BACKGROUND:Reducing the rate of abnormal fertilization is an effective approach to improving the efficacy of in vitro fertilization and reducing patients’financial strain.However,the current research on abnormal fertilization has focused on exploring the types of prokaryotic nuclei and their generation mechanisms,as well as analyzing embryos formed by abnormal fertilization,chromosomal ploidy and utilization value.There is a lack of clinical prediction models for abnormal fertilization based on retrospective studies.OBJECTIVE:To construct a nomogram model to predict abnormal female factors in in vitro fertilization.METHODS:A total of 5075 patients undergoing treatment for conventional in vitro fertilization at Nanxishan Hospital of Guangxi Zhuang Autonomous Region from March 2017 to March 2022 were retrospectively analyzed.The male confounders were calibrated on a 1:1 propensity score with a match tolerance of 0.02,and 1672 cases were successfully matched.According to the Vienna Consensus,patients with≥60%normal fertilization capacity were included in the normal fertilization group(n=836)and those with<60%normal fertilization capacity were included in the abnormal fertilization group(n=836).The model and validation groups were obtained by random sampling at a ratio of 7:3.Factors related to the occurrence of abnormal fertilization following conventional in vitro fertilization in the model group were screened using univariate analysis and the best matching factors were selected using the Least Absolute Shrinkage and Selection Operator(LASSO)and included in a multifactorial forward stepwise Logistic regression to identify their independent influencing factors and plot a nomogram.Finally,the prediction model was validated for discrimination,accuracy and clinical application efficacy using receiver operating characteristic curves,calibration curves,clinical decision curves and clinical impact curves.RESULTS AND CONCLUSION:The univariate analysis indicated the factors influencing the occurrence of abnormal fertilization were age,controlled ovarian hyperstimulation protocol,number of assisted pregnancies,years of infertility,infertility factors,anti-mullerian hormone,sinus follicle count,basal luteinizing hormone,luteinizing hormone concentration on the human chorionic gonadotropin day,and estradiol level on human chorionic gonadotropin injection day(P<0.05).LASSO regression further identified the best matching factors,including age,microstimulation protocol,number of assisted pregnancies,years of infertility,anti-mullerian hormone,luteinizing hormone level on human chorionic gonadotropin injection day,and estradiol level on human chorionic gonadotropin injection day(P<0.05).Multifactorial forward stepwise Logistic regression results showed that age,microstimulation protocol,number of assisted conceptions,years of infertility,anti-mullerian hormone,and estradiol level on human chorionic gonadotropin injection day were independent influencing factors for the occurrence of abnormal fertilization following conventional in vitro fertilization.The receiver operating characteristic curves showed an area under the curve of 0.761(0.746,0.777)for the model group and 0.767(0.733,0.801)for the validation group,indicating that the model has good discrimination.The mean absolute error of the calibration curve was 0.044,and the Hosmer-Lemeshow test indicated that there was no significant difference between the predicted probability of abnormal fertilization and the actual probability of abnormal fertilization(P>0.05),indicating the prediction model has good consistency and accuracy.The clinical decision curves and clinical impact curves showed that the model and validation groups had the maximum net clinical benefit at valve probability values of 0.00-0.52 and 0.00-0.48,respectively,and there was a good clinical application efficacy in this valve probability range.To conclude,the nomogram model has good discrimination and accuracy as well as clinical application efficacy for predicting the occurrence of abnormal fertilization in women undergoing conventional in vitro fertilization based on age,microstimulation protocol,number of assisted conceptions,years of infertility,anti-mullerian hormone,and estradiol level on human chorionic gonadotropin injection day.
作者
周超
李欢
庾广聿
于春梅
陈迪
唐程民
莫秋菊
覃仁利
黄新梅
Zhou Chao;Li Huan;Yu Guangyu;Yu Chunmei;Chen Di;Tang Chengmin;Mo Qiuju;Qin Renli;Huang Xinmei(Nanxishan Hospital of Guangxi Zhuang Autonomous Region,Guilin 541000,Guangxi Zhuang Autonomous Region,China;The 924th Hospital of PLA Joint Logistic Support Force,Guilin 541000,Guangxi Zhuang Autonomous Region,China;Changzhou Maternal and Child Health Care Hospital,Changzhou 213000,Jiangsu Province,China;Liuzhou People’s Hospital,Liuzhou 545000,Guangxi Zhuang Autonomous Region,China;Fuchuan Yao Autonomous County People’s Hospital,Hezhou 542700,Guangxi Zhuang Autonomous Region,China)
出处
《中国组织工程研究》
CAS
北大核心
2024年第11期1696-1703,共8页
Chinese Journal of Tissue Engineering Research
关键词
异常受精
体外受精
预测模型
正常受精
列线图
女方因素
abnormal fertilization
in vitro fertilization
prediction model
normal fertilization
nomogram
female factor
