生殖医学杂志

目的 通过分析胚胎动力学和胚胎致密化的相关性，构建胚胎正常致密化的预测模型并加以验证。方法 选取2023年1—6月在邢台不孕不育专科医院生殖医学中心行IVF-ET治疗患者的394枚胚胎作为研究对象，采用留出法按照常用比例(8∶2)分为训练集(315枚)和验证集(79枚),训练集胚胎根据授精后(92±2)h(第4天)有无致密化分为两组：未发生任何致密化的为未致密化组(122枚),完全致密化的为致密化组(193枚),比较各组患者的临床资料。采用多因素Logistic回归分析胚胎致密化的影响因素，建立第4天胚胎正常致密化的预测模型。通过校准曲线、受试者工作特征(ROC)曲线和决策曲线(DCA)分别在训练集和验证集中进行内部和外部验证，以评估预测模型的精确度、区分度和临床适用性。结果 训练集和验证集胚胎的动力学参数(tPNa、tPNf、t2、t3、t4、t5、t6、t7、t8、CC2和S2等)及胚胎致密化比例比较均无显著性差异(P>0.05)。致密化组和未致密化组间胚胎t3、t6、t7、t8和t8-tPNf比较均无显著性差异(P>0.05);与未致密化组比较，致密化组胚胎tPNa、tPNf、t2、t4和S2显著缩短，而t5、CC2和t5-tPNf显著延长(P均<0.05)。多因素Logistic回归分析结果显示，tPNa[OR=0.86,95%CI(0.76,0.99),P=0.031]和t2[OR=0.64,95%CI(0.56,0.73),P<0.001]是胚胎致密化的独立负向影响因素，t5[OR=1.18,95%CI(1.12,1.24),P<0.001]是胚胎致密化的独立正向影响因素。基于上述影响因素建立了列线图致密化预测模型，经Bootstrap法验证显示预测发生率与实际发生率曲线分布相接近，内部验证相对平均误差为0.016,外部验证相对平均误差为0.079,模型校准度良好。ROC曲线结果显示，内部验证ROC曲线下面积为[0.808(0.761,0.855)],外部验证ROC曲线下面积为[0.810(0.739,0.880)]。DCA曲线结果显示，在训练集中，如果阈值概率在10%～87%时，使用列线图预测胚胎致密化会产生较高的临床净效益；在验证集中，当阈值概率在10%～75%时，会产生良好的临床净效益。结论 tPNa、t2和t5是胚胎致密化的独立影响因素；基于此构建的致密化预测模型能有效预测体外受精第4天胚胎正常致密化的发生，具有一定的临床推广价值。

Objective:To analyze and validate the correlation between morphokinetic parameters and embryonic compaction by constructing a nomogram predictive model.Methods:A total of 394 embryos from patients undergoing IVF-ET at Reproductive Center of Xingtai Infertility Specialist Hospital from January 2023 to June 2023 were recruited. The embryos were divided into a training set(n=315) and a validation set(n=79) according to the leave-out method with the usual ratio(8∶2). Based on the presence or absence of compaction on the fourth day [(92±2)h],the embryos were divided into two groups in the training set as no-compaction group(no compaction occurred, n=122) and compaction group(complete compaction occurred, n=193). The clinical data of the two groups in the training set and the validation set were analyzed. The risk factors for embryonic compaction were determined through multivariate logistic regression analysis and a predictive model for the normal embryonic compaction on the fourth day was established. The accuracy, discriminatory, and clinical applicability of the predictive model were evaluated through calibration curves, the receiver operating characteristic(ROC),and decision curve analysis(DCA) in the training set and validation set.Results:There were no significant differences in the dynamic parameters(tPNa, tPNf, t2,t3,t4,t5,t6,t7,t8,CC2,S2,etc.) and embryonic compaction ratio between the training and validation sets(P>0.05). There were no significant differences in embryo t3,t6,t7,t8,and t8-tPNf between the compaction group and the no-compaction groups(P>0.05). Compared with the no-compaction group, the embryos in the compaction group showed significant shortening of tPNa, tPNf, t2,t4,and S2,while t5,CC2,and t5-tPNf were significantly prolonged(all P<0.05). Multivariate logistic regression analysis showed that tPNa [OR=0.86,95%CI(0.76,0.99),P=0.031] and t2 [OR=0.64,95%CI(0.56,0.73),P<0.001] were independent negative influencing factors, while t5 [OR=1.18,95%CI(1.12,1.24), P<0.001] was independent positive influencing factor for successful embryonic compaction. Based on these factors, a column chart compaction predictive model was established. The Bootstrap method showed that the distribution of the predicted occurrence rate was similar to the actual occurrence rate curve, and the relative average error for internal validation was 0.016,and the relative average error for external validation was 0.079,indicating good model calibration. The area under the ROC curve was [0.808(0.761,0.855)] in the internal validation and that of [0.810(0.739,0.880)] in the external validation. The DCA curve results showed that in the training set, using the column line chart to predict the embryonic compaction would result in higher net clinical benefits when the threshold probability was between 10% and 87%. In the validation set, good clinical net benefits would be achieved when the threshold probability was between 10% and 75%.Conclusions:tPNa, t2 and t5 were independent influencing factors for successful embryonic compaction. The compaction predictive model based on morphokinetic parameters can effectively predict the occurrence rate of successful compaction on the fourth day of IVF embryos and obtained clinical net benefits.