PDF(Pubmed)
Abstract：
BACKGROUND: For women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.
METHODS: A prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.
RESULTS: In the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer-Lemeshow test was 7.427, and P = 0.505.
CONCLUSIONS: Predicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.
BACKGROUND: This study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).
METHODS: A prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.
RESULTS: In the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer-Lemeshow test was 7.427, and P = 0.505.
CONCLUSIONS: Predicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.
BACKGROUND: This study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).
摘要：
背景：对于经历过反复妊娠丢失（RPL）的女性，不仅治疗它们，而且评估复发的风险也是至关重要的。该研究旨在开发一种风险预测模型，以根据孕前数据预测患有RPL的女性随后的早期妊娠丢失（EPL）。
方法：前瞻性，动态人群队列研究在兰州大学第二医院进行。从2019年9月到2022年12月，共有1050名非妊娠RPL妇女参加。到2023年12月，605名妇女有随后的妊娠结局，并按3：1的比例随机分为训练和验证组。在训练组中,对具有随后EPL结局的RPL患者进行单变量筛查.利用最小绝对收缩和选择算子(LASSO)回归和多变量逻辑回归来选择变量，分别。采用广义线性模型(GLM)构建后续EPL预测模型，梯度增压机（GBM），随机森林(RF)，深度学习（DP）然后建立LASSO回归和多变量logistic回归选择的变量，并使用最佳预测模型进行比较。AUC,校正曲线,和决策曲线（DCA）进行评估，以评估最佳模型的预测性能。使用验证组验证了最佳模型。最后,根据最佳预测特征建立列线图.
结果：在训练组中，GBM模型以最高的AUC（0.805）达到最佳性能。通过LASSO回归（16变量）和逻辑回归（9变量）模型筛选的变量之间的AUC没有显着差异（AUC：0.805vs.0.777,P=0.1498)。同时,9变量模型在验证组中表现出良好的判别性能，AUC值为0.781（95CI0.702，0.843）。DCA显示该模型表现良好，对于做出有益的临床决策是可行的。校准曲线揭示了模型预测值与实际值之间的拟合优度，Hosmer-Lemeshow检验为7.427,P=0.505。
结论：使用GBM模型预测RPL患者的后续EPL具有重要的临床意义。需要未来的前瞻性研究来验证其临床适用性。
背景：本研究在中国临床试验注册中心注册，注册号为ChiCTR2000039414（2020年10月27日）。
方法：前瞻性，动态人群队列研究在兰州大学第二医院进行。从2019年9月到2022年12月，共有1050名非妊娠RPL妇女参加。到2023年12月，605名妇女有随后的妊娠结局，并按3：1的比例随机分为训练和验证组。在训练组中,对具有随后EPL结局的RPL患者进行单变量筛查.利用最小绝对收缩和选择算子(LASSO)回归和多变量逻辑回归来选择变量，分别。采用广义线性模型(GLM)构建后续EPL预测模型，梯度增压机（GBM），随机森林(RF)，深度学习（DP）然后建立LASSO回归和多变量logistic回归选择的变量，并使用最佳预测模型进行比较。AUC,校正曲线,和决策曲线（DCA）进行评估，以评估最佳模型的预测性能。使用验证组验证了最佳模型。最后,根据最佳预测特征建立列线图.
结果：在训练组中，GBM模型以最高的AUC（0.805）达到最佳性能。通过LASSO回归（16变量）和逻辑回归（9变量）模型筛选的变量之间的AUC没有显着差异（AUC：0.805vs.0.777,P=0.1498)。同时,9变量模型在验证组中表现出良好的判别性能，AUC值为0.781（95CI0.702，0.843）。DCA显示该模型表现良好，对于做出有益的临床决策是可行的。校准曲线揭示了模型预测值与实际值之间的拟合优度，Hosmer-Lemeshow检验为7.427,P=0.505。
结论：使用GBM模型预测RPL患者的后续EPL具有重要的临床意义。需要未来的前瞻性研究来验证其临床适用性。
背景：本研究在中国临床试验注册中心注册，注册号为ChiCTR2000039414（2020年10月27日）。
