Predictive models for live birth outcomes of FET: Improving clinical decision-making based on machine learning analysis

Predictive models for live birth outcomes of FET: Improving clinical decision-making based on machine learning analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive models for live birth outcomes of FET: Improving clinical decision-making based on machine learning analysis Xinyue Hu, Xuejiao Wang, Mingjing Xia, Yubin Ding, Tian Li, Zhaohui Zhong, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3430829/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: This study used multiple machine learning algorithms to predict live births from frozen embryo transfers (FET) based on patient demographics, laboratory test results, and parameters associated with the FET cycle. Methods: Data from 33,915 cycles of frozen-thaw embryo transfer performed at Chengdu Xinan Gynecological Hospital between January 2015 and December 2021 were used. The dataset was randomly divided into a training set (70%) and a test set (30%). Features were ranked for importance based on the random forest model, and features with the top 25 contribution values were used to develop logistic regression models, random forest models, support vector machine models, and XGBoost models. Shapley was used to interpret the results of the best-performing models. Receiver operating characteristic curves (AUC) under area and calibration curves were to be assessed for the performance of machine learning prediction models. Results: Ranking the importance of features based on the stable random forest algorithm showed that the most predictive features included AMH, Basal PRL, Basal T, Basal FSH, etc. The XGBoost model had the highest AUC (0.750, 95% CI 0.746-0.755). The XGBoost-based SHAP summary plot indicated that patients with lower age, shorter years of infertility, and D5 embryo type for transfer had a greater likelihood of live birth outcome after freeze-thaw embryo transfer. Conclusion: The XGBoost model performed best in predicting the outcome of freeze-thaw embryo transfer. The algorithm combined with the interpretability of SHAP summary plot can assist clinicians in the decision-making process of freeze-thaw embryo transfer. FET live birth machine learning predictive model random forest Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 What does this study adds to the clinical work This study offered improved predictive models, highlighted essential predictive features, optimized decision-making through machine learning, and provided a practical tool for guiding clinicians in the selection and management of patients undergoing frozen embryo transfers. These advancements have the potential to enhance the overall success rates and outcomes of FET procedures, benefiting both patients and healthcare providers. Introduction In vitro fertilization-embryo transfer techniques have been widely used to treat infertility[ 1 , 2 ]. With the maturation of vitrification techniques used for embryo cryopreservation, the success rate of frozen embryo transfer (FET) cycles is now close to that of fresh cycles[ 3 , 4 ]. In addition, compared to fresh embryo transfer, FET provides sufficient time to adjust the patient's uterine environment, effectively prevent the onset of ovarian hyperstimulation, and manipulate the timing of transfer, thereby increasing the chances of live birth after embryo transfer[ 5 – 7 ]. The development of FET technology offers a safe and effective approach to treat infertility, and as a result, an increasing number of embryos are being selectively frozen and retained for later transfer[ 8 ]. Determining the factors that influence the success of FET cycles remains a challenge. Previous studies have found that factors affecting the success of FET cycles include a woman's age, years of infertility, endometrial thickness, and embryo type[ 9 , 10 ]. However, most studies have been stratified or threshold analyses of a single factor[ 11 , 12 ], and others have screened for features that predict live birth based on a priori knowledge, with predictive models mostly based on traditional statistics[ 13 – 17 ]. Machine learning methods are increasingly being used in clinical settings[ 18 – 20 ], including in a variety of fields such as ophthalmology and psychiatry[ 21 , 22 ], and their accuracy has been shown to outperform traditional statistical models[ 23 , 24 ]. In this study, we included as many characteristic variables as possible and explained the effects of the included characteristics on live birth outcomes, providing a more comprehensive understanding of the factors affecting FET cycle outcomes, more effective predictive models, and more clues to beneficial treatments obtained that may provide new insights for clinicians and infertile couples. Materials and Method Participants Patients who underwent IVF/ICSI at Chengdu Xinan Gynecological Hospital between January 2015 and December 2021 were retrospectively identified in the database for a total of 33,915 FET cycles. Of these, 16,585 cycles (48.9%) resulted in a live birth and 17,300 cycles (51.1%) resulted in a failed FET. Exclusion criteria included cycles involving preimplantation genetic testing, sperm donation, egg donation, fresh embryo transfer only, and cancellation. Endometrial preparation protocols Endometrial preparation protocols include natural cycle, HRT cycle, down-regulated HRT cycle and ovulation-inducing cycle. The natural cycle is indicated for patients with normal ovulation and regular menstruation who are monitored for follicular development and luteinizing hormone levels. Transfer of oogenic embryos or blastocysts is performed on day 3 or 5 after ovulation. Hormone replacement cycle (HRT) is indicated for patients with irregular menstruation and abnormal ovulation. Estradiol valerate (Progynova, Berlin, Germany) is administered at 4 mg daily for 10 days from day 2 to 4 of the menstrual cycle, and cleavage stage or blastocyst transfer is performed on day 3 or 5 after endometrial transformation. The down-regulated HRT cycle is mainly used in patients with endometriosis and polycystic ovary syndrome. Intramuscular injection of 3.75mg GnRH-a (Dabigat, Germany) on day 2–3 of menstruation for pituitary hyporegulation and check if the hyporegulation criteria are met after 28–30 days and use hormone replacement protocol if eligible. The vulation-inducing cycle is mainly indicated for patients with ovulation disorders, luteal insufficiency and thin endometrium. Letrozole 2.5-5 mg or tamoxifen 20–40 mg for 5 consecutive days starting on day 2–5 of menstruation, with embryo transfer at the cleavage stage or blastocyst transfer on day 3 or 5 after ovulation. Luteal support Oral estradiol is gradually reduced until discontinued at 8–9 weeks of gestation. Intramuscular progesterone 60 mg/d or vaginal dosing of progesterone (crinone 90 mg/d or utrogestan 0.6 mg/d) or oral progesterone (dydrogesterone 30–40 mg/d or femoston yellow tablets 2 tablets/d) until 10–12 weeks of gestation. Predictor screening Patient demographic characteristics, laboratory test results, and parameters associated with FET cycles were collected. Demographic characteristics included female age, infertility years, BMI, male age, and days of abstinence; laboratory test results included basal FSH, basal AMH, basal LH, and HBcAb; and FET cycle-related parameters included number and type of embryos, and endometrial thickness on the day before and on the day of transfer. Laboratory test results and FET cycle-related parameters were gathered by uniformly trained staff. Variables with > 15% missing data were excluded, and if missing ≤ 15%, they were populated using statistical measures (mean, plurality, or median) depending on their characteristics. Live birth is defined as births with more than one live baby after the 24th week of pregnancy. Model development and evaluation Variables with p-values less than 0.05 in the univariate analysis were included in the next step of feature selection. Feature importance ranking was performed by a performance-stabilized random forest model, and the best number of features was found by incorporating the five groups of features from highest to lowest importance into the prediction model. Four machine learning algorithms: logistic regression, random forest, SVM, and XGBoost, were used to develop models for predicting FET outcomes. Logistic regression is a linear model with the advantage of being easy to implement and interpret. Random Forest is an ensemble learning method that can handle high-dimensional datasets with many features, and its ability to handle noisy and missing data makes it a robust algorithm. Support vector machines are powerful models that can handle linear and nonlinear data, transforming the data into a high-dimensional space, making it easier to separate and achieve high accuracy. Extreme gradient boosting (XGBoost) is a popular machine learning algorithm that has gained significant attention in recent years. XGBoost iteratively builds a sequence of decision trees to make highly accurate predictions[ 25 ]. The algorithm combines advanced features such as regularization techniques, adaptive learning rate and parallel processing to make it efficient and effective in dealing with large and complex datasets. Due to its scalability and high efficiency, XGBoost has become a popular algorithm of choice and is widely used. The validation of the robustness of the machine learning model is performed by randomly dividing the dataset into a 70% training set and a 30% test set. Machine learning models are trained in the training set by learning the relationships between samples. The model learns the weights between different features in the training set in order to make predictions on new data. Test sets is employed to validate the performance of the model on unseen data to ensure that the model has good generalization capabilities. The results of the test set can be used to evaluate the model's accuracy, precision, recall, and other performance metrics[ 26 , 27 ]. Calibration curves can help assess the accuracy and reliability of a machine learning model by showing the relationship between the probability predicted by the model and the actual observed frequency. Ideally, the predicted probability of a model should exactly match the actual frequency, indicating that its predicted probability is very reliable, otherwise it needs to be adjusted or improved. Statistical analysis Python 3.9 and SPSS software were implemented for statistical analysis and model building. Continuous variables were compared using the t test or Mann-Whitney U test, and categorical variables were compared using the chi-square test. Continuous data are expressed as mean ± standard deviation or median, and categorical data are expressed as counts. A p-values < 0.05 was deemed statistically significant. Accuracy, precision, recall, F1 score and area under the ROC curve were used to quantify the ability of the model to distinguish between live-born and non-live-born events[ 28 ]; all evaluation metrics were in the range of 0–1. The impact of each feature on the prediction results and the interpretation of the prediction results of the best prediction model were calculated by the shap library. Calibration curves were used to assess the accuracy of the prediction model. Results Baseline Characteristics The demographic characteristics of the study participants, laboratory findings and parameters associated with FET cycles are shown in Table 1 . Table 1 Comparison of baseline characteristics between the live birth group and the non-live birth group Variable’s name Total (n = 33915) Live birth (n = 16585) No Live birth (n = 17330) P-value Female age (year) 31 ± 4.41 30 ± 3.76 32 ± 4.79 < 0.01 Infertility years (years) 3 ± 3.12 3 ± 2.67 3 ± 3.46 < 0.01 Menarche age 13 ± 1.29 13 ± 1.23 13 ± 1.34 < 0.01 No. of pregnancies =2 9897(29.2) 4297(25.9) 5600(32.3) No. of deliver < 0.01 0 30075(88.7) 15186(91.6) 14889(85.9) <=1 3840(11.3) 1399(8.4) 2441(14.1) Basal FSH (MIU/mL) 7.05 ± 1.78 6.98 ± 1.72 7.07 ± 1.82 < 0.01 Basal E2 (pg/mL) 45 ± 17.59 45 ± 17.41 45 ± 17.75 < 0.01 Basal LH (MIU/mL) 4.16 ± 1.89 4.16 ± 1.91 4.16 ± 1.88 < 0.01 Basal P (ng/mL) 0.6 ± 0.32 0.6 ± 0.32 0.6 ± 0.32 0.138 Basal PRL (MIU/mL) 256.45 ± 123.84 258.16 ± 124.39 254.82 ± 123.29 < 0.01 Basal T (ng/ml?/dl) 38.61 ± 19.6 38.86 ± 19.77 38.38 ± 19.42 < 0.01 AFC 16 ± 9.19 17 ± 9.18 14 ± 8.97 < 0.01 AMH (ng/mL) 3.73 ± 2.82 4.14 ± 2.83 3.28 ± 2.74 < 0.01 Male age (year) 33 ± 5.47 32 ± 4.91 34 ± 5.83 < 0.01 Days of Abstinence 3.93 ± 1.75 3 ± 1.71 4 ± 1.78 < 0.05 No. of retrieved oocytes 13 ± 7.1 14 ± 6.96 11 ± 6.95 < 0.01 No. of embryos thawed 2 ± 0.46 2 ± 0.4 2 ± 0.49 < 0.01 Thickness(mm) on the day before ET 9.45 ± 1.53 9.5 ± 1.48 9.48 ± 1.55 < 0.01 Thickness(mm) on the day of ET 9.61 ± 1.96 10 ± 1.9 9 ± 1.99 < 0.01 No. of embryos transferred < 0.01 1 10376(30.6) 3418(20.6) 6958(40.2) 2 23539(69.4) 13167(79.4) 10372(59.8) No. of high-quality embryos transferred < 0.01 0 9871(29.1) 4083(24.6) 5788(33.4) 1 11594(34.2) 5394(32.5) 6200(35.8) 2 12450(36.7) 7108(42.9) 5342(30.8) Female HBCAB positive 8661(25.5) 4047(24.4) 4614(26.6) < 0.01 Female HBSAG positive 2454(7.2) 1170(7.1) 1280 4(7.4) 0.2077 Female HBSAB positive 19129(56.4) 9327(56.2) 9802(56.6) 0.5484 Female HBEAG positive 409(1.2) 198(1.2) 211(1.2) 0.8416 Female HCVAB positive 125(0.4) 57(0.3) 68(0.4) 0.4594 Female chromosome abnormality 1398(4.1) 690(4.2) 708(4.1) 0.7284 Female toxoplasma positive 19642(57.9) 9551(57.6) 10091(58.2) 0.2325 Female HPV positive 2105(6.2) 1037(6.3) 1068(6.2) 0.7316 Male chromosome abnormality 157(0.5) 76(0.5) 81(0.5) 0.9012 Endometrial Preparation Protocols < 0.01 Ovulation-inducing cycle 3090(9.1) 1382(8.3) 1708(9.9) HRT cycle 22710(67.0) 11324(68.3) 11386(65.7) Nature cycles 4128(12.2) 1995(12.0) 2133(12.3) Down-regulated HRT cycle 3987(11.8) 1884(11.4) 2103(12.1) Type of culture medium < 0.01 COOK 22511(66.4) 10899(65.7) 11612(67.0) G1.3 9333(27.5) 4699(28.3) 4634(26.7) G1.5 1638(4.8) 773(4.7) 865(5.0) Other types 433(1.3) 214(1.3) 219(1.3) Embryo type < 0.01 Day3 8719(25.7) 2763(16.7) 5956(34.4) Day5 17671(52.1) 10524(63.5) 7147(41.2) Day6 7525(22.2) 3298(19.9) 4227(24.4) ovulation induction protocol < 0.01 GnRH antagonist protocol 12565(37.0) 6671(40.2) 5894(34.0) Short-acting GnRH-a long protocol 8730(25.7) 4638(28.0) 4092(23.6) Long-acting GnRH-a long protocol 7849(23.1) 3751(22.6) 4098(23.6) Mild stimulation protocol 4137(12.2) 1182(7.1) 2955(17.1) Ultra-long GnRH agonist protocol 127(0.4) 77(0.5) 50(0.3) Other protocol 507(1.5) 266(1.6) 241(1.4) Cause of infertility male factor 11669(34.4) 5773(34.8) 5896(34.0) 0.1274 pelvic factor 27183(80.2) 13190(79.5) 13993(80.7) < 0.01 Ovulatory dysfunction 7092(20.9) 3330(20.1) 3762(21.7) < 0.01 Unexplained infertility 603(1.8) 326(2.0) 277(1.6) < 0.01 Other causes 2038(6.0) 902(5.4) 1136(6.6) < 0.02 Female BMI (kg/m 2 ) < 0.01 < 18.5 3746(11.0) 1911(11.5) 1835(10.6) 18.5∽23.9 23018(67.9) 11408(68.8) 11610(67.0) 24.0∽27.9 719(2.1) 299(1.8) 420(2.4) ≥ 28 6432(19.0) 2967(17.9) 3465(20.0) Male BMI (kg/m 2 ) < 0.01 < 18.5 1193(3.5) 620(3.7) 573(3.3) 18.5∽23.9 16120(47.5) 8037(48.5) 8083(46.6) 24.0∽27.9 3322(9.8) 1492(9.0) 1830(10.6) ≥ 28 13280(39.2) 6436(38.8 6844(39.5) Infertility type (n, %) < 0.01 Primary 16646(49.1) 8536(51.5) 7933(45.8) Secondary 17446(51.4) 8049(48.5) 9397(54.2) RSA preprocessing 1513(4.5) 615(3.7) 898(5.2) < 0.01 Values of continuous variables are expressed as mean ± standard deviation or median; values of categorical variables are expressed as numbers (percentages). FSH follicle-stimulating hormone, E2 estradiol, LH luteinizing hormone, P progesterone, PRL prolactin, T testosterone, AFC antral follicle count, AMH anti-Mullerian hormone, AFC antral follicle count, BMI body mass index Patients who had a live birth outcome were older and had higher endometrial thickness and AFC levels at the day of transplantation, with statistically significant differences. Patients with a FET outcome of live birth were younger (30 ± 3.76 vs 32 ± 4.79, P < 0.01), had thicker endometrial thickness at the day of transplantation (10 ± 1.9 vs 9 ± 1.99, P < 0.01), and had a greater number of AFCs (17 ± 9.18 vs 14 ± 8.97, P < 0.01). Feature Importance Based on the Gini index of random forest, the features were ranked in importance using 5-fold cross-validation and grid search. The importance ranking of all candidate features is shown in Fig. 1 . Three aspects of characteristics ranked in order of importance, including demographic characteristics, laboratory test results, and FET cycle-related parameters. The feature importance ranking chart demonstrates the contribution of each feature to the model prediction. x-axis indicates the importance score of the feature; y-axis indicates the feature name, from top to bottom expresses the decreasing importance of the feature. According to the performance of the importance ranking graph, the number of transferred embryos was the most important predictor of live birth events, followed by embryo type, number of thawed embryos, female age, and basal AMH. Five groups of five were incorporated into the prediction model according to their highest to lowest importance, as shown in Fig. 2 , and it was found that the AUC value of the model stabilized and hardly increased after the number of features reached 25, so the top 25 features in the order of importance were selected as predictors of live birth events. Selection of the number of features to be included in the model. The x-axis represents the number of features; the y-axis represents the AUC value of the machine learning model when a certain number of features are incorporated. SHAP was used to explain the results of the random forest model by calculating the contribution of each variable to the prediction, and a summary plot of the re-SHAP for the 25 candidate factors is shown in Fig. 3 . SHAP summary chart of 25 features. Red represents higher values, blue represents lower values, and a positive SHAP value means a positive effect on the outcome, while a negative one means a negative effect on the outcome. Model Construction Twenty-five variables were selected for input into a logistic regression model, a random forest model, a support vector machine model, and an XGBoost model to predict live birth events. The performance evaluation of the models generated by the four machine learning algorithms is shown in Table 2 . The receiver operating characteristic (ROC) curves for the prediction models of the four machine learning algorithms on the training and test sets were plotted as shown in Fig. 4 . The XGBoost model had the highest AUC (0.750, 95% CI 0.746–0.755), accuracy, precision, recall, and f1 score. The calibration curves of the four models in the test set are shown in Fig. 5 . Table 2 Performance of the four models predictive models accuracy precision recall F1 AUC(95%CI) Logistic regression 0.668 0.660 0.668 0.664 0.724(0.720–0.728) Random forest 0.673 0.669 0.660 0.664 0.739(0.735–0.743) SVM 0.666 0.667 0.638 0.652 0.720(0.717–0.724) XGBoost 0.678 0.672 0.670 0.671 0.750(0.746–0.755) ROC curves of the four models on the training and test sets. AUC, area under the curve; ROC, receiver operating characteristic Calibration curves for the four models. The x-axis represents the predicted probability and the y-axis represents the observed frequency. The dashed line is the reference line, and the calibration curve shows a good correlation between the predicted and actual results. Discussion In our study, data from 33,915 frozen-thawed embryo transfer cycles were analyzed and 25 features were extracted from 46 candidate predictors that could have an impact on live birth events, from which logistic regression models, random forest models, support vector machine models, and XGBoost models were constructed. Comparison of model performance revealed that the prediction model based on the XGBoost algorithm had the largest area under the ROC curve (0.750, 95% CI 0.746–0.755) and outperformed the other models in terms of accuracy, precision, recall, and F1 score performance. Female age is one of the most important factors influencing the outcome of frozen-thaw embryo transfer. Many studies have shown that older women have lower clinical pregnancy and live birth rates than younger women, which may be associated with a decrease in ovarian function after the age of 35 years in women[ 29 ], and the results of this study confirm this. The type of embryo and the number of embryos transferred are also among the factors affecting FET live births. The results of Pan, Y et al.[ 30 ] showed that the number of frozen embryos transferred had a significant effect on the live birth rate, with higher live birth rates for cycles in which two embryos were selected for transfer than for cycles in which one embryo was transferred. It is also evident from the shap summary plot in our study that the number of embryos transferred with two has a positive effect on live birth events. The choice of embryo type can likewise have an impact on pregnancy outcome. The SHAP summary plot of this study has indicated that the choice of blastocyst for transfer is more likely to result in live birth than cleavage stage embryos, consistent with most previous studies[ 31 , 32 ]. This may be due to the fact that the period of cleavage stage to blastocyst development itself eliminates nearly half of the embryos with developmental potential before they reach the blastocyst stage[ 33 , 34 ]. Endometrial thickness can also have an impact on the outcome of in vitro fertilization-embryo transfer. A large study conducted by Liu, KE et al.[ 35 ]showed a significant decrease in clinical pregnancy and live birth rates in frozen-thawed embryo transfer cycles with endometrial thickness below 7 mm. Bu et al.[ 36 ] analyzed the relationship between endometrial thickness on the day of embryo transfer and pregnancy outcome in FET cycles and found a significant effect of endometrial thickness on the outcome of the FET cycle. Our study was consistent with the findings of these studies. To date, several studies have been used to predict outcomes in IVF-embryo transfer, including prediction of outcomes such as biochemical pregnancy, clinical pregnancy, early miscarriage, and embryo development[ 37 , 38 ]. Live birth events are the outcomes of most concern to clinicians and infertile couples, and there are relatively few predictive models for live birth outcomes[ 39 ]. Traditional statistical methods models are usually based on specific assumptions and a priori knowledge, do not take full advantage of the information in the data and in most cases require manual adjustment of the parameters of the prediction model. In contrast, machine learning methods can automatically uncover patterns in the data and are more flexible. In terms of adjusting model parameters, machine learning models are also automated and can easily handle large amounts of data and complex models, reducing the cost and error of human intervention and providing higher prediction accuracy, especially in complex nonlinear data analysis and prediction tasks[ 40 , 41 ]. Our study had several advantages that could enrich the current body of knowledge on the outcome of frozen-thawed embryo transfer. First, our study employed a large sample, which may be more representative and generalizable compared to smaller studies. Second, interpretability is crucial for clinical decision making, and the black box of machine learning has been difficult to convince clinicians and patients for predictive models related to clinical outcomes. In our study, the SHAP algorithm was used to explain the contribution of each predictor to the final outcome, allowing clinicians to understand the impact of each factor on the likelihood of live birth. Third, we used four different machine learning algorithms to develop predictive models for freeze-thaw embryo transfer outcomes, reducing the risk of bias as well as overfitting that can occur when using a single model and improving the robustness of the study results. Fourth, the use of the developed prediction models has the potential to improve the outcomes of frozen-thaw embryo transfer and to reduce medical costs and the psychological and financial burden on patients to some extent. Our study also had some limitations. First, all data were collected from one center, which may affect the effectiveness of our prediction model in applying it to populations from other health care institutions. Future studies may consider including populations from multiple centers or different regions, which may provide a more comprehensive picture of the predictive factors affecting FET outcomes. Second, although our study attempted to include as many predictors as possible, some factors not included may still be relevant, and further studies of a broader range of potential predictors may achieve better results. In summary, we incorporated a total of 46 predictors for patient demographics, laboratory findings, and FET cycle-related parameters, and identified 25 features after feature screening to construct prediction models, and developed four different algorithms for predicting live birth for frozen-thaw embryo transfer, with the best performing XGBoost algorithm providing the possibility of identifying live birth and non-live birth populations, thus aiding clinicians in their decision making. Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions X.J.T, Z.H.Z, and Y.B.D designed the study and interpreted the data. T.L, X.J.W, M.J.X, H.C.Z, M.W, and Q.W provided clinical guidance and advice on statistical analysis. X.Y.H performed the statistical analysis. X.Y.H and X.J.W co-drafted the manuscript. X.J.T and Y.B.D further revised the manuscript. The final manuscript was approved by all co-authors. Conflicts of interest The authors declare that there is no confict of interest. Ethics approval The procedures used in this study have adhered to the tenets of the Declaration of Helsinki. The study protocol involved human participants were approved by the Ethics Committee of Chongqing Medical University (2021060). Consent to participate Due to the retrospective nature, the data were anonymised and the written informed consent was waived by the Ethics Committee of Chongqing Medical University Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 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Uddin S, Khan A, Hossain ME, Moni MA: Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 2019, 19(1):16. Scott IA: Demystifying machine learning: a primer for physicians. Intern Med J 2021, 51(9):1388-1400. Kermany DS, Goldbaum M, Cai WJ, Valentim CCS, Liang HY, Baxter SL, McKeown A, Yang G, Wu XK, Yan FB et al: Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell 2018, 172(5):1122-+. Sharma M: RESEARCH AND GOOGLE TREND FOR HUMAN NEUROPSYCHIATRIC DISORDERS AND MACHINE LEARNING: A BRIEF REPORT. Psychiatr Danub 2021, 33(3):354-357. Bonderman D: Artificial intelligence in cardiology. Wien Klin Wochen 2017, 129(23-24):866-868. Fernandez-Delgado M, Cernadas E, Barro S, Amorim D: Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? J Mach Learn Res 2014, 15:3133-3181. Fernandez-Delgado M, Sirsat MS, Cernadas E, Alawadi S, Barro S, Febrero-Bande M: An extensive experimental survey of regression methods. Neural Netw 2019, 111:11-34. Shung DL, Au B, Taylor RA, Tay JK, Laursen SB, Stanley AJ, Dalton HR, Ngu J, Schultz M, Laine L: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding. Gastroenterology 2020, 158(1):160-167. Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, McGinn T, Guyatt G: Discrimination and Calibration of Clinical Prediction Models Users' Guides to the Medical Literature. JAMA-J Am Med Assoc 2017, 318(14):1377-1384. Liu Y, Chen PHC, Krause J, Peng L: How to Read Articles That Use Machine Learning Users' Guides to the Medical Literature. JAMA-J Am Med Assoc 2019, 322(18):1806-1816. Janssens A, Martens FK: Reflection on modern methods: Revisiting the area under the ROC Curve. Int J Epidemiol 2020, 49(4):1397-1403. Tian HQ, Zhang HJ, Qiu H, Yang XJ, La XL, Cui L: Influence of Maternal Age on the Relationship Between Endometrial Thickness and Ongoing Pregnancy Rates in Frozen-Thawed Embryo Transfer Cycles: A Retrospective Analysis of 2,562 Cycles. Front Endocrinol 2022, 13:8. Pan Y, Hao GM, Wang QM, Liu H, Wang Z, Jiang Q, Shi YH, Chen ZJ: Major Factors Affecting the Live Birth Rate After Frozen Embryo Transfer Among Young Women. Front Med 2020, 7:8. Zhu QQ, Lin JY, Gao HY, Wang NL, Wang B, Wang Y: The Association Between Embryo Quality, Number of Transferred Embryos and Live Birth Rate After Vitrified Cleavage-Stage Embryos and Blastocyst Transfer. Frontiers in Physiology 2020, 11:7. Glujovsky D, Blake D, Farquhar C, Bardach A: Cleavage stage versus blastocyst stage embryo transfer in assisted reproductive technology. Cochrane Database Syst Rev 2012(7):112. Eftekhar M, Aflatoonian A, Mohammadian F, Tabibnejad N: Transfer of blastocysts derived from frozen-thawed cleavage stage embryos improved ongoing pregnancy. Arch Gynecol Obstet 2012, 286(2):511-516. Zhao P, Li M, Lian Y, Zheng XY, Liu P, Qiao J: The clinical outcomes of day 3 4-cell embryos after extended in vitro culture. Journal of Assisted Reproduction and Genetics 2015, 32(1):55-60. Liu KE, Hartman M, Hartman A, Luo ZC, Mahutte N: The impact of a thin endometrial lining on fresh and frozen-thaw IVF outcomes: an analysis of over 40 000 embryo transfers. Human Reproduction 2018, 33(10):1883-1888. Bu ZQ, Wang KY, Dai W, Sun YP: Endometrial thickness significantly affects clinical pregnancy and live birth rates in frozen-thawed embryo transfer cycles. Gynecol Endocrinol 2016, 32(7):524-528. Dirvanauskas D, Maskeliunas R, Raudonis V, Damasevicius R: Embryo development stage prediction algorithm for automated time lapse incubators. Comput Meth Programs Biomed 2019, 177:161-174. Blank C, Wildeboer RR, DeCroo I, Tilleman K, Weyers B, de Sutter P, Mischi M, Schoot BC: Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertility and Sterility 2019, 111(2):318-326. Liang R, An J, Zheng YJ, Li JQ, Wang Y, Jia YY, Zhang J, Lu Q: predicting and improving the probability of live birth for women undergoing frozen-thawed embryo transfer: a data-driven estimation and simulation model. Comput Meth Programs Biomed 2021, 198:7. Speiser JL, Callahan KE, Houston DK, Fanning J, Gill TM, Guralnik JM, Newman AB, Pahor M, Rejeski WJ, Miller ME: Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. J Gerontol Ser A-Biol Sci Med Sci 2021, 76(4):647-654. Barnett-Itzhaki Z, Elbaz M, Butterman R, Amar D, Amitay M, Racowsky C, Orvieto R, Hauser R, Baccarelli AA, Machtinger R: Machine learning vs. classic statistics for the prediction of IVF outcomes. Journal of Assisted Reproduction and Genetics 2020, 37(10):2405-2412. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3430829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":246004843,"identity":"7461d6d7-50e0-42ba-a205-315c4c8cf341","order_by":0,"name":"Xinyue Hu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Hu","suffix":""},{"id":246004844,"identity":"11d76934-5a72-42b1-9cba-f0831789be33","order_by":1,"name":"Xuejiao 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Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYNACAwYGxgYGxgcILh7Ag6SFGaaUGC0QwCZBlBZ79rOHX/MU2Mkxz8g9VvGjbFtiA3vzNgmGmju4beHJS7OcYZBszDgjL+1mz7nbiQ08x8okGI49w+OwHDODDwbMiY0zcsxuM7YBtUjkmEkwNhzGrYX/jZlBgkE9WEsxWIv8GwJaJHKMH3wwOAzWwgyxhYeAlhtvzBhnGBw3Zux5YywJ9ItxG09asUXCMdxa2PtzjD/z/KmWM2zPMfzwo+y2bD/74Y03PtTg1sIAiw7DBjAbjBgYEvBpYGBg/gAi5RmgWkbBKBgFo2AUoAMATBxR8EcT4wwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3486-1020","institution":"Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2023-10-11 07:45:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3430829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3430829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46052211,"identity":"ffe93783-ab5c-4578-9125-88234cb4b1b4","added_by":"auto","created_at":"2023-11-08 00:07:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7147125,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3430829/v1/62575d0b9e0b1b30de39ed57.jpg"},{"id":46053309,"identity":"8c7fa9cf-26f3-4fd7-86e4-59ed3b66bd6e","added_by":"auto","created_at":"2023-11-08 00:15:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2123790,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3430829/v1/b58caf1d52db587e32d5c344.jpg"},{"id":46052209,"identity":"fc4dd424-a35e-447e-b81d-1ef664ba04d9","added_by":"auto","created_at":"2023-11-08 00:07:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":483633,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3430829/v1/156508ed647073fa776c438a.jpg"},{"id":46052214,"identity":"a3c9041f-cb46-4566-bb60-f187697b9ed3","added_by":"auto","created_at":"2023-11-08 00:07:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":691155,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3430829/v1/de40d756c95f55362efba19c.jpg"},{"id":46052213,"identity":"2f8ed9cf-b772-4ed4-8783-231929b3ab2a","added_by":"auto","created_at":"2023-11-08 00:07:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":193768,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3430829/v1/b8662e120430877245f289d0.jpg"},{"id":47673803,"identity":"dd4db131-39a1-41c8-b89c-00f159ab1d0e","added_by":"auto","created_at":"2023-12-06 03:50:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1040588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3430829/v1/63956400-493f-4f11-a63d-87df209bd122.pdf"}],"financialInterests":"","formattedTitle":"Predictive models for live birth outcomes of FET: Improving clinical decision-making based on machine learning analysis","fulltext":[{"header":"What does this study adds to the clinical work","content":"\u003cp\u003eThis study offered improved predictive models, highlighted essential predictive features, optimized decision-making through machine learning, and provided a practical tool for guiding clinicians in the selection and management of patients undergoing frozen embryo transfers. These advancements have the potential to enhance the overall success rates and outcomes of FET procedures, benefiting both patients and healthcare providers.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eIn vitro fertilization-embryo transfer techniques have been widely used to treat infertility[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the maturation of vitrification techniques used for embryo cryopreservation, the success rate of frozen embryo transfer (FET) cycles is now close to that of fresh cycles[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, compared to fresh embryo transfer, FET provides sufficient time to adjust the patient's uterine environment, effectively prevent the onset of ovarian hyperstimulation, and manipulate the timing of transfer, thereby increasing the chances of live birth after embryo transfer[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The development of FET technology offers a safe and effective approach to treat infertility, and as a result, an increasing number of embryos are being selectively frozen and retained for later transfer[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDetermining the factors that influence the success of FET cycles remains a challenge. Previous studies have found that factors affecting the success of FET cycles include a woman's age, years of infertility, endometrial thickness, and embryo type[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, most studies have been stratified or threshold analyses of a single factor[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and others have screened for features that predict live birth based on a priori knowledge, with predictive models mostly based on traditional statistics[\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMachine learning methods are increasingly being used in clinical settings[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], including in a variety of fields such as ophthalmology and psychiatry[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and their accuracy has been shown to outperform traditional statistical models[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, we included as many characteristic variables as possible and explained the effects of the included characteristics on live birth outcomes, providing a more comprehensive understanding of the factors affecting FET cycle outcomes, more effective predictive models, and more clues to beneficial treatments obtained that may provide new insights for clinicians and infertile couples.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003ePatients who underwent IVF/ICSI at Chengdu Xinan Gynecological Hospital between January 2015 and December 2021 were retrospectively identified in the database for a total of 33,915 FET cycles. Of these, 16,585 cycles (48.9%) resulted in a live birth and 17,300 cycles (51.1%) resulted in a failed FET. Exclusion criteria included cycles involving preimplantation genetic testing, sperm donation, egg donation, fresh embryo transfer only, and cancellation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEndometrial preparation protocols\u003c/h2\u003e \u003cp\u003eEndometrial preparation protocols include natural cycle, HRT cycle, down-regulated HRT cycle and ovulation-inducing cycle. The natural cycle is indicated for patients with normal ovulation and regular menstruation who are monitored for follicular development and luteinizing hormone levels. Transfer of oogenic embryos or blastocysts is performed on day 3 or 5 after ovulation. Hormone replacement cycle (HRT) is indicated for patients with irregular menstruation and abnormal ovulation. Estradiol valerate (Progynova, Berlin, Germany) is administered at 4 mg daily for 10 days from day 2 to 4 of the menstrual cycle, and cleavage stage or blastocyst transfer is performed on day 3 or 5 after endometrial transformation. The down-regulated HRT cycle is mainly used in patients with endometriosis and polycystic ovary syndrome. Intramuscular injection of 3.75mg GnRH-a (Dabigat, Germany) on day 2\u0026ndash;3 of menstruation for pituitary hyporegulation and check if the hyporegulation criteria are met after 28\u0026ndash;30 days and use hormone replacement protocol if eligible. The vulation-inducing cycle is mainly indicated for patients with ovulation disorders, luteal insufficiency and thin endometrium. Letrozole 2.5-5 mg or tamoxifen 20\u0026ndash;40 mg for 5 consecutive days starting on day 2\u0026ndash;5 of menstruation, with embryo transfer at the cleavage stage or blastocyst transfer on day 3 or 5 after ovulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLuteal support\u003c/h2\u003e \u003cp\u003eOral estradiol is gradually reduced until discontinued at 8\u0026ndash;9 weeks of gestation. Intramuscular progesterone 60 mg/d or vaginal dosing of progesterone (crinone 90 mg/d or utrogestan 0.6 mg/d) or oral progesterone (dydrogesterone 30\u0026ndash;40 mg/d or femoston yellow tablets 2 tablets/d) until 10\u0026ndash;12 weeks of gestation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePredictor screening\u003c/h2\u003e \u003cp\u003ePatient demographic characteristics, laboratory test results, and parameters associated with FET cycles were collected. Demographic characteristics included female age, infertility years, BMI, male age, and days of abstinence; laboratory test results included basal FSH, basal AMH, basal LH, and HBcAb; and FET cycle-related parameters included number and type of embryos, and endometrial thickness on the day before and on the day of transfer. Laboratory test results and FET cycle-related parameters were gathered by uniformly trained staff.\u003c/p\u003e \u003cp\u003eVariables with \u0026gt;\u0026thinsp;15% missing data were excluded, and if missing\u0026thinsp;\u0026le;\u0026thinsp;15%, they were populated using statistical measures (mean, plurality, or median) depending on their characteristics.\u003c/p\u003e \u003cp\u003eLive birth is defined as births with more than one live baby after the 24th week of pregnancy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eModel development and evaluation\u003c/h2\u003e \u003cp\u003eVariables with p-values less than 0.05 in the univariate analysis were included in the next step of feature selection. Feature importance ranking was performed by a performance-stabilized random forest model, and the best number of features was found by incorporating the five groups of features from highest to lowest importance into the prediction model.\u003c/p\u003e \u003cp\u003eFour machine learning algorithms: logistic regression, random forest, SVM, and XGBoost, were used to develop models for predicting FET outcomes.\u003c/p\u003e \u003cp\u003eLogistic regression is a linear model with the advantage of being easy to implement and interpret. Random Forest is an ensemble learning method that can handle high-dimensional datasets with many features, and its ability to handle noisy and missing data makes it a robust algorithm. Support vector machines are powerful models that can handle linear and nonlinear data, transforming the data into a high-dimensional space, making it easier to separate and achieve high accuracy.\u003c/p\u003e \u003cp\u003eExtreme gradient boosting (XGBoost) is a popular machine learning algorithm that has gained significant attention in recent years. XGBoost iteratively builds a sequence of decision trees to make highly accurate predictions[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The algorithm combines advanced features such as regularization techniques, adaptive learning rate and parallel processing to make it efficient and effective in dealing with large and complex datasets. Due to its scalability and high efficiency, XGBoost has become a popular algorithm of choice and is widely used.\u003c/p\u003e \u003cp\u003eThe validation of the robustness of the machine learning model is performed by randomly dividing the dataset into a 70% training set and a 30% test set. Machine learning models are trained in the training set by learning the relationships between samples. The model learns the weights between different features in the training set in order to make predictions on new data. Test sets is employed to validate the performance of the model on unseen data to ensure that the model has good generalization capabilities. The results of the test set can be used to evaluate the model's accuracy, precision, recall, and other performance metrics[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCalibration curves can help assess the accuracy and reliability of a machine learning model by showing the relationship between the probability predicted by the model and the actual observed frequency. Ideally, the predicted probability of a model should exactly match the actual frequency, indicating that its predicted probability is very reliable, otherwise it needs to be adjusted or improved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePython 3.9 and SPSS software were implemented for statistical analysis and model building. Continuous variables were compared using the t test or Mann-Whitney U test, and categorical variables were compared using the chi-square test. Continuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median, and categorical data are expressed as counts. A p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant. Accuracy, precision, recall, F1 score and area under the ROC curve were used to quantify the ability of the model to distinguish between live-born and non-live-born events[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]; all evaluation metrics were in the range of 0\u0026ndash;1. The impact of each feature on the prediction results and the interpretation of the prediction results of the best prediction model were calculated by the shap library. Calibration curves were used to assess the accuracy of the prediction model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eThe demographic characteristics of the study participants, laboratory findings and parameters associated with FET cycles are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of baseline characteristics between the live birth group and the non-live birth group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u0026rsquo;s name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;33915)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLive birth\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16585)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Live birth\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17330)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale age (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility years (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenarche age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15571(45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8114(48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7457(43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8447(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4174(25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4273(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9897(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4297(25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5600(32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of deliver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30075(88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15186(91.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14889(85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3840(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1399(8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2441(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal FSH (MIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal E2 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;17.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u0026thinsp;\u0026plusmn;\u0026thinsp;17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal LH (MIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal P (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal PRL (MIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256.45\u0026thinsp;\u0026plusmn;\u0026thinsp;123.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e258.16\u0026thinsp;\u0026plusmn;\u0026thinsp;124.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254.82\u0026thinsp;\u0026plusmn;\u0026thinsp;123.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal T (ng/ml?/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.61\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.86\u0026thinsp;\u0026plusmn;\u0026thinsp;19.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.38\u0026thinsp;\u0026plusmn;\u0026thinsp;19.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;9.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMH (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale age (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays of Abstinence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of retrieved oocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of embryos thawed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness(mm) on the day before ET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThickness(mm) on the day of ET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of embryos transferred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10376(30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3418(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6958(40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23539(69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13167(79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10372(59.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of high-quality embryos transferred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9871(29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4083(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5788(33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11594(34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5394(32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6200(35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12450(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7108(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5342(30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale HBCAB positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8661(25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4047(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4614(26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale HBSAG positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2454(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1170(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1280\u003c/p\u003e \u003cp\u003e4(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale HBSAB positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19129(56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9327(56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9802(56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale HBEAG positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e211(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale HCVAB positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale chromosome abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1398(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e690(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e708(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale toxoplasma positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19642(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9551(57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10091(58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale HPV positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2105(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1037(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1068(6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale chromosome abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial Preparation Protocols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvulation-inducing cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3090(9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1382(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1708(9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRT cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22710(67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11324(68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11386(65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature cycles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4128(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995(12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2133(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDown-regulated HRT cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3987(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1884(11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2103(12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of culture medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOOK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22511(66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10899(65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11612(67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9333(27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4699(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4634(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1638(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e773(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e865(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbryo type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8719(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2763(16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5956(34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17671(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10524(63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7147(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7525(22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3298(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4227(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eovulation induction protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnRH antagonist protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12565(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6671(40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5894(34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-acting GnRH-a long protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8730(25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4638(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4092(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong-acting GnRH-a long protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7849(23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3751(22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4098(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild stimulation protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4137(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1182(7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2955(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltra-long GnRH agonist protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause of infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11669(34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5773(34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5896(34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epelvic factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27183(80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13190(79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13993(80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvulatory dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7092(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3330(20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3762(21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e326(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e277(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther causes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2038(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e902(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1136(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3746(11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1911(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1835(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5∽23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23018(67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11408(68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11610(67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.0∽27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e719(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6432(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2967(17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3465(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1193(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e620(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e573(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5∽23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16120(47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8037(48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8083(46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.0∽27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3322(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1492(9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1830(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13280(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6436(38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6844(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility type (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16646(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8536(51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7933(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17446(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8049(48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9397(54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSA preprocessing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1513(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e615(3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e898(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues of continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median; values of categorical variables are expressed as numbers (percentages). FSH follicle-stimulating hormone, E2 estradiol, LH luteinizing hormone, P progesterone, PRL prolactin, T testosterone, AFC antral follicle count, AMH anti-Mullerian hormone, AFC antral follicle count, BMI body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePatients who had a live birth outcome were older and had higher endometrial thickness and AFC levels at the day of transplantation, with statistically significant differences. Patients with a FET outcome of live birth were younger (30\u0026thinsp;\u0026plusmn;\u0026thinsp;3.76 vs 32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), had thicker endometrial thickness at the day of transplantation (10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 vs 9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and had a greater number of AFCs (17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18 vs 14\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFeature Importance\u003c/h2\u003e \u003cp\u003eBased on the Gini index of random forest, the features were ranked in importance using 5-fold cross-validation and grid search. The importance ranking of all candidate features is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThree aspects of characteristics ranked in order of importance, including demographic characteristics, laboratory test results, and FET cycle-related parameters. The feature importance ranking chart demonstrates the contribution of each feature to the model prediction. x-axis indicates the importance score of the feature; y-axis indicates the feature name, from top to bottom expresses the decreasing importance of the feature.\u003c/p\u003e \u003cp\u003eAccording to the performance of the importance ranking graph, the number of transferred embryos was the most important predictor of live birth events, followed by embryo type, number of thawed embryos, female age, and basal AMH.\u003c/p\u003e \u003cp\u003eFive groups of five were incorporated into the prediction model according to their highest to lowest importance, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and it was found that the AUC value of the model stabilized and hardly increased after the number of features reached 25, so the top 25 features in the order of importance were selected as predictors of live birth events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSelection of the number of features to be included in the model. The x-axis represents the number of features; the y-axis represents the AUC value of the machine learning model when a certain number of features are incorporated.\u003c/p\u003e \u003cp\u003eSHAP was used to explain the results of the random forest model by calculating the contribution of each variable to the prediction, and a summary plot of the re-SHAP for the 25 candidate factors is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSHAP summary chart of 25 features. Red represents higher values, blue represents lower values, and a positive SHAP value means a positive effect on the outcome, while a negative one means a negative effect on the outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Construction\u003c/h2\u003e \u003cp\u003eTwenty-five variables were selected for input into a logistic regression model, a random forest model, a support vector machine model, and an XGBoost model to predict live birth events. The performance evaluation of the models generated by the four machine learning algorithms is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The receiver operating characteristic (ROC) curves for the prediction models of the four machine learning algorithms on the training and test sets were plotted as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The XGBoost model had the highest AUC (0.750, 95% CI 0.746\u0026ndash;0.755), accuracy, precision, recall, and f1 score. The calibration curves of the four models in the test set are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of the four models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epredictive models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprecision\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erecall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.724(0.720\u0026ndash;0.728)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.739(0.735\u0026ndash;0.743)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.720(0.717\u0026ndash;0.724)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.750(0.746\u0026ndash;0.755)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eROC curves of the four models on the training and test sets. AUC, area under the curve; ROC, receiver operating characteristic\u003c/p\u003e \u003cp\u003eCalibration curves for the four models. The x-axis represents the predicted probability and the y-axis represents the observed frequency. The dashed line is the reference line, and the calibration curve shows a good correlation between the predicted and actual results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, data from 33,915 frozen-thawed embryo transfer cycles were analyzed and 25 features were extracted from 46 candidate predictors that could have an impact on live birth events, from which logistic regression models, random forest models, support vector machine models, and XGBoost models were constructed. Comparison of model performance revealed that the prediction model based on the XGBoost algorithm had the largest area under the ROC curve (0.750, 95% CI 0.746\u0026ndash;0.755) and outperformed the other models in terms of accuracy, precision, recall, and F1 score performance.\u003c/p\u003e \u003cp\u003eFemale age is one of the most important factors influencing the outcome of frozen-thaw embryo transfer. Many studies have shown that older women have lower clinical pregnancy and live birth rates than younger women, which may be associated with a decrease in ovarian function after the age of 35 years in women[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and the results of this study confirm this. The type of embryo and the number of embryos transferred are also among the factors affecting FET live births. The results of Pan, Y et al.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] showed that the number of frozen embryos transferred had a significant effect on the live birth rate, with higher live birth rates for cycles in which two embryos were selected for transfer than for cycles in which one embryo was transferred. It is also evident from the shap summary plot in our study that the number of embryos transferred with two has a positive effect on live birth events. The choice of embryo type can likewise have an impact on pregnancy outcome. The SHAP summary plot of this study has indicated that the choice of blastocyst for transfer is more likely to result in live birth than cleavage stage embryos, consistent with most previous studies[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This may be due to the fact that the period of cleavage stage to blastocyst development itself eliminates nearly half of the embryos with developmental potential before they reach the blastocyst stage[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Endometrial thickness can also have an impact on the outcome of in vitro fertilization-embryo transfer. A large study conducted by Liu, KE et al.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]showed a significant decrease in clinical pregnancy and live birth rates in frozen-thawed embryo transfer cycles with endometrial thickness below 7 mm. Bu et al.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] analyzed the relationship between endometrial thickness on the day of embryo transfer and pregnancy outcome in FET cycles and found a significant effect of endometrial thickness on the outcome of the FET cycle. Our study was consistent with the findings of these studies.\u003c/p\u003e \u003cp\u003eTo date, several studies have been used to predict outcomes in IVF-embryo transfer, including prediction of outcomes such as biochemical pregnancy, clinical pregnancy, early miscarriage, and embryo development[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Live birth events are the outcomes of most concern to clinicians and infertile couples, and there are relatively few predictive models for live birth outcomes[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Traditional statistical methods models are usually based on specific assumptions and a priori knowledge, do not take full advantage of the information in the data and in most cases require manual adjustment of the parameters of the prediction model. In contrast, machine learning methods can automatically uncover patterns in the data and are more flexible. In terms of adjusting model parameters, machine learning models are also automated and can easily handle large amounts of data and complex models, reducing the cost and error of human intervention and providing higher prediction accuracy, especially in complex nonlinear data analysis and prediction tasks[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study had several advantages that could enrich the current body of knowledge on the outcome of frozen-thawed embryo transfer. First, our study employed a large sample, which may be more representative and generalizable compared to smaller studies. Second, interpretability is crucial for clinical decision making, and the black box of machine learning has been difficult to convince clinicians and patients for predictive models related to clinical outcomes. In our study, the SHAP algorithm was used to explain the contribution of each predictor to the final outcome, allowing clinicians to understand the impact of each factor on the likelihood of live birth. Third, we used four different machine learning algorithms to develop predictive models for freeze-thaw embryo transfer outcomes, reducing the risk of bias as well as overfitting that can occur when using a single model and improving the robustness of the study results. Fourth, the use of the developed prediction models has the potential to improve the outcomes of frozen-thaw embryo transfer and to reduce medical costs and the psychological and financial burden on patients to some extent.\u003c/p\u003e \u003cp\u003eOur study also had some limitations. First, all data were collected from one center, which may affect the effectiveness of our prediction model in applying it to populations from other health care institutions. Future studies may consider including populations from multiple centers or different regions, which may provide a more comprehensive picture of the predictive factors affecting FET outcomes. Second, although our study attempted to include as many predictors as possible, some factors not included may still be relevant, and further studies of a broader range of potential predictors may achieve better results.\u003c/p\u003e \u003cp\u003eIn summary, we incorporated a total of 46 predictors for patient demographics, laboratory findings, and FET cycle-related parameters, and identified 25 features after feature screening to construct prediction models, and developed four different algorithms for predicting live birth for frozen-thaw embryo transfer, with the best performing XGBoost algorithm providing the possibility of identifying live birth and non-live birth populations, thus aiding clinicians in their decision making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.J.T, Z.H.Z, and Y.B.D designed the study and interpreted the data. T.L, X.J.W, M.J.X, H.C.Z, M.W, and Q.W provided clinical guidance and advice on statistical analysis. X.Y.H performed the statistical analysis. X.Y.H and X.J.W co-drafted the manuscript. X.J.T and Y.B.D further revised the manuscript. The final manuscript was approved by all co-authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no confict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedures used in this study have adhered to the tenets of the Declaration of Helsinki. The study protocol involved human participants were approved by the Ethics Committee of Chongqing Medical University (2021060).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the retrospective nature, the data were anonymised and the written informed consent was waived by the Ethics Committee of Chongqing Medical University\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKushnir VA, Barad DH, Albertini DF, Darmon SK, Gleicher N: Systematic review of worldwide trends in assisted reproductive technology 2004-2013. 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Comput Meth Programs Biomed 2019, 177:161-174.\u003c/li\u003e\n\u003cli\u003eBlank C, Wildeboer RR, DeCroo I, Tilleman K, Weyers B, de Sutter P, Mischi M, Schoot BC: Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective. Fertility and Sterility 2019, 111(2):318-326.\u003c/li\u003e\n\u003cli\u003eLiang R, An J, Zheng YJ, Li JQ, Wang Y, Jia YY, Zhang J, Lu Q: predicting and improving the probability of live birth for women undergoing frozen-thawed embryo transfer: a data-driven estimation and simulation model. Comput Meth Programs Biomed 2021, 198:7.\u003c/li\u003e\n\u003cli\u003eSpeiser JL, Callahan KE, Houston DK, Fanning J, Gill TM, Guralnik JM, Newman AB, Pahor M, Rejeski WJ, Miller ME: Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults. 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Journal of Assisted Reproduction and Genetics 2020, 37(10):2405-2412.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"FET, live birth, machine learning, predictive model, random forest","lastPublishedDoi":"10.21203/rs.3.rs-3430829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3430829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eThis study used multiple machine learning algorithms to predict live births from frozen embryo transfers (FET) based on patient demographics, laboratory test results, and parameters associated with the FET cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from 33,915 cycles of frozen-thaw embryo transfer performed at Chengdu Xinan Gynecological Hospital between January 2015 and December 2021 were used. The dataset was randomly divided into a training set (70%) and a test set (30%). Features were ranked for importance based on the random forest model, and features with the top 25 contribution values were used to develop logistic regression models, random forest models, support vector machine models, and XGBoost models. Shapley was used to interpret the results of the best-performing models. Receiver operating characteristic curves (AUC) under area and calibration curves were to be assessed for the performance of machine learning prediction models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eRanking the importance of features based on the stable random forest algorithm showed that the most predictive features included AMH, Basal PRL, Basal T, Basal FSH, etc. The XGBoost model had the highest AUC (0.750, 95% CI 0.746-0.755). The XGBoost-based SHAP summary plot indicated that patients with lower age, shorter years of infertility, and D5 embryo type for transfer had a greater likelihood of live birth outcome after freeze-thaw embryo transfer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe XGBoost model performed best in predicting the outcome of freeze-thaw embryo transfer. The algorithm combined with the interpretability of SHAP summary plot can assist clinicians in the decision-making process of freeze-thaw embryo transfer.\u003c/p\u003e","manuscriptTitle":"Predictive models for live birth outcomes of FET: Improving clinical decision-making based on machine learning analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-08 00:07:20","doi":"10.21203/rs.3.rs-3430829/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87d7d337-b782-46aa-9b68-3870c4990418","owner":[],"postedDate":"November 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-06T03:42:44+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-08 00:07:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3430829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3430829","identity":"rs-3430829","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}