How to estimate the probability of a live birth after one or more complete IVF cycles？The development of a novel model in a single-center

How to estimate the probability of a live birth after one or more complete IVF cycles？The development of a novel model in a single-center | 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 How to estimate the probability of a live birth after one or more complete IVF cycles？The development of a novel model in a single-center Xiangyi Kong, Zhiqiang Liu, Chunyu Huang, Xiuyu Hu, Meilan Mo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3048402/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 12 You are reading this latest preprint version Abstract Objective To estimate the probability of a live birth for an infertile couple after one or more complete cycles of in vitro fertilization (IVF) by using a Cox regression and Nomogram model. Methods A retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Urology Hospital. A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. 70% of the patients were randomly placed into the training set (n = 3089) and the remaining 30% of the patients were placed into the validation set (n = 1324) randomly. Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for one retrieval cycle and the subsequent five frozen embryo transfer (FET) cycles. Proportional Hazards (PH) Assumption test was used for selecting the parameter in the predictive model. A Cox regression model was built based on the basis of training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Subsequently, the validation set was applied to verify the validity of the model. Finally, for a more intuitive assessment of the CLBR more intuitively for clinicians and patients, a Nomogram model was established based on predictive model. By calculating the scores of the model, the clinicians could more effectively predict the probability for an individual patient to obtain at least one live birth. Result(s): In the fresh embryo transfer cycle, the LBR was 38.7%. In the first to fifth FET cycle, the optimal estimate and conservative estimate CLBRs were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Based on PH test results, the potential predictive factors for live birth were insemination method, infertility factors, serum progesterone level (R = 0.043, p = 0.059), and luteinizing hormone level (R = 0.015, p = 0.499) on the day initiated with gonadotropin, basal follicle-stimulating hormone (R = -0.042, p = 0.069) and BMI (R = -0.035, p = 0.123). We used ROC curve to test the predictive power of the model. The AUC was 0.782 (p < 0.01, 95% CI: 0.764–0.801). Then the model was verified using the validation data. The AUC was 0.801 (p < 0.01, 95% CI: 0.774–0.828). A Nomogram model was built based on potential predictive factors that might influence the event of a live birth. Conclusion(s): The Cox regression and Nomogram prediction models effectively predicted the probability of infertile couples having a live birth. Therefore, this model could assist clinicians with making clinical decisions and providing guidance for patients. Trial registration: N/A. In vitro Fertilization cumulative live birth rate Cox regression model Nomogram model predictive factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Infertility has become a global public health tissue, and the prevalence of infertility has increased gradually, from 9–26%, and even to 31.1% in low-to-middle-income countries( 1 – 3 ). In vitro fertilization (IVF) has become an effective method for infertile patients to fulfill their fertility wishes. Globally, an estimated 7 million children have been born as a result of IVF since 1978( 4 ),and in the year 2013, almost 1,160,474 embryo transfers (ETs) were performed resulting in > 344,317 babies( 5 ). As for patients who came to an infertility center for the first time, their most important question was:“what is the probability of taking a baby home?” There are many indicators to evaluate the outcome of IVF, and the most efficient indicator is cumulative live birth rate (CLBR) after complete cycles, which is defined as all fresh and frozen-thawed embryos transfer (FET) attempts resulting from one episode of ovarian stimulation( 6 , 7 ). Many scholars have studied different factors that might have an effect on live birth outcomes. In 1995, Collins firstly studied the influence of pregnancy history, duration of infertility and female partner's age on live birth, and proposed a negative correlation between age and live birth. The prediction score based on these factors would be accurate in approximately 62% of cases. While the subjects of the study were untreated infertile couples, the estimation of live birth among them was sufficiently accurate to be useful in the clinical management of infertility and their future planning.( 8 ). In 1996, Templeton et al. studied a large-scale retrospective analysis by using logistic regression to explore the influence of age on cumulative live birth; they pointed out that the highest live birth rate was in patients aged 25–30 years Moreover, there was a significant decrease in age-adjusted live birth rates with increasing duration of infertility between 1 and 12 years( 9 ). This research was a major leap forward, because few pervious studies had taken account of these factors before. In 2016, McLernon model has been established, which could provide an individualized estimate of a couple’s cumulative chances of having a baby over a complete package of IVF both before treatment and after the first fresh embryo transfer( 10 ). This discrete time logistic regression model would help to shape couples’ expectations allowing them to plan their treatment more efficiently and to prepare emotionally and financially. A subsequent study in a different geographical context has been conducted to verify and confirm its effectiveness( 11 ). Recently, a Nomogram predictive model by using multivariable linear regression based on a retrospective database was built to estimate the likelihood of a live birth after surgery followed by assisted reproductive technology (ART) for endometriosis-related infertility. Though the AUC of the Nomogram model was 0.77, the sample was small( 12 ). Laura van Loendersloot provided a significant contribution in building prediction models in IVF. She proposed three phases of prediction model development and provided key principles to assess the effectiveness of different prediction models( 13 ). Many previous studies have used different predictors to explore the effect of each factor on live birth and to build predictive models. We utilized a more rigorous variable screening approach for different divisions of patient infertility factors to create a new predictive model to assess the reproductive potential of patients at our center after transfer of all fresh and subsequently frozen-thawed embryos in a full cycle of treatment. Furthermore, we also wanted to verify whether this model could help clinicians make clinical decisions and provide guidance for patients. Methods Study Design and Patients A retrospective study was conducted in the IVF clinical center of Shenzhen Zhongshan Urology Hospital. From January 2012 to April 2015, a total of 4413 patients who completed ovarian stimulation treatment and a fresh cycle with subsequent FET were involved in this study. This project was approved by the Institutional Review Board of the Shenzhen Zhongshan Urology Hospital on 15 February 2021 (FM-YXLL-026). Inclusion and Exclusion Criteria The inclusion criteria were: ( 1 ) women who were diagnosed with infertility; ( 2 ) women aged 18–45 years; ( 3 ) women who had undergone their first cycle of IVF or ICSI; and ( 4 ) women with a retrieved number of oocytes > 0. The exclusion criteria were: ( 1 ) women who undergone preimplantation genetic testing (PGT);( 2 ) reproductive malformation or intrauterine adhesions; ( 3 ) untreated unilateral or bilateral hydrosalpinx; ( 4 ) recurrent pregnancy loss or repeated implantation failure. Treatment Protocols All patients underwent a short luteal gonadotropin releasing hormone (GnRH) protocol. Injections of 0.1mg of GnRH agonist (Triptorelin, Ferring, Germany) were administered daily from day 21 of the patient’s menstrual cycle (or 16 days after the start of an oral contraceptive). Controlled ovarian stimulation was achieved using a daily dose of gonadotropin (150–300 UI/d), recombinant follicle-stimulating hormone (rFSH; Gonal-F, Merck-Serono, Germany), or human menopausal gonadotropin (hMG; Menopur, China) ) after confirmation of pituitary down-regulation (level of serum estradiol (E2) < 183.5pmol/L, with no ovarian cysts and the diameter of the largest follicle was < 8mm). The dosage of rFSH was adjusted according to female age, ovarian response: antral follicle count (AFC), which was assessed by ultrasound、serum hormone、anti-mullerian hormone (AMH, ng/ml, measured by using the chemical luminescence method (Yahuilong Company, Shenzhen, China)) and body mass index (BMI, kg/m2)( 14 – 17 ). When a mean diameter of two follicles reached > 18mm, human chorionic gonadotropin (hCG; LiZhu, Zhuhai, China) was given, and oocyte retrieval was performed 36 h later. Fresh embryo transfer was performed on Day 3 (cleavage embryos) or Day 5 (blastocysts). Subsequent FET was performed on Day 3 (cleavage embryos) or Day 5 (blastocysts) after ovulation in a natural cycle. In a hormone replacement cycle, by using oral estradiol 4–8 mg per day (estradiol valerate tablets; Progynova, Bayer, France) for 12 to 15 d and intramuscular progesterone (60 mg/d) for 4 or 6 days, the FET was performed on Day 4 (cleavage embryos) or Day 6 (blastocysts) after progesterone administration The number of embryos transferred varied from one to three based on the recommendation of the Health Ministry of China and the requests of patients. Luteal support was performed using dydrogesterone tablets 20 mg twice a day (Abbott, America) plus vaginal or anal progesterone (90 mg/d, Merck-Serono, Germany, or 200 mg, three times a day; Cyndea Pharma, S.L). Basal Characteristics Data Collection All the data were obtained from the electrical medical record system of IVF in the fertility center of Shenzhen Zhongshan Urology Hospital. The following baseline characteristics were recorded and analyzed: female age (years), duration of infertility (years), infertility factors (ovulation disorder, endometriosis, tubal factor, male factor, couple factors(the cause of infertility is mainly by both female and male factors), unexplained factor), body mass index (BMI, kg/m2), antral follicle count (AFC), basal follicle-stimulating hormone (b-FSH) levels (pg/mL), initiated gonadotropin (Gn) dosage, Gn days, serum progesterone (P) level (pg/mL)、E2 level (pg/mL)、follicle-stimulating hormone (FSH) level (pg/mL) and luteinizing hormone (LH) level (pg/mL) on the day initiated with Gn (GnP、GnE2、GnFSH、GnLH), serum P level (pg/mL)、E2 level (pg/mL) and LH level (pg/mL) on the day of trigger (tP、tE2、tLH), number of follicles on the day of HCG trigger, insemination method (IVF/ICSI), number of retrieved oocytes, metaphase II (MII), fertilized oocytes, two pronucleus zygotes (2PN zygotes), good quality embryos and transferred embryos. Reproductive Outcomes The primary outcome was cumulative live birth rate. Live birth was defined as a neonate showing any sign of life, irrespective of gestational age, as defined by the World Health Organization (WHO). CLBR within one complete IVF/ICSI treatment cycle was defined as the probability of a live birth from an ovarian stimulation, including all embryo transfers (fresh and frozen) from that stimulation( 18 – 20 ). The second outcome was fresh live birth. The optimal estimate is based on the observed data and assumes that the CLBR in women who discontinue ART treatment without a live birth would be equal to the rate in those who continue. The conservative estimate assumes that those who did not continue the ART treatment would not have a live birth( 21 ). Statistical Methods Statistical analysis was performed using SPSS v. 26.0 and R v. 4.0.3. The basal characteristics of patients and stimulation cycles are listed in Table 1 according to live birth or not. Normally distributed data were expressed as mean \(\pm\) SD, whereas skewed distributed data were expressed as median (interquartile range). Categorical variables were shown as n and were compared using the chi-square test. Comparison of means of continuous variables was performed using the Mann–Whitney U-test or Student’s t-test depending on data distribution. Categorical variables were plotted using the graphical method of Kaplan-Meier survival curves grouped by that variable, or by plotting ln[-ln(S ̂(t)) against survival time t grouped by that variable, and observing whether the curves crossed to determine whether the PH assumption was satisfied. Continuous variables were tested for the PH assumption of the Cox regression model using the Schoenfeld residual method, and if the PH assumption held, the biased residuals were plotted (Schoenfeld residuals) and estimated for the Cox model, which should fluctuate above or below zero level with time. Table 1 Characteristics of patients and stimulation cycles were described according to live birth or not Variable no live birth(N = 1691) live birth(N = 2722) P value Female age (years) 32.00 (29.00,36.00) 31.00 (28.00,33.00) 0.000 Duration of infertility (years) 3.00 (2.00,5.00) 3.00 (2.00,5.00) 0.001 BMI (kg/m 2 ) 20.96 (19.33,23.05) 20.70 (19.20,22.72) 0.007 AFC 8.00 (6.00,10.00) 10.00 (8.00,12.00) 0.000 Infertility factors 0.024 Female 1038(37.5%) 1729(62.5%) Tubal factor 815 (37.4%) 1366 (62.6%) Endometriosis 145 (44.3%) 182 (55.7%) Ovulation disorder 78 (30.1%) 181 (69.9%) Male 249(35.9%) 445(64.1%) Couple factors 2(50.0%) 2(50.0%) Unexplained 402(42.4%) 546(57.6%) b-FSH 5.38 (4.55,6.50) 5.34 (4.54,6.36) 0.148 Gn P(pg/mL) 0.10 (0.10,0.20) 0.10 (0.10,0.20) 0.125 GnLH (pg/mL) 0.60 (0.45,0.79) 0.61 (0.46,0.81) 0.648 GnE 2 (pg/mL) 12.00 (10.00,15.00) 12.00 (10.00,14.00) 0.028 Gn FSH(pg/mL) 2.53 (2.05,3.11) 2.36 (1.93,2.88) 0.000 tP (pg/mL) 0.60 (0.40,0.80) 0.60 (0.40,0.80) 0.251 tLH (pg/mL) 0.71 (0.50,0.98) 0.66 (0.48,0.91) 0.001 tE 2 (pg/mL) 2105.00 (1417.00,2908.00) 2700.00 (1910.00,3597.25) 0.000 No. of follicles on the day of HCG trigger 8.00 (6.00,10.00) 10.00 (8.00,11.00) 0.000 Initiated Gn dosage (75/IU) 4.00 (3.00,4.00) 3.00 (2.00,4.00) 0.000 Gn days 9.00 (8.00,10.00) 9.00 (8.00,10.00) 0.156 Insemination method 0.917 IVF 1562(38.3%) 2512(61.7%) ICSI 129(38.1%) 210(61.9%) No. of retrieved oocytes 11.00 (7.00,15.00) 15.00 (11.00,19.00) 0.000 No. of MII 9.00 (6.00,13.00) 13.00 (10.00,17.00) 0.000 No. of fertilized oocytes 8.00 (5.00,12.00) 12.00 (8.00,16.00) 0.000 No. of 2PN zygotes 5.00 (3.00,8.00) 8.00 (5.00,12.00) 0.000 No. of transferred embryos 4.00 (2.00,6.00) 7.00 (5.00,9.00) 0.000 No. of good quality embryos 3.00 (1.00,5.00) 6.00 (4.00,9.00) 0.000 Sample Stratified and Model Building According to the pre-experimental data, the incidence of successful pregnancy was 61.68%, the standard deviation of the variable was 1.204, the expected log-hazard ratio was 0.185 (lnHR = 0.185), and the coefficient of determination was 0.047. Under the condition of a significance level of 0.05 and test power of 0.90, the sample size was calculated using PASS, and the estimated sample size was 361(22.23). In order to verify the rationality of sampling among variables of the training set and the validation set, we conducted stratified sampling for the whole sample (n = 4413) according to live birth, no birth and loss of follow-up. 70% of the subjects were randomly placed into a training set (n = 3089) and 30% of the subjects were placed into a validation set (n = 1324). A Cox regression model was built on the basis of the training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Finally, the validation set was used to verify the validity of the model. To assess the possibility of CLBR more intuitively for clinicians, we established a Nomogram model on the basis of various clinical factors that might affect CLB. Calculating the goals of the model, the clinicians might predict the probability for patients to obtain at least one live birth within 1 to 3 years. Results A total of 4413 patients who had undergone their first cycle of IVF/ICSI in the fertility center of Shenzhen Zhongshan Urology Hospital were involved in this study. The data analysis flowchart is shown in Fig. 1 . Table 2 and Fig. 2 show the cumulative live birth rate among different embryo transfer cycles and the relationship among them. In the fresh embryo transfer cycles, the live birth rate was 38.7%. In the first to fifth FET cycles, the optimal estimate and conservative estimate CLBR were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Table 2 Cumulative live birth rates of different cycles. Cycles No. of cycles Cycles of live birth Cycles of no birth Cycles of loss of follow-up Live birth rate of the cycle Optimistic estimate Conservative estimate Cycles of Correction Live birth rate of the cycle Cumulative live birth rate Cycles of Correction Live birth rate of the cycle Cumulative live birth rate Fresh 4413 1708 553 400 38.70% 4413 38.70% 38.70% 4413 38.70% 38.70% FET1 1752 799 302 200 45.61% 2305 34.66% 59.95% 2705 29.54% 56.81% FET2 451 178 124 56 39.47% 1306 13.63% 65.41% 1906 9.34% 60.84% FET3 93 29 26 24 31.18% 1072 2.71% 66.35% 1728 1.68% 61.50% FET4 14 7 2 3 50.00% 1019 0.69% 66.58% 1699 0.41% 61.66% FET5 2 1 1 50.00% 1009 0.10% 66.61% 1692 0.06% 61.68% Basal Characteristics of the Training and Validation Sets The basal characteristics of patients and cycles between the training set and the validation set are listed in Tables 3 and 4 . There was no difference among the data of two cohorts, which indicated that stratified sampling based on live birth, no birth, and lost follow-up was reasonable. Table 3 Characteristics of patients between training set and validation set(median and interquartile ranges). 70% of the samples were divided into training set (N = 3089) and 30% of the samples were divided into validation set (N = 1324). Variable Training set (N = 3089) Validation set (N = 1324) P value Female age (years) 31.382 (28.000,34.000) 31.520 (29.000,34.000) 0.273 Duration of infertility (years) 3.779 (2.000,5.000) 3.857 (2.000,5.000) 0.680 BMI (kg/m 2 ) 21.267 (19.230,22.865) 21.165 (19.343,22.760) 0.844 AFC 10.030 (8.000,10.000) 10.040 (8.000,10.000) 0.769 Infertility factors 0.170 Female 1907 (68.9%) 860 (31.1%) Tubal factor 1512 (69.3%) 669 (30.7%) Endometriosis 211 (64.5%) 116 (35.5%) Ovulation disorder 184 (71.0%) 75 (29.0%) Male 507 (73.1%) 187 (26.9%) Couple factors 3 (75%) 1 (25%) Unexplained 672 (70.9%) 276 (29.1%) b-FSH 5.683 (4.550,6.390) 5.708 (4.533,6.470) 0.976 Table 4 Characteristics of cycles between training set and validation set Variable Training set (N = 3089) Validation set (N = 1324) P value GnP (pg/mL) 0.164 (0.100,0.200) 0.1635 (0.1000,0.2000) 0.465 GnLH(pg/mL) 0.679 (0.460,0.810) 0.657 (0.460,0.790) 0.461 GnE 2 (pg/mL) 13.790 (10.000,14.000) 13.951 (10.000,14.000) 0.711 GnFSH (pg/mL) 2.515 (1.970,2.940) 2.520 (1.970,3.010) 0.485 tP (pg/mL) 0.668 (0.400,0.800) 0.663 (0.400,0.800) 0.744 tLH(pg/mL) 0.758 (0.480,0.930) 0.773 (0.490,0.960) 0.119 tE 2 (pg/mL) 2690.370 (1704.000,3381.5000) 2681.920 (1684.750,3344.750) 0.584 No. of follicles on the day of HCG trigger 8.973 (7.000,11.000) 8.938 (7.000,11.000) 0.537 Initiated Gn dosage (75/IU) 3.135 (2.000,4.000) 3.155 (2.000,4.000) 0.482 Gn days 9.407 (8.000,10.000) 9.412 (8.000,10.000) 0.677 Insemination method 0.368 IVF 2859 (70.2%) 1215 (29.8%) ICSI 230 (67.8%) 109 (32.2%) No. of retrieved oocytes 13.809 (9.000,18.000) 13.785 (9.000,18.000) 0.646 No. of MII 12.519 (8.000,16.000) 12.516 (8.000,16.000) 0.677 No. of fertilized oocytes 11.257 (7.000,15.000) 11.252 (7.000,15.000) 0.780 No. of 2PN zygotes 7.706 (4.000,10.000) 7.619 (4.000,10.000) 0.268 No. of transferred embryos 6.092 (3.000,8.000) 6.074 (3.000,8.000) 0.504 No. of good quality embryos 5.549 (2.000,8.000) 5.496 (2.000,7.000) 0.495 Model Building PH test and Cox regression model The PH test is a test of whether the effect of the covariates on the live birth rate changes over time; in other words, it is a test of whether the risk ratio h(t)/h0(t) is fixed. Based on the results of the PH test, the potential predictive factors of live birth were insemination method, infertility factor, GnP level (pg/mL, R = 0.043, p = 0.059) and GnLH level (pg/mL, R = 0.015, p = 0.499), basal FSH (R = -0.042, p = 0.069) and BMI (R = -0.035, p = 0.123), which are shown in Table 5 and Fig. 3 . Table 5 Predictive factors for continuous variables of live birth in PH test. Variables R* P -value Female age (years) -0.144 0.000 Duration of infertility (years) -0.051 0.026 BMI (kg/m 2 ) -0.035 0.123** AFC 0.102 0.000 b-FSH -0.042 0.069** GnP (pg/mL) 0.043 0.059** GnLH(pg/mL) 0.015 0.499** GnE 2 (pg/mL) -0.045 0.048 GnFSH(pg/mL) -0.112 0.000 tP (pg/mL) 0.217 0.000 tLH (pg/mL) -0.102 0.000 No. of follicleson the day of HCG trigger 0.263 0.000 Initiated Gn dosage (75/IU) -0.144 0.000 Gn days 0.068 0.003 No. of retrieved oocytes 0.317 0.000 No. of 2PN zygotes 0.296 0.000 No. of transferred embryos 0.317 0.000 No. of good quality embryos 0.293 0.000 * R means the correlation coefficient between Schoenfeld residuals estimated by Cox model and time rank ** P-value > 0.05, means Schoenfeld residual is considered to have a wireless relationship with time rank, which satisfies the pH assumption. The variables that finally meet the pH assumption including GnP(pg/mL)、GnLH(pg/mL), basal FSH and BMI. Based on the results of the PH test, the Cox regression model was built. The relationship of predictive factors and live births is shown in Table 6 . Considering the statistically significant (p < 0.05) data, BMI and b-FSH levels increased as the live birth rate decreased, with the HR for b-FSH and BMI being 0.970 (95% CI: 0.945–0.996, p = 0.025) and 0.982 (95% CI: 0.966–0.998, p = 0.025), respectively. While for infertility factors, the live birth rate was 1.204-fold higher for couples with male factor than for those with unexplained infertility factor (HR = 1.204, 95% CI: 1.036–1.398; p = 0.015). Table 6 The relationship between predictive factors and live birth. HR 95% CI P Lower Upper GnP(pg/ml) 0.965 0.548 1.698 0.901 GnLH(pg/ml) 1.029 0.916 1.157 0.631 b-FSH 0.970 0.945 0.996 0.025* BMI 0.982 0.966 0.998 0.025* Infertility factor Unexplained 1.000 - - - Female factor 1.113 0.991 1.250 0.070 Male factor 1.204 1.036 1.398 0.015* Couple factors 0.479 0.067 3.407 0.462 Insemination method ICSI 1.000 - - - IVF 0.953 0.799 1.136 0.589 * p < 0.05, ** p < 0.01,*** p < 0.001 ROC curve and internal verification We used the ROC curve to test the specificity and sensitivity of the CLBR prediction model. When the maximum value of sensitivity plus specificity was 1.531, the cut-off value was 0.410 and AUC was 0.782 (p < 0.01, 95%CI: 0.764–0.801). That is to say, the sensitivity was 0.877, specificity was 0.654, and accuracy was 0.792. Subsequently, the model was verified in the validation data. When the maximum value of sensitivity plus specificity was 1.541, the cut-off value was 0.392 and AUC was 0.801 (p < 0.01, 95%CI: 0.774–0.828). As the sensitivity was 0.902, specificity was 0.639, and accuracy was 0.801. The ROC curve is shown in Fig. 4 . In addition, when we categorized infertility factors as tubal factor, endometriosis, and ovulation disorder, the Cox regression analysis was as shown in Table S1 . Considering the statistically significance (p < 0.05), with the increasing of b- FSH, the live birth rate decreased. Furthermore, with the increase of BMI, the live birth rate decreased; meanwhile for infertility factors, the live birth rate with ovulation disorder and male factor was higher than that in couples with unexplained infertility. Nomogram model A nomogram model was built based on possible factors that might influence the live birth rate of training data (Fig. 5 A). The calibration curve of 1 to 3 years to evaluate the accuracy of the model is shown in Fig. 5 B. The closer the predictive calibration curve was to the standard curve, the better the prediction ability of the histogram. For the validation data, the calibration curve of 1 to 3 years to evaluate the accuracy of the model is shown in Fig. 5 C. Discussion We have developed a Cox regression and Nomogram model to predict the probability of at least a live birth after a complete IVF cycle and its FET cycles for infertile couples. Furthermore, the model was verified effectively by inside validation. The model showed that the predictive factors of live birth were insemination method, infertility factor, GnP level, GnLH level, b- FSH and BMI. In the model, b-FSH had a negative relationship with live birth rate. It is well known that serum b-FSH is a marker for evaluating the ovarian reserve, and an elevated b-FSH reflects not only a quantitative but also qualitative decline in the ovarian reserve. The average number of oocytes collected and average number of available embryos for transfer were significantly reduced in patients with elevated FSH. Moreover, high levels of FSH predict a poor pregnancy outcome. With an increasing basal level of FSH, pregnancy rate and live birth rate were decreased ( 24 ). A predictive model to estimate the relationship between factors and live birth rate was recently built. The model shows that women delivering a live birth had a significantly lower median age and FSH and a significantly higher AMH and AFC. The authors suggested that age, FSH and AMH were independent predictors( 25 ). High BMI was considered to negatively impact live birth following IVF( 26 ). Researchers in Spain noted that live birth rates were reduced progressively with each unit of BMI (kg/m 2 ) with a significant odds ratio of 0.981 (95% CI: 0.967–0.995); however, the embryo quality was not affected. This might be attributed to an alteration in the uterine environment when the BMI was increased( 27 ). Nevertheless, a recent study has suggested an “inverted U shape” association between BMI and CLBR, with the turning point of BMI being 18.5 and 30.4 kg/m 2 . The CLBR increases in underweight women, plateaus in normal weight and overweight women, and then decreases in obese women( 28 ). In our model, with the increase of BMI, the live birth rate decreased. The mechanism of how obesity affects the pregnancy outcome is still not clear. Some researchers suggest that obesity negatively impacts the developmental competence of oocytes and embryo quality( 29 , 30 ); while others consider that the altered endometrial gene expression in obese patients might contribute to lower implantation rates( 31 ). AMH is also an important predictor of ovarian reserve and response( 32 ). One study reported that AMH was the best predictor for identifying patients with poor and high ovarian response( 33 ). An AFA model for assessing the true ovarian reserve has also been established in China( 34 ) which showed a positive association between AMH and CLBR after fresh and FET cycles( 25 ). Some researchers took AMH into consideration when exploring the relationship with live birth rate( 35 – 37 ). However, in our model, AMH was not an independent predictor for live birth. A meta-analysis considered that AMH had some association with predicting live birth rate, while the predictive accuracy was poor( 38 ). As we know, age is also a significant factor to the outcome of pregnancy; many studies and predictive models have described a negative influence on outcome( 8 , 9 ). We found that the risk ratio h(t)/h0(t) for the age variable was not a fixed value, the influence of age on the live birth rate decreased slightly with time (R=-0. 144, p < 0.001). As the time of live birth went on, more patients with short time of live birth were better basic conditions, but more patients with long time of live birth were recurrent implantation failure or recurrent miscarriage, which led to the influence of age on the live birth rate could not be consistent with time, which violated the PH hypothesis. However, violating the PH hypothesis may lead to biased effect estimates in Cox regression analysis ( 39 ). We will not include the patient age into the model to obtain the unbiased effect estimator of the model. In our study, the age range of patients was wide, and some of the patients had advanced age. In order to analyze the influence of age in pregnancy, we analyzed those patients who were older than 35 years. Based on PH test results, the Cox regression model showed the same result that we described previously (Table S2). Though the chance of success of IVF in this cohort of patients was low, the model could effectively predict the pregnancy outcome. As we known, infertility is a couple’s concern, where the male factor must be taken into account. In our analysis, live birth rate was 1.204-fold higher for couples with male factor than those with unexplained infertility factor. Furthermore, a retrospective cohort study reported that more than half of couples under consultation for male infertility succeeded in having a child( 40 ). Though the subgroup Cox regression analysis showed that the live birth rate with ovulation disorder was higher than that in couples with unexplained infertility, it might not indicate that ovulation disorder was considered with live birth. The Nomogram method was used to make an earlier prediction of the probability of live birth ( 41 ) and it was more efficient and rapid at assessing the probability of having a baby. For example, a patient’s BMI was 22.58 kg/m 2 , b-FSH was 5.62 pg/mL, female infertility factor, traditional IVF insemination method, GnLH was 1.19pg/mL, GnP was 0.2 pg/mL; she wanted to know the probability of taking a baby home after a course of IVF treatment. According to the Nomogram model, the score of these factors was 74.5、78、89、73、77.5 and 79, and the total score was 471. So, the chance of having a live birth was 60.1% in the first year of the treatment, 62.8% in the second year, and 62.9% in the third year (Fig. 5 A). The use of this model can provide a more personalized analysis for pregnancy evaluation for patients. In our study, the approach used was to model the Cox regression for variables that met the PH assumptions. The confidence degree of the statistical analysss methods was higher when compared to univariate or multivariate regression analysis. Though the efficacy and specificity of the Nomogram model were validated internally with a high level of success, this study was a retrospective analysis of a single-center, which was limited by sample size. In the future, a large-scale multicenter study should be conducted to verify the efficacy. Try to collect more samples and their characteristics, and use big data mining technology such as machine learning to establish a prediction model to improve the accuracy and generalization of the model. Concussions In conclusion, the potential predictive factors of live birth were insemination method, infertility factor, GnP level (pg/mL), GnLH level (pg/mL) and BMI. The new model could effectively predict the probability of infertile couples having a live birth. In addition, this model could also support clinicians making clinical decisions and providing guidance for patients. Abbreviations BMI: body mass index; AFC: antral follicle count; GnRH: gonadotropin releasing hormone b-FSH: basal follicle-stimulating hormone levels; GnP、GnE2、GnFSH、GnLH: serum progesterone level、E2 level、follicle-stimulating hormone level and luteinizing hormone level on the day initiated with Gn; tP、tE2、tLH: serum P level、E2 level and LH level on the day of trigger; MII: metaphase II; 2PN zygotes: 2 pronucleus zygotes; IVF: In vitro fertilization; LBR: live birth rate; CLBR: cumulative LBR; FET: frozen embryo transfer; ET: embryo transfer; ART: assisted reproductive technology; CI: Confdential interval; HR = Hazards Risk; PH test: Proportional Hazards test; Declarations Ethics approval and consent to participate This project was approved by the Institutional Review Board of the Shenzhen Zhongshan Urology Hospital on 15 February 2021 (FM-YXLL-026). Patient informed consent was waived by the Ethics Committee of Shenzhen Zhongshan Urology Hospital as the study used data from the electronic medical record system. This study is in compliance with the Helsinki declaration, relevant guidelines, and regulations. Consent for publication Not applicable. Availability of data and materials The dataset used in the current study provided by Shenzhen Zhongshan Urology Hospital is not publicly available, due to reasonable privacy and security concerns. Please contact the corresponding author to obtain access to a de-identified version of the data that supports the findings of this study through a data-use agreement. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by National Key Research & Developmental Program of China (2018YFC1003900/2018YFC1003904). Authors’ contributions XK and ZL conceived and wrote the paper. XH carried out the data collection. ZL and CH cleared and analyzed the data. CH and MM revised the manuscript. HZ and YZ designed and guided the study. All authors reviewed the results and approved the final version of the manuscript. Acknowledgments We thank all clinicians, embryologists, scientists, and researchers involved in this study for the ART treatment and data collection. And we thank all the patients for the contribution to the study. References Boivin J, Bunting L, Collins JA, Nygren KG. International estimates of infertility prevalence and treatment-seeking: potential need and demand for infertility medical care. Hum Reprod. 2007;22(6):1506–12. Dhalwani N, Fiaschi L, West J, Tata L. Occurrence of fertility problems presenting to primary care: population-level estimates of clinical burden and socioeconomic inequalities across the UK. Human reproduction (Oxford, England). 2013;28(4):960–8. Polis C, Cox C, Tunçalp Ö, McLain A, Thoma M. Estimating infertility prevalence in low-to-middle-income countries: an application of a current duration approach to Demographic and Health Survey data. Human reproduction (Oxford, England). 2017;32(5):1064-74. Adamson G, de Mouzon J, Chambers G, Zegers-Hochschild F, Mansour R, Ishihara O et al. International Committee for Monitoring Assisted Reproductive Technology: world report on assisted reproductive technology, 2011. Fertility and sterility. 2018;110(6):1067-80. Banker M, Dyer S, Chambers G, Ishihara O, Kupka M, de Mouzon J et al. International Committee for Monitoring Assisted Reproductive Technologies (ICMART): world report on assisted reproductive technologies, 2013. Fertility and sterility. 2021. Maheshwari A, McLernon D, Bhattacharya S. Cumulative live birth rate: time for a consensus? Human reproduction. (Oxford England). 2015;30(12):2703–7. Paul R, Fitzgerald O, Lieberman D, Venetis C, Chambers G. Cumulative live birth rates for women returning to ART treatment for a second ART-conceived child. Hum Reprod (Oxford England). 2020;35(6):1432–40. Collins J, Burrows E, Wilan A. The prognosis for live birth among untreated infertile couples. Fertil Steril. 1995;64(1):22–8. Templeton A, Morris J, Parslow W. Factors that affect outcome of in-vitro fertilisation treatment. Lancet (London England). 1996;348(9039):1402–6. McLernon D, Steyerberg E, Te Velde E, Lee A, Bhattacharya S. Predicting the chances of a live birth after one or more complete cycles of in vitro fertilisation: population based study of linked cycle data from 113 873 women. BMJ (Clinical research ed). 2016;355:i5735. Leijdekkers J, Eijkemans M, van Tilborg T, Oudshoorn S, McLernon D, Bhattacharya S, et al. Predicting the cumulative chance of live birth over multiple complete cycles of in vitro fertilization: an external validation study. Hum Reprod (Oxford England). 2018;33(9):1684–95. Benoit L, Boujenah J, Poncelet C, Grynberg M, Carbillon L, Nyangoh Timoh K, et al. Predicting the likelihood of a live birth for women with endometriosis-related infertility. Eur J Obstet Gynecol Reprod Biol. 2019;242:56–62. van Loendersloot L, Repping S, Bossuyt P, van der Veen F, van Wely M. Prediction models in in vitro fertilization; where are we? A mini review. J Adv Res. 2014;5(3):295–301. Jayaprakasan K, Deb S, Batcha M, Hopkisson J, Johnson I, Campbell B, et al. The cohort of antral follicles measuring 2–6 mm reflects the quantitative status of ovarian reserve as assessed by serum levels of anti-Müllerian hormone and response to controlled ovarian stimulation. Fertil Steril. 2010;94(5):1775–81. Jamil Z, Fatima S, Cheema Z, Baig S, Choudhary R. Assessment of ovarian reserve: Anti-Mullerian hormone versus follicle stimulating hormone. J Res Med sciences: official J Isfahan Univ Med Sci. 2016;21:100. La Marca A, Argento C, Sighinolfi G, Grisendi V, Carbone M, D'Ippolito G, et al. Possibilities and limits of ovarian reserve testing in ART. Curr Pharm Biotechnol. 2012;13(3):398–408. Jayaprakasan K, Campbell B, Hopkisson J, Johnson I, Raine-Fenning N. A prospective, comparative analysis of anti-Müllerian hormone, inhibin-B, and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation. Fertil Steril. 2010;93(3):855–64. Paul R, Fitzgerald O, Lieberman D, Venetis C, Chambers G. Cumulative live birth rates for women returning to ART treatment for a second ART-conceived child. Hum Reprod (Oxford England). 2020;35(6):1432–40. Smith A, Tilling K, Nelson S, Lawlor D. Live-Birth Rate Associated With Repeat In Vitro Fertilization Treatment Cycles. JAMA. 2015;314(24):2654–62. Li Y, Li X, Yang X, Cai S, Lu G, Lin G, et al. in vitroCumulative Live Birth Rates in Low Prognosis Patients According to the POSEIDON Criteria: An Analysis of 26,697 Cycles of Fertilization/Intracytoplasmic Sperm Injection. Front Endocrinol. 2019;10:642. Luke B, Brown M, Wantman E, Lederman A, Gibbons W, Schattman G, et al. Cumulative birth rates with linked assisted reproductive technology cycles. N Engl J Med. 2012;366(26):2483–91. Hsieh F, Lavori P. Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. Control Clin Trials. 2000;21(6):552–60. Schoenfeld D. Sample-size formula for the proportional-hazards regression model. Biometrics. 1983;39(2):499–503. Abdalla H, Thum M. An elevated basal FSH reflects a quantitative rather than qualitative decline of the ovarian reserve. Hum Reprod (Oxford England). 2004;19(4):893–8. Balachandren N, Salman M, Diu N, Schwab S, Rajah K, Mavrelos D. Ovarian reserve as a predictor of cumulative live birth. Eur J Obstet Gynecol Reprod Biol. 2020;252:273–7. Sermondade N, Huberlant S, Bourhis-Lefebvre V, Arbo E, Gallot V, Colombani M, et al. Female obesity is negatively associated with live birth rate following IVF: a systematic review and meta-analysis. Hum Reprod Update. 2019;25(4):439–51. Bellver J, Ayllón Y, Ferrando M, Melo M, Goyri E, Pellicer A, et al. Female obesity impairs in vitro fertilization outcome without affecting embryo quality. Fertil Steril. 2010;93(2):447–54. Xue X, Shi W, Zhou H, Tian L, Zhao Z, Zhou D, et al. in vitroCumulative Live Birth Rates According to Maternal Body Mass Index After First Ovarian Stimulation for Fertilization: A Single Center Analysis of 14,782 Patients. Front Endocrinol. 2020;11:149. Metwally M, Cutting R, Tipton A, Skull J, Ledger W, Li T. Effect of increased body mass index on oocyte and embryo quality in IVF patients. Reprod Biomed Online. 2007;15(5):532–8. Depalo R, Garruti G, Totaro I, Panzarino M, Vacca M, Giorgino F, et al. Oocyte morphological abnormalities in overweight women undergoing in vitro fertilization cycles. Gynecol endocrinology: official J Int Soc Gynecol Endocrinol. 2011;27(11):880–4. Comstock I, Diaz-Gimeno P, Cabanillas S, Bellver J, Sebastian-Leon P, Shah M, et al. Does an increased body mass index affect endometrial gene expression patterns in infertile patients? A functional genomics analysis. Fertil Steril. 2017;107(3):740–8e2. La Marca A, Sighinolfi G, Radi D, Argento C, Baraldi E, Artenisio AC, et al. Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART). Hum Reprod Update. 2010;16(2):113–30. Arce J, La Marca A, Mirner Klein B, Nyboe Andersen A, Fleming R. Antimüllerian hormone in gonadotropin releasing-hormone antagonist cycles: prediction of ovarian response and cumulative treatment outcome in good-prognosis patients. Fertil Steril. 2013;99(6):1644–53. Xu H, Shi L, Feng G, Xiao Z, Chen L, Li R, et al. An Ovarian Reserve Assessment Model Based on Anti-Müllerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study. J Med Internet Res. 2020;22(9):e19096. Yarde F, Voorhuis M, Dólleman M, Knauff E, Eijkemans M, Broekmans F. Antimüllerian hormone as predictor of reproductive outcome in subfertile women with elevated basal follicle-stimulating hormone levels: a follow-up study. Fertil Steril. 2013;100(3):831–8. Lukaszuk K, Liss J, Kunicki M, Jakiel G, Wasniewski T, Woclawek-Potocka I, et al. Anti-Müllerian hormone (AMH) is a strong predictor of live birth in women undergoing assisted reproductive technology. Reprod Biol. 2014;14(3):176–81. Tal R, Seifer DB, Tal R, Granger E, Wantman E, Tal O. AMH Highly Correlates with Cumulative Live Birth Rate in Women with Diminished Ovarian Reserve Independent of Age. J Clin Endocrinol Metab. 2021. Iliodromiti S, Kelsey T, Wu O, Anderson R, Nelson S. The predictive accuracy of anti-Müllerian hormone for live birth after assisted conception: a systematic review and meta-analysis of the literature. Hum Reprod Update. 2014;20(4):560–70. Kuitunen I, Ponkilainen V, Uimonen M, Eskelinen A, Reito A. Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review. BMC Musculoskelet Disord. 2021;22(1):489. Walschaerts M, Bujan L, Isus F, Parinaud J, Mieusset R, Thonneau P. Cumulative parenthood rates in 1735 couples: impact of male factor infertility. Hum Reprod (Oxford England). 2012;27(4):1184–90. Imani B, Eijkemans M, te Velde E, Habbema J, Fauser B. A nomogram to predict the probability of live birth after clomiphene citrate induction of ovulation in normogonadotropic oligoamenorrheic infertility. Fertil Steril. 2002;77(1):91–7. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 04 Oct, 2024 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 03 Jun, 2024 Reviewers agreed at journal 16 Oct, 2023 Reviews received at journal 29 Sep, 2023 Reviewers agreed at journal 29 Sep, 2023 Reviewers invited by journal 26 Sep, 2023 Editor assigned by journal 26 Sep, 2023 Editor invited by journal 27 Jun, 2023 Submission checks completed at journal 27 Jun, 2023 First submitted to journal 11 Jun, 2023 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. 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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-3048402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":213510926,"identity":"8f0839be-c096-477d-b475-083a5f54e307","order_by":0,"name":"Xiangyi Kong","email":"","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiangyi","middleName":"","lastName":"Kong","suffix":""},{"id":213510927,"identity":"4c86ab3b-e19a-4ea7-91f6-746082da6ab9","order_by":1,"name":"Zhiqiang Liu","email":"","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Liu","suffix":""},{"id":213510928,"identity":"166fd266-da57-47ef-9ecb-849bc43a7b74","order_by":2,"name":"Chunyu Huang","email":"","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Huang","suffix":""},{"id":213510929,"identity":"634449f4-0813-498d-b283-34fb6e82d8c5","order_by":3,"name":"Xiuyu Hu","email":"","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiuyu","middleName":"","lastName":"Hu","suffix":""},{"id":213510930,"identity":"d7e12d07-23e3-4430-a455-de6444cf4fc1","order_by":4,"name":"Meilan Mo","email":"","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meilan","middleName":"","lastName":"Mo","suffix":""},{"id":213510931,"identity":"55390aa2-3e83-4185-90a4-79c13f6a8eec","order_by":5,"name":"Hongzhan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACNijNw8DMfODAhx+kaWFLPDizhzQLeYwPc7ARVsbAJ3342IOPbTYyBsd5PhwGWibPL3aAgMP40tINZ7al8Rgc5t1wuMCCwXDm7AQCWnh4zKR5tx2GaJnBw5BgcJugFv5v0n+3/Qdq4XlwmIeNKC08bNKM2w6AtDAQq4XNTLL3XzKP5GE2A2AgSxD2i3wP8zOJH2fs7PnOH3784cMPG3l+aQJa4EDhAJiSIFI52LoGEhSPglEwCkbByAIA4Lc+So8b75cAAAAASUVORK5CYII=","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hongzhan","middleName":"","lastName":"Zhang","suffix":""},{"id":213510932,"identity":"0aa4be5d-417a-4dbb-a94b-5ba7e1b7173a","order_by":6,"name":"Yong Zeng","email":"","orcid":"","institution":"Reproductive Center of Shenzhen Zhongshan Urology Hospital, Shenzhen, Guangdong Province, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2023-06-11 06:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3048402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3048402/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-024-07017-6","type":"published","date":"2025-01-30T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39330017,"identity":"1e214fa9-f7cd-4823-9975-f8e28423dedb","added_by":"auto","created_at":"2023-06-29 21:16:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":309019,"visible":true,"origin":"","legend":"\u003cp\u003eThe data analysis flowchart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/7a1fda6bc463107f5134f617.png"},{"id":39330849,"identity":"2426e389-774d-4ca9-8323-ae3eb819845f","added_by":"auto","created_at":"2023-06-29 21:24:50","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34042,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve for the treatment period of the patient. (A) Cumulative live birth rate of patient embryo transfer cycles; (B) Cumulative live birth rate per month for the patient\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/402e2820918cc41ca561da38.jpg"},{"id":39330015,"identity":"8f1f02fc-d146-4d42-b05c-cf6d29672610","added_by":"auto","created_at":"2023-06-29 21:16:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42518,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive factors for categorical variables of live birth in PH test. The Kaplan Meier curve and the relation graph of ln [-ln(S ̂(t)) ] for (A) infertility factor and (B) insemination method. There was no cross among the curves which meant these two factors satisfied the PH test\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/41a57c6c0123a718a4217e72.jpg"},{"id":39330013,"identity":"8151dfde-f977-4de0-b1c4-0c429f824e9b","added_by":"auto","created_at":"2023-06-29 21:16:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29750,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the CLBR predictive model in the training and validation data.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/96dcc08007347fec519d2e80.jpg"},{"id":39330850,"identity":"18a772cc-2e74-4471-92a5-ec9cfd28b000","added_by":"auto","created_at":"2023-06-29 21:24:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61923,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram model of various predictive factors for CLBR of training and validation data. (A) The nomogram model of predictive factors and live birth, (B) showed the calibration curve of 1 year、2 years and 3 years to evaluate the accuracy of the model in the training data, (C) Showed the calibration curve of 1 year、2 years and 3 years to evaluate the accuracy of the model in the validation data\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/6e304a41a6f5829dff12936f.png"},{"id":75351353,"identity":"3b5eb710-2355-4015-b2b7-b562479494d8","added_by":"auto","created_at":"2025-02-03 16:10:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1794366,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/2e44e68e-d7ba-4e50-94cc-e5211e598f65.pdf"},{"id":39330851,"identity":"5d448afa-78fe-4c5f-89dd-1ebe259187e5","added_by":"auto","created_at":"2023-06-29 21:24:50","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17401,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3048402/v1/3f95ab1eb55d32697d4debd1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"How to estimate the probability of a live birth after one or more complete IVF cycles？The development of a novel model in a single-center","fulltext":[{"header":"Background","content":"\u003cp\u003eInfertility has become a global public health tissue, and the prevalence of infertility has increased gradually, from 9\u0026ndash;26%, and even to 31.1% in low-to-middle-income countries(\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In vitro fertilization (IVF) has become an effective method for infertile patients to fulfill their fertility wishes. Globally, an estimated 7\u0026nbsp;million children have been born as a result of IVF since 1978(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e),and in the year 2013, almost 1,160,474 embryo transfers (ETs) were performed resulting in \u0026gt;\u0026thinsp;344,317 babies(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). As for patients who came to an infertility center for the first time, their most important question was:\u0026ldquo;what is the probability of taking a baby home?\u0026rdquo; There are many indicators to evaluate the outcome of IVF, and the most efficient indicator is cumulative live birth rate (CLBR) after complete cycles, which is defined as all fresh and frozen-thawed embryos transfer (FET) attempts resulting from one episode of ovarian stimulation(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany scholars have studied different factors that might have an effect on live birth outcomes. In 1995, Collins firstly studied the influence of pregnancy history, duration of infertility and female partner's age on live birth, and proposed a negative correlation between age and live birth. The prediction score based on these factors would be accurate in approximately 62% of cases. While the subjects of the study were untreated infertile couples, the estimation of live birth among them was sufficiently accurate to be useful in the clinical management of infertility and their future planning.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In 1996, Templeton et al. studied a large-scale retrospective analysis by using logistic regression to explore the influence of age on cumulative live birth; they pointed out that the highest live birth rate was in patients aged 25\u0026ndash;30 years Moreover, there was a significant decrease in age-adjusted live birth rates with increasing duration of infertility between 1 and 12 years(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This research was a major leap forward, because few pervious studies had taken account of these factors before. In 2016, McLernon model has been established, which could provide an individualized estimate of a couple\u0026rsquo;s cumulative chances of having a baby over a complete package of IVF both before treatment and after the first fresh embryo transfer(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This discrete time logistic regression model would help to shape couples\u0026rsquo; expectations allowing them to plan their treatment more efficiently and to prepare emotionally and financially. A subsequent study in a different geographical context has been conducted to verify and confirm its effectiveness(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recently, a Nomogram predictive model by using multivariable linear regression based on a retrospective database was built to estimate the likelihood of a live birth after surgery followed by assisted reproductive technology (ART) for endometriosis-related infertility. Though the AUC of the Nomogram model was 0.77, the sample was small(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Laura van Loendersloot provided a significant contribution in building prediction models in IVF. She proposed three phases of prediction model development and provided key principles to assess the effectiveness of different prediction models(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany previous studies have used different predictors to explore the effect of each factor on live birth and to build predictive models. We utilized a more rigorous variable screening approach for different divisions of patient infertility factors to create a new predictive model to assess the reproductive potential of patients at our center after transfer of all fresh and subsequently frozen-thawed embryos in a full cycle of treatment. Furthermore, we also wanted to verify whether this model could help clinicians make clinical decisions and provide guidance for patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Patients\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted in the IVF clinical center of Shenzhen Zhongshan Urology Hospital. From January 2012 to April 2015, a total of 4413 patients who completed ovarian stimulation treatment and a fresh cycle with subsequent FET were involved in this study. This project was approved by the Institutional Review Board of the Shenzhen Zhongshan Urology Hospital on 15 February 2021 (FM-YXLL-026).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003eThe inclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) women who were diagnosed with infertility; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) women aged 18\u0026ndash;45 years; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) women who had undergone their first cycle of IVF or ICSI; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) women with a retrieved number of oocytes\u0026thinsp;\u0026gt;\u0026thinsp;0. The exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) women who undergone preimplantation genetic testing (PGT);(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) reproductive malformation or intrauterine adhesions; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) untreated unilateral or bilateral hydrosalpinx; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) recurrent pregnancy loss or repeated implantation failure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTreatment Protocols\u003c/h2\u003e \u003cp\u003eAll patients underwent a short luteal gonadotropin releasing hormone (GnRH) protocol. Injections of 0.1mg of GnRH agonist (Triptorelin, Ferring, Germany) were administered daily from day 21 of the patient\u0026rsquo;s menstrual cycle (or 16 days after the start of an oral contraceptive). Controlled ovarian stimulation was achieved using a daily dose of gonadotropin (150\u0026ndash;300 UI/d), recombinant follicle-stimulating hormone (rFSH; Gonal-F, Merck-Serono, Germany), or human menopausal gonadotropin (hMG; Menopur, China) ) after confirmation of pituitary down-regulation (level of serum estradiol (E2)\u0026thinsp;\u0026lt;\u0026thinsp;183.5pmol/L, with no ovarian cysts and the diameter of the largest follicle was \u0026lt;\u0026thinsp;8mm). The dosage of rFSH was adjusted according to female age, ovarian response: antral follicle count (AFC), which was assessed by ultrasound、serum hormone、anti-mullerian hormone (AMH, ng/ml, measured by using the chemical luminescence method (Yahuilong Company, Shenzhen, China)) and body mass index (BMI, kg/m2)(\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). When a mean diameter of two follicles reached\u0026thinsp;\u0026gt;\u0026thinsp;18mm, human chorionic gonadotropin (hCG; LiZhu, Zhuhai, China) was given, and oocyte retrieval was performed 36 h later.\u003c/p\u003e \u003cp\u003eFresh embryo transfer was performed on Day 3 (cleavage embryos) or Day 5 (blastocysts). Subsequent FET was performed on Day 3 (cleavage embryos) or Day 5 (blastocysts) after ovulation in a natural cycle. In a hormone replacement cycle, by using oral estradiol 4\u0026ndash;8 mg per day (estradiol valerate tablets; Progynova, Bayer, France) for 12 to 15 d and intramuscular progesterone (60 mg/d) for 4 or 6 days, the FET was performed on Day 4 (cleavage embryos) or Day 6 (blastocysts) after progesterone administration The number of embryos transferred varied from one to three based on the recommendation of the Health Ministry of China and the requests of patients. Luteal support was performed using dydrogesterone tablets 20 mg twice a day (Abbott, America) plus vaginal or anal progesterone (90 mg/d, Merck-Serono, Germany, or 200 mg, three times a day; Cyndea Pharma, S.L).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBasal Characteristics Data Collection\u003c/h2\u003e \u003cp\u003eAll the data were obtained from the electrical medical record system of IVF in the fertility center of Shenzhen Zhongshan Urology Hospital. The following baseline characteristics were recorded and analyzed: female age (years), duration of infertility (years), infertility factors (ovulation disorder, endometriosis, tubal factor, male factor, couple factors(the cause of infertility is mainly by both female and male factors), unexplained factor), body mass index (BMI, kg/m2), antral follicle count (AFC), basal follicle-stimulating hormone (b-FSH) levels (pg/mL), initiated gonadotropin (Gn) dosage, Gn days, serum progesterone (P) level (pg/mL)、E2 level (pg/mL)、follicle-stimulating hormone (FSH) level (pg/mL) and luteinizing hormone (LH) level (pg/mL) on the day initiated with Gn (GnP、GnE2、GnFSH、GnLH), serum P level (pg/mL)、E2 level (pg/mL) and LH level (pg/mL) on the day of trigger (tP、tE2、tLH), number of follicles on the day of HCG trigger, insemination method (IVF/ICSI), number of retrieved oocytes, metaphase II (MII), fertilized oocytes, two pronucleus zygotes (2PN zygotes), good quality embryos and transferred embryos.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eReproductive Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was cumulative live birth rate. Live birth was defined as a neonate showing any sign of life, irrespective of gestational age, as defined by the World Health Organization (WHO). CLBR within one complete IVF/ICSI treatment cycle was defined as the probability of a live birth from an ovarian stimulation, including all embryo transfers (fresh and frozen) from that stimulation(\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The second outcome was fresh live birth. The optimal estimate is based on the observed data and assumes that the CLBR in women who discontinue ART treatment without a live birth would be equal to the rate in those who continue. The conservative estimate assumes that those who did not continue the ART treatment would not have a live birth(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS v. 26.0 and R v. 4.0.3. The basal characteristics of patients and stimulation cycles are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e according to live birth or not. Normally distributed data were expressed as mean\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003eSD, whereas skewed distributed data were expressed as median (interquartile range). Categorical variables were shown as \u003cem\u003en\u003c/em\u003e and were compared using the chi-square test. Comparison of means of continuous variables was performed using the Mann\u0026ndash;Whitney U-test or Student\u0026rsquo;s t-test depending on data distribution. Categorical variables were plotted using the graphical method of Kaplan-Meier survival curves grouped by that variable, or by plotting ln[-ln(S ̂(t)) against survival time \u003cem\u003et\u003c/em\u003e grouped by that variable, and observing whether the curves crossed to determine whether the PH assumption was satisfied. Continuous variables were tested for the PH assumption of the Cox regression model using the Schoenfeld residual method, and if the PH assumption held, the biased residuals were plotted (Schoenfeld residuals) and estimated for the Cox model, which should fluctuate above or below zero level with time.\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\u003eCharacteristics of patients and stimulation cycles were described according to live birth or not\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno live birth(N\u0026thinsp;=\u0026thinsp;1691)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elive birth(N\u0026thinsp;=\u0026thinsp;2722)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e 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 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.00 (29.00,36.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.00 (28.00,33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (2.00,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.96 (19.33,23.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.70 (19.20,22.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.00 (6.00,10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (8.00,12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility factors\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1038(37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1729(62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e815 (37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1366 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182 (55.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvulation disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181 (69.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249(35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e445(64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e402(42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e546(57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb-FSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.38 (4.55,6.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.34 (4.54,6.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGn P(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10 (0.10,0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.10,0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnLH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.45,0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61 (0.46,0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnE\u003csub\u003e2\u003c/sub\u003e(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00 (10.00,15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.00 (10.00,14.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGn FSH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.53 (2.05,3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36 (1.93,2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.40,0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.40,0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etLH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.50,0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.48,0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etE\u003csub\u003e2\u003c/sub\u003e(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2105.00 (1417.00,2908.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2700.00 (1910.00,3597.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of follicles on the day of HCG trigger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.00 (6.00,10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.00 (8.00,11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitiated Gn dosage (75/IU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.00 (3.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00,4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGn days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 (8.00,10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.00 (8.00,10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsemination method\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1562(38.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2512(61.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129(38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210(61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.00 (7.00,15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.00 (11.00,19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of MII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.00 (6.00,13.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.00 (10.00,17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of fertilized oocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.00 (5.00,12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.00 (8.00,16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of 2PN zygotes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00 (3.00,8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.00 (5.00,12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of transferred embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.00 (2.00,6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.00 (5.00,9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of good quality embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.00 (1.00,5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.00 (4.00,9.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSample Stratified and Model Building\u003c/h2\u003e \u003cp\u003eAccording to the pre-experimental data, the incidence of successful pregnancy was 61.68%, the standard deviation of the variable was 1.204, the expected log-hazard ratio was 0.185 (lnHR\u0026thinsp;=\u0026thinsp;0.185), and the coefficient of determination was 0.047. Under the condition of a significance level of 0.05 and test power of 0.90, the sample size was calculated using PASS, and the estimated sample size was 361(22.23). In order to verify the rationality of sampling among variables of the training set and the validation set, we conducted stratified sampling for the whole sample (n\u0026thinsp;=\u0026thinsp;4413) according to live birth, no birth and loss of follow-up. 70% of the subjects were randomly placed into a training set (n\u0026thinsp;=\u0026thinsp;3089) and 30% of the subjects were placed into a validation set (n\u0026thinsp;=\u0026thinsp;1324).\u003c/p\u003e \u003cp\u003eA Cox regression model was built on the basis of the training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Finally, the validation set was used to verify the validity of the model.\u003c/p\u003e \u003cp\u003eTo assess the possibility of CLBR more intuitively for clinicians, we established a Nomogram model on the basis of various clinical factors that might affect CLB. Calculating the goals of the model, the clinicians might predict the probability for patients to obtain at least one live birth within 1 to 3 years.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 4413 patients who had undergone their first cycle of IVF/ICSI in the fertility center of Shenzhen Zhongshan Urology Hospital were involved in this study. The data analysis flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the cumulative live birth rate among different embryo transfer cycles and the relationship among them. In the fresh embryo transfer cycles, the live birth rate was 38.7%. In the first to fifth FET cycles, the optimal estimate and conservative estimate CLBR were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively.\u003c/p\u003e \u003cp\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\u003eCumulative live birth rates of different cycles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCycles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of cycles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCycles of live birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCycles of no birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCycles of loss of follow-up\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLive birth rate of the cycle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eOptimistic estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eConservative estimate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCycles of Correction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLive birth rate of the cycle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCumulative live birth rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCycles of Correction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLive birth rate of the cycle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCumulative live birth rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e38.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e38.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e38.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFET1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e59.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e56.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFET2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e65.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e60.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFET3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e61.50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFET4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e61.66%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFET5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e61.68%\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBasal Characteristics of the Training and Validation Sets\u003c/h2\u003e \u003cp\u003eThe basal characteristics of patients and cycles between the training set and the validation set are listed in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. There was no difference among the data of two cohorts, which indicated that stratified sampling based on live birth, no birth, and lost follow-up was reasonable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients between training set and validation set(median and interquartile ranges). 70% of the samples were divided into training set (N\u0026thinsp;=\u0026thinsp;3089) and 30% of the samples were divided into validation set (N\u0026thinsp;=\u0026thinsp;1324).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set (N\u0026thinsp;=\u0026thinsp;3089)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set (N\u0026thinsp;=\u0026thinsp;1324)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e 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 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.382 (28.000,34.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.520 (29.000,34.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.779 (2.000,5.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.857 (2.000,5.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.267 (19.230,22.865)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.165 (19.343,22.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\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\u003e10.030 (8.000,10.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.040 (8.000,10.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility factors\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1907 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e860 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1512 (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e669 (30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvulation disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184 (71.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507 (73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e672 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb-FSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.683 (4.550,6.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.708 (4.533,6.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.976\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of cycles between training set and validation set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTraining set (N\u0026thinsp;=\u0026thinsp;3089)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValidation set (N\u0026thinsp;=\u0026thinsp;1324)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eGnP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.164 (0.100,0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1635 (0.1000,0.2000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnLH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.679 (0.460,0.810)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.657 (0.460,0.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnE\u003csub\u003e2\u003c/sub\u003e (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.790 (10.000,14.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.951 (10.000,14.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnFSH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.515 (1.970,2.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.520 (1.970,3.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.668 (0.400,0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.663 (0.400,0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etLH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.758 (0.480,0.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.773 (0.490,0.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etE\u003csub\u003e2\u003c/sub\u003e (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2690.370 (1704.000,3381.5000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2681.920 (1684.750,3344.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of follicles on the day of HCG trigger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.973 (7.000,11.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.938 (7.000,11.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitiated Gn dosage (75/IU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.135 (2.000,4.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.155 (2.000,4.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGn days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.407 (8.000,10.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.412 (8.000,10.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsemination method\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2859 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1215 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230 (67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.809 (9.000,18.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.785 (9.000,18.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of MII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.519 (8.000,16.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.516 (8.000,16.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of fertilized oocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.257 (7.000,15.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.252 (7.000,15.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of 2PN zygotes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.706 (4.000,10.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.619 (4.000,10.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of transferred embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.092 (3.000,8.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.074 (3.000,8.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of good quality embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.549 (2.000,8.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.496 (2.000,7.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Building\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003ePH test and Cox regression model\u003c/h2\u003e \u003cp\u003eThe PH test is a test of whether the effect of the covariates on the live birth rate changes over time; in other words, it is a test of whether the risk ratio h(t)/h0(t) is fixed.\u003c/p\u003e \u003cp\u003eBased on the results of the PH test, the potential predictive factors of live birth were insemination method, infertility factor, GnP level (pg/mL, R\u0026thinsp;=\u0026thinsp;0.043, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059) and GnLH level (pg/mL, R\u0026thinsp;=\u0026thinsp;0.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.499), basal FSH (R = -0.042, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.069) and BMI (R = -0.035, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.123), which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive factors for continuous variables of live birth in PH test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-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 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.123**\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb-FSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.069**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnLH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.499**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnE\u003csub\u003e2\u003c/sub\u003e(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnFSH(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etLH (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of follicleson the day of HCG trigger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitiated Gn dosage (75/IU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGn days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of 2PN zygotes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of transferred embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of good quality embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* R means the correlation coefficient between Schoenfeld residuals estimated by Cox model and time rank\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e** P-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, means Schoenfeld residual is considered to have a wireless relationship with time rank, which satisfies the pH assumption. The variables that finally meet the pH assumption including GnP(pg/mL)、GnLH(pg/mL), basal FSH and BMI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the results of the PH test, the Cox regression model was built. The relationship of predictive factors and live births is shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Considering the statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) data, BMI and b-FSH levels increased as the live birth rate decreased, with the HR for b-FSH and BMI being 0.970 (95% CI: 0.945\u0026ndash;0.996, p\u0026thinsp;=\u0026thinsp;0.025) and 0.982 (95% CI: 0.966\u0026ndash;0.998, p\u0026thinsp;=\u0026thinsp;0.025), respectively. While for infertility factors, the live birth rate was 1.204-fold higher for couples with male factor than for those with unexplained infertility factor (HR\u0026thinsp;=\u0026thinsp;1.204, 95% CI: 1.036\u0026ndash;1.398; p\u0026thinsp;=\u0026thinsp;0.015).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relationship between predictive factors and live birth.\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=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnP(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnLH(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eb-FSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility factor\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\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCouple factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsemination method\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\u003eICSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01,*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eROC curve and internal verification\u003c/h2\u003e \u003cp\u003eWe used the ROC curve to test the specificity and sensitivity of the CLBR prediction model. When the maximum value of sensitivity plus specificity was 1.531, the cut-off value was 0.410 and AUC was 0.782 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, 95%CI: 0.764\u0026ndash;0.801). That is to say, the sensitivity was 0.877, specificity was 0.654, and accuracy was 0.792. Subsequently, the model was verified in the validation data. When the maximum value of sensitivity plus specificity was 1.541, the cut-off value was 0.392 and AUC was 0.801 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, 95%CI: 0.774\u0026ndash;0.828). As the sensitivity was 0.902, specificity was 0.639, and accuracy was 0.801. The ROC curve is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, when we categorized infertility factors as tubal factor, endometriosis, and ovulation disorder, the Cox regression analysis was as shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Considering the statistically significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the increasing of b- FSH, the live birth rate decreased. Furthermore, with the increase of BMI, the live birth rate decreased; meanwhile for infertility factors, the live birth rate with ovulation disorder and male factor was higher than that in couples with unexplained infertility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNomogram model\u003c/h2\u003e \u003cp\u003eA nomogram model was built based on possible factors that might influence the live birth rate of training data (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The calibration curve of 1 to 3 years to evaluate the accuracy of the model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. The closer the predictive calibration curve was to the standard curve, the better the prediction ability of the histogram. For the validation data, the calibration curve of 1 to 3 years to evaluate the accuracy of the model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have developed a Cox regression and Nomogram model to predict the probability of at least a live birth after a complete IVF cycle and its FET cycles for infertile couples. Furthermore, the model was verified effectively by inside validation. The model showed that the predictive factors of live birth were insemination method, infertility factor, GnP level, GnLH level, b- FSH and BMI.\u003c/p\u003e \u003cp\u003eIn the model, b-FSH had a negative relationship with live birth rate. It is well known that serum b-FSH is a marker for evaluating the ovarian reserve, and an elevated b-FSH reflects not only a quantitative but also qualitative decline in the ovarian reserve. The average number of oocytes collected and average number of available embryos for transfer were significantly reduced in patients with elevated FSH. Moreover, high levels of FSH predict a poor pregnancy outcome. With an increasing basal level of FSH, pregnancy rate and live birth rate were decreased (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). A predictive model to estimate the relationship between factors and live birth rate was recently built. The model shows that women delivering a live birth had a significantly lower median age and FSH and a significantly higher AMH and AFC. The authors suggested that age, FSH and AMH were independent predictors(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh BMI was considered to negatively impact live birth following IVF(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Researchers in Spain noted that live birth rates were reduced progressively with each unit of BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) with a significant odds ratio of 0.981 (95% CI: 0.967–0.995); however, the embryo quality was not affected. This might be attributed to an alteration in the uterine environment when the BMI was increased(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Nevertheless, a recent study has suggested an “inverted U shape” association between BMI and CLBR, with the turning point of BMI being 18.5 and 30.4 kg/m\u003csup\u003e2\u003c/sup\u003e. The CLBR increases in underweight women, plateaus in normal weight and overweight women, and then decreases in obese women(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). In our model, with the increase of BMI, the live birth rate decreased. The mechanism of how obesity affects the pregnancy outcome is still not clear. Some researchers suggest that obesity negatively impacts the developmental competence of oocytes and embryo quality(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e); while others consider that the altered endometrial gene expression in obese patients might contribute to lower implantation rates(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAMH is also an important predictor of ovarian reserve and response(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). One study reported that AMH was the best predictor for identifying patients with poor and high ovarian response(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). An AFA model for assessing the true ovarian reserve has also been established in China(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) which showed a positive association between AMH and CLBR after fresh and FET cycles(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Some researchers took AMH into consideration when exploring the relationship with live birth rate(\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). However, in our model, AMH was not an independent predictor for live birth. A meta-analysis considered that AMH had some association with predicting live birth rate, while the predictive accuracy was poor(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs we know, age is also a significant factor to the outcome of pregnancy; many studies and predictive models have described a negative influence on outcome(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). We found that the risk ratio h(t)/h0(t) for the age variable was not a fixed value, the influence of age on the live birth rate decreased slightly with time (R=-0. 144, p \u0026lt; 0.001). As the time of live birth went on, more patients with short time of live birth were better basic conditions, but more patients with long time of live birth were recurrent implantation failure or recurrent miscarriage, which led to the influence of age on the live birth rate could not be consistent with time, which violated the PH hypothesis. However, violating the PH hypothesis may lead to biased effect estimates in Cox regression analysis (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). We will not include the patient age into the model to obtain the unbiased effect estimator of the model.\u003c/p\u003e \u003cp\u003eIn our study, the age range of patients was wide, and some of the patients had advanced age. In order to analyze the influence of age in pregnancy, we analyzed those patients who were older than 35 years. Based on PH test results, the Cox regression model showed the same result that we described previously (Table S2). Though the chance of success of IVF in this cohort of patients was low, the model could effectively predict the pregnancy outcome. As we known, infertility is a couple’s concern, where the male factor must be taken into account. In our analysis, live birth rate was 1.204-fold higher for couples with male factor than those with unexplained infertility factor. Furthermore, a retrospective cohort study reported that more than half of couples under consultation for male infertility succeeded in having a child(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Though the subgroup Cox regression analysis showed that the live birth rate with ovulation disorder was higher than that in couples with unexplained infertility, it might not indicate that ovulation disorder was considered with live birth.\u003c/p\u003e \u003cp\u003eThe Nomogram method was used to make an earlier prediction of the probability of live birth (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) and it was more efficient and rapid at assessing the probability of having a baby. For example, a patient’s BMI was 22.58 kg/m\u003csup\u003e2\u003c/sup\u003e, b-FSH was 5.62 pg/mL, female infertility factor, traditional IVF insemination method, GnLH was 1.19pg/mL, GnP was 0.2 pg/mL; she wanted to know the probability of taking a baby home after a course of IVF treatment. According to the Nomogram model, the score of these factors was 74.5、78、89、73、77.5 and 79, and the total score was 471. So, the chance of having a live birth was 60.1% in the first year of the treatment, 62.8% in the second year, and 62.9% in the third year (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The use of this model can provide a more personalized analysis for pregnancy evaluation for patients.\u003c/p\u003e \u003cp\u003eIn our study, the approach used was to model the Cox regression for variables that met the PH assumptions. The confidence degree of the statistical analysss methods was higher when compared to univariate or multivariate regression analysis. Though the efficacy and specificity of the Nomogram model were validated internally with a high level of success, this study was a retrospective analysis of a single-center, which was limited by sample size. In the future, a large-scale multicenter study should be conducted to verify the efficacy. Try to collect more samples and their characteristics, and use big data mining technology such as machine learning to establish a prediction model to improve the accuracy and generalization of the model.\u003c/p\u003e "},{"header":"Concussions","content":"\u003cp\u003eIn conclusion, the potential predictive factors of live birth were insemination method, infertility factor, GnP level (pg/mL), GnLH level (pg/mL) and BMI. The new model could effectively predict the probability of infertile couples having a live birth. In addition, this model could also support clinicians making clinical decisions and providing guidance for patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: body mass index;\u003c/p\u003e\n\u003cp\u003eAFC: antral follicle count;\u003c/p\u003e\n\u003cp\u003eGnRH: gonadotropin releasing hormone\u003c/p\u003e\n\u003cp\u003eb-FSH: basal follicle-stimulating hormone levels;\u003c/p\u003e\n\u003cp\u003eGnP、GnE2、GnFSH、GnLH: serum progesterone level、E2 level、follicle-stimulating hormone\u0026nbsp;level and luteinizing hormone level on the day initiated with Gn;\u003c/p\u003e\n\u003cp\u003etP、tE2、tLH: serum P level、E2 level and LH level on the day of trigger;\u003c/p\u003e\n\u003cp\u003eMII: metaphase II;\u003c/p\u003e\n\u003cp\u003e2PN zygotes:\u0026nbsp;2 pronucleus zygotes;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIVF: In vitro fertilization;\u003c/p\u003e\n\u003cp\u003eLBR: live birth rate;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCLBR: cumulative LBR;\u003c/p\u003e\n\u003cp\u003eFET: frozen embryo transfer;\u003c/p\u003e\n\u003cp\u003eET: embryo transfer;\u003c/p\u003e\n\u003cp\u003eART: assisted reproductive technology;\u003c/p\u003e\n\u003cp\u003eCI: Confdential interval;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR = Hazards Risk;\u003c/p\u003e\n\u003cp\u003ePH test: Proportional Hazards test;\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was approved by the Institutional Review Board of the Shenzhen Zhongshan Urology Hospital on 15 February 2021 (FM-YXLL-026). Patient informed consent was waived by the Ethics Committee of Shenzhen Zhongshan Urology Hospital as the study used data from the electronic medical record system. This study is in compliance with the Helsinki declaration, relevant guidelines, and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used in the current study provided by Shenzhen Zhongshan Urology Hospital is not publicly available, due to reasonable privacy and security concerns. Please contact the corresponding author to obtain access to a de-identified version of the data that supports the findings of this study through a data-use agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key Research \u0026amp; Developmental Program of China (2018YFC1003900/2018YFC1003904).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXK and ZL conceived and wrote the paper. XH carried out the data collection. ZL and CH cleared and analyzed the data. CH and MM revised the manuscript. HZ and YZ designed and guided the study. All authors reviewed the results and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all clinicians, embryologists, scientists, and researchers involved in this study for the ART treatment and data collection. And we thank all the patients for the contribution to the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoivin J, Bunting L, Collins JA, Nygren KG. International estimates of infertility prevalence and treatment-seeking: potential need and demand for infertility medical care. Hum Reprod. 2007;22(6):1506\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhalwani N, Fiaschi L, West J, Tata L. Occurrence of fertility problems presenting to primary care: population-level estimates of clinical burden and socioeconomic inequalities across the UK. Human reproduction (Oxford, England). 2013;28(4):960\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolis C, Cox C, Tun\u0026ccedil;alp \u0026Ouml;, McLain A, Thoma M. Estimating infertility prevalence in low-to-middle-income countries: an application of a current duration approach to Demographic and Health Survey data. Human reproduction (Oxford, England). 2017;32(5):1064-74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdamson G, de Mouzon J, Chambers G, Zegers-Hochschild F, Mansour R, Ishihara O et al. International Committee for Monitoring Assisted Reproductive Technology: world report on assisted reproductive technology, 2011. Fertility and sterility. 2018;110(6):1067-80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanker M, Dyer S, Chambers G, Ishihara O, Kupka M, de Mouzon J et al. International Committee for Monitoring Assisted Reproductive Technologies (ICMART): world report on assisted reproductive technologies, 2013. Fertility and sterility. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaheshwari A, McLernon D, Bhattacharya S. Cumulative live birth rate: time for a consensus? Human reproduction. (Oxford England). 2015;30(12):2703\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul R, Fitzgerald O, Lieberman D, Venetis C, Chambers G. Cumulative live birth rates for women returning to ART treatment for a second ART-conceived child. Hum Reprod (Oxford England). 2020;35(6):1432\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins J, Burrows E, Wilan A. The prognosis for live birth among untreated infertile couples. Fertil Steril. 1995;64(1):22\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTempleton A, Morris J, Parslow W. 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Assessment of ovarian reserve: Anti-Mullerian hormone versus follicle stimulating hormone. J Res Med sciences: official J Isfahan Univ Med Sci. 2016;21:100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Marca A, Argento C, Sighinolfi G, Grisendi V, Carbone M, D'Ippolito G, et al. Possibilities and limits of ovarian reserve testing in ART. Curr Pharm Biotechnol. 2012;13(3):398\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayaprakasan K, Campbell B, Hopkisson J, Johnson I, Raine-Fenning N. A prospective, comparative analysis of anti-M\u0026uuml;llerian hormone, inhibin-B, and three-dimensional ultrasound determinants of ovarian reserve in the prediction of poor response to controlled ovarian stimulation. Fertil Steril. 2010;93(3):855\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul R, Fitzgerald O, Lieberman D, Venetis C, Chambers G. Cumulative live birth rates for women returning to ART treatment for a second ART-conceived child. Hum Reprod (Oxford England). 2020;35(6):1432\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith A, Tilling K, Nelson S, Lawlor D. Live-Birth Rate Associated With Repeat In Vitro Fertilization Treatment Cycles. JAMA. 2015;314(24):2654\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Li X, Yang X, Cai S, Lu G, Lin G, et al. in vitroCumulative Live Birth Rates in Low Prognosis Patients According to the POSEIDON Criteria: An Analysis of 26,697 Cycles of Fertilization/Intracytoplasmic Sperm Injection. Front Endocrinol. 2019;10:642.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuke B, Brown M, Wantman E, Lederman A, Gibbons W, Schattman G, et al. Cumulative birth rates with linked assisted reproductive technology cycles. N Engl J Med. 2012;366(26):2483\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh F, Lavori P. Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. Control Clin Trials. 2000;21(6):552\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoenfeld D. Sample-size formula for the proportional-hazards regression model. Biometrics. 1983;39(2):499\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdalla H, Thum M. An elevated basal FSH reflects a quantitative rather than qualitative decline of the ovarian reserve. Hum Reprod (Oxford England). 2004;19(4):893\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalachandren N, Salman M, Diu N, Schwab S, Rajah K, Mavrelos D. Ovarian reserve as a predictor of cumulative live birth. Eur J Obstet Gynecol Reprod Biol. 2020;252:273\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSermondade N, Huberlant S, Bourhis-Lefebvre V, Arbo E, Gallot V, Colombani M, et al. Female obesity is negatively associated with live birth rate following IVF: a systematic review and meta-analysis. Hum Reprod Update. 2019;25(4):439\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellver J, Ayll\u0026oacute;n Y, Ferrando M, Melo M, Goyri E, Pellicer A, et al. Female obesity impairs in vitro fertilization outcome without affecting embryo quality. Fertil Steril. 2010;93(2):447\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue X, Shi W, Zhou H, Tian L, Zhao Z, Zhou D, et al. in vitroCumulative Live Birth Rates According to Maternal Body Mass Index After First Ovarian Stimulation for Fertilization: A Single Center Analysis of 14,782 Patients. Front Endocrinol. 2020;11:149.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetwally M, Cutting R, Tipton A, Skull J, Ledger W, Li T. Effect of increased body mass index on oocyte and embryo quality in IVF patients. Reprod Biomed Online. 2007;15(5):532\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepalo R, Garruti G, Totaro I, Panzarino M, Vacca M, Giorgino F, et al. Oocyte morphological abnormalities in overweight women undergoing in vitro fertilization cycles. Gynecol endocrinology: official J Int Soc Gynecol Endocrinol. 2011;27(11):880\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComstock I, Diaz-Gimeno P, Cabanillas S, Bellver J, Sebastian-Leon P, Shah M, et al. Does an increased body mass index affect endometrial gene expression patterns in infertile patients? A functional genomics analysis. Fertil Steril. 2017;107(3):740\u0026ndash;8e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Marca A, Sighinolfi G, Radi D, Argento C, Baraldi E, Artenisio AC, et al. Anti-Mullerian hormone (AMH) as a predictive marker in assisted reproductive technology (ART). Hum Reprod Update. 2010;16(2):113\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArce J, La Marca A, Mirner Klein B, Nyboe Andersen A, Fleming R. Antim\u0026uuml;llerian hormone in gonadotropin releasing-hormone antagonist cycles: prediction of ovarian response and cumulative treatment outcome in good-prognosis patients. Fertil Steril. 2013;99(6):1644\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu H, Shi L, Feng G, Xiao Z, Chen L, Li R, et al. An Ovarian Reserve Assessment Model Based on Anti-M\u0026uuml;llerian Hormone Levels, Follicle-Stimulating Hormone Levels, and Age: Retrospective Cohort Study. J Med Internet Res. 2020;22(9):e19096.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYarde F, Voorhuis M, D\u0026oacute;lleman M, Knauff E, Eijkemans M, Broekmans F. Antim\u0026uuml;llerian hormone as predictor of reproductive outcome in subfertile women with elevated basal follicle-stimulating hormone levels: a follow-up study. Fertil Steril. 2013;100(3):831\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLukaszuk K, Liss J, Kunicki M, Jakiel G, Wasniewski T, Woclawek-Potocka I, et al. Anti-M\u0026uuml;llerian hormone (AMH) is a strong predictor of live birth in women undergoing assisted reproductive technology. Reprod Biol. 2014;14(3):176\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTal R, Seifer DB, Tal R, Granger E, Wantman E, Tal O. AMH Highly Correlates with Cumulative Live Birth Rate in Women with Diminished Ovarian Reserve Independent of Age. J Clin Endocrinol Metab. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIliodromiti S, Kelsey T, Wu O, Anderson R, Nelson S. The predictive accuracy of anti-M\u0026uuml;llerian hormone for live birth after assisted conception: a systematic review and meta-analysis of the literature. Hum Reprod Update. 2014;20(4):560\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuitunen I, Ponkilainen V, Uimonen M, Eskelinen A, Reito A. Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review. BMC Musculoskelet Disord. 2021;22(1):489.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalschaerts M, Bujan L, Isus F, Parinaud J, Mieusset R, Thonneau P. Cumulative parenthood rates in 1735 couples: impact of male factor infertility. Hum Reprod (Oxford England). 2012;27(4):1184\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImani B, Eijkemans M, te Velde E, Habbema J, Fauser B. A nomogram to predict the probability of live birth after clomiphene citrate induction of ovulation in normogonadotropic oligoamenorrheic infertility. Fertil Steril. 2002;77(1):91\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"In vitro Fertilization, cumulative live birth rate, Cox regression model, Nomogram model, predictive factors","lastPublishedDoi":"10.21203/rs.3.rs-3048402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3048402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo estimate the probability of a live birth for an infertile couple after one or more complete cycles of in vitro fertilization (IVF) by using a Cox regression and Nomogram model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Urology Hospital. A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. 70% of the patients were randomly placed into the training set (n\u0026thinsp;=\u0026thinsp;3089) and the remaining 30% of the patients were placed into the validation set (n\u0026thinsp;=\u0026thinsp;1324) randomly. Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for one retrieval cycle and the subsequent five frozen embryo transfer (FET) cycles. Proportional Hazards (PH) Assumption test was used for selecting the parameter in the predictive model. A Cox regression model was built based on the basis of training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Subsequently, the validation set was applied to verify the validity of the model. Finally, for a more intuitive assessment of the CLBR more intuitively for clinicians and patients, a Nomogram model was established based on predictive model. By calculating the scores of the model, the clinicians could more effectively predict the probability for an individual patient to obtain at least one live birth.\u003c/p\u003e\u003ch2\u003eResult(s):\u003c/h2\u003e \u003cp\u003eIn the fresh embryo transfer cycle, the LBR was 38.7%. In the first to fifth FET cycle, the optimal estimate and conservative estimate CLBRs were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Based on PH test results, the potential predictive factors for live birth were insemination method, infertility factors, serum progesterone level (R\u0026thinsp;=\u0026thinsp;0.043, p\u0026thinsp;=\u0026thinsp;0.059), and luteinizing hormone level (R\u0026thinsp;=\u0026thinsp;0.015, p\u0026thinsp;=\u0026thinsp;0.499) on the day initiated with gonadotropin, basal follicle-stimulating hormone (R = -0.042, p\u0026thinsp;=\u0026thinsp;0.069) and BMI (R = -0.035, p\u0026thinsp;=\u0026thinsp;0.123). We used ROC curve to test the predictive power of the model. The AUC was 0.782 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, 95% CI: 0.764\u0026ndash;0.801). Then the model was verified using the validation data. The AUC was 0.801 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, 95% CI: 0.774\u0026ndash;0.828). A Nomogram model was built based on potential predictive factors that might influence the event of a live birth.\u003c/p\u003e\u003ch2\u003eConclusion(s):\u003c/h2\u003e \u003cp\u003eThe Cox regression and Nomogram prediction models effectively predicted the probability of infertile couples having a live birth. Therefore, this model could assist clinicians with making clinical decisions and providing guidance for patients.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eN/A.\u003c/p\u003e","manuscriptTitle":"How to estimate the probability of a live birth after one or more complete IVF cycles？The development of a novel model in a single-center","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-29 21:16:45","doi":"10.21203/rs.3.rs-3048402/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-04T11:05:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-08T16:08:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258436892045021762669040032252664741530","date":"2024-08-20T08:30:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79032732700968125829741428479205108724","date":"2024-06-03T05:25:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61305ada-9a75-40db-889d-0c2f5c276cbc","date":"2023-10-16T09:35:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-29T14:17:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34f034f9-5709-473d-9cd3-f12abfdfa460","date":"2023-09-29T04:10:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-09-26T11:36:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-26T06:44:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-27T05:29:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-27T05:28:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2023-06-11T06:41:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b390e7a-941f-4e6f-b472-61ca3ebb1ea6","owner":[],"postedDate":"June 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:03:45+00:00","versionOfRecord":{"articleIdentity":"rs-3048402","link":"https://doi.org/10.1186/s12884-024-07017-6","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2025-01-30 15:58:00","publishedOnDateReadable":"January 30th, 2025"},"versionCreatedAt":"2023-06-29 21:16:45","video":"","vorDoi":"10.1186/s12884-024-07017-6","vorDoiUrl":"https://doi.org/10.1186/s12884-024-07017-6","workflowStages":[]},"version":"v1","identity":"rs-3048402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3048402","identity":"rs-3048402","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}