The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles | 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 The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles Yang Wang, Ziru Niu, Liyuan Tao, Xiaoying Zheng, Yifeng Yuan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-50551/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 2 You are reading this latest preprint version Abstract Background: To study which characteristics of a pre-oocyte-retrieval patient can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. Methods: Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Data was collected from the Reproductive Center, Peking University Third Hospital of China. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Results: The predictors in the model of ‘no embryo to transfer’ are female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH level (OR= 1.205, 95% CI=1.051-1.382, P=0.0084), basal E2 level (OR=1.006, 95% CI=1.001-1.010, P=0.012) and sperm from MESA (OR=7.741, 95% CI=2.905-20.632, P<0.001). Upon assessing predictive ability, the AUC for this model was 0.799 (95% CI: 0.722–0.875, p＜0.001). The Hosmer-Lemeshow test (p=0.721) and calibration curve showed good calibration. The predictors in the cumulative live birth were the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031). The AUC for this model was 0.724 (95% CI: 0.647–0.801, p＜0.001). The Hosmer-Lemeshow test (p=0.562) and calibration curve showed good calibration for the prediction of cumulative live birth. Conclusion: The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, basal FSH and E2 level, the number of follicles with a diameter greater than 10 mm on the day of hCG administration, endometriosis and sperm from microdissection testicular sperm extraction (MESA). Laboratory Diagnostics Nomogram Oocyte freeze-thaw IVF Figures Figure 1 Figure 2 Figure 3 Background As early as 1986, researchers had applied frozen oocytes for in vitro fertilization (IVF) and achieved a successful pregnancy [1]. With the development of intracytoplasmic sperm injection (ICSI) and the improvement of cryogenic freezing technology, the recovery rate, fertilization rate and pregnancy rate of oocytes frozen cycle were significantly improved. The American society for reproductive medicine also proposed in 2013 that the freezing of mature oocytes could be widely applied in clinical practice without being confined to the experimental stage [2]. At present, the international clinical research on oocyte cryopreservation mainly focuses on the social and economic benefit analysis or the fertility preservation of malignant tumor patients. We have identified another common scenario during our daily clinical practice: patients who planned to receive assisted reproductive technology (ART) on the day of oocyte retrieval where there is an unexpected sperm collection failure, such as the donor is unable to get to the hospital for sperm collection due to sudden illness or accident, the donor fails to perform masturbation or operation, or due to other various and sundry reasons. If these patients give up oocyte retrieval, the cost and time of treatment in previous ovulation induction process would be wasted, and the risk of ovarian hyperstimulation syndrome would be significantly increased due to the excessive physiological dose of estrogen in the body. However, if the oocytes are harvested as planned, the damage caused by freezing and thawing, and the risk of injury caused by the operation itself will also cause physical and psychological damage to the patients. As there is no unified evaluation framework or reference standard for this scenario to guide doctors in their daily clinical work, doctors often advise patients to give up oocyte retrieval or oocyte cryopreservation based on their own experience, or leave patients to choose completely by themselves. IVF cannot guarantee 100% success; between 38% and 49% of couples who start IVF will remain childless, even after undergoing up to 6 IVF cycles [3]. To manage the expectations of the infertile couples, several clinical prediction models for IVF have been developed over the last three decades [4, 5]. However, all of those models are based on fresh oocyte cycles, and no prediction model exists to evaluate the recovery effect of freeze-thaw mature oocytes. Our reproductive center, in 2007, began to develop mature oocyte cryopreservation. 80% of them are emergency oocyte frozen because the sperm donor cannot come to the hospital on the day of oocyte retrieval. In this study, clinical data of emergency oocyte cryopreservation because of male reason from 2007 to 2019 were retrospectively analyzed. According to the clinical characteristic and laboratory indexes, the prediction model of pregnancy outcomes of oocyte cryopreservation was established and validated. We hope this model could provide individualized and targeted suggestions to patients when they made the decision. Methods Study design and participants From August 2007 to December 2019, 418 women who had undergone oocyte cryopreservation in the Reproductive Center, Peking University Third Hospital, China, were prospectively identified. Infertile couples who received IVF and conducted emergency oocyte cryopreservation due to issues with the sperm donor were enrolled (Figure 1). Issues with the sperm donor on the day of oocyte retrieval includes: the sperm donor cannot come to the hospital for sperm collection due to sudden illness or accident, fails to perform masturbation or operation (MESA, TESA), or fails to obtain enough sperm, as well as other unexpected sperm collection failures. Data used in the investigation data includes: female age, BMI, duration of infertility, primary/secondary infertility, causes of infertility, previous history of gestation, basal hormone levels, semen quality, gonadotropin (Gn) dosage and duration totally applied, number of follicles with a diameter greater than 10 mm and hormone levels on the day of hCG administration, oocyte storage duration and et al. The final date of follow-up was May 31, 2020. The study utilized the TRIPOD score [6] to establish and validate the models. Procedures The initial dose of gonadotropin (Gn) applied to ovulation promotion was selected according to the age of the patients, the level of basal hormone and other ovarian reserve situation. And the Gn was adjusted based on the growth of follicles. The trigger time was decided based on the diameter of follicles and the level of serum hormone. When the diameter of two or more follicles is ≥ 18mm, recombinant human chorionic gonadotropin (r-hCG, Ezer, 250ug) was administered to the patients. The oocyte retrieval would be conducted 34-38 hours later. Mature oocytes were vitrified and thawed as previously described [7]. Briefly, oocytes were firstly equilibrated in a 7.5% (v/v) EG + 7.5% (v/v) DMSO solution for 5 minutes at room temperature. These oocytes were then transferred into the vitrification solutions composed of 15% (v/v) EG +15% (v/v) DMSO + 0.5 M sucrose for less than 1 minute at room temperature. Finally, these oocytes were loaded on the sterile iVitri straw immediately and transferred directly into liquid nitrogen for storage. Thawing of the frozen oocytes was carried out step by step using different concentrations of sucrose solution. After recovery, only an oocyte with intact membrane and uniform cytoplasm was considered as having survived. Following ICSI, all embryos were further cultured for 3 days; the quality of the embryo was evaluated by experienced embryologists. The embryo which could be transferred was then transferred back to the uterus or freeze. Patients with regular menstruation and normal ovulation were grouped in natural cycles, while artificial cycles to prepare for endometrium was applied to those with irregular menstruation or anovulation used. Luteal support was given to them after embryo transfer. Outcomes ‘No embryo to transfer’ and ‘cumulative live birth’ are the two key outcomes. ‘No embryo to transfer’ means after thawing the oocyte and formation of the embryo by ICIS, there was no available embryo to transfer back to the uterus. Cumulative live birth was defined as at least one live birth from the oocyte cryopreservation cycle as of May 2020 due to either the thawing fresh embryo transfer cycle or the following frozen embryo transfer cycle. Statistical analysis Primary statistical analysis For the quantitative data, the Kolmogorov-Smirnov was used to test the normality distribution. The quantitative elements were expressed as mean± std or median(p25, p75) according to whether it conformed to the normal distribution. For the qualitative data, the n (%) was used to express the data. Statistical tests were done with R software (version 3.6.0) and SPSS (version 25.0). Statistical significance was set at two-sided p values less than 0.05. M odel development Univariable logistic regression analyses were performed to assess the association of each of the predictive factors with cumulative live birth and no embryo to transfer. A multivariable logistic regression model was used to derive the nomogram. The predictors included in the multivariable model were selected based on the result of univariable logistic regression analyses (P＜0.1). The backward procedure for variable selection was applied for the multivariable logistic regression model. Regression coefficients were used to generate a nomogram. Missing Data The entire dataset contained 211 women, and data entry was complete for all variables. There is no missing data. Predictive ability Nomogram model performance was assessed by examining discrimination and calibration in the development and validation cohorts. The discrimination was assessed by the area under the receiver-operator characteristic (ROC) and area under the curve (AUC) and its 95% CI. The calibration was constructed to examine the agreement between the predicted probabilities with the observed outcome, which was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration plots. The calibration plot was calculated by the 400 repetitions Bootstrap resampling. Ethical approval Ethical approval for this study was provided by the Ethics Committee of Peking University Third Hospital (Approval reference No:2019SZ-092; date of approval 16 December 2019). Patients provided written consent for the information to be used in the analyses, editing and publications. Results Basic characters A total of 211 patients with 215 cycles of freeze-thaw oocytes participated in this study. Among them, four patients received two freeze-thaw oocytes cycles. 40 patients with 43 cycles did not have embryos to transfer. 7 patients conducted oocyte thawed and had embryo to transfer but they did not transfer yet. 164 patients received IVF-ET/FET. Figure 1 shows how we established the eligible cohort of oocyte freeze-thaw treatment cycles. Table 1 shows the baseline characteristics of the cohort. In total, there were 2546 oocytes that were thawed. The average recovery rate of oocytes was 75.42±24.04%, the fertilization rate was 69.54±26.07% and the cleavage rate was 95.05±12.53%. The overall rate of cumulative live birth from the whole dataset was 39.63% (65/164), the rate of no embryo to transfer was 20.00% (43/215) and the live birth rate per frozen oocyte was 2.55% (65/2546). Development and validation of a nomogram for predicting no embryo to transfer The univariate associations of the potential predictors and multivariable logistic regression model for no embryo to transfer are shown in Table 2. Predictors included in the multivariable logistic regression were as follows: female age, antral follicle count (AFC), basal LH level, gonadotropin (Gn) dosage, number of follicles on the day of hCG administration, endometriosis, semen quality, sperm source, and storage duration of oocytes. The variables which showed a statistically significant increment in odds ratio of no embryo to transfer in the final model were: female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH(OR= 1.205, 95% CI=1.051-1.382, P=0.0084) and E2(OR= 1.006, 95% CI=1.001-1.010, P=0.012) level. As for the source of sperm, compared with masturbation and PESA, sperm from MESA significantly increased the risk of no embryo to transfer (OR= 7.741, 95% CI=2.905-20.632, P<0.001). The nomogram was derived from a multivariable logistic regression model. The model showed an AUC of 0.799 (95% CI: 0.722–0.875, p＜0.001), which denotes a good performance. The Hosmer-Lemeshow goodness-of-fit test, and the calibration curve showed good discrimination and calibration of nomogram in the internal validation cohort (Figure2). Development and validation of a nomogram for predicting cumulative live birth The univariate associations of the potential predictors and multivariable logistic regression model for the cumulative live birth of freeze-thaw oocytes are shown in Table 3. Predictors included in the multivariable logistic regression were as follows: age of female and male, duration of infertility, basal FSH and E2 level, Gn dosage, number of follicles on the day of hCG administration, poor ovarian response and sperm source. The model shows that the odds ratio of a successful live birth decreases with the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031). The nomogram was derived from the multivariable logistic regression model. The model showed an AUC of 0.724 (95% CI: 0.647–0.801, p＜0.001). The Hosmer-Lemeshow goodness- of-fit test, and the calibration curve showed good discrimination and calibration of nomogram in the internal validation cohort (Figure3). Discussion In the early 20th century, scientists began to preserve gametes and embryos at low temperatures. In 1999, Kuleshova [8] first reported the case of successful pregnancy and delivery after oocyte cryopreservation, which marked a breakthrough in oocyte cryopreservation. Presently thousands of children are born and benefited from this technique. Although new techniques are emerging and existing ones are always evolving, the freeze-thaw process can cause damage and changes of spindles, genetic materials, organelles and epigenetic in the oocyte [9]. Whether or not these alterations may produce a long-term negative health effect remains unclear. Existing studies have shown that the clinical pregnancy rate and live birth rate of mature oocytes after freezing and thawing are similar to those of fresh oocytes, with evidence from oocyte donation cycles. However, for patients with poor ovarian reserve function or less expected number of oocytes, doctors and patients are still worried that no embryo could be transferred after thawing the oocytes. The data from our center indicates that nearly one-fifth of the patients (40/211, 18.96%) have no embryos to transfer after thawing the oocytes. For those patients, if we can inform them of the possibility of no embryos to transfer before oocyte retrieval, it may reduce the economic loss and the risks of the operation. Main findings At present, there is no predictive model of pregnancy outcome after emergency cryopreservation of oocytes. Based on the clinical data and laboratory results of emergency oocyte cryopreservation, prediction models of pregnancy outcomes were developed to fill the gap. All the indicators in the model are available before oocyte retrieval. The internal verification of the model was also conducted. The key predictors which had significant effects on the result of the model of no embryo to transfer are: female age, duration of infertility, basal FSH, basal E2 and the source of semen. While for the model of live birth, the key predictors are: the number of follicles which diameter greater than 10mm and endometriosis. Strengths and weaknesses Ratna [10] suggested that a high-quality prediction model article should meet the following three criteria:1) a TRIPOD [6] score greater than 80%. 2) external validation and 3) the model had acceptable discrimination (c-statistic >0.7)[11]. 35 prediction models of IVF success have been published across 23 articles. These 35 models met between 29 to 95% of the items included in the TRIPOD checklist [12, 13]. Only 21% of studies met at least 80% of the checklist items, and the highest achieved a TRIPOD score of 95%. Only four models [14-17] had conducted external validation (4/23 = 17.39%), and almost all of the indicators in the models have missing values or do not describe missing values. The range of c-statistic was between 0.55 and 0.77. From research design to manuscript drafting, we strictly followed the TRIPOD list. The self-evaluation TRIPOD score is 90.91%. The AUC of the ‘no embryo to transfer’ model is 0.799 (95% CI: 0.722–0.875, p＜0.001), and the AUC of the ‘live birth ’model is 0.724 (95% CI: 0.647–0.801, p＜0.001), which are greater than 0.7. The accuracy of the prediction model is at the forefront of the existing models. On the one hand, it benefits from the guidance of TRIPOD, but on the other hand, it is closely related to the fact that this study covers almost all the prediction indicators related to the pregnancy outcome of IVF and there is no missing value. The main categories of predictors included in developed models are as follow: couple factors, gender, embryo and treatment. At present, several better prediction models recommended in the field of reproductive medicine mostly come from multicenter or national databases. Although the sample size is large, the number of prediction indicators included is limited [13]. The median number of predictors included in the existing models was 7 (range 3–14). Our model includes 26 forecast indicators. In addition to the most frequently used predictors such as female age, duration of infertility, endometriosis, et al [18], we also included basic hormone levels, AFC, male age and semen quality and hormone levels on the day of hCG injection. The information about the embryos could not be obtained due to the pretreatment model. However, compared with other pretreatment models, the numbers of oocytes were estimated through the number of follicles with a diameter of more than 10 mm on hCG administration day. Moreover, the sources of the semen were also taken into consideration to further improve the accuracy of the model prediction. One of the greatest strengths of our model is that it has highlighted the semen source as a key predictor for IVF success. Semen source is a factor that has never been used in any previous prediction models. Studies have indicated that NOA (non-azoospermia) patients could produce increased numbers of cytogenetically abnormal testicular spermatozoa despite their normal somatic karyotype, and were at increased risk to produce aneuploid gametes and of transmitting chromosome aneuploidy to the zygote [19, 20], which may lead to a reduced developmental potential of embryos. An [21] had compared 150 NOA patients who underwent micro-TESE with 174 OA patients who underwent TESA and found that developmental competence of the embryo was greatest among couples using sperm obtained by TESA rather than micro-TESE, and was not dependent on whether vitrified or fresh oocytes were utilized. Capello [22] showed that the quality and source of sperm did not affect the clinical pregnancy rate and live birth rate in vitrified oocyte donation IVF model. On the contrary, the results of our study suggest that the source of semen is an important factor leading to no embryo to transfer in freeze-thaw oocyte cycles. If the sperm comes from MESA, the risk of no embryo to transfer will be increased by 7.74 times. Female age and duration of infertility are two important predictors applied to predict the pregnancy/live birth chances after IVF [18]. They both have negative associations with treatment outcomes. Our results suggest that in terms of pregnancy outcome of oocyte cryopreservation, female age and duration of infertility also play a negative role. For every 1-year increase in female age, the risk of no embryo to transfer increases by 1.099 times. The risk of no embryo to transfer increases by 1.14 times for every 1-year extension of infertility years. Basal FSH and E2 levels are important indexes for the evaluation of ovarian reserve function. A high level reflects a reduced ovarian reserve and is associated with poor IVF treatment outcome [23]. Some studies also suggested that the FSH level on cycle day 3 was a better indicator of IVF outcome than female age [24]. High levels of basal E2 level were associated with low oocyte yields, low pregnancy rates and higher cancellation rate independent of FSH levels [25, 26]. Our results are consistent with the above studies. In the multiple logistic regression equation, the effect of basal FSH and basal E2 on the adverse outcome of no embryo to transfer is even beyond the female age. Endometriosis is one of the important factors leading to female infertility. 57% (20/35) of the prediction models take endometriosis as one of the important indicators to evaluate the success rate of IVF. After balancing many other prediction indicators, only the number of follicles larger than 10 mm on the day of hCG administration and endometriosis entered the final equation, which shows that the expected number of retrieved oocytes and endometriosis are closely related to the outcome of oocytes cryopreservation. One of the weaknesses of our model is insufficient external validation. This is because there are a limited numbers of patients which require emergency oocyte cryopreservation. In one of the largest assisted reproductive centers in China, in over 10 years only 211 patients received emergency oocyte cryopreservation and returned to the hospital for follow-up treatment. The number is expected to be lower in other relatively smaller scale assisted reproductive centers. Therefore, it is difficult to carry out external verification at this moment. In addition, the sample size used to derive this prediction model is small, and all of them are from a single center. Whether the research results can be extended to other races and regions remains to be further verified. Given the complexities of assisted reproductive technology, many other confounders can have an effect at different points in time. Although we can use the expected retrieved number of eggs and sperm sources to make a preliminary assessment of the embryo, our model is for pretreatment counseling only. We appreciate that IVF success rates depend on more than the factors in this model alone. Therefore, when using the model, it is important for clinicians to ensure that their patients understand the probability of having a successful outcome will invariably change as they progress through their treatment and thus should be interpreted as a baseline prediction only. Comparison to existing models The existing prediction models of IVF success rate are all for fresh oocyte; there is no available model for frozen oocyte. Therefore, there is no comparability of clinical indicators and prediction accuracy between this model and existing models. Different from the published clinical model of assisted reproduction, a nomogram was applied in this research to display the prediction model, which is more practical and intuitive. The nomogram makes it convenient for clinicians and patients to calculate the benefits of oocytes cryopreservation in each treatment cycle according to their own conditions. This can help reduce psychological pressure on both doctors and patients and make the decision easier under certain expectations. It may also reduce the economic and psychological pressure on patients when facing no embryo to transfer. We believe this prediction tool is an important and valuable addition in the counseling process for patients at this critical decision-making point in their journey. Conclusions Our results show that, as fresh oocyte cycle, for the oocyte cryopreservation cycle, with the increase of age, the prolongation of infertility, the decrease of ovarian reserve function and endometriosis, the risk of no embryo to transfer could be increased and the live birth rate could be decreased. We have illustrated not only the clinical use of this model but also how a couple’s characteristics might affect their prognosis. This model provides a personalized approach to counseling and estimates the chances of success based on individual information. This can be applied by clinicians when counseling couples before emergency oocyte cryopreservation. For example, take the case of an infertile patient and sperm donor where the woman is 32 years old with three years of infertile history, basal FSH, 7.5MIU/ml, basal E2:131mmol/L. Their IVF indicators are involved: endometriosis and severe oligozoospermia. The number of follicles with a diameter greater than 10 mm on the day of hCG administration is 8. The sperm donor could not come to the hospital due to an emergency on the day of oocyte retrieval. If the man can obtain sperm by masturbation, the possibility of no embryo to transfer is 25.30%. If it is necessary to extract sperm by MESA, the possibility of no embryo to transfer is 48.9%, and the possibility of live birth is 39.39%. The results from our model might assist the couples to decide whether to freeze or give up oocyte retrieval. The next step for this model is to further validate the research findings by performing external validation in other assisted reproduction centers in China and worldwide. Furthermore, this model may be developed into both a user-friendly web-based decision aid platform and as a mobile application to assist both clinicians and patients. Abbreviations IVF: in vitro fertilization ICSI: intracytoplasmic sperm injection ART: assisted reproductive technology MESA: Microsurgical Epididymal Sperm Aspiration TESA: Testicular Sperm Aspiration Gn: gonadotropin Hcg: human chorionic gonadotropin ROC：receiver-operator characteristic AUC：area under the curve AFC:antral follicle count Declarations Ethics approval and consent to participate Ethical approval for this study was provided by the Ethics Committee of Peking University Third Hospital (Approval reference No:2019SZ-092; date of approval 16 December 2019). Patients provided written consent for the information to be used in the analyses, editing and publications. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was fund by the National Natural Science Foundation of China (81801447). Authors' contributions Yang Wang and Rong Li design the study. Yang Wang and Ziru Niu collected the data and drafted the manuscript. Liyuan Tao conducted the statistical analysis. Xiaoying zheng and Yifeng yuan conducted the oocyte frozen and thawing. Ping Liu and Rong Li revised the manuscript. Acknowledgements All the staff in the reproductive center of Peking University Third Hospital were acknowledged here. References [1]. Chen, C., Pregnancy after human oocyte cryopreservation. Lancet (London, England), 1986. 1(8486): p. 884. [2]. Practice, C.O.A.S. and F.A.R.T. Society, Mature oocyte cryopreservation: a guideline. Fertility and sterility, 2013. 99(1): p. 37. [3]. Malizia, B.A., M.R. Hacker and A.S. Penzias, Cumulative live-birth rates after in vitro fertilization. N Engl J Med, 2009. 360(3): p. 236-43. [4]. Wiegerinck, M.A., et al., How concordant are the estimated rates of natural conception and in-vitro fertilization/embryo transfer success? Hum Reprod, 1999. 14(3): p. 689-93. [5]. van der Steeg, J.W., et al., Do clinical prediction models improve concordance of treatment decisions in reproductive medicine? BJOG, 2006. 113(7): p. 825-31. [6]. Collins, G.S., et al., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Medicine, 2015. 13(1): p. 1. [7]. Zhang, L., et al., L-proline: a highly effective cryoprotectant for mouse oocyte vitrification. Sci Rep, 2016. 6: p. 26326. [8]. Kuleshova, L., et al., Birth following vitrification of a small number of human oocytes: case report. Hum Reprod, 1999. 14(12): p. 3077-9. [9]. Iussig, B., et al., A brief history of oocyte cryopreservation: Arguments and facts. Acta Obstet Gynecol Scand, 2019. 98(5): p. 550-558. [10]. Ratna, M.B., et al., A systematic review of the quality of clinical prediction models in in vitro fertilisation. Human Reproduction, 2020. 35(1): p. 100-116. [11]. Swets, J.A., Measuring the accuracy of diagnostic systems. Science, 1988. 240(4857): p. 1285. [12]. Hughes, E.G., C. King and E.C. Wood, A prospective study of prognostic factors in in vitro fertilization and embryo transfer. Fertil Steril, 1989. 51(5): p. 838-44. [13]. McLernon, D.J., et al., 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, 2016: p. i5735. [14]. Dhillon, R.K., et al., Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool. Hum Reprod, 2016. 31(1): p. 84-92. [15]. van Loendersloot, L.L., et al., Individualized decision-making in IVF: calculating the chances of pregnancy. Hum Reprod, 2013. 28(11): p. 2972-80. [16]. Cai, Q.F., et al., Factors predicting the cumulative outcome of IVF/ICSI treatment: a multivariable analysis of 2450 patients. Hum Reprod, 2011. 26(9): p. 2532-40. [17]. Stolwijk, A.M., et al., External validation of prognostic models for ongoing pregnancy after in-vitro fertilization. Hum Reprod, 1998. 13(12): p. 3542-9. [18]. van Loendersloot, L.L., et al., Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis. Human Reproduction Update, 2010. 16(6): p. 577-589. [19]. Onofre, J., et al., Cryopreservation of testicular tissue or testicular cell suspensions: a pivotal step in fertility preservation. Hum Reprod Update, 2016. 22(6): p. 744-761. [20]. Vozdova, M., et al., Testicular sperm aneuploidy in non-obstructive azoospermic patients. Hum Reprod, 2012. 27(7): p. 2233-9. [21]. An, G., et al., Outcome of Oocyte Vitrification Combined with Microdissection Testicular Sperm Extraction and Aspiration for Assisted Reproduction in Men. Medical Science Monitor, 2018. 24: p. 1379-1386. [22]. Capelouto, S.M., et al., Impact of male partner characteristics and semen parameters on in vitro fertilization and obstetric outcomes in a frozen oocyte donor model. Fertility and Sterility, 2018. 110(5): p. 859-869. [23]. Scott, R.T., et al., Reprint of: Follicle-stimulating hormone levels on cycle day 3 are predictive of in vitro fertilization outcome. Fertil Steril, 2019. 112(4 Suppl1): p. e174-e177. [24]. Toner, J.P., et al., Basal follicle-stimulating hormone level is a better predictor of in vitro fertilization performance than age. Fertil Steril, 1991. 55(4): p. 784-91. [25]. Evers, J.L., et al., Elevated levels of basal estradiol-17beta predict poor response in patients with normal basal levels of follicle-stimulating hormone undergoing in vitro fertilization. Fertil Steril, 1998. 69(6): p. 1010-4. [26]. Jiang, Z., et al., A combination of follicle stimulating hormone, estradiol and age is associated with the pregnancy outcome for women undergoing assisted reproduction: a retrospective cohort analysis. Sci China Life Sci, 2019. 62(1): p. 112-118. Tables Table 1. Baseline characteristics of the cohort ‘no embryo to transfer’ cohort（N=215) Live birth rate(N=164) Female age (years)（Mean±SD） 29.91±4.95 29.41±4.85 BMI(kg/㎡) （Mean±SD） 22.32±3.56 22.41±3.61 Duration of infertility (years)（Mean±SD） 3.44±3.24 3.13±2.68 Types of infertility Primary infertility（n（%）） 174(80.93%) 131(79.88%) Secondary infertility（n（%）） 41(19.07%) 33(20.12%) Gravidity (times）（Median，(min,max)） 0.00(0, 5) 0.00(0, 5) Delivery (times）（Median，(min,max)） 0.00(0, 2) 0.00(0, 2) Endometriosis（n（%）） 17(7.91%) 14(8.54%) PCOS（n（%）） 24(11.16%) 18(10.98%) POR（n（%））a 22(10.23%) 13(7.93%) Tubal factor （n（%）） 28(13.02%) 22(13.41%) IVF failure history （n（%）） 21(9.77%) 16(9.76%) basal FSH (MIU/ml) （Mean±SD） 6.11±2.82 5.86±2.38 basal LH (MIU/ml)（Mean±SD） 3.64±1.99 3.64±1.95 basal E2 (mmol/L)（Mean±SD） 164.58±88.24 157.86±58.30 AFC 14.55±6.86 14.85±6.69 Duration of Gn applied (Days)（Mean±SD） 11.47±2.59 11.47±2.53 total Gn applied （units）（Mean±SD） 2505.62±1140.04 2428.80±1106.91 LH on the day of hCG (MIU/ml) （Mean±SD） 1.73±2.40 1.63±2.17 E2 on the day of hCG (mmol/L) （Mean±SD） 11036.42±7532.33 11622.27±7552.50 P on the day of hCG (pmol/L)（Mean±SD） 2.85±1.86 2.89±1.59 The number of follicles with a diameter greater than 10 mm on the day of hCG(number) (Mean±SD） 16.08±6.88 16.76±6.81 Male age (years)（Mean±SD） 31.20±6.21 30.70±5.99 Semen quality Azoospermia（n（%）） 158(73.49%) 123(75.00%) Oligozoospermia（n（%）） 21(9.77%) 14(8.54%) Normal semen（n（%）） 36(16.74%) 27(16.46%) Semen source AID （n（%）） 104(48.37%) 90(54.88%) TESA/PESA （n（%）） 10(4.65%) 7(4.27%) MESA （n（%）） 42(19.53%) 22(13.41%) Masturbation （n（%）） 59(27.44%) 45(27.44%) Duration of oocyte frozen (month）（Mean±SD） 8.23±8.94 8.51±9.12 Oocyte retrival time grouped by year 2007-2011 （n（%）） 24(11.16%) 23(14.02%) 2012-2015 （n（%）） 76(35.35%) 54(32.93%) 2016-2019 （n（%）） 115(53.49%) 87(53.05%) BMI:body mass index; AFC:antral follicle count; PCOS: polycystic ovary syndrome; POR-poor ovarian response diagnosis according to Bologna dianosis criteria； AID:artificial insemination by donor; TESA:testicular sperm aspiration; PESA：percutaneous epididymal sperm aspiration; MESA：microdissection testicular sperm extraction. Table 2. Potential predictors and multivariable logistic regression model for no embryo to transfer Predictor univariate analysis multivariable analysis OR (95%CI) P OR (95%CI) P Female age (years) 1.098(1.028，1.172) 0.005* 1.099(1.003,1.205) 0.044* BMI(kg/㎡) 0.975(0.885，1.075) 0.616 Duration of infertility (years) 1.149(1.047，1.262) 0.004* 1.140(1.018,1.276) 0.024* Secondary infertility 1.575 (0.616, 4.029) 0.343 Gravidity (times） 0.881 (0.568,1.367) 0.573 Delivery (times） 0.842 (0.126,5.603) 0.859 Endometriosis 1.181(0.324,4.310) 0.801 PCOS 0.944(0.331,2.690) 0.914 POR 0.309(0.122,0.781) 0.013* Tubal factor 1.518(0.518，4.825) 0.421 IVF failure history 1.069(0.341,3.358) 0.909 basal FSH (MIU/ml) 1.156(1.029，1.299) 0.014* 1.205(1.051,1.382) 0.008* basal LH (MIU/ml) 1.014(0.858,1.198) 0.871 basal E2 (mmol/L) 1.003(1.000，1.007) 0.059 1.006(1.001,1.010) 0.012* AFC 0.960(0.912，1.010) 0.115 Duration of Gn applied (Days) 0.994(0.873，1.131) 0.926 total Gn applied （units） 1.003(1.000，1.001) 0.095 LH on the day of hCG (MIU/ml) 1.066(0.944,1.203) 0.302 E2 on the day of hCG (mmol/L) 1.000(1.000，1.000) 0.204 P on the day of hCG (pmol/L) 0.972(0.804，1.174) 0.765 The number of follicles with a diameter greater than 10 mm on the day of hCG(number) 0.950(0.901，1.001) 0.056 Male age (years) 1.066(1.014，1.121) 0.012* Semen quality Azoospermia 1 0.516 Oligozoospermia 1.779(0.636,4.978) 0.272 Normal semen 1.271(0.526,3.073) 0.595 Semen source AID 1 0.001* 1 ＜0.001* TESA/PESA 4.029(0.898,18.081) 0.069 2.180(0.383,12.403) 0.380 MESA 6.392(2.607,15.675) ＜0.001* 7.741(2.905,20.632) ＜0.001* Masturbation 2.675(1.084,6.512) 0.033 1.399(0.486,4.034) 0.534 Duration of oocyte frozen (month） 0.978(0.936,1.022) 0.323 Oocyte retrival time grouped by year 2007-2011 1 0.190 2012-2015 6.133(0.769,48.931) 0.087 2016-2019 6.719(0.866,52.154) 0.068 BMI:body mass index; AFC:antral follicle count; PCOS: polycystic ovary syndrome; POR-poor ovarian response diagnosis according to Bologna dianosis criteria； AID:artificial insemination by donor; TESA:testicular sperm aspiration; PESA：percutaneous epididymal sperm aspiration; MESA：microdissection testicular sperm extraction. Table 3. Potential predictors and multivariable logistic regression model for live birth rate Predictor univariate analysis multivariable analysis OR（95%CI） P OR（95%CI） P Female age (years) 0.934（0.872，1.001） 0.055 BMI(kg/㎡) 0.976(0.894,1.066) 0.593 Duration of infertility (years) 0.928（0.817，1.055） 0.255 Secondary infertility 1.991（0.859，4.615） 0.108 Gravidity (times） 0.795(0.523,1.211) 0.286 Delivery (times） 1.842(0.386,8.799) 0.444 Endometriosis 4.345(0.939,20.099) 0.060 0.172(0.035,0.853) 0.031* PCOS 0.622(0.233,1.662) 0.344 POR 0.431(0.114,1.629) 0.215 Tubal factor 1.174(0.462,2.978) 0.736 IVF failure history 1.105(0.381,3.203) 0.854 basal FSH (MIU/ml) 0.904（0.790，1.034） 0.141 basal LH (MIU/ml) 0.865（0.731，1.025） 0.095 basal E2 (mmol/L) 0.998(0.992,1.003) 0.393 AFC 1.046（0.997，1.097） 0.066 Duration of Gn applied (Days) 0.911（0.801，1.037） 0.158 total Gn applied （units） 1.000（0.999，1.000） 0.034* LH on the day of hCG (MIU/ml) 1.036(0.898,1.194) 0.630 E2 on the day of hCG (mmol/L) 1.000（1.000，1.000） 0.256 P on the day of hCG (pmol/L) 0.861（0.699，1.061） 0.161 The number of follicles with a diameter greater than 10 mm on the day of hCG(number) 1.071（1.019，1.125） 0.006* 1.088(1.030,1.149) 0.002* Male age (years) 0.955（0.901，1.011） 0.113 Semen quality Azoospermia 1 0.051 Oligozoospermia 0.687(0.218,2.168) 0.522 Normal semen 0.281(0.100,0.790) 0.016 Semen source AID 1 0.064 TESA/PESA 0.228(0.026,1.974) 0.179 MESA 1.977(0.767,5.097) 0.159 Masturbation 0.556(0.258,1.199) 0.134 Duration of oocyte frozen (month） 1.028(0.992,1.065) 0.130 Oocyte retrival time grouped by year 2007-2011 1 0.653 2012-2015 1.500(0.545,4.127) 0.432 2016-2019 1.146(0.438,2.996) 0.781 BMI:body mass index; AFC:antral follicle count; PCOS: polycystic ovary syndrome; POR-poor ovarian response diagnosis according to Bologna dianosis criteria； AID:artificial insemination by donor; TESA:testicular sperm aspiration; PESA：percutaneous epididymal sperm aspiration; MESA：microdissection testicular sperm extraction. 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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-50551","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":1153212,"identity":"9002fbe3-967a-42cf-a79b-23524e2528c2","order_by":0,"name":"Yang Wang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Wang","suffix":""},{"id":1153213,"identity":"b419bb4d-9201-4c26-a9cf-18c43dec9064","order_by":1,"name":"Ziru Niu","email":"","orcid":"https://orcid.org/0000-0002-2629-3484","institution":"Peking University Third Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ziru","middleName":"","lastName":"Niu","suffix":""},{"id":1153214,"identity":"4e53dbb6-c462-42d8-a5c5-23b76c9c8fa3","order_by":2,"name":"Liyuan Tao","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Tao","suffix":""},{"id":1153215,"identity":"fe228313-941b-4808-bf8b-aaff9cb4227a","order_by":3,"name":"Xiaoying Zheng","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Zheng","suffix":""},{"id":1153216,"identity":"b135afef-de31-45e0-88d6-bd9debbd1f0e","order_by":4,"name":"Yifeng Yuan","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yifeng","middleName":"","lastName":"Yuan","suffix":""},{"id":1153217,"identity":"e102b0c7-0a34-447d-b912-6c8f4d6f7ce3","order_by":5,"name":"Ping Liu","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Liu","suffix":""},{"id":1153218,"identity":"312ed245-6737-4126-944b-b3c87e758622","order_by":6,"name":"Rong Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYLCCBwYSPGzsDQwHiNeSYGAjx8dzgCQtDGnGchIJRKo2b29/+CCh4HBim+Tzh4cLahjk+cUIWCZz5oyxQYIBUIt0jsHhGccYDGfOJmCdhEQOmwRUC8NhHjagv24T1JL+/AdYi+TxB4d5/hGlJcEMqCzNmE2CweAwbxsxWnjOGEuAApmNB+gX3j4JIvzC3v7ww4c/Ejzy7ccff+b5ZiPPL01AC4YRpCkfBaNgFIyCUYAdAAD+iT7vP/sxTAAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2020-07-29 10:30:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-50551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-50551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1779181,"identity":"9f188a1e-453d-4734-90b0-35f3d8a88bd0","added_by":"auto","created_at":"2020-08-04 17:23:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83308,"visible":true,"origin":"","legend":"Flowchart of the study","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-50551/v1/figure1.jpg"},{"id":1779182,"identity":"00e06fe0-18ab-45a8-b5b8-59afee6e5ea2","added_by":"auto","created_at":"2020-08-04 17:23:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59141,"visible":true,"origin":"","legend":"Discrimination and calibration of a model to predict no embryo to transfer of emergency oocyte frozen-thawed cycles (A-AUC, B- Calibration curve, C- nomogram). AUC,\narea under the ROC curve; FSH, follicle stimulating hormone; E2, estradiol; AID, artificial insemination by donor; TESA, testicular sperm aspiration; PESA, percutaneous epididymal sperm aspiration; MESA, microdissection testicular sperm extraction.\n","description":"","filename":"figure2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-50551/v1/figure2.JPG"},{"id":1779183,"identity":"eb5ead10-5618-49ee-80a3-b0852ac70ae2","added_by":"auto","created_at":"2020-08-04 17:23:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53734,"visible":true,"origin":"","legend":"Discrimination and calibration of a model to predict cumulative live birth of emergency oocyte frozen-thawed cycles (A-AUC, B- Calibration curve, C- nomogram). AUC,\narea under the ROC curve; hCG, human chorionic gonadotropin.\n\n","description":"","filename":"figure3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-50551/v1/figure3.JPG"},{"id":13568267,"identity":"e199cc44-7968-4190-88d4-eb015cf447f7","added_by":"auto","created_at":"2021-09-17 03:35:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":484678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-50551/v1/2fbc3a10-e23a-40f9-b7bf-feae77d90d42.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAs early as 1986, researchers had applied frozen oocytes for in vitro fertilization (IVF) and achieved a successful pregnancy [1]. With the development of intracytoplasmic sperm injection (ICSI) and the improvement of cryogenic freezing technology, the recovery rate, fertilization rate and pregnancy rate of oocytes frozen cycle were significantly improved. The American society for reproductive medicine also proposed in 2013 that the freezing of mature oocytes could be widely applied in clinical practice without being confined to the experimental stage [2].\u003c/p\u003e\n\u003cp\u003eAt present, the international clinical research on oocyte cryopreservation mainly focuses on the social and economic benefit analysis or the fertility preservation of malignant tumor patients. We have identified another common scenario during our daily clinical practice: patients who planned to receive assisted reproductive technology (ART) on the day of oocyte retrieval where there is an unexpected sperm collection failure, such as the donor is unable to get to the hospital for sperm collection due to sudden illness or accident, the donor fails to perform masturbation or operation, or due to other various and sundry reasons. If these patients give up oocyte retrieval, the cost and time of treatment in previous ovulation induction process would be wasted, and the risk of ovarian hyperstimulation syndrome would be significantly increased due to the excessive physiological dose of estrogen in the body. However, if the oocytes are harvested as planned, the damage caused by freezing and thawing, and the risk of injury caused by the operation itself will also cause physical and psychological damage to the patients. As there is no unified evaluation framework or reference standard for this scenario to guide doctors in their daily clinical work, doctors often advise patients to give up oocyte retrieval or oocyte cryopreservation based on their own experience, or leave patients to choose completely by themselves.\u003c/p\u003e\n\u003cp\u003eIVF cannot guarantee 100% success; between 38% and 49% of couples who start IVF will remain childless, even after undergoing up to 6 IVF cycles [3]. To manage the expectations of the infertile couples, several clinical prediction models for IVF have been developed over the last three decades [4, 5]. However, all of those models are based on fresh oocyte cycles, and no prediction model exists to evaluate the recovery effect of freeze-thaw mature oocytes. Our reproductive center, in 2007, began to develop mature oocyte cryopreservation. 80% of them are emergency oocyte frozen because the sperm donor cannot come to the hospital on the day of oocyte retrieval. In this study, clinical data of emergency oocyte cryopreservation because of male reason from 2007 to 2019 were retrospectively analyzed.\u003c/p\u003e\n\u003cp\u003eAccording to the clinical characteristic and laboratory indexes, the prediction model of pregnancy outcomes of oocyte cryopreservation was established and validated. We hope this model could provide individualized and targeted suggestions to patients when they made the decision.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy design and participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom August 2007 to December 2019, 418 women who had undergone oocyte cryopreservation in the Reproductive Center, Peking University Third Hospital, China, were prospectively identified. Infertile couples who received IVF and conducted emergency oocyte cryopreservation due to issues with the sperm donor were enrolled (Figure 1). Issues with the sperm donor on the day of oocyte retrieval includes: the sperm donor cannot come to the hospital for sperm collection due to sudden illness or accident, fails to perform masturbation or operation (MESA, TESA), or fails to obtain enough sperm, as well as other unexpected sperm collection failures. Data used in the investigation data includes: female age, BMI, duration of infertility, primary/secondary infertility, causes of infertility, previous history of gestation, basal hormone levels, semen quality, gonadotropin (Gn) dosage and duration totally applied, number of follicles with a diameter greater than 10 mm and hormone levels on the day of hCG administration, oocyte storage duration and et al. The final date of follow-up was May 31, 2020. The study utilized the TRIPOD score [6] to establish and validate the models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eProcedures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial dose of gonadotropin (Gn) applied to ovulation promotion was selected according to the age of the patients, the level of basal hormone and other ovarian reserve situation. And the Gn was adjusted based on the growth of follicles. The trigger time was decided based on the diameter of follicles and the level of serum hormone. When the diameter of two or more follicles is \u0026ge; 18mm, recombinant human chorionic gonadotropin (r-hCG, Ezer, 250ug) was administered to the patients. The oocyte retrieval would be conducted 34-38 hours later.\u003c/p\u003e\n\u003cp\u003eMature oocytes were vitrified and thawed as previously described [7]. Briefly, oocytes were firstly equilibrated in a 7.5% (v/v) EG + 7.5% (v/v) DMSO solution for 5 minutes at room temperature. These oocytes were then transferred into the vitrification solutions composed of 15% (v/v) EG +15% (v/v) DMSO + 0.5 M sucrose for less than 1 minute at room temperature. Finally, these oocytes were loaded on the sterile iVitri straw immediately and transferred directly into liquid nitrogen for storage. Thawing of the frozen oocytes was carried out step by step using different concentrations of sucrose solution. After recovery, only an oocyte with intact membrane and uniform cytoplasm was considered as having survived. Following ICSI, all embryos were further cultured for 3 days; the quality of the embryo was evaluated by experienced embryologists. The embryo which could be transferred was then transferred back to the uterus or freeze. Patients with regular menstruation and normal ovulation were grouped in natural cycles, while artificial cycles to prepare for endometrium was applied to those with irregular menstruation or anovulation used. Luteal support was given to them after embryo transfer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOutcomes \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;No embryo to transfer\u0026rsquo; and \u0026lsquo;cumulative live birth\u0026rsquo; are the two key outcomes. \u0026lsquo;No embryo to transfer\u0026rsquo; means after thawing the oocyte and formation of the embryo by ICIS, there was no available embryo to transfer back to the uterus. Cumulative live birth was defined as at least one live birth from the oocyte cryopreservation cycle as of May 2020 due to either the thawing fresh embryo transfer cycle or the following frozen embryo transfer cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrimary statistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the quantitative data, the Kolmogorov-Smirnov was used to test the normality distribution. The quantitative elements were expressed as mean\u0026plusmn; std or median(p25, p75) according to whether it conformed to the normal distribution. For the qualitative data, the n (%) was used to express the data. Statistical tests were done with R software (version 3.6.0) and SPSS (version 25.0). Statistical significance was set at two-sided p values less than 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003cem\u003eodel\u003c/em\u003e\u003cem\u003edevelopment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable logistic regression analyses were performed to assess the association of each of the predictive factors with cumulative live birth and no embryo to transfer. A multivariable logistic regression model was used to derive the nomogram. The predictors included in the multivariable model were selected based on the result of univariable logistic regression analyses (P＜0.1). The backward procedure for variable selection was applied for the multivariable logistic regression model. Regression coefficients were used to generate a nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMissing Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe entire dataset contained 211 women, and data entry was complete for all variables. There is no missing data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredictive ability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNomogram model performance was assessed by examining discrimination and calibration in the development and validation cohorts. The discrimination was assessed by the area under the receiver-operator characteristic (ROC) and area under the curve (AUC) and its 95% CI. The calibration was constructed to examine the agreement between the predicted probabilities with the observed outcome, which was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration plots. The calibration plot was calculated by the 400 repetitions Bootstrap resampling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was provided by the Ethics Committee of Peking University Third Hospital (Approval reference No:2019SZ-092; date of approval 16 December 2019). Patients provided written consent for the information to be used in the analyses, editing and publications.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBasic characters\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 211 patients with 215 cycles of freeze-thaw oocytes participated in this study. Among them, four patients received two freeze-thaw oocytes cycles. 40 patients with 43 cycles did not have embryos to transfer. 7 patients conducted oocyte thawed and had embryo to transfer but they did not transfer yet. 164 patients received IVF-ET/FET. Figure 1 shows how we established the eligible cohort of oocyte freeze-thaw treatment cycles. Table 1 shows the baseline characteristics of the cohort. In total, there were 2546 oocytes that were thawed. The average recovery rate of oocytes was 75.42\u0026plusmn;24.04%, the fertilization rate was 69.54\u0026plusmn;26.07% and the cleavage rate was 95.05\u0026plusmn;12.53%. The overall rate of cumulative live birth from the whole dataset was 39.63% (65/164), the rate of no embryo to transfer was 20.00% (43/215) and the live birth rate per frozen oocyte was 2.55% (65/2546).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDevelopment and validation of a nomogram for predicting no embryo to transfer\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate associations of the potential predictors and multivariable logistic regression model for no embryo to transfer are shown in Table 2. Predictors included in the multivariable logistic regression were as follows: female age, antral follicle count (AFC), basal LH level, gonadotropin (Gn) dosage, number of follicles on the day of hCG administration, endometriosis, semen quality, sperm source, and storage duration of oocytes. The variables which showed a statistically significant increment in odds ratio of no embryo to transfer in the final model were: female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH(OR= 1.205, 95% CI=1.051-1.382, P=0.0084) and E2(OR= 1.006, 95% CI=1.001-1.010, P=0.012) level. As for the source of sperm, compared with masturbation and PESA, sperm from MESA significantly increased the risk of no embryo to transfer (OR= 7.741, 95% CI=2.905-20.632, P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eThe nomogram was derived from a multivariable logistic regression model. The model showed an AUC of 0.799 (95% CI: 0.722\u0026ndash;0.875, p＜0.001), which denotes a good performance. The Hosmer-Lemeshow goodness-of-fit test, and the calibration curve showed good discrimination and calibration of nomogram in the internal validation cohort (Figure2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDevelopment and validation of a nomogram for predicting cumulative live birth\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate associations of the potential predictors and multivariable logistic regression model for the cumulative live birth of freeze-thaw oocytes are shown in Table 3. Predictors included in the multivariable logistic regression were as follows: age of female and male, duration of infertility, basal FSH and E2 level, Gn dosage, number of follicles on the day of hCG administration, poor ovarian response and sperm source. The model shows that the odds ratio of a successful live birth decreases with the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031).\u003c/p\u003e\n\u003cp\u003eThe nomogram was derived from the multivariable logistic regression model. The model showed an AUC of 0.724 (95% CI: 0.647\u0026ndash;0.801, p＜0.001). The Hosmer-Lemeshow goodness- of-fit test, and the calibration curve showed good discrimination and calibration of nomogram in the internal validation cohort (Figure3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the early 20th century, scientists began to preserve gametes and embryos at low temperatures. In 1999, Kuleshova [8] first reported the case of successful pregnancy and delivery after oocyte cryopreservation, which marked a breakthrough in oocyte cryopreservation. Presently thousands of children are born and benefited from this technique. Although new techniques are emerging and existing ones are always evolving, the freeze-thaw process can cause damage and changes of spindles, genetic materials, organelles and epigenetic in the oocyte [9]. Whether or not these alterations may produce a long-term negative health effect remains unclear. Existing studies have shown that the clinical pregnancy rate and live birth rate of mature oocytes after freezing and thawing are similar to those of fresh oocytes, with evidence from oocyte donation cycles. However, for patients with poor ovarian reserve function or less expected number of oocytes, doctors and patients are still worried that no embryo could be transferred after thawing the oocytes. The data from our center indicates that nearly one-fifth of the patients (40/211, 18.96%) have no embryos to transfer after thawing the oocytes. For those patients, if we can inform them of the possibility of no embryos to transfer before oocyte retrieval, it may reduce the economic loss and the risks of the operation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMain findings\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt present, there is no predictive model of pregnancy outcome after emergency cryopreservation of oocytes. Based on the clinical data and laboratory results of emergency oocyte cryopreservation, prediction models of pregnancy outcomes were developed to fill the gap. All the indicators in the model are available before oocyte retrieval. The internal verification of the model was also conducted. The key predictors which had significant effects on the result of the model of no embryo to transfer are: female age, duration of infertility, basal FSH, basal E2 and the source of semen. While for the model of live birth, the key predictors are: the number of follicles which diameter greater than 10mm and endometriosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStrengths and weaknesses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRatna [10] suggested that a high-quality prediction model article should meet the following three criteria:1) a TRIPOD [6] score greater than 80%. 2) external validation and 3) the model had acceptable discrimination (c-statistic \u0026gt;0.7)[11]. 35 prediction models of IVF success have been published across 23 articles. These 35 models met between 29 to 95% of the items included in the TRIPOD checklist [12, 13]. Only 21% of studies met at least 80% of the checklist items, and the highest achieved a TRIPOD score of 95%. Only four models [14-17] had conducted external validation (4/23 = 17.39%), and almost all of the indicators in the models have missing values or do not describe missing values. The range of c-statistic was between 0.55 and 0.77.\u003c/p\u003e\n\u003cp\u003eFrom research design to manuscript drafting, we strictly followed the TRIPOD list. The self-evaluation TRIPOD score is 90.91%. The AUC of the \u0026lsquo;no embryo to transfer\u0026rsquo; model is 0.799 (95% CI: 0.722\u0026ndash;0.875, p＜0.001), and the AUC of the \u0026lsquo;live birth \u0026rsquo;model is 0.724 (95% CI: 0.647\u0026ndash;0.801, p＜0.001), which are greater than 0.7. The accuracy of the prediction model is at the forefront of the existing models. On the one hand, it benefits from the guidance of TRIPOD, but on the other hand, it is closely related to the fact that this study covers almost all the prediction indicators related to the pregnancy outcome of IVF and there is no missing value.\u003c/p\u003e\n\u003cp\u003eThe main categories of predictors included in developed models are as follow: couple factors, gender, embryo and treatment. At present, several better prediction models recommended in the field of reproductive medicine mostly come from multicenter or national databases. Although the sample size is large, the number of prediction indicators included is limited [13]. The median number of predictors included in the existing models was 7 (range 3\u0026ndash;14). Our model includes 26 forecast indicators. In addition to the most frequently used predictors such as female age, duration of infertility, endometriosis, et al [18], we also included basic hormone levels, AFC, male age and semen quality and hormone levels on the day of hCG injection. The information about the embryos could not be obtained due to the pretreatment model. However, compared with other pretreatment models, the numbers of oocytes were estimated through the number of follicles with a diameter of more than 10 mm on hCG administration day. Moreover, the sources of the semen were also taken into consideration to further improve the accuracy of the model prediction.\u003c/p\u003e\n\u003cp\u003eOne of the greatest strengths of our model is that it has highlighted the semen source as a key predictor for IVF success. Semen source is a factor that has never been used in any previous prediction models. Studies have indicated that NOA (non-azoospermia) patients could produce increased numbers of cytogenetically abnormal testicular spermatozoa despite their normal somatic karyotype, and were at increased risk to produce aneuploid gametes and of transmitting chromosome aneuploidy to the zygote [19, 20], which may lead to a reduced developmental potential of embryos. An [21] had compared 150 NOA patients who underwent micro-TESE with 174 OA patients who underwent TESA and found that developmental competence of the embryo was greatest among couples using sperm obtained by TESA rather than micro-TESE, and was not dependent on whether vitrified or fresh oocytes were utilized. Capello [22] showed that the quality and source of sperm did not affect the clinical pregnancy rate and live birth rate in vitrified oocyte donation IVF model. On the contrary, the results of our study suggest that the source of semen is an important factor leading to no embryo to transfer in freeze-thaw oocyte cycles. If the sperm comes from MESA, the risk of no embryo to transfer will be increased by 7.74 times.\u003c/p\u003e\n\u003cp\u003eFemale age and duration of infertility are two important predictors applied to predict the pregnancy/live birth chances after IVF [18]. They both have negative associations with treatment outcomes. Our results suggest that in terms of pregnancy outcome of oocyte cryopreservation, female age and duration of infertility also play a negative role. For every 1-year increase in female age, the risk of no embryo to transfer increases by 1.099 times. The risk of no embryo to transfer increases by 1.14 times for every 1-year extension of infertility years.\u003c/p\u003e\n\u003cp\u003eBasal FSH and E2 levels are important indexes for the evaluation of ovarian reserve function. A high level reflects a reduced ovarian reserve and is associated with poor IVF treatment outcome [23]. Some studies also suggested that the FSH level on cycle day 3 was a better indicator of IVF outcome than female age [24]. High levels of basal E2 level were associated with low oocyte yields, low pregnancy rates and higher cancellation rate independent of FSH levels [25, 26]. Our results are consistent with the above studies. In the multiple logistic regression equation, the effect of basal FSH and basal E2 on the adverse outcome of no embryo to transfer is even beyond the female age.\u003c/p\u003e\n\u003cp\u003eEndometriosis is one of the important factors leading to female infertility. 57% (20/35) of the prediction models take endometriosis as one of the important indicators to evaluate the success rate of IVF. After balancing many other prediction indicators, only the number of follicles larger than 10 mm on the day of hCG administration and endometriosis entered the final equation, which shows that the expected number of retrieved oocytes and endometriosis are closely related to the outcome of oocytes cryopreservation.\u003c/p\u003e\n\u003cp\u003eOne of the weaknesses of our model is insufficient external validation. This is because there are a limited numbers of patients which require emergency oocyte cryopreservation. In one of the largest assisted reproductive centers in China, in over 10 years only 211 patients received emergency oocyte cryopreservation and returned to the hospital for follow-up treatment. The number is expected to be lower in other relatively smaller scale assisted reproductive centers. Therefore, it is difficult to carry out external verification at this moment. In addition, the sample size used to derive this prediction model is small, and all of them are from a single center. Whether the research results can be extended to other races and regions remains to be further verified.\u003c/p\u003e\n\u003cp\u003eGiven the complexities of assisted reproductive technology, many other confounders can have an effect at different points in time. Although we can use the expected retrieved number of eggs and sperm sources to make a preliminary assessment of the embryo, our model is for pretreatment counseling only. We appreciate that IVF success rates depend on more than the factors in this model alone. Therefore, when using the model, it is important for clinicians to ensure that their patients understand the probability of having a successful outcome will invariably change as they progress through their treatment and thus should be interpreted as a baseline prediction only.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison to existing models\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe existing prediction models of IVF success rate are all for fresh oocyte; there is no available model for frozen oocyte. Therefore, there is no comparability of clinical indicators and prediction accuracy between this model and existing models. Different from the published clinical model of assisted reproduction, a nomogram was applied in this research to display the prediction model, which is more practical and intuitive. The nomogram makes it convenient for clinicians and patients to calculate the benefits of oocytes cryopreservation in each treatment cycle according to their own conditions. This can help reduce psychological pressure on both doctors and patients and make the decision easier under certain expectations. It may also reduce the economic and psychological pressure on patients when facing no embryo to transfer. We believe this prediction tool is an important and valuable addition in the counseling process for patients at this critical decision-making point in their journey.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur results show that, as fresh oocyte cycle, for the oocyte cryopreservation cycle, with the increase of age, the prolongation of infertility, the decrease of ovarian reserve function and endometriosis, the risk of no embryo to transfer could be increased and the live birth rate could be decreased. We have illustrated not only the clinical use of this model but also how a couple\u0026rsquo;s characteristics might affect their prognosis. This model provides a personalized approach to counseling and estimates the chances of success based on individual information. This can be applied by clinicians when counseling couples before emergency oocyte cryopreservation.\u003c/p\u003e\n\u003cp\u003eFor example, take the case of an infertile patient and sperm donor where the woman is 32 years old with three years of infertile history, basal FSH, 7.5MIU/ml, basal E2:131mmol/L. Their IVF indicators are involved: endometriosis and severe oligozoospermia. The number of follicles with a diameter greater than 10 mm on the day of hCG administration is 8. The sperm donor could not come to the hospital due to an emergency on the day of oocyte retrieval. If the man can obtain sperm by masturbation, the possibility of no embryo to transfer is 25.30%. If it is necessary to extract sperm by MESA, the possibility of no embryo to transfer is 48.9%, and the possibility of live birth is 39.39%. The results from our model might assist the couples to decide whether to freeze or give up oocyte retrieval.\u003c/p\u003e\n\u003cp\u003eThe next step for this model is to further validate the research findings by performing external validation in other assisted reproduction centers in China and worldwide. Furthermore, this model may be developed into both a user-friendly web-based decision aid platform and as a mobile application to assist both clinicians and patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIVF: in vitro fertilization\u003c/p\u003e\n\u003cp\u003eICSI: intracytoplasmic sperm injection\u003c/p\u003e\n\u003cp\u003eART: assisted reproductive technology\u003c/p\u003e\n\u003cp\u003eMESA: Microsurgical Epididymal Sperm Aspiration\u003c/p\u003e\n\u003cp\u003eTESA: Testicular Sperm Aspiration\u003c/p\u003e\n\u003cp\u003eGn: gonadotropin\u003c/p\u003e\n\u003cp\u003eHcg: human chorionic gonadotropin\u003c/p\u003e\n\u003cp\u003eROC：receiver-operator characteristic\u003c/p\u003e\n\u003cp\u003eAUC：area under the curve\u003c/p\u003e\n\u003cp\u003eAFC:antral follicle count\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was provided by the Ethics Committee of Peking University Third Hospital (Approval reference No:2019SZ-092; date of approval 16 December 2019). Patients provided written consent for the information to be used in the analyses, editing and publications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was fund by the National Natural Science Foundation of China (81801447).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Wang and Rong Li design the study. Yang Wang and Ziru Niu collected the data and drafted the manuscript. Liyuan Tao conducted the statistical analysis. Xiaoying zheng and Yifeng yuan conducted the oocyte frozen and thawing. Ping Liu and Rong Li revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the staff in the reproductive center of Peking University Third Hospital were acknowledged here.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e\u0026nbsp;[1]. Chen, C., Pregnancy after human oocyte cryopreservation. Lancet (London, England), 1986. 1(8486): p. 884.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[2]. Practice, C.O.A.S. and F.A.R.T. Society, Mature oocyte cryopreservation: a guideline. Fertility and sterility, 2013. 99(1): p. 37.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[3]. Malizia, B.A., M.R. Hacker and A.S. Penzias, Cumulative live-birth rates after in vitro fertilization. N Engl J Med, 2009. 360(3): p. 236-43.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[4]. Wiegerinck, M.A., et al., How concordant are the estimated rates of natural conception and in-vitro fertilization/embryo transfer success? Hum Reprod, 1999. 14(3): p. 689-93.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[5]. van der Steeg, J.W., et al., Do clinical prediction models improve concordance of treatment decisions in reproductive medicine? BJOG, 2006. 113(7): p. 825-31.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[6]. Collins, G.S., et al., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Medicine, 2015. 13(1): p. 1.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[7]. Zhang, L., et al., L-proline: a highly effective cryoprotectant for mouse oocyte vitrification. Sci Rep, 2016. 6: p. 26326.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[8]. Kuleshova, L., et al., Birth following vitrification of a small number of human oocytes: case report. Hum Reprod, 1999. 14(12): p. 3077-9.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;[9]. Iussig, B., et al., A brief history of oocyte cryopreservation: Arguments and facts. Acta Obstet Gynecol Scand, 2019. 98(5): p. 550-558.\u003c/p\u003e\n\u003cp\u003e[10]. Ratna, M.B., et al., A systematic review of the quality of clinical prediction models in in vitro fertilisation. Human Reproduction, 2020. 35(1): p. 100-116.\u003c/p\u003e\n\u003cp\u003e[11]. Swets, J.A., Measuring the accuracy of diagnostic systems. Science, 1988. 240(4857): p. 1285.\u003c/p\u003e\n\u003cp\u003e[12]. Hughes, E.G., C. King and E.C. Wood, A prospective study of prognostic factors in in vitro fertilization and embryo transfer. Fertil Steril, 1989. 51(5): p. 838-44.\u003c/p\u003e\n\u003cp\u003e[13]. McLernon, D.J., et al., 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\u0026thinsp;873 women. BMJ, 2016: p. i5735.\u003c/p\u003e\n\u003cp\u003e[14]. Dhillon, R.K., et al., Predicting the chance of live birth for women undergoing IVF: a novel pretreatment counselling tool. Hum Reprod, 2016. 31(1): p. 84-92.\u003c/p\u003e\n\u003cp\u003e[15]. van Loendersloot, L.L., et al., Individualized decision-making in IVF: calculating the chances of pregnancy. Hum Reprod, 2013. 28(11): p. 2972-80.\u003c/p\u003e\n\u003cp\u003e[16]. Cai, Q.F., et al., Factors predicting the cumulative outcome of IVF/ICSI treatment: a multivariable analysis of 2450 patients. Hum Reprod, 2011. 26(9): p. 2532-40.\u003c/p\u003e\n\u003cp\u003e[17]. Stolwijk, A.M., et al., External validation of prognostic models for ongoing pregnancy after in-vitro fertilization. Hum Reprod, 1998. 13(12): p. 3542-9.\u003c/p\u003e\n\u003cp\u003e[18]. van Loendersloot, L.L., et al., Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis. Human Reproduction Update, 2010. 16(6): p. 577-589.\u003c/p\u003e\n\u003cp\u003e[19]. Onofre, J., et al., Cryopreservation of testicular tissue or testicular cell suspensions: a pivotal step in fertility preservation. Hum Reprod Update, 2016. 22(6): p. 744-761.\u003c/p\u003e\n\u003cp\u003e[20]. Vozdova, M., et al., Testicular sperm aneuploidy in non-obstructive azoospermic patients. Hum Reprod, 2012. 27(7): p. 2233-9.\u003c/p\u003e\n\u003cp\u003e[21]. An, G., et al., Outcome of Oocyte Vitrification Combined with Microdissection Testicular Sperm Extraction and Aspiration for Assisted Reproduction in Men. Medical Science Monitor, 2018. 24: p. 1379-1386.\u003c/p\u003e\n\u003cp\u003e[22]. Capelouto, S.M., et al., Impact of male partner characteristics and semen parameters on in\u0026nbsp;vitro fertilization and obstetric outcomes in a frozen oocyte donor model. Fertility and Sterility, 2018. 110(5): p. 859-869.\u003c/p\u003e\n\u003cp\u003e[23]. Scott, R.T., et al., Reprint of: Follicle-stimulating hormone levels on cycle day 3 are predictive of in\u0026nbsp;vitro fertilization outcome. Fertil Steril, 2019. 112(4 Suppl1): p. e174-e177.\u003c/p\u003e\n\u003cp\u003e[24]. Toner, J.P., et al., Basal follicle-stimulating hormone level is a better predictor of in vitro fertilization performance than age. Fertil Steril, 1991. 55(4): p. 784-91.\u003c/p\u003e\n\u003cp\u003e[25]. Evers, J.L., et al., Elevated levels of basal estradiol-17beta predict poor response in patients with normal basal levels of follicle-stimulating hormone undergoing in vitro fertilization. Fertil Steril, 1998. 69(6): p. 1010-4.\u003c/p\u003e\n\u003cp\u003e[26]. Jiang, Z., et al., A combination of follicle stimulating hormone, estradiol and age is associated with the pregnancy outcome for women undergoing assisted reproduction: a retrospective cohort analysis. Sci China Life Sci, 2019. 62(1): p. 112-118.\u003c/p\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" width=\"100%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"100%\"\u003e\n\u003cp\u003eTable 1. Baseline characteristics of the cohort\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e\u0026lsquo;no embryo to transfer\u0026rsquo;\u003c/p\u003e\n\u003cp\u003ecohort（N=215)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003eLive birth rate(N=164)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eFemale age (years)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e29.91\u0026plusmn;4.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e29.41\u0026plusmn;4.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eBMI(kg/㎡)\u0026nbsp; （Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e22.32\u0026plusmn;3.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e22.41\u0026plusmn;3.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eDuration of infertility (years)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e3.44\u0026plusmn;3.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e3.13\u0026plusmn;2.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eTypes of infertility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Primary infertility（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e174(80.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e131(79.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Secondary infertility（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e41(19.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e33(20.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eGravidity (times）（Median，(min,max)）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e0.00(0, 5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e0.00(0, 5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eDelivery (times）（Median，(min,max)）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e0.00(0, 2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e0.00(0, 2)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Endometriosis（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e17(7.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e14(8.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; PCOS（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e24(11.16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e18(10.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; POR（n（%））a\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e22(10.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e13(7.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Tubal factor （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e28(13.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e22(13.41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eIVF failure history （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e21(9.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e16(9.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003ebasal FSH (MIU/ml) （Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e6.11\u0026plusmn;2.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e5.86\u0026plusmn;2.38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003ebasal LH (MIU/ml)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e3.64\u0026plusmn;1.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e3.64\u0026plusmn;1.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003ebasal E2 (mmol/L)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e164.58\u0026plusmn;88.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e157.86\u0026plusmn;58.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eAFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e14.55\u0026plusmn;6.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e14.85\u0026plusmn;6.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eDuration of Gn applied\u0026nbsp; (Days)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e11.47\u0026plusmn;2.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e11.47\u0026plusmn;2.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003etotal Gn applied （units）（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e2505.62\u0026plusmn;1140.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e2428.80\u0026plusmn;1106.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eLH on the day of hCG (MIU/ml) （Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e1.73\u0026plusmn;2.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e1.63\u0026plusmn;2.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eE2 on the day of hCG (mmol/L) （Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e11036.42\u0026plusmn;7532.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e11622.27\u0026plusmn;7552.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eP on the day of hCG (pmol/L)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e2.85\u0026plusmn;1.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e2.89\u0026plusmn;1.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eThe number of follicles with a diameter greater than 10 mm on the day of hCG(number) (Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e16.08\u0026plusmn;6.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e16.76\u0026plusmn;6.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eMale age (years)（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e31.20\u0026plusmn;6.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e30.70\u0026plusmn;5.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eSemen quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Azoospermia（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e158(73.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e123(75.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Oligozoospermia（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e21(9.77%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e14(8.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Normal semen（n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e36(16.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e27(16.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eSemen source\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; AID （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e104(48.37%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e90(54.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; TESA/PESA （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e10(4.65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e7(4.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; MESA （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e42(19.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e22(13.41%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; Masturbation （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e59(27.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e45(27.44%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eDuration of oocyte frozen (month）（Mean\u0026plusmn;SD）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e8.23\u0026plusmn;8.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e8.51\u0026plusmn;9.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003eOocyte retrival time grouped by year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; 2007-2011 （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e24(11.16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e23(14.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; 2012-2015 （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e76(35.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e54(32.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"46%\"\u003e\n\u003cp\u003e\u0026nbsp; 2016-2019 （n（%））\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"34%\"\u003e\n\u003cp\u003e115(53.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\n\u003cp\u003e87(53.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"80%\"\u003e\n\u003cp\u003eBMI:body mass index; AFC:antral follicle count; PCOS: polycystic ovary syndrome;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"80%\"\u003e\n\u003cp\u003ePOR-poor ovarian response diagnosis according to Bologna dianosis criteria；\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"80%\"\u003e\n\u003cp\u003eAID:artificial insemination by donor; TESA:testicular sperm aspiration;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"19%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"100%\"\u003e\n\u003cp\u003ePESA：percutaneous epididymal sperm aspiration; MESA：microdissection testicular sperm extraction.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" width=\"117%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"100%\"\u003e\n\u003cp\u003eTable 2. Potential predictors and multivariable logistic regression model for no embryo to transfer\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"26%\"\u003e\n\u003cp\u003ePredictor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"38%\"\u003e\n\u003cp\u003e\u0026nbsp;univariate analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"35%\"\u003e\n\u003cp\u003emultivariable analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003eOR (95%CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eOR (95%CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eFemale age (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.098(1.028，1.172)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.005*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1.099(1.003,1.205)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e0.044*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eBMI(kg/㎡)\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.975(0.885，1.075)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.616\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eDuration of infertility (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.149(1.047，1.262)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.004*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1.140(1.018,1.276)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e0.024*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Secondary infertility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.575 (0.616, 4.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.343\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eGravidity (times）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.881 (0.568,1.367)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.573\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eDelivery (times）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.842 (0.126,5.603)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.859\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Endometriosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.181(0.324,4.310)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.801\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; PCOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.944(0.331,2.690)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.914\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; POR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.309(0.122,0.781)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.013*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Tubal factor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.518(0.518，4.825)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eIVF failure history\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.069(0.341,3.358)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003ebasal FSH (MIU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.156(1.029，1.299)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.014*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1.205(1.051,1.382)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e0.008*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003ebasal LH (MIU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.014(0.858,1.198)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003ebasal E2 (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.003(1.000，1.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1.006(1.001,1.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e0.012*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eAFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.960(0.912，1.010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.115\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eDuration of Gn applied (Days)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.994(0.873，1.131)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.926\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003etotal Gn applied （units）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.003(1.000，1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eLH on the day of hCG (MIU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.066(0.944,1.203)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eE2 on the day of hCG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.000(1.000，1.000)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.204\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eP on the day of hCG (pmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.972(0.804，1.174)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.765\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eThe number of follicles with a diameter greater than 10 mm on the day of hCG(number)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.950(0.901，1.001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eMale age (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.066(1.014，1.121)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.012*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eSemen quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Azoospermia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.516\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Oligozoospermia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.779(0.636,4.978)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Normal semen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1.271(0.526,3.073)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.595\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eSemen source\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; AID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e＜0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; TESA/PESA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e4.029(0.898,18.081)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e2.180(0.383,12.403)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e0.380\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; MESA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e6.392(2.607,15.675)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e＜0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e7.741(2.905,20.632)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e＜0.001*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; Masturbation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e2.675(1.084,6.512)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1.399(0.486,4.034)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e0.534\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eDuration of oocyte frozen (month）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e0.978(0.936,1.022)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003eOocyte retrival time grouped by year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; 2007-2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; 2012-2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e6.133(0.769,48.931)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"26%\"\u003e\n\u003cp\u003e\u0026nbsp; 2016-2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"23%\"\u003e\n\u003cp\u003e6.719(0.866,52.154)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"49%\"\u003e\n\u003cp\u003eBMI:body mass index; AFC:antral follicle count; PCOS: polycystic ovary syndrome;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"49%\"\u003e\n\u003cp\u003ePOR-poor ovarian response diagnosis according to Bologna dianosis criteria；\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"49%\"\u003e\n\u003cp\u003eAID:artificial insemination by donor; TESA:testicular sperm aspiration;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"14%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"64%\"\u003e\n\u003cp\u003ePESA：percutaneous epididymal sperm aspiration; MESA：microdissection testicular sperm extraction.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"13%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" width=\"114%\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" width=\"100%\"\u003e\n\u003cp\u003eTable 3. Potential predictors and multivariable logistic regression model for live birth rate\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"20%\"\u003e\n\u003cp\u003ePredictor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"44%\"\u003e\n\u003cp\u003e\u0026nbsp;univariate analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"34%\"\u003e\n\u003cp\u003emultivariable analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003eOR（95%CI）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003eOR（95%CI）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eFemale age (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.934（0.872，1.001）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eBMI(kg/㎡)\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.976(0.894,1.066)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eDuration of infertility (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.928（0.817，1.055）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.255\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Secondary infertility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.991（0.859，4.615）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eGravidity (times）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.795(0.523,1.211)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.286\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eDelivery (times）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.842(0.386,8.799)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Endometriosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e4.345(0.939,20.099)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e0.172(0.035,0.853)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0.031*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; PCOS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.622(0.233,1.662)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; POR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.431(0.114,1.629)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.215\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Tubal factor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.174(0.462,2.978)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eIVF failure history\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.105(0.381,3.203)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003ebasal FSH (MIU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.904（0.790，1.034）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003ebasal LH (MIU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.865（0.731，1.025）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003ebasal E2 (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.998(0.992,1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eAFC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.046（0.997，1.097）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eDuration of Gn applied\u0026nbsp; (Days)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.911（0.801，1.037）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003etotal Gn applied （units）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.000（0.999，1.000）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.034*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eLH on the day of hCG (MIU/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.036(0.898,1.194)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.630\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eE2 on the day of hCG (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.000（1.000，1.000）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eP on the day of hCG (pmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.861（0.699，1.061）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eThe number of follicles with a diameter greater than 10 mm on the day of hCG(number)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.071（1.019，1.125）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.006*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e1.088(1.030,1.149)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e0.002*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eMale age (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.955（0.901，1.011）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eSemen quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Azoospermia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Oligozoospermia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.687(0.218,2.168)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Normal semen\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.281(0.100,0.790)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eSemen source\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; AID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; TESA/PESA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.228(0.026,1.974)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.179\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; MESA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.977(0.767,5.097)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.159\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; Masturbation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e0.556(0.258,1.199)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eDuration of oocyte frozen (month）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.028(0.992,1.065)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003eOocyte retrival time grouped by year\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; 2007-2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.653\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; 2012-2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.500(0.545,4.127)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"20%\"\u003e\n\u003cp\u003e\u0026nbsp; 2016-2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"28%\"\u003e\n\u003cp\u003e1.146(0.438,2.996)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\n\u003cp\u003e0.781\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\n\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"49%\"\u003e\n\u003cp\u003eBMI:body mass index; AFC:antral follicle count; PCOS: polycystic ovary syndrome;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"49%\"\u003e\n\u003cp\u003ePOR-poor ovarian response diagnosis according to Bologna dianosis criteria；\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"49%\"\u003e\n\u003cp\u003eAID:artificial insemination by donor; TESA:testicular sperm aspiration;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"16%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"22%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" width=\"88%\"\u003e\n\u003cp\u003ePESA：percutaneous epididymal sperm aspiration; MESA：microdissection testicular sperm extraction.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"11%\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nomogram, Oocyte freeze-thaw, IVF","lastPublishedDoi":"10.21203/rs.3.rs-50551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-50551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e To study which characteristics of a pre-oocyte-retrieval patient can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Data was collected from the Reproductive Center, Peking University Third Hospital of China. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The predictors in the model of ‘no embryo to transfer’ are female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH level (OR= 1.205, 95% CI=1.051-1.382, P=0.0084), basal E2 level (OR=1.006, 95% CI=1.001-1.010, P=0.012) and sperm from MESA (OR=7.741, 95% CI=2.905-20.632, P\u0026lt;0.001). Upon assessing predictive ability, the AUC for this model was 0.799 (95% CI: 0.722–0.875, p＜0.001). The Hosmer-Lemeshow test (p=0.721) and calibration curve showed good calibration. The predictors in the cumulative live birth were the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031). The AUC for this model was 0.724 (95% CI: 0.647–0.801, p＜0.001). The Hosmer-Lemeshow test (p=0.562) and calibration curve showed good calibration for the prediction of cumulative live birth. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, basal FSH and E2 level, the number of follicles with a diameter greater than 10 mm on the day of hCG administration, endometriosis and sperm from microdissection testicular sperm extraction (MESA).\u003c/p\u003e","manuscriptTitle":"The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-04 17:23:43","doi":"10.21203/rs.3.rs-50551/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2020-08-02T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-07-28T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9f516f9-e101-4b50-a75b-c72b4768c74e","owner":[],"postedDate":"August 4th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":238259,"name":"Laboratory Diagnostics"}],"tags":[],"updatedAt":"2020-08-04T17:23:43+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-04 17:23:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-50551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-50551","identity":"rs-50551","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}