Predicting single-cycle cumulative live birth rate in POSEIDON Group 2 Patients: a prediction model based on matching learning

Predicting single-cycle cumulative live birth rate in POSEIDON Group 2 Patients: a prediction model based on matching learning | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting single-cycle cumulative live birth rate in POSEIDON Group 2 Patients: a prediction model based on matching learning Chunyan Chen, Xinliu Zeng, Hanke Zhang, Yanhui Li, Ying Gao, Lin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3521867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Outcomes in patients with poor ovarian response (POR) have been less favorable and there is a need for improvement. The patient-oriented strategy encompassing individualized oocyte number (POSEIDON) criteria，proposed in 2016, are now widely accepted and used in clinical practice. POSEIDON Group 2 is considered as “Unexpected low response”, which is a challenge for clinicians. Currently, multiple reviews have retrospectively analysed the ART outcomes in the hyporesponsive populations of the POSEIDON Groups. However, no study has systematically examined the influencing factors specifically associated with the single-cycle cumulative live birth rate in POSEIDON Group 2. A prediction model was developed to predict the cumulative single-cycle live birth rate in POSEIDON Group 2 Patients. Methods: A total of 565 assisted reproductive cycles from the low-response population of POSEIDON Group 2 were retrospectively analyzed from January 2018 to December 2021 at the center for Reproductive Medicine, Wuhan Union Hospital, Tongji Medical College. Cases were randomized 7:3 into two groups. Baseline levels were compared among the total, training and validation groups. A total of 26 variables were included and analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO)regression with "lambda.min" as the screening criterion. To construct a predictive model of cumulative live birth rate, the selected variables were subjected to multivariate logistic regression. The predictive performance of the model was validated in the validation group. Results: After randomization, 392 cases were assigned to the training group and 173 cases to the validation group. There were no statistical differences in baseline characteristics among the three groups. Seven variables were screened out by LASSO regression, including female age, assisted reproduction cycles, type of infertility, normal fertilization rate, blastocyst formation rate, number of frozen embryos, and whether fresh embryos were transferred. Furthermore, logistic regression was performed on these seven variables to construct a regression model，which had a ROC (Receiver Operating Characteristic) curve of 0.818 in the training group and 0.7971 in the validation group, with good predictive power and goodness-of-fit tests >0.05 in both the training and validation groups. The model had an area under the ROC curve of 0.818 in the training group and 0.7971 in the validation group. The prediction efficiency was good, and the Goodness of fit test in both the training group and the validation group was>0.05. Conclusions: In this study, the prediction model constructed had good predictive performance with female age, normal fertilization rate, blastocyst formation rate, number of frozen embryos, and fresh embryo transfer. These factors work as independent predictors of single cycle cumulative live birth rate in patients with POSEIDON Group 2. Trial registration: This is a retrospective study, and the study was ethically approved by Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China. LASSO regression machine learning prediction model POR POSEIDON Group Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The goal of assisted reproductive technology is the birth of a healthy baby. As clinicians, we constantly have to answer questions about the probability of a successful birth upon assisted reproductive technology raised by patients during outpatient consultations. With the advances of in vitro fertilization technology, the pregnancy rate and live birth rate of in vitro fertilization have been increased steadily, but the pregnancy outcomes of older patients with poor ovarian response are still not satisfactory. In response to the challenge, in 2016 the POSEIDON (Patient-Oriented Strategies Encompassing Individualized Oocyte Number) Group proposed a new stratification for patients who have a reduced ovarian reserve or an unexpected inappropriate ovarian response to exogenous gonadotropins[1, 2]. In brief, four subgroups are defined in terms of both quantitative and qualitative parameters, namely, (i) age and the expected aneuploidy rate, (ii) ovarian parameters (i.e., antral follicle count [AFC] and anti-Müllerian hormone [AMH]), and (iii) number of oocytes retrieved by standard ovarian hyperstimulation. The POSEIDON criteria included two new categories of impaired response: a ‘suboptimal response’ and a ‘hyporesponse’, corresponding to suboptimal response and hyporesponse, by combining ‘‘qualitative’’ and ‘‘quantitative’’ parameters: age, biomarkers, and functional indicators (i.e., AMH and AFC). POSEIDON Group 1 and 2 have a ‘suboptimal response’ or unexpected poor response. In Group 1, patients are younger than 35 years with adequate ovarian reserve (AFC≥5, AMH≥1.2 ng/mL) and an unexpected poor or suboptimal ovarian response. Patients in Group 2 are older than 35 years but have sufficient pre-stimulation ovarian reserve (AFC≥5, AMH≥1.2 ng/mL) and an unexpected poor or suboptimal ovarian response. In particular, clinicians hope to help patients in POSEIDON Group 2 to achieve a more favorable outcome with assisted reproductive technology (ART). As early as in 2010, a retrospective analysis identified nine factors predictive of outcome of ART, including: female age, duration of infertility, follicle-stimulating hormone (FSH) levels, number of oocytes retrieved and embryo quality[3]. Subsequently, multiple studies suggested that the older female age, protracted infertility time and higher basal follicle-stimulating hormone (bFSH) levels were negative predictors of ART pregnancy rates, while increased oocyte retrieval and superior embryo quality were positive predictors[4, 5]. Previous data consistently suggested a robust correlation between the number of oocytes retrieved and live birth rate[6, 7]. Questions present themselves: What can be done clinically to improve ART outcomes in the POSEIDON Group 2, and what are the factors involved in pregnancy and live birth in these patients? Currently, multiple reviews have retrospectively analyzed the ART outcomes in the hyporesponsive populations of POSEIDON Groups, but no studies systematically examined the influencing factors specifically implicated in the single cycle cumulative live birth rate in the POSEIDON Group 2[8, 9]. In this study, we developed a nomogram for the prediction of single cycle cumulative live birth rate in POSEIDON Group 2 by employing machine learning and retrospective analysis (LASSO-logistic)[10], with an attempt to work out better clinical solutions and ovarian stimulation protocols for these patients. Materials and methods 1. Patient selection This retrospective study recruited patients who underwent ART at the Center for Reproductive Medicine, Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China, from January 2018 to December 2021. 2. Inclusion and exclusion criteria Inclusion criteria were as follows: (1) Patients aged ≥ 35 years old, with AFC ≥ 5 or AMH ≥ 1.2 ng/ml; (2) Number of oocytes retrieved ≤ 9; (3) Main follow-up endpoint: This study entailed a follow-up to the pregnancy outcome or live birth of all embryos transferred during the oocytes retrieval cycle. (4) Clinical data were complete. Exclusion criteria included: (1) Those who underwent Pre-implantation Genetic Testing (PGT) or either spouse with chromosomal abnormalities; (2) Presence of uterine adhesions, severe endometriosis, submucosal fibroids, hydrosalpinx and other factors that significantly affect embryo implantation. A total of 565 patients who met the inclusion criteria were enrolled in this study, and the study was ethically approved by Tongji Medical College, HUST, Wuhan, China. 3. Data collection and follow-up Cases were randomized, at a ratio of 7:3, into two groups: a training group and a validation group, with 392 cases assigned into the training group and 173 cases into the validation group. The calculation method for data in the study was as follows: Ovarian Sensitivity Index (OSI)[11] = Total amount of gonadotrophin (Gn) / number of oocytes retrieved. Metaphase II (MII) ooctyes Rate (MII R) = (Number of MII oocytes / number of oocytes retrieved) ×100%. Normal Fertilization Rate (NFR) includes Normal fertilization rate of in-vitro fertilization (IVF) and Normal fertilization rate of intracytoplasmic sperm injection (ICSI): Normal Fertilization Rate of IVF = (Number of fertilized oocytes by IVF/ number of oocytes retrieved) ×100%; Normal Fertilization Rate of ICSI = (number of fertilized oocytes by ICSI / number of M II oocytes) ×100%. The normal fertilization rate of patients undergoing IVF and rescue-ICSI in this study was defined as the average of normal fertilization rate of IVF plus normal fertilization rate of ICSI combined. Cleavage Rate (CR): (Number of fertilized embryos / number of fertilized oocytes) ×100%. High Quality Embryo Rate (HQER): (High quality embryos/ number of fertilized embryos) ×100%. Blastocyst Formation Rate (BFR）: (Number of formed blastocysts / number of cultured blastocysts) ×100%. Cumulative Pregnancy Rate (CPR): (Number of clinical pregnancy cycles after embryo transfer in one oocytes retrieval cycle / number of oocyte retrieval cycles) ×100%. Cumulative Live Birth Rate (CLBR): (Number of live birth cycles after embryo transfer in one oocytes retrieval cycle/ number of oocytes retrieval cycles) ×100%. 4. Statistical analysis SPSS 27.0 and R software version 4.2.3 and the “glmnet” package (R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analyses. The data of training and validation groups, and the overall data were compared in terms of baseline characteristics. Data of normal distribution were expressed as Mean ± Standard deviation (SD), and comparison between groups were made by utilizing the single factor method, while non-normal distribution data were presented as median [IQR, 25th-75th percentile], and were compared among the groups by using the Kruskal-Wallis single factor method. The R software "glmnet" package was employed to randomly group the data, at a ratio of 7:3, and to define the training and verification groups. The "glmnet" package was used to perform LASSO regression on the data of the training group data to screen out variables, and the Lambda(λ) value of "lambda.min" was used for variable screening[12]. The variables identified were subjected to multivariate regression analysis, and the "rms" package of R software was utilized for multivariate logistic regression on the included data, and a predictive model of cumulative live birth rate was constructed, and a line table was drawn to assess the predictive power of the model. The R software "ResourceSelection" package was run to test the "goodness-of-fit" of the prediction model for the training and verification groups, and the "pROC" package was employed to plot the AUC curves for the training and verification groups and calculate the area under the curves (AUC). The flow chart of this study is shown in Figure 1. Results 1. Baseline data The "glmnet" package of R software was used to randomly divide the data into two groups at a ratio of 7:3, i.e., the training group and the verification group. With the overall data that were not grouped, there were three groups in all. The baseline information from the three data sets was compared, and the results are listed in Table 1. Table 1. Baseline information Global group (n=565) Training group (n=392) Validation group (n=173) z or χ 2 P * Female age 38 [36-40] 38 [36-40.5] 37 [36-39] 1.394 0.498 Age of male spouse 391 [36-43] 39 [36-44] 39 [36-42] 0.063 0.969 Progestation cycle 1[1-2] 1 [1-2] 1 [1-2] 0.535 0.765 BMI 22.71[20.82-24.75] 22.60[20.82-24.74] 22.83 [20.82-24.84] 0.806 0.668 Infertility years 3 [2-7] 3 [1.5-7] 4 [2-8] 1.423 0.491 bFSH 7.65 [6.48-8.93] 7.61 [6.49-8.90] 7.82 [6.45-8.90] 0.063 0.969 AMH 1.98 [1.50-2.72] 1.98[1.49-2.71] 2.00[1.52-2.74] 0.443 0.801 OSI 480 [344-750] 482 [348-750] 475 [338-775] 0.170 0.918 Infertility type 0.093 0.955 Primary infertility 139 (24.6%) 95 (24.2%) 44 (25.4%) Secondary infertility 426 (75.4%) 297 (75.8%) 129 (75.4%) Causes of infertility 0.867 0.990 Female factor 440 (77.9%) 308(78.6%) 132(76.3%) Male factor 49 (8.7%) 33(8.4%) 16(9.2%) Bilateral factors 19 (10.1%) 14(3.6%) 5(3.4%) Unknown reasons 57 (10.1%) 37 (9.4%) 20(11.6%) COS protocols 5.800 0.670 GnRHa long protocol 128 (22.7%) 80 (20.4%) 48(27.7%) Modified-long GnRHa protocol 35 (6.2%) 26 (6.6%) 9(5.2%) Antagonist protocol 63 (11.2%) 46 (11.7%) 17(9.8%) Non-downregulation protocol 244 (43.2%) 178 (45.4%) 66(38.2%) Luteal phase protocol and others 95(16.8%) 62 (15.8%) 33(19.1%) Progestation tecnology 0.906 0.924 IVF 319(56.5%) 226 (57.7%) 93(53.8%) ICSI 225(39.8%) 151(38.5%) 74(16.4%) RICSI 21(3.7%) 15(3.8%) 6(3.5%) Whether fresh embryo was transferred 0.094 0.954 Fresh embryo transfer 165 (29.2%) 116(29.6%) 49(28.3%) Fresh embryo not transplanted 400 (70.8%) 276(70.4%) 124(71.7%) OPU 6 [4-7] 6[4-7] 6[4-8] 0.033 0.984 MII ooctyes rate (%) 88.89[75.00-100] 88.89[75-100] 88.89[77.78-100] 0.165 0.921 Fertilization rate (%) 60[40-77.78] 57.14 [40-77.78] 60 [37.5-77.78] 0.002 0.999 Cleavage rate (%) 100[100-100] 100 [100-100] 100 [100-100] 6.876 0.076 High quality embryo rate (&) 33.33[0-66.67] 33.33 [0-62.5] 37.5 [0-66.67] 0.016 0.992 Blastocyst formation rate (%) 50[0-85.71] 50 [0-86.61] 50 [0-83.33] 0.555 0.758 Number of frozen embryos 1 [0-2] 1 [0-2] 1 [0-3] 0.129 0.938 Cumulative pregnancy rate 215/565(38.1%) 143/372 (36.5%) 72/173 (41.6%) 1.337 0.513 Cumulative active production rate 135/565(23.9%) 86/392 (21.9%) 49/173 (28.3%) 2.635 0.268 *P<0.05: A statistically significant difference was found. The results in Table 1 suggest that, after randomization, there was no statistical difference in the baseline data among the total, training and validation groups. COS: controlled ovarian hyperstimulation; GnRHa: Gonadotropin releasing hormone agonist 2. Variable screening All variables (age of spouses, assisted pregnancy cycles, infertility years, body mass index(BMI), infertility type, infertility cause, bFSH, AMH, AFC, number of follicles ( ≥14 mm) in Human Chorionic Gonadotropin (HCG) days, OSI, Gn days, Gn initiation dose, controlled ovarian hyperstimulation(COS) protocols, the number of oocytes retrieved, MII rate, normal fertilization rate (NFR), cleavage rate, high quality embryo rate, blastocyst formation rate (BFR), fresh embryo transfer rate, frozen embryo number, etc. A total of 26 variables were included, and LASSO regression analysis was used for evaluation. The lambda.min (λ=0.03394075) was used for model construction. Seven variables were singled out by LASSO regression analysis as factors that impact the single cycle cumulative live birth rate in older patients with low response, i.e., female age, assisted pregnancy cycles, infertility type, normal fertilization rate, blastocyst formation rate, frozen embryo number, and whether fresh embryos were transferred. The results are given in Figure 2. 3. Construction and verification of the prediction model Seven variables selected by LASSO regression analysis were incorporated into multi-factor logistic regression, and the results are shown in Table 2. Table 2. Multivariate regression analysis of single-cycle cumulative live birth rate in older patients with low response β SD P* OR 95%CI Lower limit Upper limit Female age -0.208 0.062 < 0.001 0.812 0.719 0.917 Progestation cycle -0.258 0.178 0.145 0.772 0.545 1.093 Secondary infertility/ Primary infertility -0.388 0.313 0.215 0.678 0.367 1.254 Normal fertilization rate (%) 0.013 0.006 0.038 1.013 1.001 1.025 Blastocyst formation rate (%) 0.10 0.005 0.047 1.010 1.000 1.020 Fresh embryo Transfer/Fresh embryo not transplanted 0.781 0.333 0.019 2.183 1.136 4.193 Number of frozen blastocysts 0.392 0.122 0.001 1.480 1.164 1.882 constant 4.915 2.357 0.037 136.355 *P<0.05, indicating that the difference was statistically significant. Five variables, including female age, normal fertilization rate, blastocyst formation rate, fresh embryo transfer and frozen blastocyst number, were independent contributors to single cycle cumulative live birth rate in older patients with low response. The logistic regression model was presented by a column graph, and the results are shown in Figure 3. Based on logistic regression analysis, a clinical prediction model of single-cycle cumulative live birth rate in older low-response patients was obtained in POSEIDON Groups 2. For older low-response patients, the predicted cumulative live birth rate of a single cycle (P) is as follows: The prediction probability of the prediction model was verified in the verification group, and the goodness of fit of the model in the training group and the verification group was calculated by using the "ResourceSelection" package of the R software. The goodness of fit test of the model yielded a P of 0.3677 in the training group, and the P was 0.4176 in the verification group. The goodness of fit test P for both of them was greater than 0.05, indicating that the calibration degree of the model was acceptable. The “pROC” package of R software was used to draw ROC curves for the data of the training and verification groups. The results showed that the AUC of the prediction model in the training group was 0.818, and the predictive power was good. The AUC of the prediction model in the verification group was 0.7971, and the predictive power was good. Therefore, the prediction model of single cycle cumulative live birth rate in older patients with low response has good predictive power. Female age, normal fertilization rate, blastocyst formation rate, fresh embryo transfer and frozen embryo number were independently contributed to the single cycle cumulative live birth rate in older patients with low response. Discussion Developments in assisted reproductive technology (ART) are helping more and more infertile couples to achieve pregnancy and live birth through IVF/ICSI. However, the pregnancy rate and the live birth rate of IVF/ICSI still need to be improved, especially for people with POR. A European study conducted over a period of 18 years reported[5] that there was a strong correlation between the number of oocytes retrieved and the cumulative live birth rate in a single COS cycle. The cumulative live birth rate increased as the number of oocytes retrieved increased. The cumulative live birth rate tended to stabilize when the number of oocytes retrieved was 15-20, and it gradually dropped when the number of oocytes retrieved was more than 20. Patients with poor response to ovulation induction and low oocytes retrieval represent a major challenge in ART. In fact, clinicians have been striving to improve the outcome of assisted conception in patients with POR and to achieve a more accurate assessment pre-ART for the counselling of patients. Before 2011, there was no precise definition of low ovarian response. According to the systematic review by Polyzos and Devroey, 47 randomized trials of low ovarian response proposed up to 41 different diagnostic criteria for low ovarian response[13]. In the same year, ESHRE formulated the Bologna criteria for the diagnosis of low ovarian response[14] to standardize the definition of POR and reduce the heterogeneity of studies. According to the Bologna criteria, at least two of the following conditions must be met in order to diagnose POR: (1) advanced age (age ≥40 years) and (2) obtaining ≤3 oocytes with conventional stimulation or low ovarian reserve function (AFC<7 or AMH<1.1 ng/ml). In addition, in the absence of advanced age or low ovarian reserve function, a diagnosis of low response could be established, if the number of oocytes retrieved after two maximum stimulations is less than 3. The Bologna consensus fails to consider the heterogeneity of clinical responses in patients with low response and applies a blanket approach to age, rendering it difficult to inform patients against this standard in ART consultation. In response, POSEIDON Organization proposed the POSEIDON criteria for POR in 2016[1, 2], which categorizes patients with low response into four groups in terms of age and ovarian reserve. Patients in POSEIDON Group 1 are young and have good ovarian reserve. Although the number of oocytes obtained from them is relatively small, they can still achieve good clinical outcomes. Yan E et al. retrospectively analyzed 4105 patients of POSEIDON Group 1, and the cumulative live birth rate in 4 cycles of ovulation promotion reached 67.9%[15]. In POSEIDON Group 2, although the patients are older, their ovarian reserve is still good, and they belong to the patients with unexpected low response. Lebovitz O et al.[16] retrospectively reviewed 751 patients with POR who satisfied the Bologna criteria and identified several contributors to the single-cycle cumulative live birth rate in patients with low ovarian response through logistic regression. Their results suggested that female age and the number of oocytes retrieved were independent predictors of cumulative live birth rate. Using a retrospective analysis, Gong et al.[17] explored the factors that may influence the cumulative live birth rate of patients with standard low ovarian response diagnosed according to POSEIDON criteria. Their multivariate logistic regression analysis revealed that female age, bFSH, BMI, AMH and the number of normal fertilized oocytes were independent factors for POSEIDON patients with standard low ovarian response. However, this study did not stratify patients in the POSEIDON groups, and the patients in different POSEIDON groups were highly heterogeneous. Therefore, the influencing factors of cumulative live birth rate should be different. This study focused on patients of POSEIDON Group 2, who had good ovarian reserve but were older. In this group of patients with an unexpected low response, both patients and clinicians expect a positive outcome. This point is that it is of paramount importance to accurately identify the factors that may influence cumulative live births per cycle in this group. In this study, machine learning regularization analysis (LASSO) was used to screen out possible influencing factors, and the selected factors were subjected to logistic regression to construct a prediction model for predicting the single-cycle cumulative live birth rate of patients in POSEIDON Group 2. The area under the ROC curve (AUC) of the prediction model was 0.818 and the area under the ROC curve of the verification set was 0.7971, indicating that the model had a good predictive power. Female age, normal fertilization rate, blastocyst formation rate, number of fresh embryo transfer and frozen embryos were independent factors influencing the cumulative live birth rate in single cycle of older low-response patients in POSEIDON Group 2. The correlation between age and cumulative live birth rate has currently been generally accepted. Studies on normal fertilization rate, blastocyst formation rate and number of frozen embryos all suggest that increasing the number of available embryos is important to the improvement of the assisted pregnancy outcome in older patients with low response. This predictive model can help reproductive clinicians to better inform patients during pregnancy counselling and to develop better strategies for pregnancy assistance. Conclusions In this study, we used machine learning LASSO to screen out the relevant variables, and subjected them to logistic regression to construct a prediction model for predicting the single-cycle cumulative live birth rate in patients of POSEIDON Group 2. The predictive power of the prediction model was good, and the goodness of fit test also indicated that both the training set and the verification set were well fitted. Female age, normal fertilization rate, blastocyst formation rate, number of frozen embryos and fresh embryo transfer were independent predictors of low response patients in the POSEIDON Group 2. However, the data in this study came from a single center, and the data were only verified internally. In the future, we will conduct a multi-center prospective study to further evaluate and verify the predictive efficacy of this model. Abbreviations POR: poor ovarian response; POSEIDON: patient-oriented strategy encompassing individualized oocyte number; LASSO: Least Absolute Shrinkage and Selection Operator; ROC: Receiver Operating Characteristic; AMH: antral follicle count; AMH: anti-Müllerian hormone; ART: assisted reproductive technology; FSH: follicle stimulating hormone; bFSH: basal follicle-stimulating hormone; HUST: Huazhong University of Science and Technology; PGT: Pre-implantation Genetic Testing; OSI: Ovarian Sensitivity Index; Gn: gonadotrophin; MII: Metaphase II; NFR: Normal Fertilization Rate; ICSI: intracytoplasmic sperm injection; IVF: in-vitro fertilization; CR: Cleavage Rate; HQER: High Quality Embryo Rate; BFR: Blastocyst Formation Rate; CPR: Cumulative Pregnancy Rate; CLBR: Cumulative Live Birth Rate; SD: Standard deviation; AUC: area under the curves; COS: controlled ovarian hyperstimulation; GnRHa: Gonadotropin releasing hormone agonist; HCG: Human Chorionic Gonadotropin. Declarations Acknowledgements Not applicable. Authors’ contributions The first author, Chunyan Chen collected the data, analyzed the data, made all the tables in this manuscript and drafted the manuscript. The co-first author xinliu Zeng drafted and modified the manuscript. Hanke Zhang helped to collect data. Yanhui Li gave the suggestions on revision. The corresponding author, Ying Gao and Lin Liu have designed the research and guided writing. All authors have read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China under Grant number 82201818, 82303128. Availability of data and materials The datasets used and analysed during this study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate The study was ethically approved by Tongji Medical College, HUST, Wuhan, China. The ethic approval number: [ 2023]-S015 Consent for publication Not applicable. 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Polyzos NP, Devroey P: A systematic review of randomized trials for the treatment of poor ovarian responders: is there any light at the end of the tunnel? Fertil Steril 2011, 96 (5):1058-1061.e1057. Ferraretti AP, La Marca A, Fauser BC, Tarlatzis B, Nargund G, Gianaroli L: ESHRE consensus on the definition of 'poor response' to ovarian stimulation for in vitro fertilization: the Bologna criteria . Hum Reprod 2011, 26 (7):1616-1624. Yan E, Li W, Jin H, Zhao M, Chen D, Hu X, Chu Y, Guo Y, Jin L: Cumulative live birth rates and birth outcomes after IVF/ICSI treatment cycles in young POSEIDON patients: A real-world study . Front Endocrinol (Lausanne) 2023, 14 :1107406. Lebovitz O, Haas J, Mor N, Zilberberg E, Aizer A, Kirshenbaum M, Orvieto R, Nahum R: Predicting IVF outcome in poor ovarian responders . BMC Womens Health 2022, 22 (1):395. Gong X, Zhang Y, Zhu Y, Wang P, Wang Z, Liu C, Zhang M, La X: Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI . Front Endocrinol (Lausanne) 2023, 14 :1027805. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-3521867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":244910866,"identity":"1c3722ba-b18d-474b-a2b2-06285499ee25","order_by":0,"name":"Chunyan Chen","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Chen","suffix":""},{"id":244910867,"identity":"1870dd72-3090-4a8c-9fd3-7c9dcc05ddf5","order_by":1,"name":"Xinliu Zeng","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinliu","middleName":"","lastName":"Zeng","suffix":""},{"id":244910868,"identity":"96795b0d-5a53-49ab-b058-7b9102044d7b","order_by":2,"name":"Hanke Zhang","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hanke","middleName":"","lastName":"Zhang","suffix":""},{"id":244910869,"identity":"042148ca-0d42-4fbe-b63b-597b321af413","order_by":3,"name":"Yanhui Li","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanhui","middleName":"","lastName":"Li","suffix":""},{"id":244910870,"identity":"43deef38-dec1-4467-ab27-4de7149c6ebb","order_by":4,"name":"Ying Gao","email":"","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Gao","suffix":""},{"id":244910871,"identity":"d7a27bad-18ae-4de2-9dd7-d9f7d94f09f7","order_by":5,"name":"Lin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACA2YGxgMJIAYDD+NjmCAhLQwwLczGQFqCsBYgPgBh8LBJE6eFnffAgYdtDInbJXKPVRe21dUxsDdvk2CouYPHYXwJBxKBWnb2nEu7PbPtsAQDz7EyCYZjz/Bo4TEAacndcLzH7DZv2wEJBokcMwnGhsNEaDnMY1bM21YnwSD/hlgtQFuYeduYgbbwEKEl4RxD/YYzZ4ylec4dlmzjSSu2SDiGW4t9/xnDhz/KGIwNbuQYfuYpq+PnZz+88caHGtxaoOA/gskGIhIIaRgFo2AUjIJRgBcAAI/4TUCCvfxmAAAAAElFTkSuQmCC","orcid":"","institution":"Union Hospital, Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-10-31 08:44:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3521867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3521867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45829177,"identity":"beaf8bf5-7170-4760-8fb9-de26e9dcd57e","added_by":"auto","created_at":"2023-11-03 17:44:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137745,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of model development (PGT: Preimplantation Genetic Testing; ICSI: \u0026nbsp;Intracytoplasmic Sperm Injection)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3521867/v1/a0a4756f92dbfbc024347450.png"},{"id":45828516,"identity":"5d588531-c23d-46e0-8354-9df26e3102a3","added_by":"auto","created_at":"2023-11-03 17:36:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175819,"visible":true,"origin":"","legend":"\u003cp\u003eVariables screened out for the prediction model. (A) Likelihood deviation of some LASSO coefficient profiles. The red dashed line represents the cross-validation curve, and the error columns are the upper and lower standard deviations along the λ sequence. (B) Selection of variables for LASSO regression model. A coefficient profile was drawn based on the Lamabda (λ) sequence, with each curve corresponding to a variable. Each curve corresponds to a variable, which displays the vector path of its coefficients under different λ values relative to the entire coefficient vector. This study used lambda.min (λ=0.03394075) and acquired 7 non-zero coefficient variables.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3521867/v1/de64d1e5ab69a5e53dc02a4d.png"},{"id":45828513,"identity":"e1ad5aa0-592d-4258-a70a-6fc3984614e7","added_by":"auto","created_at":"2023-11-03 17:36:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91376,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram of cumulative live birth rate in one cycle in older patients with low response (NFR: Normal Fertilization Rate; BFR: Blastocyst Formation Rate; CBR: Cumulative Birth Rate)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3521867/v1/6de9d0a5c8ef2ecb2e10fcff.png"},{"id":45828515,"identity":"f5267f67-e03a-43e3-8645-fd3a987e334e","added_by":"auto","created_at":"2023-11-03 17:36:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175405,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The AUC of ROC curve of the prediction model was 0.818 in the training set. (B) The AUC was 0.7971 in the verification; (C) Validity curve of the prediction model in the training group; (D) The validity curve in the verification set. (AUC: Area Under the Curve; ROC: Receiver Operating Characteristic; CLBR: Cumulative Live Birth Rate)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3521867/v1/bd1337b4f3b06782c1d7f557.png"},{"id":45877267,"identity":"afc3725f-1cba-4edb-a013-aa99d6b69476","added_by":"auto","created_at":"2023-11-05 05:52:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1411588,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3521867/v1/2cca8361-0608-47a6-ab93-81a602284b01.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting single-cycle cumulative live birth rate in POSEIDON Group 2 Patients: a prediction model based on matching learning","fulltext":[{"header":"Background","content":"\u003cp\u003eThe goal of assisted reproductive technology is the birth of a healthy baby. As clinicians, we constantly have to answer questions about the probability of a successful birth upon assisted reproductive technology raised by patients during outpatient consultations. With the advances of in vitro fertilization technology, the pregnancy rate and live birth rate of in vitro fertilization have been increased steadily, but the pregnancy outcomes of older patients with poor ovarian response are still not satisfactory. In response to the challenge, in 2016 the POSEIDON (Patient-Oriented Strategies Encompassing Individualized Oocyte Number) Group proposed a new stratification for patients who have a reduced ovarian reserve or an unexpected inappropriate ovarian response to exogenous gonadotropins[1, 2]. In brief, four subgroups are defined in terms of both quantitative and qualitative parameters, namely, (i) age and the expected aneuploidy rate, (ii) ovarian parameters (i.e., antral follicle count [AFC] and anti-M\u0026uuml;llerian hormone [AMH]), and (iii) number of oocytes retrieved by standard ovarian hyperstimulation. The POSEIDON criteria included two new categories of impaired response: a \u0026lsquo;suboptimal response\u0026rsquo; and a \u0026lsquo;hyporesponse\u0026rsquo;, corresponding to suboptimal response and hyporesponse, by combining \u0026lsquo;\u0026lsquo;qualitative\u0026rsquo;\u0026rsquo; and \u0026lsquo;\u0026lsquo;quantitative\u0026rsquo;\u0026rsquo; parameters: age, biomarkers, and functional indicators (i.e., AMH and AFC). POSEIDON Group 1 and 2 have a \u0026lsquo;suboptimal response\u0026rsquo; or unexpected poor response. In Group 1, patients are younger than 35 years with adequate ovarian reserve (AFC\u0026ge;5, AMH\u0026ge;1.2 ng/mL) and an unexpected poor or suboptimal ovarian response. Patients in Group 2 are older than 35 years but have sufficient pre-stimulation ovarian reserve (AFC\u0026ge;5, AMH\u0026ge;1.2 ng/mL) and an unexpected poor or suboptimal ovarian response. In particular, clinicians hope to help patients in POSEIDON Group 2 to achieve a more favorable outcome with assisted reproductive technology (ART).\u003c/p\u003e\n\u003cp\u003eAs early as in 2010, a retrospective analysis identified nine factors predictive of outcome of ART, including: female age, duration of infertility, follicle-stimulating hormone (FSH) levels, number of oocytes retrieved and embryo quality[3]. Subsequently, multiple studies suggested that the older female age, protracted infertility time and higher basal follicle-stimulating hormone (bFSH) levels were negative predictors of ART pregnancy rates, while increased oocyte retrieval and superior embryo quality were positive predictors[4, 5]. Previous data consistently suggested a robust correlation between the number of oocytes retrieved and live birth rate[6, 7]. Questions present themselves: What can be done clinically to improve ART outcomes in the POSEIDON Group 2, and what are the factors involved in pregnancy and live birth in these patients? Currently, multiple reviews have retrospectively analyzed the ART outcomes in the hyporesponsive populations of POSEIDON Groups, but no studies systematically examined the influencing factors specifically implicated in the single cycle cumulative live birth rate in the POSEIDON Group 2[8, 9]. In this study, we developed a nomogram for the prediction of single cycle cumulative live birth rate in POSEIDON Group 2 by employing machine learning and retrospective analysis (LASSO-logistic)[10], with an attempt to work out better clinical solutions and ovarian stimulation protocols for these patients.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e1. Patient selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study recruited patients who underwent ART at the Center for Reproductive Medicine, Wuhan Union Hospital, Tongji Medical College,\u0026nbsp;Huazhong\u003c/p\u003e\n\u003cp\u003eUniversity of Science and Technology (HUST), Wuhan, China, from January 2018 to December 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Inclusion and exclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInclusion criteria were as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(1) Patients aged \u0026ge; 35 years old, with AFC \u0026ge; 5 or AMH \u0026ge; 1.2 ng/ml;\u003c/p\u003e\n\u003cp\u003e(2) Number of oocytes retrieved \u0026le; 9;\u003c/p\u003e\n\u003cp\u003e(3) Main follow-up endpoint: This study entailed a follow-up to the pregnancy outcome or live birth of all embryos transferred during the oocytes retrieval cycle.\u003c/p\u003e\n\u003cp\u003e(4) Clinical data were complete.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included:\u003c/p\u003e\n\u003cp\u003e(1) Those who underwent Pre-implantation Genetic Testing (PGT) or either spouse with chromosomal abnormalities;\u003c/p\u003e\n\u003cp\u003e(2) Presence of uterine adhesions, severe endometriosis, submucosal fibroids, hydrosalpinx and other factors that significantly affect embryo implantation.\u003c/p\u003e\n\u003cp\u003eA total of 565 patients who met the inclusion criteria were enrolled in this study, and the study was ethically approved by Tongji Medical College, HUST, Wuhan, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Data collection and follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCases were randomized, at a ratio of 7:3, into two groups: a training group and a validation group, with 392 cases assigned into the training group and 173 cases into the validation group. The calculation method for data in the study was as follows:\u003c/p\u003e\n\u003cp\u003eOvarian Sensitivity Index (OSI)[11]\u0026nbsp;= Total amount of\u0026nbsp;\u003ca href=\"http://beta_dict.eudic.net/dicts/en/gonadotrophin\"\u003egonadotrophin\u003c/a\u003e (Gn) / number of oocytes retrieved.\u003c/p\u003e\n\u003cp\u003eMetaphase II (MII) ooctyes Rate (MII R) = (Number of MII oocytes / number of oocytes retrieved) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003eNormal Fertilization Rate (NFR) includes Normal fertilization rate of in-vitro fertilization (IVF) and Normal fertilization rate of intracytoplasmic sperm injection (ICSI):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormal Fertilization Rate of IVF = (Number of fertilized oocytes by IVF/ number of oocytes retrieved) \u0026times;100%;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNormal Fertilization Rate of ICSI = (number of fertilized oocytes by ICSI / number of M II oocytes) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003eThe normal fertilization rate of patients undergoing IVF and rescue-ICSI in this study was defined as the average of normal fertilization rate of IVF plus normal fertilization rate of ICSI combined.\u003c/p\u003e\n\u003cp\u003eCleavage Rate (CR): (Number of fertilized embryos / number of fertilized oocytes) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003eHigh Quality Embryo Rate (HQER): (High quality embryos/ number of fertilized embryos) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003eBlastocyst Formation Rate (BFR）: (Number of formed blastocysts / number of cultured blastocysts) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003eCumulative Pregnancy Rate (CPR): (Number of clinical pregnancy cycles after embryo transfer in one oocytes retrieval cycle / number of oocyte retrieval cycles) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003eCumulative Live Birth Rate (CLBR): (Number of live birth cycles after embryo transfer in one oocytes retrieval cycle/ number of oocytes retrieval cycles) \u0026times;100%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 27.0 and R software version 4.2.3 and the \u0026ldquo;glmnet\u0026rdquo; package (R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analyses. The data of training and validation groups, and the overall data were compared in terms of baseline characteristics. Data of normal distribution were expressed as Mean \u0026plusmn; Standard deviation (SD), and comparison between groups were made by utilizing the single factor method, while non-normal distribution data were presented as median [IQR, 25th-75th percentile], and were compared among the groups by using the Kruskal-Wallis single factor method.\u003c/p\u003e\n\u003cp\u003eThe R software \u0026quot;glmnet\u0026quot; package was employed to randomly group the data, at a ratio of 7:3, and to define the training and verification groups. The \u0026quot;glmnet\u0026quot; package was used to perform LASSO regression on the data of the training group data to screen out variables, and the Lambda(\u0026lambda;) value of \u0026quot;lambda.min\u0026quot; was used for variable screening[12]. The variables identified were subjected to multivariate regression analysis, and the \u0026quot;rms\u0026quot; package of R software was utilized for multivariate logistic regression on the included data, and a predictive model of cumulative live birth rate was constructed, and a line table was drawn to assess the predictive power of the model. The R software \u0026quot;ResourceSelection\u0026quot; package was run to test the \u0026quot;goodness-of-fit\u0026quot; of the prediction model for the training and verification groups, and the \u0026quot;pROC\u0026quot; package was employed to plot the AUC curves for the training and verification groups and calculate the area under the curves (AUC).\u003c/p\u003e\n\u003cp\u003eThe flow chart of this study is shown in Figure 1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Baseline data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;glmnet\u0026quot; package of R software was used to randomly divide the data into two groups at a ratio of 7:3, i.e., the training group and the verification group. With the overall data that were not grouped, there were three groups in all. The baseline information from the three data sets was compared, and the results are listed in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline information\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=565)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraining group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=392)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=173)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ez or \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e38 [36-40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e38 [36-40.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e37 [36-39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e1.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of male spouse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e391 [36-43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e39 [36-44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e39 [36-42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgestation cycle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e1[1-2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e1 [1-2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e1 [1-2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e22.71[20.82-24.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e22.60[20.82-24.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e22.83 [20.82-24.84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfertility years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e3 [2-7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e3 [1.5-7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e4 [2-8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e1.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ebFSH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e7.65 [6.48-8.93]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e7.61 [6.49-8.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e7.82 [6.45-8.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAMH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e1.98 [1.50-2.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e1.98[1.49-2.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e2.00[1.52-2.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e480 [344-750]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e482 [348-750]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e475 [338-775]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfertility type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003ePrimary infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e139 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e95 (24.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e44 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eSecondary infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e426 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e297 (75.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e129 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCauses of infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eFemale factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e440 (77.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e308(78.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e132(76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eMale factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e49 (8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e33(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e16(9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eBilateral factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e19 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e14(3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e5(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown reasons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e57 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e37 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e20(11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOS protocols\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e5.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eGnRHa long protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e128 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e80 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e48(27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eModified-long GnRHa protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e35 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e26 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e9(5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eAntagonist protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e63 (11.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e46 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e17(9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eNon-downregulation protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e244 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e178 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e66(38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eLuteal phase protocol and others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e95(16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e62 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e33(19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgestation tecnology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eIVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e319(56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e226 (57.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e93(53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eICSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e225(39.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e151(38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e74(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eRICSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e21(3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e15(3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e6(3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhether fresh embryo was transferred\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eFresh embryo transfer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e165 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e116(29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e49(28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003eFresh embryo not transplanted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e400 (70.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e276(70.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e124(71.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOPU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e6 [4-7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e6[4-7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e6[4-8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMII ooctyes rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e88.89[75.00-100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e88.89[75-100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e88.89[77.78-100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFertilization rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e60[40-77.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e57.14 [40-77.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e60 [37.5-77.78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCleavage rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e100[100-100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e100 [100-100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e100 [100-100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e6.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh quality embryo rate (\u0026amp;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e33.33[0-66.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e33.33 [0-62.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e37.5 [0-66.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlastocyst formation rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e50[0-85.71]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e50 [0-86.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e50 [0-83.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of frozen embryos\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e1 [0-2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e1 [0-2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e1 [0-3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative pregnancy rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e215/565(38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e143/372 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e72/173 (41.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e1.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.646748681898067%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative active production rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.411247803163445%\" valign=\"top\"\u003e\n \u003cp\u003e135/565(23.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.91388400702988%\" valign=\"top\"\u003e\n \u003cp\u003e86/392 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.15641476274165%\" valign=\"top\"\u003e\n \u003cp\u003e49/173 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.787346221441124%\" valign=\"top\"\u003e\n \u003cp\u003e2.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.084358523725834%\" valign=\"top\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*P\u0026lt;0.05: A\u0026nbsp;statistically significant difference was found.\u003c/p\u003e\n\u003cp\u003eThe results in Table 1 suggest that, after randomization, there was no statistical difference in the baseline data among the total, training and validation groups.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCOS: controlled ovarian hyperstimulation; GnRHa: Gonadotropin releasing hormone agonist\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Variable screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll variables (age of spouses, assisted pregnancy cycles, infertility years, body mass index(BMI), infertility type, infertility cause, bFSH, AMH, AFC, number of follicles ( \u0026ge;14 mm) in Human Chorionic Gonadotropin (HCG) days, OSI, Gn days, Gn initiation dose, controlled ovarian hyperstimulation(COS) protocols, the number of oocytes retrieved, MII rate, normal fertilization rate (NFR), cleavage rate, high quality embryo\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003erate, blastocyst formation rate (BFR), fresh embryo transfer rate, frozen embryo number, etc. A total of 26 variables were included, and LASSO regression analysis was used for evaluation. The lambda.min (\u0026lambda;=0.03394075) was used for model construction. Seven variables were singled out by LASSO regression analysis as factors that impact the single cycle cumulative live birth rate in older patients with low response, i.e., female age, assisted pregnancy cycles, infertility type, normal fertilization rate, blastocyst formation rate, frozen embryo number, and whether fresh embryos were transferred. The results are given in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Construction and verification of the prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeven variables selected by LASSO regression analysis were incorporated into multi-factor logistic regression, and the results are shown in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2. Multivariate regression analysis of single-cycle cumulative live birth rate in older patients with low response\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSD \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eP*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.608695652173914%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLower limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eUpper limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e-0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgestation cycle\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e-0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary infertility/ Primary infertility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e-0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal fertilization rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlastocyst formation rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFresh embryo Transfer/Fresh embryo not transplanted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e2.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e4.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of frozen blastocysts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e1.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.56521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003econstant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.434782608695652%\" valign=\"top\"\u003e\n \u003cp\u003e4.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.956521739130435%\" valign=\"top\"\u003e\n \u003cp\u003e2.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.130434782608695%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e136.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.304347826086957%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*P\u0026lt;0.05, indicating that the difference was\u0026nbsp;statistically significant.\u003c/p\u003e\n\u003cp\u003eFive variables, including female age, normal fertilization rate, blastocyst formation rate, fresh embryo transfer and frozen blastocyst number, were independent contributors to single cycle cumulative live birth rate in older patients with low response. The logistic regression model was presented by a column graph, and the results are shown in Figure 3.\u003c/p\u003e\n\u003cp\u003eBased on logistic regression analysis, a clinical prediction model of single-cycle cumulative live birth rate in older low-response patients was obtained in POSEIDON Groups 2. \u0026nbsp;For older low-response patients, the predicted cumulative live birth rate of a single cycle (P) is as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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8cy60a8pp5QDw1rU2dsZo099PzReEz/lTf5VvT7vxsu963eutX537nA8ww057muTLu3oF304u461xdo5+NeL9vZ2viuvxlDbjpjudCJ7x1q93abdnCN95knnRkxqjnedfl/ZZP+ZWvunOJyZ/+wx8w3gglW7IK0AC22XcM59Zr3C4IK5YttKFu0sRhZtvzzQpw6evezsZNHHIeDiYD6bLzazgSlvOgirXMfdmtjqP7NB3sJT+6rN07OyV7l21D/JpA1+xAy2/WCoc7RzxXHq9xBnE5tku+bOsEUvOlb+V1unZ3VV+7UP/3uBB/rqeGXo61Tv7DNG1EdFfycdR23V5klPlX1L/zSHNmIMN3gercuVDGOCj/VwVz65gnz6vbgwzthwgTtTZLgaW+1Yjant2IDMnkvw6PntJXOXK1X2ZGuNof1TvcoF55+1Qb6Tjl3bGfnKXq2Lo/7KavXsPt7tIWfJI3yYctY12y9iypvyTbbI7Dk82ad/q1gxBznmPTVn7CRbe3cxoc+iHyv2jqs+2VbrVb/tnTtztbXr1vaJx05etcfn3fgz3JHD+sWmGmvuMLRNMVB3rVe+MmZnhyyqrN0c/XXeqq5sd7ZVvTs7GafuKdbaUeUdzTnS549S5LCF9Yyu6aXbMdYrm+yvtbY452w9sahyn/38+05woyWCqQmlKCH53XqXKI75nmoXoLx29T0JxWaF7H6YTay1YYrrNL636dM99naZt3x3o8afqWjn9NI6jafNw/jopYmxvkigZypyri9I2EJ7vzg6342vXzbtr/WVtYZf2EnMvBRpX88D1z02avvqYuvYLqPauXs2RiuGu7mP7PNwgcnki6yJ+ZXCeGI5XUCvyFnFaifDOJ+9yKhjJVMGjDtbvIT6C6gyOg/z4OyeMtmq7LMyug/3zu/y7vnuPrHyxXUn11t0rfZP21dniWcNMTCOdf2s8s18rJftld36R0x2BV1e4s2Jum8x19w6u8eo+2i8+bJaD6t+26fYrmzVt4nHTh7+c6bARL+UNel3zKSnxoG4I6fuh8ytF/Y6fnpe+crYnR3a32Wu5hzx6XL4vrOtjl/pdMxO98qP3Zwjfej1xwTPbnw5c59g7som/TlTaz/1O5bzp9vCu12QVoCFxtyUPYEVw/2sudeLLYvkqKj31sR2U3l2jN288Wcq5u+Zg5r5bDQcBHzcdCa5tnkpnJi7DvqGpU3THOQ6b+WTuuvYozh4CePwRL6+aUvPA/q55HjRwRaYYLNztUMZK38ct6qZh/xb56/k3tLugdN5IEs7r1wM4I1ML5e32OQcGPGZbHNMrV0bR7lR56ijttVndB+NqeN59ocAuXmB6+Pke9beyY5b7Kt2OP+sDXXuo5/dz1e26P89uaW/yKrlTCzUjwzKSlaViy/MO7PWHav8Kmd6Zl9Crmu4cjvjT5Wp7iM7j3xe9dtebVS/tnbdnbfjqVfyyA33cdahubIajyx9Z8xR8XxAri/TZ+Ypd+XrkR2yUI71ynb9Zd7ZsrOtyljpdIy6p1iv/NjNOdKHXvZYZHvvIU49n7Sv1yub+rjdd+2nfsdyPksW3u2CtAIstClRFmq+2+YVw6tAODQ4MFgg/WI7yVLvLrEZs/pLjS8Mq/5J50e1eVBOvrixr34B7TbtLrr42jef3eXGCyMya3FT8yJZ+3g++rW3jnet1V/9aj/PKzvoW61vbOy+drl+ry9ltl2pnX9W3xXZV8ea15Mtxg2eZ4rcp/HEuO+P6GTNTeuXi4lrduqf7DG2XpamMb3tSAe6HXNFrmtUP6a1at4z9kzRjjq22nd2zdf52tBjU8d81rOs8LMXL6nsb/cU94+uw9xdxWLirE3IWuUG+xT905rofpi/xORKqXY4z7ie5eWetNqjlVs5TD7LseeT3Hs7ct0H+h4kOy/D2kCtf13e6vzb6Zf7tEarTp49/+DF/tj19/H9+8pXxu3sIId6zu7m1Did3Rd2tlU/dnYybsd65cduzpE+dLp3kDPkIOsYBkelcjoau+vXfup3LH/ecS96sQvSLUG/qP7LD18xxHH7+gY6QWGDZ3FMm/c0Xtm7xGbMtOBYXLTTf3YTmmx4VJsbHBt3LS7eVTv2V/89fKaDCT85hHosdixcO52Rdk1ssd8LetdVfavPxrK21Wf5dHlurp2D9vXxVWZ9djxypvyrjOs8n+V0NM7xH1nrS79gGefe7vjOkJgT3+nihSza8buWVZwYY1/P5Tq/PnuhOXNBrfPOxMIxk2+rS64XUfh1huqXMSynNdhlM45PLzv70DHlqDLkfDb3nfdRtb70OGpnb3c8tcWxfR+i376eVzUW07pEL+yrHuSpf+KHfuas9j3ttda2SZZjkDflCjlWc6P6M40nJxhjqeu65wvjKhN9Jsd7sa/HSfmdH/NXftc1VG3l2ZeqLg8GlYP2sXZp7+Pp1+bqo/N6jW7kEFM+3c8+vn9f+cq4nR0rv5zT94oqb9q38KPHeWdb9UOdNU/rWtvFeuXHbs6RPm0zJ1gLfX07ptc7vX3s7rtyqN+x/PlUueiFG1BfEIIh8HURI95NlcClrAl4UZ8YMstFVRdklQZ3WMOZOPWFX8fWZy9VyJ82e8eqn43GjQAdLsieE8777BoO2uShiL20TYd0zd26sM11/Z7qKRY13+Uk42k8fHw5wkbjhh+MRy+b49niIVh9qXO1z4ONcehBh3bUgwZ7Jt9tw+Y6vvqDDmQin9wyLivbmItc5r1KkQk1MeFjW/eD73KpfY63b6p7jI29cYIHus0lWPJ9V4gL+VBl7Mb3PnVhy6qQH8jHJ2xiLJ9drGtO7fYNcxXZ+IFc8oi1iU79Z53J1DWnvSv75NK5O4+aPuQi4xWKvuC7+UXN98kP7a99MDRW+nUmr4wFuup6p502YrJjTy4ZL2wmP7qsHWNsxe7qSx9Pf5dpDhPvWvSHOeYWbGTGcy2uYeTzTD8136tNxgi56Kbgt/Ope6kx6X3O62czrNGNHtgjw7VBfGRR5Xmm4S8xYBy2y4iY9IIcPjXmfUz9jmxtqu1nnmXffWXuyo669s0vdbkHTfJqnBgHPz7Y32OKvJ1t6qOWJfailw/yXBurWFc/HKvc1Zwz+pRR7eryHdNr9ZrHvf/sd3INHtTvWO56cTIJAUAisPFQSEAXJH11A2WxubjpqxvMOwL8KJs7JxjCtRb48SGZeyGx4cyiNy59TP1eNw3l9rrLYQ6bQM8DN+Eq/9nPHlTmHjUHUN9YsdNFjf8u7NrWudTvUyyQSTwrJ56PDh76PXTUwXrpcThiy3jmT4eFc7HbNUutHx7S8Kr22q5dU93zFTsqA/QgZ7dpoxPZjHulUnlhH3Hq/mJvzRtzifaJV2/reyO5CkN0mcfMgSn2TLksM2xjLDLZG3ZjnTPVHubo3BX01dx1vU2MlINMcuKokBM9j8htfUKHuYzPPHe9+EFOVY5Hawv5yjuy8TP7O2v8df12O/DRPLAPFoynr/I4m1ed49E8OBKvHqOjvUB7a60MY1/7eEaPPuM3n519rNGet3yva7fq6PsAsqdL5ZV8Y43XOGC/BX36Qd3j3HNBpnUfquuhj8dX2lzn6Kj68U/92Dj5qq3W7uH4daUQO3V1X7sdnk3YbU4wh2faKD0POjvGnI1Ttw3Ou1LHy5jxq1gTg+4Hbbs5Vf9KXx3jfnZke50jd5nWvivP5uNqXV2R9Yyxd704PcPg6AyBELhOgE2Yg251wbgikUNqdWiyobq5TgfTFT2M9bC7d6O+qjfj1wQ4aLmUPPrQI0cfkTNry+/rwTb8vnoBvE9rZu8IEIt+qd6NT9/nE/Dl4BFnz+db/3U1cqZe2cd92bnyorWip6xHnyErfY9uf5sXJxffIwCQLNOHC18OxUcQjoxXI+CvfvwSdW/xJWwlxwvmvZfgR9q8sjXt1wlw4PLizH75qOL+/qqXq4/w+VHsvnc55CH5mB9XXjMTXv0Hkdek9vFW8QJU/5p4pDHr7HdCL//ixBspC88Xnd9Nv/2JDVaZSqGNxEDPvRc+ZaYOgVci4J/v7/1xgDXCRWVV1HPPr0n8s4RHX85X9qb9OgFfam/99ZEcdJ/lZemVL1fYl0vD9Rz5rBn1pfZVX7w/i8Ur6OEy7kus/5OBxOX5keFcZt+mcL5yjp89o/1XBs5/vjfPteClX5wIEgFjEXoZexQu/wlQlYcekolDPCUEviIBLqzkuOvqFh9di/XfaiOH9eNfm269UHPAIoOXpltl3OJT5lwn4Mste+nZA1gtxpia+byYvGJhvXAeYF8uf68Yof/ZRGyIEbG694eh1/XyPSzjfOFs4JxgH89l+zXi5j7Lnss6IT5HhX2decTR/43V0Zzvof+lX5xqAAg2C/JRhWSY5NE2tT9Kb+SEwLMJ8ILDS8k9l1UuJ26orhk2Vw7Mq5foygO77pVR5eX5Ywn4ostBTF6dLfwSbd4Q81d8KSHHWSO5iJ+N6vPHJWbPjwHnAGubPSEvTc+PhxZwrhIX4sN9+qiwnxNDxr7i/nxk/0f2P+5N5COtLP/fBY9SM704kSgkFgmWEgIhEAIhEAIhEAIhEAIhEAIS+BIvTr4E+QvmVDOmFufYxp8h+XWRuff8Yq681CEQAiEQAiEQAiEQAiEQAl+HwJd4cbolHL441Zes/BOhW0hmTgiEQAiEQAiEQAiEQAh8fQLf/YvT1w9xPAyBEAiBEAiBEAiBEAiBELiXQF6c7iWY+SEQAiEQAiEQAiEQAiEQAl+ewFu8OPEfbfCf1j3iv9Lify2Ef6aX/8Til8/xOBgCIRACIRACIRACIRACdxN4+Ren+r9Bqs9n/nOKKzpVDs/5j0GsSKU9BEIgBEIgBEIgBEIgBEIAAi//4pQwhUAIhEAIhEAIhEAIhEAIhMCzCeTF6dkRiP4QCIEQCIEQCIEQCIEQCIGXJ5AXp5cPUQwMgRAIgRAIgRAIgRAIgRB4NoG8OD07AtEfAiEQAiEQAiEQAiEQAiHw8gTy4vTyIYqBIRACIRACIRACIRACIRACzyaQF6dnRyD6QyAEQiAEQiAEQiAEQiAEXp5AXpxePkQxMARCIARCIARCIARCIARC4NkE8uL07AhEfwiEQAiEQAiEQAiEQAiEwMsTyIvTy4coBoZACIRACIRACIRACIRACDybQF6cnh2B6A+BEAiBEAiBEAiBEAiBEHh5AnlxevkQxcAQCIEQCIEQCIEQCIEQCIFnE8iL07MjEP0hEAIhEAIhEAIhEAIhEAIvTyAvTi8fohgYAiEQAiEQAiEQAiEQAiHwbAJ5cXp2BKI/BEIgBEIgBEIgBEIgBELg5QnkxenlQxQDQyAEQiAEQiAEQiAEQiAEnk0gL07PjkD0h0AIhEAIhEAIhEAIhEAIvDyBvDi9fIhiYAiEQAiEQAiEQAiEQAiEwLMJ5MXp2RGI/hAIgRAIgRAIgRAIgRAIgZcnkBenlw9RDAyBEAiBEAiBEAiBEAiBEHg2gbw4PTsC0R8CIRACIRACIRACIRACIfDyBPLi9PIhioEhEAIhEAIhEAIhEAIhEALPJpAXp2dHIPpDIARCIARCIARCIARCIARenkBenF4+RDEwBEIgBEIgBEIgBEIgBELg2QTy4vTsCER/CIRACIRACIRACIRACITAyxPIi9PLhygGhkAIhEAIhEAIhEAIhEAIPJtAXpyeHYHoD4EQCIEQCIEQCIEQCIEQeHkCeXF6cIj+/e9/f/v73//+2+fBoj9F3F//+tdv//jHP77997///RR9UXIfgXfNN9cI9qe8PoFff/31288///yN/eE///nPHwyufT/88MM3Pox9VsG+v/3tb7/ZgS3JsX0k/vWvf/2B148//rifcENv1vsN0J44pa7pV1/vT8T0jbXzl7/85f/2mmfacqT7e9kX8ZNzijOIPP6I8rAXJw9VD042yr7gzjjwyy+//GET5wCk7Uzhso/eo42fcT/99NNv47SX5GfuPYcsMpFTA4btBFE9jJkKNjuG8fXFhTn27bjOHAIAABlmSURBVOrqd7049DnI5+Vo8hW99OEHm8KrlnfKNxiygOVKPODL93sW9pRvbho95vU7efLPf/7zT6Hth0Cd05+dfDRHXd1PvhNDOKzWhDqeWd+zH2E3PnZ203fG9QJb9qQ6fsWzz+U765t9YJI9jV+1mVPonvYMdPAhpuwf7oEreZ/V7r472fxZNry6HvcQYkz8iDGfR5fvZb3DjXxjb69nsGduPdc7Y/aaut49I1ZzaGcfZw5j3SfQe8/Z/a7rvfP8rO/EQfafpfMePeblV94X8Y19jHVHPj+6POTFiU2CxPEyhtEsZD5XjGaxI8cLJQlpkHcbgZuyybvb+LGRccg1cZhvO31nX9RqMNC58rcuLOSvmCCDfuzpxZenyTd59z7Z1YsTttTL3Iqrsahzu03P+v5O+QYj4kksyA9zznyjfYr3EdtdviGPPOKjPu0grthBX88XxpD7zu021PVY+1ZzaDcHV36yFrBnsqXqeMaza+DqflRtNVdluqphW4vzWH/mB2O8WMFzVeo49N2zhskfZGDPVMzjW/bMSd4j28gpbK9r4JHy310Waw8+n/nDxVdf765Patc0teuZvc72mj/2EwvXO3nL+GmOsSN+rME+59a4vvN6rzw/+xnefN6hfE/7ouvq0WfA3ZH2ctE3X9t54ztT2ExIvH4hsJ2+acNh02BjYaPyF8bdJcyXhgmkkHfzJ198qZlkOh77sXPy0TEmtN9rrd0r2+jvfcqbLk76ik2ros5HXYrwfbJlpX9qN6/eJd/wQdado+3UV8rZfIP3lJPYQR8fDuBaGG9fbffZfr9T28a8Xur6Za1Oxfk9ptPYM23kfV8LZ+bVMdp9dT+qMnjGjtVLI/3sWexdtfgysvLBfY61UAuXJxi6H7rf3LrmYICMlR3opo+4E8NXK4+0DR93HF7N9zP2uL/fmh9ndExjvvp6Z834IlP9d9123p5pfR9grn0992TYZdU5035c7enP777euz+f+R3WV3l/pn1V1yP3xSr3VZ/xlzVJfj+q/Pmmc1EylwISpl/AEGMyTX1djZfB6XJloHsfcukTiAcBbbeUW+ajGz+PdDLGSw3P3Rfs1c/J9ltsU960ubrxYssqPmz+JBwb/iMKuiZbrsh+t3yDIX7DsRdjMPX1sX6/km/oRcdU6Jv6tYm+s+Vojnk4XQzU4RjXsu231Mjic0+5ZT+a9GFHf8FxnJeiHiNZrNaKtvV+9hT6vLQdydGOVe2Lfbevjid3r+RKnfvRz/q/s/+MDeY38r5SYT1Oe8Bn+Ghsvtp637HT575uPdP6D2vKIkZ8zrIyX5mzOtuVXet3X+/Vl89+NkafrfcWfebhvfviLbqfMcf1cPUH6p2t529HgxQvcSTNVAxQ3yimsf4aMwWT+ehA3q6cHbeS4cbB5eNscc5q01MO9mMflxueuXB4wXGMvPxe61t8Ux5zezGZsGVi7ngvaavLn+PO1DI4M3Ya84755uV4lbswOYpBZXEl31ZyK8d+sNa8qHp3z0dzzMMVA2T7V7BHbG7o2ena+WLfI/YjZa1qbOTS1Avtu7XimpzWdZV1JKeO7c/myGRfHWv+1rZXedb/3f52xtZb9t4zcp895lF8bvHje1zv7in9ruAaWuWpcerzVtyP9uNp3ldY75Nfn9VmDD9L3z16zKdVvt0j+1Xn+uMEef6IMr/xnJTsAl39RcJL3u6XZlWZeJNjXj6Pfpm/54DTFwD3FxptnGpsOrKLefjnRccg9kuivCY9K988gKY5LhD11jHOO7KdizW2n4lhlT89VwZT/1GbMXqnfDNuPdb66mE6/QXSMbW+km/wnjZHbSI/epExc2vxL2eTvNUc55vvRz6e9U25qxq/Jt9W46d2/Odzz340ybVNZtMPErv4MN+cmWKhfOrd+q/jpmd/4FnFDC7+xUJW1sirfe4/1MSYcbapGw7mCf089zGOpb2OZTy+9oul/sOJ/HV/ZTz2ndnnfUllTv9gBzqNB/0U9kx126bt2IIddQ5j+w8YVS/9FGLhPOrurzrO8Knyq181p4gxthozarhVWxlTYzGxrjK1kfp7Wu/EBM7TOSD/FSdzCRlnimvXvLky5xXXO+sU3819eOFbz385yXOqa+7C5UyOV37EqO5t5HDdV+rY3fNZn+o6NZ51H2Dt6dPZPU5Oda26xpHX8xD29mtD3fdoY06ND3Jga6l+dB3GoM5H5nQ2Ig9/iYE2EWfm0rYqrolVfq/mrdr/eDtajVq0uxkIsw876nc80E1y22p91O/Ys/ocT6Ih28OB+bSdLdq18r/KwT/kU5xHG88W7fd7re2rurDVRVzH+szYqtd2audpU+3rz7vY9LG77ytbdnNq38TgSr9jK3/ban3U79gjexi3i8GZfnVRa1fNgdpfn41ZzS82KG0m/lOuq4P5tTivyrN/NYd+NjlksclN+pRBLatJRx139IycM4xWcnb+MOeofyW3tmMfm/1U4MThAjf2Jg8gDxjaV4dKlSdPYne1OHcVC2ysHHj2oy73GA7ZernA/mqTfR5qyLaNA7cWZep/HVtlMkcfvGggi48HLrKOCj556Hrg66dxMcfp51n5+MnH4sVg8nPKBfXiFzzwh2f9Qo82qOMsH3wwx9CjT/Ck6Adj1MEYdE569ZmxytV/5k1FP1b905ypDTl8bi3o19ZJxlH/NIc2WJD72EZ8zdk+Xt0rDnLq+d3l8J34GQtieLaoY2UDcisHnv2ow9x75HpHL/mET9pWcwxdFnyo42o7jPtecjXHiR9y6prAFtnRd6Zc8Ql5q33AdYbP+ML3vsexb/SivchlDmPqvoIfPXe0AflyYBwf5FFqfuBjL8an9hmDGrca3ylm6mSceplP+6pom7auxp1tX2s6IQHYOjENP+p3jk6tHD/qV85ZfYyvMtHLxsb8GlTlrmr1UR8VdNRxXgxIXIvy/F5r+5AzfepYn10gVS+J6gbXk9J5vVYOzO4pncFVWTJYJf9Rv/pq7G2r9VG/Y8/ok12NgfOpj/rrWPWtZNWxU47YRvxXeV59d3ytpxyoc7QB+WywbGisrb4RO67WV/yr8/ozTFc50sdO3yd/6rij/jp2enb+URxZn5W9z6sLWNd1Jbf6XHX19v59N854Ygf7HTnhQanvXHyQMR3wHoYekNSMnWLr/l3t03/k1PxTJ7LOFOM16XU+srCB/ZzxFPf4OmaSwVw+1UbmqJe+HnN09fZb+WivdlLjB/Jlb58XqB4vWVMzhuIFa5JPv/lhLqjjao3OietZOZXzNOeof5qjbzDkA88eQ+ftzmPWjOvgDCdlrXSps9fa2dv79904fSYWj1jv6HYP7P6wVrCFdWBBb2dkvsK/n3lXcpx1QBz4dDmuO+w5U674hLyaf5VDzY0rexycsLXPQZd95FEt2kA/3PAZ/TzTZnFf6nsZ/ejrd05jgPxaKtPa555a25gHlyP+9B+NqTbsns9FeiGhLpRpyFG/cwzKyqmjfuWc1ed4a+S74UzJ5Lheq4/6qOBbHVeT3oNGeZMs+2qSIsNEn+bYZ8JYX/ERucrpyTrp3LV1BruxU9/EoI476nfsUT4d9SvnjD7ZMXYqR/11jvpWsupYY11jxmbEBkP8+dQ+5658d7M/mqNe637BUs9UX/Fvmm8bTPncWlYMlHfU77hVjW3wZ/2uirxh4jgOIw+aM1yv5Fa3w/j19v59N8547mLhvjvllfZ7WVgdpt0mv/f5tlPv7K7jeDbeOz+UN/nR5fXv2tnnqpeY9+IFou4Ft/Lper2UTv5qU72wYps+VHu6zf27+XFlTpeh7snWaezUpk/EcCpH/dMc21i7vKi7bqldz46BN/sB+r0LMIa8h7N9rgPn9VqeR+P6PL6bv1NfbduNU/8uFlfWO3r1vdrgs7aQ91Oh3fn9In81x30BW+272jLZ0du0qbfzXTnVJ/Nv2gd2605ZXY9zzLXav/pRSRuwvedvnS+n/oJETmJP9csY9L1EeeZKleW+R9/ODmXUesWjjjn7PO8UJ2cfLZSjftUYFBybylG/c87qc3yvDdRu4dc56qM+KvjWx5lkJqPyJln2ddtM9GmOC0S9JJqJN23gkwzalEMcjop2mqRX6rOyOwPnqXvV77ijfDrqV84ZfbIzBs61Pup3HLX6VrLqWLlPMdM/867Os4/5tZA7tO3k1TmMQz5tZw/yK/5VO9Fx5TP5UH2tsmu7z0f9jptq564OYOa4L7Af9UIcznK9kltdjzx7e/++G3cmns7f1TXffaFkPP7tckv/p3irr/szfTdmyFuVK/K6jJWdO70rto/go2x9WtXVj5UPdUx/Vk+Nbx/jd1msbNm1T/FXLnWVXdt9Pup33K5m3fpr/OQvl0jvH/jC+cw4913a+uW/6vNiuttX6vj+LL/e3r/vxp2Jp/N3tXwq9934VXzNSeVVX7R1J5c+y04WY5Tj+FV9i0/OwYZetGtisLJpN2eVbzsbqk28GKGXM6oW8rnnpjGY/GLu1F9fhtHBfldfxqrO/rzi0ced+f57ZpwZ3cZ4acegqeh4fWOcxtVgTQlg0KY37ipPfatA1LHT85E/fY76qI8KjKZx+EQfSaW8SZZ9V3xzgXS9buA9kSe9tClnis1qztS+YjCNndqO4iOjV8o3LzKruMkW346K/vV4TvNgzWcVM/v7r06uNfrPltUcD3Nkndncrvi3sw2mK967efY9aj9SXq2N946He8LqpYB1C9PpxWrSdSZf6jyezY/e3r/vxp2J525+1+V3uLiHMZ/naf3IeloDV/Sa37ucOiuPuMMFWdUH5nc7d3p3bO/ls5NtDHq9Y93H+v0WPc6tNbp3saljp+ePXO9V39FZUMf6TL6Y47b12nPx7Hne5/P9bP7uxp2J525+t2uX/31s/+6PT6s74xlbq0zzm3lTOevXLT7t5mgXY3pZ2bSbgwznVZk7G7pefwBwT3Zuf/E3Bqu1u+pnTXgGaivfWce74tjdmLN9529Hg0T/1IZBUzFA/XI2jfVX1Bosxwnw6KLguFUglLeqDfDKnz5PfdRHBZnTuKrTZJhkqeuKb/LveuuFtifzpFs5U2ym8au2FYPV+N7+jvnmBr6KG0z4nImDOdDj2TnxXbmrmBnTLqvm4yR3atvN8XJ4tHaRe8W/yQ7b8G3F2zFH9SP2o67D/D1icRQ7OR35uIpxt2v6rg1TX23bjdPOnmPT/FWe1rH9mTn6iB1dhn29HTk7uyc9jN/xPiPPizP7PDZ50K/sZMxK7xm2zFc2cjoH+3q7snf+dkYrWX1c/a6eXX7U8atndF+xdZLzEeu969HfK7Z6fqwYsadgOzl1TzmTv8jfjdO/la11fs+5yXbG7PRNc2hzn2Xu6lzV1rOxYBzyVr6dtfMWn5wz2apdjOllZdNuDjKcV3/c29nQ9Xq/9JwjNyfbj2Jw1M/+yRjX7qSj2qZfte3W5/mN54I0jZ4C56WpBmAlGsg4NiWmLxQEZFeOQO/m0mdyYMeZor6jv3Aga+Ubffq3C6y6jpKj2u0CmZgamzPy/AV8inHVd/S8Y3A01/53y7e6ieuD9ZlfEx1LbQ6czTd4r2JmrvUfNa6uAezazXET3dmij14sp3x1zJmanD6T1ztZj9iPunzX+Somjndt9tjYLyfk7cpu/e/m0Wd+eLlfjXfc1G++7uLp3nK0t0/ybZNr16P/E++d3cq1Nr93OXUkz3UwxWxl507vGbbaf5WPf8EgD8+WlQ+7+eZxj9tuztSH7l1spjm97SPWe9dhzM7aytrjvCMO0zqkjfXDZ+qnfcr9bhffzd9JTh3vuNrms/7t4nllvWOL+s7cIbVDHav9k3FXc9xc9WVAXdTVzto+PdexZ33a7QO7dSe7bodz/ItQ7fe+Qt7VsrOhjvPZe5rypjw0Bl2XMlb7lv3W3qPwd1Uq99WYK+1rTSeluFj6gSDo3u747qTj+2aNw24eRyYpe7cxOWZKWvu6zSu92rzT51z8Rf5U9HGV6MzRtjO61OECmfRqOzp53pWdXbt5vW/HoI9dfZdDj5H+9HbHo7sWx390vqHTOPTLobb19mpnfdbmMzlgzKbYKocxfR3Uvqp793w0R/+P7HbcZPNOf+9DzpGuPqd/16cr+SHzab25uXOoHxXzYuUDNqHriJM8J3uObHDukQ59Zg/rRT92+h2DT5OMqp+x0yHrS0nXs/NBu7vN03dzAd3Vxrp2juTpZ7fRi8UUT/VOeTDJo+0WPv0CVc+j6fKJzZUDzHasJ6a3zplkoXtiNI1dtcn67Hp3fI8bdnQZ6pTR2f2eSzrxhPdUvMxP/dpHfaZo29F487zHHx1TTnbdjjmz3pmrXasXlu678plXizmtf37HnzM57iWf8V0ndw65VJ2r56s+GcvuE/KVpV9V58om55A/vciv36N2NnQZfDc3ifNqPdQY7Pagvs9O8UIH/q7KVftXcmxfa3LEQY3z/Q0fR2nrBw2iDMzkpAlIjVw+tk2JUU1TJ3LRW2HXcerHPhcAeggGc2nn+9mCrsmXOh9bGNOTsY5R/0qWiTgxrXLqs3FZ6XUBrTYxZJlw08ZVdZ15xjf431OIjX65gIz9xMZ4T1zNLWrk8rHtUfmGr+QZtvFRLjXfp81wx4c5ky91Dn4who/66KedQ1sZ0wFeD4g6t8rvz3XOtO6Qoz3GrMvgO2Ow7d4C06tcJ53mwtn80Mcpx5U1Me+6iZM5zjyZUrMO0TPpqHLML8bCAplXivvRTk/Ns37woQu96J8OaG2pvrIPMRad+M138kHbaUdeZyIr93Nle5h25tXuPse5vXbNoAs7qPWr5re29vnYgO3I4Zk5yIGRudEZ6i+6enGONtDv+LN85FZlqEd7sZmcQ7b20sZzLbTx6T7UMf2Z8fC4t8CQz71FptTEkY9txKuWGvPaZ873GCiHelfQCXtiQ/6u8pO9QOa7utq20/uK6x17PTvxESbmITlJ7tS4M9accs/UZ/lXHldz3PWCXvcpYoQc95pVvLTjqk+Mx2f9r3J41qa+Hskj86KzMEfdi5RpDiCT+bXsbKjjfDYW2ACfVTEG2GJssFe/+lzjW/cZ7d7dUbWfsY8od784YYQbDM7rGIna4TNWBxg3FfpNQsYAY5eMLgjGTh9k1UJQ0EHyaC/zCBTtk811fn9Wfw1kHUN796f212eTpbbxzCKtviFvpc+5ynLedDjW5F7JVHdPYPVcqbEFxveWd8o3fYU1uWw84H0Li6N8Q0/NN/VZ04cMNyntoybGdU3wfGRjn4P8ab1W36cNjnzGRmy7t7C264F6jzz8rzx3+5GMJ2b0IedKYZOv65h4oH+KnXKrrdpT67N82SeZh/6p0F91YVvdk9w31L3Ty3runNFLW92P8Rv/u96JSeembd1uZNk3+WlbXVfMMca1HV/pW8Wn+lhlwIa5lWFfVzWfkSNXam25wgd5kwz9pVae41b511mfuZy8+3qHjVxqvHkm9ztfvh+doXCT8dHYngPa0utqW41tf37F9a6N2MYagY3+TTzZB+xf1Z0H3+s8+fdx2NLvHdjgOPUx/yh2yDrr024f6Ha7D0x7XD2TsRuejK9r1z2p7rnYOtmAjqOCbGQelSsxmNYWevCl21316ucZu+u81fP89rIanfY/ESAQLBqS8asVEpGN4Ezyn/EdTi7uM+Mz5s8Evmq+sX7Ij0dsbMj6iuvxz9nwsS1e6L0cfKy2SP+eCGS9v160s95fLybvbJEvYs/2gfOLuwX5/aiSF6cHkPTX1TO/XD5A3aeJ+Kp+fRrAD1L01eLir8/4lfI6BHiJ5YcTfq1LCYFHEch6fxTJx8rJen8sz+9Zmn+l2v0V6LP4cH5xjj3iR1ltzouTJO6sDU79k+idIp86ncTnLT2X2aeGYan8q+Qb6yWX82WYn97hJfeRv9Y93akY8DQCWe9PQ39Kcdb7KUwZ1AhwX/RfE/Gy9Cp/bfKvqOT1I0tenB5IkyBxCTz695YPVPlwUbyVm2wshpTXJfDO+cbmyjphveBHyusS8LLLP6/KP9t73Ti9smVZ768cnT/alvX+Rx75dkzAs5yac+LZ/0qBcwo7uF+Qz48ueXF6MFECxv9o710vgyQ8tj/yz5oPRhxxhcC75hs5xjrJRbwE84UfvfjyS2L2hhcO1IualvX+ooFZmJX1vgCT5pEA/2ET/oWS/1si8udZhfPJv3h9lB15cXpWdKM3BEIgBEIgBEIgBEIgBELgbQjkxeltQhVDQyAEQiAEQiAEQiAEQiAEnkUgL07PIh+9IRACIRACIRACIRACIRACb0MgL05vE6oYGgIhEAIhEAIhEAIhEAIh8CwCeXF6FvnoDYEQCIEQCIEQCIEQCIEQeBsCeXF6m1DF0BAIgRAIgRAIgRAIgRAIgWcRyIvTs8hHbwiEQAiEQAiEQAiEQAiEwNsQyIvT24QqhoZACIRACIRACIRACIRACDyLQF6cnkU+ekMgBEIgBEIgBEIgBEIgBN6GQF6c3iZUMTQEQiAEQiAEQiAEQiAEQuBZBPLi9Czy0RsCIRACIRACIRACIRACIfA2BPLi9DahiqEhEAIhEAIhEAIhEAIhEALPIpAXp2eRj94QCIEQCIEQCIEQCIEQCIG3IZAXp7cJVQwNgRAIgRAIgRAIgRAIgRB4FoG8OD2LfPSGQAiEQAiEQAiEQAiEQAi8DYH/B7SZMCo8SEUfAAAAAElFTkSuQmCC\" width=\"846\" height=\"118\"\u003e\u003c/p\u003e\n\u003cp\u003eThe prediction probability of the prediction model was verified in the verification group, and the goodness of fit of the model in the training group and the verification group was calculated by using the \u0026quot;ResourceSelection\u0026quot; package of the R software. The goodness of fit test of the model yielded a P of 0.3677 in the training group, and the P was 0.4176 in the verification group. The goodness of fit test P for both of them was greater than 0.05, indicating that the calibration degree of the model was acceptable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;pROC\u0026rdquo; package of R software was used to draw ROC curves for the data of the training and verification groups. The results showed that the AUC of the prediction model in the training group was 0.818, and the predictive power was good. The AUC of the prediction model in the verification group was 0.7971, and the predictive power was good. Therefore, the prediction model of single cycle cumulative live birth rate in older patients with low response has good predictive power. Female age, normal fertilization rate, blastocyst formation rate, fresh embryo transfer and frozen embryo number were independently contributed to the single cycle cumulative live birth rate in older patients with low response.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDevelopments in assisted reproductive technology (ART) are helping more and more infertile couples to achieve pregnancy and live birth through IVF/ICSI. However, the pregnancy rate and the live birth rate of IVF/ICSI still need to be improved, especially for people with POR. A European study conducted over a period of 18 years reported[5]\u0026nbsp;that there was a strong correlation between the number of oocytes retrieved and the cumulative live birth rate in a single COS cycle. The cumulative live birth rate increased as the number of oocytes retrieved increased. The cumulative live birth rate tended to stabilize when the number of oocytes retrieved was 15-20, and it gradually dropped when the number of oocytes retrieved was more than 20. Patients with poor response to ovulation induction and low oocytes retrieval represent a major challenge in ART. In fact, clinicians have been striving to improve the outcome of assisted conception in patients with POR and to achieve a more accurate assessment pre-ART for the counselling of patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBefore 2011, there was no precise definition of low ovarian response. According to the systematic review by Polyzos and Devroey, 47 randomized trials of low ovarian response proposed up to 41 different diagnostic criteria for low ovarian response[13]. In the same year, ESHRE formulated the Bologna criteria for the diagnosis of low ovarian response[14]\u0026nbsp;to standardize the definition of POR and reduce the heterogeneity of studies. According to the Bologna criteria, at least two of the following conditions must be met in order to diagnose POR: (1) advanced age (age \u0026ge;40 years) and (2) obtaining \u0026le;3 oocytes with conventional stimulation or low ovarian reserve function (AFC\u0026lt;7 or AMH\u0026lt;1.1 ng/ml). In addition, in the absence of advanced age or low ovarian reserve function, a diagnosis of low response could be established, if the number of oocytes retrieved after two maximum stimulations is less than 3. The Bologna consensus fails to consider the heterogeneity of clinical responses in patients with low response and applies a blanket approach to age, rendering it difficult to inform patients against this standard in ART consultation. In response, POSEIDON Organization proposed the POSEIDON criteria for POR in 2016[1, 2], which categorizes patients with low response into four groups in terms of age and ovarian reserve.\u003c/p\u003e\n\u003cp\u003ePatients in POSEIDON Group 1 are young and have good ovarian reserve. Although the number of oocytes obtained from them is relatively small, they can still achieve good clinical outcomes. Yan E et al. retrospectively analyzed 4105 patients of POSEIDON Group 1, and the cumulative live birth rate in 4 cycles of ovulation promotion reached 67.9%[15]. In POSEIDON Group 2, although the patients are older, their ovarian reserve is still good, and they belong to the patients with unexpected low response. Lebovitz O et al.[16] retrospectively reviewed 751 patients with POR who satisfied the Bologna criteria and identified several contributors to the single-cycle cumulative live birth rate in patients with low ovarian response through logistic regression. Their results suggested that female age and the number of oocytes retrieved were independent predictors of cumulative live birth rate. Using a retrospective analysis, Gong et al.[17] \u0026nbsp;explored the factors that may influence the cumulative live birth rate of patients with standard low ovarian response diagnosed according to POSEIDON criteria. Their multivariate logistic regression analysis revealed that female age, bFSH, BMI, AMH and the number of normal fertilized oocytes were independent factors for POSEIDON patients with standard low ovarian response. However, this study did not stratify patients in the POSEIDON groups, and the patients in different POSEIDON groups were highly heterogeneous. Therefore, the influencing factors of cumulative live birth rate should be different. This study focused on patients of POSEIDON Group 2, who had good ovarian reserve but were older. In this group of patients with an unexpected low response, both patients and clinicians expect a positive outcome. This point is that it is of paramount importance to accurately identify the factors that may influence cumulative live births per cycle in this group. In this study, machine learning regularization analysis (LASSO) was used to screen out possible influencing factors, and the selected factors were subjected to logistic regression to construct a prediction model for predicting the single-cycle cumulative live birth rate of patients in POSEIDON Group 2. The area under the ROC curve (AUC) of the prediction model was 0.818 and the area under the ROC curve of the verification set was 0.7971, indicating that the model had a good predictive power. Female age, normal fertilization rate, blastocyst formation rate, number of fresh embryo transfer and frozen embryos were independent factors influencing the cumulative live birth rate in single cycle of older low-response patients in POSEIDON Group 2. The correlation between age and cumulative live birth rate has currently been generally accepted. Studies on normal fertilization rate, blastocyst formation rate and number of frozen embryos all suggest that increasing the number of available embryos is important to the improvement of the assisted pregnancy outcome in older patients with low response. This predictive model can help reproductive clinicians to better inform patients during pregnancy counselling and to develop better strategies for pregnancy assistance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we used machine learning LASSO to screen out the relevant variables, and subjected them to logistic regression to construct a prediction model for predicting the single-cycle cumulative live birth rate in patients of POSEIDON Group 2. The predictive power of the prediction model was good, and the goodness of fit test also indicated that both the training set and the verification set were well fitted. Female age, normal fertilization rate, blastocyst formation rate, number of frozen embryos and fresh embryo transfer were independent predictors of low response patients in the POSEIDON Group 2. However, the data in this study came from a single center, and the data were only verified internally. In the future, we will conduct a multi-center prospective study to further evaluate and verify the predictive efficacy of this model.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePOR: poor ovarian response; POSEIDON: patient-oriented strategy encompassing individualized oocyte number; LASSO: Least Absolute Shrinkage and Selection Operator; ROC: Receiver Operating Characteristic; AMH: antral follicle count; AMH: anti-M\u0026uuml;llerian hormone; ART: assisted reproductive technology; FSH: follicle stimulating hormone; bFSH: basal follicle-stimulating hormone; HUST: Huazhong University of Science and Technology; PGT: Pre-implantation Genetic Testing; OSI: Ovarian Sensitivity Index; Gn: gonadotrophin; MII: Metaphase II; NFR: Normal Fertilization Rate; ICSI: intracytoplasmic sperm injection; IVF: in-vitro fertilization; CR: Cleavage Rate; HQER: High Quality Embryo Rate; BFR: Blastocyst Formation Rate; CPR: Cumulative Pregnancy Rate; CLBR: Cumulative Live Birth Rate; SD: Standard deviation; AUC: area under the curves; COS: controlled ovarian hyperstimulation; GnRHa: Gonadotropin releasing hormone agonist; HCG: Human Chorionic Gonadotropin.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first author, Chunyan Chen collected the data, analyzed the data, made all the tables in this manuscript and drafted the manuscript. The co-first author xinliu Zeng drafted and modified the manuscript. Hanke Zhang helped to collect data. Yanhui Li gave the suggestions on revision. The corresponding author, Ying Gao and Lin Liu have designed the research and guided writing. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China under Grant number 82201818, 82303128.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during this study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was ethically approved by Tongji Medical College, HUST, Wuhan, China. The ethic approval number: [\u003cstrong\u003e2023]-S015\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eDepartment of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUniversity of Science and Technology, Wuhan, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlviggi C, Andersen CY, Buehler K, Conforti A, De Placido G, Esteves SC, Fischer R, Galliano D, Polyzos NP, Sunkara SK\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA new more detailed stratification of low responders to ovarian stimulation: from a poor ovarian response to a low prognosis concept\u003c/strong\u003e. \u003cem\u003eFertil Steril \u003c/em\u003e2016, \u003cstrong\u003e105\u003c/strong\u003e(6):1452-1453.\u003c/li\u003e\n\u003cli\u003eHumaidan P, Alviggi C, Fischer R, Esteves SC: \u003cstrong\u003eThe novel POSEIDON stratification of \u0026apos;Low prognosis patients in Assisted Reproductive Technology\u0026apos; and its proposed marker of successful outcome\u003c/strong\u003e. \u003cem\u003eF1000Res \u003c/em\u003e2016, \u003cstrong\u003e5\u003c/strong\u003e:2911.\u003c/li\u003e\n\u003cli\u003evan Loendersloot LL, van Wely M, Limpens J, Bossuyt PM, Repping S, van der Veen F: \u003cstrong\u003ePredictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis\u003c/strong\u003e. \u003cem\u003eHum Reprod Update \u003c/em\u003e2010, \u003cstrong\u003e16\u003c/strong\u003e(6):577-589.\u003c/li\u003e\n\u003cli\u003eSteward RG, Lan L, Shah AA, Yeh JS, Price TM, Goldfarb JM, Muasher SJ: \u003cstrong\u003eOocyte number as a predictor for ovarian hyperstimulation syndrome and live birth: an analysis of 256,381 in vitro fertilization cycles\u003c/strong\u003e. \u003cem\u003eFertil Steril \u003c/em\u003e2014, \u003cstrong\u003e101\u003c/strong\u003e(4):967-973.\u003c/li\u003e\n\u003cli\u003eSunkara SK, Rittenberg V, Raine-Fenning N, Bhattacharya S, Zamora J, Coomarasamy A: \u003cstrong\u003eAssociation between the number of eggs and live birth in IVF treatment: an analysis of 400 135 treatment cycles\u003c/strong\u003e. \u003cem\u003eHum Reprod \u003c/em\u003e2011, \u003cstrong\u003e26\u003c/strong\u003e(7):1768-1774.\u003c/li\u003e\n\u003cli\u003eMagnusson \u0026Aring;, K\u0026auml;llen K, Thurin-Kjellberg A, Bergh C: \u003cstrong\u003eThe number of oocytes retrieved during IVF: a balance between efficacy and safety\u003c/strong\u003e. \u003cem\u003eHum Reprod \u003c/em\u003e2018, \u003cstrong\u003e33\u003c/strong\u003e(1):58-64.\u003c/li\u003e\n\u003cli\u003ePolyzos NP, Drakopoulos P, Parra J, Pellicer A, Santos-Ribeiro S, Tournaye H, Bosch E, Garcia-Velasco J: \u003cstrong\u003eCumulative live birth rates according to the number of oocytes retrieved after the first ovarian stimulation for in vitro fertilization/intracytoplasmic sperm injection: a multicenter multinational analysis including \u003c/strong\u003e\u003cstrong\u003e\u0026sim;15,000 women\u003c/strong\u003e. \u003cem\u003eFertil Steril \u003c/em\u003e2018, \u003cstrong\u003e110\u003c/strong\u003e(4):661-670.e661.\u003c/li\u003e\n\u003cli\u003eSunkara SK, Ramaraju GA, Kamath MS: \u003cstrong\u003eManagement Strategies for POSEIDON Group 2\u003c/strong\u003e. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2020, \u003cstrong\u003e11\u003c/strong\u003e:105.\u003c/li\u003e\n\u003cli\u003eChinta P, Antonisamy B, Mangalaraj AM, Kunjummen AT, Kamath MS: \u003cstrong\u003ePOSEIDON classification and the proposed treatment options for groups 1 and 2: time to revisit? A retrospective analysis of 1425 ART cycles\u003c/strong\u003e. \u003cem\u003eHum Reprod Open \u003c/em\u003e2021, \u003cstrong\u003e2021\u003c/strong\u003e(1):hoaa070.\u003c/li\u003e\n\u003cli\u003eHuang A, Xu S, Cai X: \u003cstrong\u003eEmpirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping\u003c/strong\u003e. \u003cem\u003eBMC Genet \u003c/em\u003e2013, \u003cstrong\u003e14\u003c/strong\u003e:5.\u003c/li\u003e\n\u003cli\u003eYadav V, Malhotra N, Mahey R, Singh N, Kriplani A: \u003cstrong\u003eOvarian Sensitivity Index (OSI): Validating the Use of a Marker for Ovarian Responsiveness in IVF\u003c/strong\u003e. \u003cem\u003eJ Reprod Infertil \u003c/em\u003e2019, \u003cstrong\u003e20\u003c/strong\u003e(2):83-88.\u003c/li\u003e\n\u003cli\u003eWang H, Xu Q, Zhou L: \u003cstrong\u003eLarge unbalanced credit scoring using Lasso-logistic regression ensemble\u003c/strong\u003e. \u003cem\u003ePLoS One \u003c/em\u003e2015, \u003cstrong\u003e10\u003c/strong\u003e(2):e0117844.\u003c/li\u003e\n\u003cli\u003ePolyzos NP, Devroey P: \u003cstrong\u003eA systematic review of randomized trials for the treatment of poor ovarian responders: is there any light at the end of the tunnel?\u003c/strong\u003e \u003cem\u003eFertil Steril \u003c/em\u003e2011, \u003cstrong\u003e96\u003c/strong\u003e(5):1058-1061.e1057.\u003c/li\u003e\n\u003cli\u003eFerraretti AP, La Marca A, Fauser BC, Tarlatzis B, Nargund G, Gianaroli L: \u003cstrong\u003eESHRE consensus on the definition of \u0026apos;poor response\u0026apos; to ovarian stimulation for in vitro fertilization: the Bologna criteria\u003c/strong\u003e. \u003cem\u003eHum Reprod \u003c/em\u003e2011, \u003cstrong\u003e26\u003c/strong\u003e(7):1616-1624.\u003c/li\u003e\n\u003cli\u003eYan E, Li W, Jin H, Zhao M, Chen D, Hu X, Chu Y, Guo Y, Jin L: \u003cstrong\u003eCumulative live birth rates and birth outcomes after IVF/ICSI treatment cycles in young POSEIDON patients: A real-world study\u003c/strong\u003e. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1107406.\u003c/li\u003e\n\u003cli\u003eLebovitz O, Haas J, Mor N, Zilberberg E, Aizer A, Kirshenbaum M, Orvieto R, Nahum R: \u003cstrong\u003ePredicting IVF outcome in poor ovarian responders\u003c/strong\u003e. \u003cem\u003eBMC Womens Health \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(1):395.\u003c/li\u003e\n\u003cli\u003eGong X, Zhang Y, Zhu Y, Wang P, Wang Z, Liu C, Zhang M, La X: \u003cstrong\u003eDevelopment and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI\u003c/strong\u003e. \u003cem\u003eFront Endocrinol (Lausanne) \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1027805.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"LASSO regression, machine learning, prediction model, POR, POSEIDON Group","lastPublishedDoi":"10.21203/rs.3.rs-3521867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3521867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eOutcomes in patients with poor ovarian response (POR) have been less favorable and there is a need for improvement. The patient-oriented strategy encompassing individualized oocyte number (POSEIDON) criteria，proposed in 2016, are now widely accepted and used in clinical practice. POSEIDON Group 2 is considered as “Unexpected low response”, which is a challenge for clinicians. Currently, multiple reviews have retrospectively analysed the ART outcomes in the hyporesponsive populations of the POSEIDON Groups. However, no study has systematically examined the influencing factors specifically associated with the single-cycle cumulative live birth rate in POSEIDON Group 2. A prediction model was developed to predict the cumulative single-cycle live birth rate in POSEIDON Group 2 Patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 565 assisted reproductive cycles from the low-response population of POSEIDON Group 2 were retrospectively analyzed from January 2018 to December 2021 at the center for Reproductive Medicine, Wuhan Union Hospital, Tongji Medical College. Cases were randomized 7:3 into two groups. Baseline levels were compared among the total, training and validation groups. A total of 26 variables were included and analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO)regression with \"lambda.min\" as the screening criterion. To construct a predictive model of cumulative live birth rate, the selected variables were subjected to multivariate logistic regression. The predictive performance of the model was validated in the validation group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e After randomization, 392 cases were assigned to the training group and 173 cases to the validation group. There were no statistical differences in baseline characteristics among the three groups. Seven variables were screened out by LASSO regression, including female age, assisted reproduction cycles, type of infertility, normal fertilization rate, blastocyst formation rate, number of frozen embryos, and whether fresh embryos were transferred. Furthermore, logistic regression was performed on these seven variables to construct a regression model，which had a ROC (Receiver Operating Characteristic) curve of 0.818 in the training group and 0.7971 in the validation group, with good predictive power and goodness-of-fit tests \u0026gt;0.05 in both the training and validation groups. The model had an area under the ROC curve of 0.818 in the training group and 0.7971 in the validation group. The prediction efficiency was good, and the Goodness of fit test in both the training group and the validation group was\u0026gt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In this study, the prediction model constructed had good predictive performance with female age, normal fertilization rate, blastocyst formation rate, number of frozen embryos, and fresh embryo transfer. These factors work as independent predictors of single cycle cumulative live birth rate in patients with POSEIDON Group 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eThis is a retrospective study, and the study was ethically approved by Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, China.\u003c/p\u003e","manuscriptTitle":"Predicting single-cycle cumulative live birth rate in POSEIDON Group 2 Patients: a prediction model based on matching learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-03 17:36:35","doi":"10.21203/rs.3.rs-3521867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2bc6389f-db04-45e9-a368-cf5be635d619","owner":[],"postedDate":"November 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-05T05:44:18+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-03 17:36:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3521867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3521867","identity":"rs-3521867","version":["v1"]},"buildId":"369fNeqWncA4NS6XSWjrt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}