Prognostic determinants for clinical pregnancy rate following IVF in women with ovarian endometriomas? A propensity score-matched retrospective cohort study

Prognostic determinants for clinical pregnancy rate following IVF in women with ovarian endometriomas? 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A propensity score-matched retrospective cohort study Yixin Wang, Yuqing Zhang, Xinzhe Wang, Na Duan, Zhengao Sun, Ruihua Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8614148/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Ovarian endometrioma (OMA) is associated with reduced in vitro fertilization (IVF) success rates. Therefore, the purpose of the study is to investigate risk factors associated with pregnancy outcomes following IVF in women with OMA and assess the predictive utility of maximum OMA diameter. Methods This propensity score-matched retrospective cohort study enrolled 2416 women who underwent IVF between December 2020 and November 2025. A total of 248 transvaginal ultrasound (TVUS)-confirmed OMA patients who underwent IVF were allocated to the OMA group. Conversely, 2,168 concurrently enrolled patients without ultrasonographically confirmed OMA who underwent IVF were assigned to the non-OMA group. A sample size of 738 was calculated to achieve a statistical power of 99.9% and a two-sided α of 0.05. Age, body mass index (BMI), and controlled ovarian hyperstimulation (COH) protocols were adjusted between the two groups using propensity score matching (PSM) at a 1:3 ratio. After PSM, 248 OMA patients were matched to 738 patients without OMA who underwent IVF. Additionally, 248 OMA patients were stratified into pregnant and non-pregnant groups according to clinical pregnancy outcome, and factors associated with clinical pregnancy among these patients were identified. Results After PSM, multivariate logistic regression revealed that type of infertility, number of abortions and cesarean deliveries, and level of sex hormones were independent factors for OMA, and OMA independently influenced clinical pregnancy and early miscarriage rates ( P < 0.05). In the OMA group, multivariate logistic regression identified age, miscarriage number, COH protocols, and maximum OMA diameter as independent predictors of clinical pregnancy outcomes. The integrated predictive model exhibited an area under the receiver operating characteristic curve (AUC) of 0.857, with optimal predicted cutoff values of 32.5 years for age and 21.25 mm for maximum OMA diameter. Conclusions The retrospective analysis delineated infertility type, obstetric history, and sex hormone profiles as independent risk factors for ovarian cysts. OMA subgroup analysis demonstrated age, miscarriage count, COH protocols, and maximum OMA diameter as key predictors of IVF outcomes. Registry : the Ethics Review Committee of the Jiangsu Provincial Hospital of Traditional Chinese Medicine, approval number:2021NL-045-03, Registration date: 16 January 2021. Ovarian endometrioma Female infertility IVF-ET Clinical pregnancy rate Propensity score matching Risk factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Endometriosis is a chronic condition characterized by heterogeneous clinical manifestations, encompassing not only variability in lesion distribution but also diversity in signs and symptoms, with core symptoms including pain and infertility [ 1 ] .The exact prevalence of endometriosis remains unknown, but estimates vary from 2–10% in women of reproductive age up to 50% in infertile women [ 2 ] , while approximately 17–44% of women with OMA [ 3 ] .A chronic inflammatory process impairing ovarian, tubal, and endometrial has been proposed as a key mechanism underlying reduced fertility. The diagnosis of OMA in patients with endometriosis is clinically paramount, with TVUS and magnetic resonance imaging (MRI) recognized as optimal imaging modalities that exhibit notably high diagnostic accuracy for OMA. TVUS fulfills the predetermined criteria for the Specificity in Prevalence-adjusted Negative Predictive Value (SPIN) triage test, with a specificity of 95%, and is generally low-cost, widely accessible, and notably better tolerated compared with MRI [ 4 ] . In women with OMA undergoing surgery for infertility, excision of the endometrioma capsule confers an increase in the spontaneous postoperative pregnancy rate [ 5 ] . The utility of cystectomy prior to assisted reproductive technology (ART) for improving reproductive outcomes in women with OMA is assessed, with limited consistency in result interpretation given the lack of significant difference in pregnancy rates [ 6 ] . Whereas prior surgical intervention for endometriosis emerges as a definitive risk factor for this condition in the context of OMA per se [ 7 ] . Surgical management of ovarian endometriomas fails to consistently enhance reproductive outcomes in subfertile women undergoing ART, and for cases in which endometrioma surgery is contemplated, preoperative specialized ultrasound for the assessment of disease complexity is recommended, with ovarian stripping constituting the preferred surgical modality. The European Society of Human Reproduction and Embryology (ESHRE) guideline affirms the general consensus that OMA larger than 4 cm should be surgically removed to reduce pain and improve spontaneous conception rates, while no evidence indicates that cystectomy prior to ART enhances pregnancy rates in infertile women with OMA larger than 3 cm [ 8 ] . To date, no conclusive evidence exists to delineate the nature of the correlation between maximum OMA diameter and pregnancy outcomes in women undergoing IVF, thereby precluding definitive guidance for clinicians regarding the indication for laparoscopic ovarian cystectomy. Therefore, the present study seeks to delineate the factors predictive of pregnancy outcomes in women affected by OMA undergoing IVF, and to characterize the association between maximum OMA diameter and pregnancy outcomes. MATERIALS AND METHODS Study subjects Ethical approval for this study was granted by the Ethics Review Committee of the Jiangsu Provincial Hospital of Traditional Chinese Medicine (approval number 2021NL-045-03), and all participants provided written informed consent. Data from 2,168 IVF cycles treated between December 2019 and November 2024 were retrieved for potential inclusion. To homogenize the study population, patients were excluded if they had: (1) autoimmune, infectious, or inflammatory diseases within 3 months prior to oocyte retrieval; (2) malignancies of any type; (3) previous endometriosis, ovarian, or pelvic surgery; (4) history of prior IVF treatment; (5) polycystic ovary syndrome (PCOS); (6) severe male factor infertility; (7) follicle-stimulating hormone (FSH) > 10 mIU/mL; (8) anti-Müllerian hormone (AMH) < 0.5 ng/mL; (9) history of abdomino-pelvic radiotherapy or chemotherapy; or (10) concomitant leiomyoma and adenomyosis. Patients underwent two-dimensional transvaginal sonography (2D-TVS) on days 3–5 of their menstrual cycle. Following bladder emptying, patients were placed in the lithotomy position. An ultrasound probe covered with a condom was inserted into the vagina to the cervix. The first operator conducted a routine assessment of the uterus and ovaries, including size, shape, and echogenicity, with data grouped based on the presence or absence of OMA. Subsequently, the second operator utilized two-dimensional ultrasound to measure the maximum diameter of OMA. The demographic data, clinical records and ultrasound characteristics of all the patients with endometriosis were collected through their clinical records. Statistics Statistical analyses used SPSS 26.0 and R 4.3.1. Continuous variables were summarized as mean ± SD for normal distribution or median (25th–75th percentiles) for non-normal distribution, and categorical variables as frequencies with percentages. Group comparisons employed chi-square test for categorical variables, Student’s t-test for parametric continuous variables, and Mann-Whitney U test for non-parametric ones. Univariable logistic regression identified potential predictors ( P < 0.1) incorporated into a multivariable model guided by clinical and statistical relevance. A nomogram with integrated DCA and ROC analysis (R PROC/RMDA packages) was developed, with performance assessed via AUC (1,000 bootstraps) and Hosmer-Lemeshow test (SPSS). Significance was set at two-tailed P < 0.05, with results reported as ORs with 95% CIs. Propensity score matching In the preliminary analysis of the matched cohort, the researchers sought to investigate the association between OMA and pregnancy outcomes. To mitigate potential confounding, propensity score matching (PSM) was utilized, with a 1:3 nearest-neighbor matching approach without replacement and a caliper width of 0.05 to generate the matched cohort. The propensity score was defined as the predicted probability of OMA exposure based on age, BMI, and COH protocols. To validate the PSM model, baseline characteristics were compared between the two groups in the matched cohort, followed by multivariate logistic regression analysis conducted in the PSM cohort. Subgroup analyses The cohorts were stratified into subgroups, with women with OMA further categorized into a clinical pregnancy group and a non-clinical pregnancy group according to clinical pregnancy status, with factors associated with pregnancy outcomes evaluated. RESULTS Demographic and clinical data Statistically significant differences (P < 0.05) were observed between the two groups for the following variables: type of infertility (primary/secondary), number of abortions, number of cesarean deliveries, tubal patency (left/right), and levels of reproductive hormones (prolactin[PRL], progesterone[P], estradiol[E2], anti-Müllerian hormone[AMH], and testosterone[T]). Among women with ovarian endometriomas (OMA), secondary infertility was twice as prevalent as primary infertility, accounting for 68.15% versus 31.85% of cases. In contrast, primary and secondary infertility accounted for nearly equivalent proportions among women without OMA. Notably, patients with OMA exhibited significantly lower serum AMH levels (median: 2.79; IQR: 1.40–4.26) than their non-OMA counterparts (median: 3.73; IQR: 1.75–4.81). In contrast, no statistically significant differences were noted between the two groups for age, body mass index (BMI), age at menarche, antral follicle count (AFC), or reproductive hormone levels (follicle-stimulating hormone[FSH] and luteinizing hormone[LH]), with all P values > 0.05. Comprehensive results are summarized in Table 1 . Table 1 Comparison of clinical data Variables Non-OMA group (n = 738) % OMA group (n = 248) % t/Z/ \(\:{\chi\:}\) 2 P Age(y) 32(30,35.25) 32(29,37) -0.380 0.704 BMI 23.17 ± 3.60 23.08 ± 3.21 0.379 0.705 Age at menarche(y) 2(1,3.2) 2(1.13,3.73) -0.527 0.598 Type of infertility 50.774 < 0.001 Primary 428(57.99) 79(31.85) Secondary 310(42.01) 169(68.15) Number of abortions 0(0,0) 0(0,1) -11.777 < 0.001 Number of cesarean deliveries 0(0,0) 0(0,0) -3.354 0.001 Level of sex hormones FSH 7.63(6.09,9.42) 7.96(6.27,9.75) -1.544 0.123 LH 4.65(3.19,6.41) 4.10(3.01,5.91) -1.566 0.117 E2 48(31,66.58) 43(26.57,55) -3.625 < 0.001 T 39.12(32.65,42.16) 39.01(37.39,42.48) -3.034 0.002 PRL 18.91(18.08,18.91) 16.09(16.09,16.09) -11.850 < 0.001 P 0.9(0.9,0.9) 0.69(0.69,0.69) -14.502 < 0.001 AMH 3.73(1.75,4.81) 2.79(1.40,4.26) -3.158 0.002 Tubal patency (L) 14.381 0.001 Patent 405(54.88) 170(68.55) Partial obstruction 206(27.91) 50(20.16) Obstruction 127(17.21) 28(11.29) Tubal patency (R) 32.993 < 0.001 Patent 414(56.10) 190(76.61) Partial obstruction 188(25.47) 35(14.11) Obstruction 136(18.43) 23(9.27) AFC (L) 7(5,10) 7(4,10) -0.722 0.470 AFC (R) 7(5,10) 7(5,10) -1.285 0.199 IVF information and pregnancy outcomes Statistically significant differences were noted between the two study cohorts for a panel of key IVF cycle parameters and early pregnancy outcomes (all P < 0.05), including the number of aspirated follicles, retrieved oocytes, fertilized oocytes, cleaved embryos, 2PN embryos, frozen day 3 embryos, frozen day 5 embryos, clinical pregnancy rate, early miscarriage rate, and late miscarriage rate. Notably, the clinical pregnancy rate among women with OMA was only half that documented in their non-OMA counterparts, highlighting the specific impact of OMA on early pregnancy establishment, whereas the rates of ongoing pregnancy and live birth did not differ significantly between the two groups. By contrast, no statistically significant variations were detected across core treatment modalities and downstream pregnancy endpoints, which encompassed COH protocols, duration of gonadotropin (Gn) therapy, biochemical pregnancy rate, ongoing pregnancy rate, and live birth rate (all P > 0.05). A detailed breakdown of all study metrics and outcomes is presented in Table 2 . Table 2 Comparison of IVF information and pregnancy outcomes Variables Non-OMA group (n = 738) % OMA group (n = 248) % t / Z / \(\:{\chi\:}\) 2 P COH protocols 2.007 0.571 Long GnRH agonist 231(31.30) 77(31.05) GnRH antagonist 366(49.59) 114(45.97) Progestin-Primed Ovarian Stimulation 90(12.20) 35(14.11) Minimal ovarian stimulation 51(6.91) 22(8.87) Gn duration (d) 12.16 ± 2.47 12.08 ± 2.60 0.409 0.683 Number of aspirated follicles 12(6,17) 10(5,14) -3.837 < 0.001 Number of retrieved oocytes 10(5,14) 8(4,13) -3.110 0.002 Number of fertilized oocytes 8(5,11) 7(4,9) -4.306 < 0.001 Number of cleaved embryos 7(4,9) 6(3,7) -3.547 < 0.001 Number of 2PN embryos 7(4,9) 6(3,8) -3.512 < 0.001 Number of frozen Day 3 embryos 2(1,2) 2(1,2) -2.369 0.018 Number of frozen Day 5 embryos 2(0,3) 2(0,2) -2.905 0.004 Biochemical pregnancy rate 244(33.06) 73(29.44) 1.119 0.290 Clinical pregnancy rate 146(19.78) 27(10.89) 10.154 < 0.001 Early miscarriage rate 98(13.28) 46(18.55) 4.132 0.042 Ongoing pregnancy rate 51(6.91) 15(6.05) 0.221 0.638 Late miscarriage rate 2(0.27) 4(1.61) 5.526 0.019 Live birth rate 49(6.64) 11(4.44) 1.578 0.209 Multivariate logistic regression analysis Univariable analysis identified statistically significant differences between the non-OMA and OMA cohorts for the following parameters (all P < 0.05): type of infertility, number of abortions, number of cesarean deliveries, number of aspirated follicles, number of fertilized oocytes, clinical pregnancy rate, early miscarriage rate, and reproductive hormone levels (PRL and P). Parameters yielding a univariable significance level of P < 0.05 were subsequently incorporated into the multivariable logistic regression model. Multivariable logistic regression analysis confirmed that type of infertility, number of abortions, clinical pregnancy rate, and reproductive hormone levels (PRL and P) were independently associated with OMA (all P < 0.05). The corresponding odds ratios (ORs) and 95% confidence intervals (CIs) for these variables were as follows: PRL (OR = 0.942, 95% CI: 0.908–0.978, P = 0.002); P (OR = 0.177, 95% CI: 0.097–0.323, P < 0.001); clinical pregnancy rate (OR = 0.557, 95% CI: 0.344–0.903, P = 0.017); number of abortions (OR = 1.878, 95% CI: 1.549–2.277, P < 0.001); and type of infertility (OR = 2.159, 95% CI: 1.526–3.056, P < 0.001). The statistically significant factors associated with OMA identified in the multivariable analysis were visualized using a forest plot, as illustrated in Fig. 1 . To further evaluate the predictive utility of the key independent correlates of OMA identified via multivariable logistic regression, a receiver operating characteristic (ROC) curve was constructed for the composite predictive model incorporating these variables. The results demonstrated that this integrated model yielded an area under the ROC curve (AUC) of 0.804, with a corresponding 95% confidence interval (95% CI) of 0.722–0.835. This AUC value indicates that the model exhibits favorable discriminative capacity for differentiating between OMA-positive and OMA-negative patients. The ROC curve visualizing the discriminative efficacy of the predictive model is depicted graphically in Fig. 2 . Subgroup analysis of OMA patients All participants were OMA patients undergoing IVF treatment, stratified into pregnant and non-pregnant subgroups based on clinical pregnancy outcomes. Univariable logistic regression analysis detected statistically significant variations across the two subgroups in age, number of abortions, BMI, COH protocols, Gn duration, and maximum OMA diameter (all P < 0.05). Candidate variables that achieved statistical significance at the P < 0.05 level in univariable logistic regression analyses were thereafter subjected to multivariable logistic regression modeling. After adjusting for confounding factors, the analysis confirmed that age, number of abortions, COH protocols, and maximum OMA diameter were independent factors associated with clinical pregnancy outcomes in OMA patients undergoing IVF (all P < 0.05). Significant factors linked to clinical pregnancy rates, as identified via multivariable analysis, were depicted in a forest plot (Fig. 3 ). Predictive performance of the combined model and individual predictors derived from multivariable logistic regression was assessed via ROC curve analysis. The combined model yielded an AUC of 0.857 (95% CI: 0.784–0.930), while individual factors exhibited moderate discriminative capacity, with respective AUC values of 0.727 (age), 0.635 (number of abortions), 0.713 (COH protocols), and 0.684 (maximum OMA diameter). ROC curve analysis identified optimal cutoff values of 32.5 years for age and 21.25 mm for OMA diameter, respectively. Corresponding ROC curves illustrating the discriminative efficacy of the combined model and its individual component predictors are presented in Fig. 4 . To enable individualized prediction of clinical pregnancy rates in OMA patients undergoing IVF, we developed a nomogram (Fig. 5 ) incorporating independent predictors (age, number of abortions, COH protocols, maximum OMA diameter) identified via multivariable logistic regression. Variable-specific point distributions intuitively reflect the differential contribution of each factor to pregnancy outcomes. Notably, the nomogram reveals a clear gradient across COH protocol coding (1 = Long GnRH agonist, 2 = GnRH antagonist, 3 = Progestin-Primed Ovarian Stimulation, 4 = Minimal ovarian stimulation): the Long GnRH agonist protocol correlates with the highest points, while the Minimal protocol corresponds to the lowest. Both age and maximum OMA diameter exhibit a significant negative correlation with clinical pregnancy rate in OMA patients undergoing IVF. To evaluate the clinical utility of our nomogram (for predicting IVF clinical pregnancy outcomes in OMA patients, stratified into pregnant/non-pregnant groups), we performed decision curve analysis (DCA) (Fig. 6 ). Across all threshold probabilities, the nomogram’s net benefit curve consistently outperformed the “predict all as non-pregnant” strategy. Notably, between 0 and ~ 0.2 threshold probability, it also exceeded the “predict all as pregnant” strategy. The nomogram achieved a maximum net benefit of 0.049, with a peak incremental benefit of 0.032 vs. “predict all as pregnant.” Shaded regions denote threshold ranges where the model’s net benefit surpasses these two non-discriminatory strategies. These findings confirm that using the nomogram for prognostic risk stratification (identifying high/low pregnancy risk) within the relevant threshold range yields greater clinical net benefit than binary, one-size-fits-all prediction—validating the model’s practical value for outcome-focused risk assessment in this OMA cohort. We constructed a nomogram from the independent predictors and assessed its discriminative performance via ROC curve analysis (Fig. 7 ). The model’s AUC—a gold-standard metric for discriminative performance—was 0.744 (95% CI: 0.538–0.95), with its ROC curve deviating markedly from the null diagonal reference line. These findings confirm the nomogram has moderate discriminative capacity, reliably distinguishing between pregnant and non-pregnant OMA patients undergoing IVF, and serving as a quantitative tool for clinical pregnancy outcome risk stratification. DISCUSSION Ovarian endometrioma is a cystic lesion arising within the ovaries secondary to endometriosis, a prevalent gynecological disorder characterized by the ectopic implantation and proliferation of endometrial tissue outside the uterine cavity [ 9 ] . This condition typically manifests with clinical symptoms including dysmenorrhea, chronic pelvic pain, and infertility. Endometriosis is well recognized to exert an adverse impact on fertility. While a causal relationship between endometriosis and infertility has not been definitively established, a clear association has been identified. Notably, 30–50% of women with endometriosis suffer from fertility impairment, and ovarian endometriomas are commonly detected during infertility evaluations [ 10 ] . Women with concomitant ovarian endometriomas, often require ART to achieve clinical pregnancy [ 11 ] . However, it is widely postulated that mechanisms may impair IVF outcomes in women with this condition. These include blunted ovarian stimulation responsiveness, perturbed steroidogenesis, impaired oocyte quality, compromised fertilization and embryo developmental potential, and defective endometrial implantation [ 12 ] . The 5-year retrospective review of medical records and in vitro fertilization (IVF) pregnancy outcomes was conducted. Propensity score matching and multivariate logistic regression analyses identified type of infertility, number of abortions and cesarean deliveries, and level of sex hormones profiles as independent risk factors for ovarian cysts. Additionally, subgroup analysis stratified by clinical pregnancy outcomes among OMA patients revealed that age, miscarriage number, COH protocols, and maximum OMA diameter are independent predictors of IVF-associated clinical pregnancy outcomes. Progesterone, a steroid hormone synthesized by the ovaries, adrenal cortex, and placenta, exerts pivotal regulatory effects on the intricate cascade of female reproductive functions [ 13 ] . Ovarian endometriomas originate from ectopic endometrial implantation and cyclic hemorrhage within the ovarian parenchyma, processes that progressively erode ovarian cortical tissue and compromise follicular reserve. Given that folliculogenesis and subsequent corpus luteum formation represent the primary sources of progesterone secretion, structural and functional follicular damage in OMA patients impairs follicular maturation and induces luteal insufficiency, thereby directly reducing basal progesterone levels. Progesterone resistance is widely recognized to represent a key pathological hallmark of endometriosis, as an expanding body of evidence has demonstrated that the loss of progesterone signaling occurs in both eutopic and ectopic endometrial tissues [ 14 ] . Prolactin secretion exhibits diurnal variation and is highly sensitive to stress, nipple stimulation, and recent meal consumption. The present study strictly defined basal prolactin as concentrations measured in the fasting state between 8:00 and 10:00 a.m.—a standardized protocol that ensured the reliability and comparability of hormone assay data. Prolactin levels serve as a promising prognostic biomarker to distinguish endometriosis stages III/IV from I/II and differentiate infertile women with endometriosis from non-endometriosis counterparts [ 15 ] . The association between abortion and endometriosis has remained inconsistent and contentious across the existing body of observational literature. A Mendelian randomization (MR) analysis was designed to elucidate the potential causal relationship between these two conditions, and the resultant findings did not support the existence of a robust, definitive causal link [ 16 ] . Notably, this controversy largely stems from the failure to distinguish between spontaneous abortion and induced abortion, which exert divergent effects on endometriosis pathogenesis. Intrauterine manipulations performed during induced abortion procedures can induce endometrial injury, thereby elevating the risk of ovarian endometrioma development. Similarly, dilation and curettage procedures, along with other intrauterine surgical interventions including cesarean section and hysteroscopic procedures, may disrupt the endometrial-myometrial interface and facilitate the migration, invasion, implantation, shedding, and development of endometrial colonies within the myometrium [ 17 ] . By virtue of these interconnected pathophysiological mechanisms, namely endometrial injury and endometrial-myometrial interface disruption induced by intrauterine procedures, women with a history of one or more miscarriages (including induced abortion) exhibit an elevated susceptibility to endometriosis. This risk is further amplified by intrauterine surgical procedures, which are widely recognized as well-established risk factors for this condition [ 18 ] . Subsequently, ovarian endometriosis arising from such intrauterine procedure-mediated pathways may exert deleterious effects on embryo quality, which in turn contributes to an increased likelihood of adverse pregnancy outcomes [ 19 ] . Follicular density in the ovarian tissue adjacent to endometriotic cysts has been consistently demonstrated to be significantly lower than that in healthy ovarian tissue, yet this pathological alteration does not appear to result from tissue stretching in the adjacent area induced by cyst presence. Endometriomas harbor free iron, reactive oxygen species (ROS), proteolytic enzymes, and inflammatory molecules at concentrations tens to hundreds of times greater than those detected in peripheral blood or other benign cyst types [ 20 ] . Cyst fluid induces significant alterations in the endometriotic cells it bathes, ranging from gene expression modifications to genetic mutations. The physical barrier separating cyst contents from normal ovarian tissue is a thin wall consisting of the ovarian cortex per se or fibroreactive tissue. ROS that potentially permeate surrounding tissues and proteolytic substances that degrade adjacent tissues are prone to induce the replacement of normal ovarian cortical tissue with fibrous tissue, with a reduction in cortex-specific stroma [ 21 ] . This fibrosis is associated with smooth muscle metaplasia and is subsequently accompanied by follicular loss and intraovarian vascular injury. In general, the influence of endometriosis on pregnancy is mediated by multiple distinct pathophysiological mechanisms [ 22 ] . First, toxic pelvic microenvironmental factors impair oocyte and embryo quality via direct, sustained deleterious effects on gametes and embryos during their transit through the distal fallopian tube. Second, inflammation-dependent perturbations of the eutopic endometrium induce aberrant endometrial remodeling during the window of implantation (WOI), compromise predecidual transformation, and ultimately culminate in defective placentation. Such pathological alterations predispose affected individuals to a spectrum of obstetric complications, including early pregnancy loss and notably recurrent pregnancy loss (RPL) [ 23 ] . Notably, in the context of ART, endometriosis exerts deleterious effects exclusively on the eutopic endometrium, with a particular emphasis on defective placentation, thereby potentially predisposing to early pregnancy loss and RPL. Furthermore, chronic inflammation of the eutopic endometrium exhibits a strong association with chronic endometritis. The ESHRE guidelines [ 1 ] indicate that routine surgery for ovarian endometriomas prior to ART is not recommended to improve live birth rates. This is because current evidence does not demonstrate a beneficial effect, and surgery is likely to adversely affect ovarian reserve. Currently, surgical treatment for endometriomas typically involves stripping them away or draining and destroying the cysts using an electric current or laser. A meta-analysis from the Cochrane Library compared two surgical modalities for endometriomas and concluded that women who underwent ovarian cystectomy may be associated with a lower AFC and reduced ovarian response compared with those who underwent endometrioma ablation [ 24 ] . In infertile women, surgical intervention for endometriomas smaller than 3 cm does not enhance IVF success rates. Conversely, for endometriomas larger than 6 cm located within the ovary, cystectomy results in a thin rim of ovarian tissue with compromised vascularity. Certain uncertainty persists regarding the potential benefits of cystectomy prior to IVF. Therefore, a comprehensive assessment is warranted, incorporating the patient’s miscarriage history and sex hormone profiles. The ESHRE guidelines further note that a specific protocol for ART in women with endometriosis cannot be recommended. Instead, both GnRH antagonist and agonist protocols may be utilized according to patient and physician preferences, as no significant difference in pregnancy rates or live birth rates has been demonstrated. Endometriosis exerts a negligible impact on ovarian response. Specifically, a 4 cm diameter threshold distinguishes endometriomas that do or do not interfere with ovarian response [ 25 ] . This study is not without limitations, which should be taken into account when interpreting the findings. First, occult deep infiltrating endometriosis (DIE) was not excluded. Such lesions can indirectly compromise pregnancy outcomes by perturbing the pelvic microenvironment and impairing endometrial receptivity, potentially introducing bias into the accuracy of the study results. Second, only pregnancy outcomes of the first embryo transfer cycle were analyzed, with data from multiple transfer cycles not being captured. This precludes the evaluation of cumulative pregnancy rates, thereby limiting the generalizability of the conclusions to cohorts undergoing repeated embryo transfers. Third, miscarriage history was not stratified with sufficient granularity, failing to distinguish between unplanned miscarriage and embryonic arrest. These two entities exhibit distinct pathophysiological etiologies: the former is often linked to maternal factors (e.g., endocrine disorders, immune dysfunction), while the latter is predominantly attributed to embryonic chromosomal abnormalities. This lack of stratification may introduce confounding bias into the analysis, compromising the precision of the assessment regarding the association between endometriosis and miscarriage. Fourth, endometrial preparation regimens for transfer cycles were not documented. Different clinical regimens (e.g., natural cycle, ovulation induction cycle, artificial cycle) exert differential regulatory effects on endometrial receptivity, and the absence of this variable precludes the elimination of its confounding influence on pregnancy outcomes. Finally, embryo grading—a key surrogate for embryo quality—was not incorporated into the analysis. As embryo quality represents a core prognostic factor for transfer outcomes, failure to adjust for this variable may compromise the robustness of the analysis investigating the impact of endometriosis on pregnancy outcomes. CONCLUSION Collectively, this study delineates critical independent determinants of ovarian cysts and prognostic factors for IVF clinical pregnancy outcomes in patients with OMA. These findings enable precise clinical risk stratification for ovarian cyst development and refine individualized prognostic assessment and therapeutic regimens for OMA patients undergoing IVF, thereby offering actionable clinical implications to guide evidence-based reproductive medicine practice. Declarations Consent for publication Not applicable. Competing interests The authors have no competing interests to declare. FUNDINGS Supported by the National Key R&D Program of China Award Number: 2024YFC3505800 Author Contribution Y.W. collected clinical data, drafted the main manuscript text, and conducted preliminary data collation. Y.Z. assisted with clinical data verification and literature supplementation. X.W. designed the study protocol and performed statistical analyses including propensity score matching and multivariate logistic regression. N.D. sorted out follow-up data and prepared reference lists. Z.S. provided professional guidance on the interpretation of reproductive endocrinology indicators. R.Z. supervised the research implementation and revised the manuscript for important intellectual content. Y.Y. conceived the research framework, secured funding support, critically revised the manuscript, and approved the final version for submission. All authors reviewed and approved the final manuscript. Data Availability All data supporting the findings of this study are available within the paper and its Supplementary Information. References BECKER C M, BOKOR A, HEIKINHEIMO O, et al. ESHRE guideline: endometriosis [J]. Hum Reprod Open. 2022;2022(2):hoac009. MEULEMAN C, VANDENABEELE B. High prevalence of endometriosis in infertile women with normal ovulation and normospermic partners [J]. Fertil Steril. 2009;92(1):68–74. FARKAS A H, ABUMUSA H. Structural Gynecological Disease: Fibroids, Endometriosis, Ovarian Cysts [J]. Med Clin North Am. 2023;107(2):317–28. KANTI FS, GORAK SAVARD R, BERGERON F, et al. Transvaginal ultrasound and magnetic resonance imaging in the diagnosis of endometrioma: a systematic review and meta-analysis of diagnostic test accuracy studies [J]. J Obstet Gynaecol. 2024;44(1):2311664. HART R J, HICKEY M, MAOURIS P et al. Excisional surgery versus ablative surgery for ovarian endometriomata [J]. Cochrane Database Syst Rev, 2008, (2): Cd004992. BENSCHOP L, FARQUHAR C, VAN DER POEL N, et al. Interventions for women with endometrioma prior to assisted reproductive technology [J]. Cochrane Database Syst Rev. 2010;2010(11):Cd008571. SANTULLI P, LAMAU M C MARCELLINL, et al. 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Physiology, production and action of progesterone [J]. Acta Obstet Gynecol Scand, 2015, 94 Suppl 161: 8–16. ZHANG P. WANG G. Progesterone Resistance in Endometriosis: Current Evidence and Putative Mechanisms [J]. Int J Mol Sci, 2023, 24(8). MIRABI P, ALAMOLHODA S H, GOLSORKHTABARAMIRI M, et al. Prolactin concentration in various stages of endometriosis in infertile women [J]. JBRA Assist Reprod. 2019;23(3):225–9. HUANG Y, ZHANG D, ZHOU Y, et al. Causal Relationship Between Abortion and Endometriosis: A Bidirectional Two-Sample Mendelian Randomization Study [J]. Am J Reprod Immunol. 2025;93(3):e70064. KHAN K N FUJISHITAA, MORI T. Pathogenesis of Human Adenomyosis: Current Understanding and Its Association with Infertility [J]. J Clin Med, 2022, 11(14). PARAZZINI F, VERCELLINI P. Risk factors for adenomyosis [J]. Hum Reprod. 1997;12(6):1275–9. MO X, ZENG Y. The relationship between ovarian endometriosis and clinical pregnancy and abortion rate based on logistic regression model [J]. Saudi J Biol Sci. 2020;27(1):561–6. SANCHEZ A M, VIGANò P. The distinguishing cellular and molecular features of the endometriotic ovarian cyst: from pathophysiology to the potential endometrioma-mediated damage to the ovary [J]. Hum Reprod Update. 2014;20(2):217–30. KARUPUTHULA N B, CHATTOPADHYAY R. Oxidative status in granulosa cells of infertile women undergoing IVF [J]. Syst Biol Reprod Med. 2013;59(2):91–8. PIRTEA P, CICINELLI E, DE NOLA R, et al. Endometrial causes of recurrent pregnancy losses: endometriosis, adenomyosis, and chronic endometritis [J]. Fertil Steril. 2021;115(3):546–60. PALLACKS C, HIRCHENHAIN J, KRüSSEL JS, et al. Endometriosis doubles odds for miscarriage in patients undergoing IVF or ICSI [J]. Eur J Obstet Gynecol Reprod Biol. 2017;213:33–8. KALRA R, MCDONNELL R, STEWART F, et al. Excisional surgery versus ablative surgery for ovarian endometrioma [J]. Cochrane Database Syst Rev. 2024;11(11):Cd004992. SOMIGLIANA E, PALOMINO M C, CASTIGLIONI M, et al. The impact of endometrioma size on ovarian responsiveness [J]. Reprod Biomed Online. 2020;41(2):343–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 03 Jun, 2026 Reviews received at journal 06 May, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 17 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 15 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8614148","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581212774,"identity":"b959febb-5312-4769-af3e-54ac428f0ce7","order_by":0,"name":"Yixin Wang","email":"","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Wang","suffix":""},{"id":581212775,"identity":"21b93b4c-bda1-41aa-9a23-2f37a2836698","order_by":1,"name":"Yuqing Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuqing","middleName":"","lastName":"Zhang","suffix":""},{"id":581212778,"identity":"8faec356-58b2-4975-823c-5fc1c3036068","order_by":2,"name":"Xinzhe Wang","email":"","orcid":"","institution":"Guang'anmen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinzhe","middleName":"","lastName":"Wang","suffix":""},{"id":581212779,"identity":"1135192b-3b62-4263-8a26-9a1e7bf75445","order_by":3,"name":"Na Duan","email":"","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Duan","suffix":""},{"id":581212780,"identity":"da888335-dc16-4289-9586-fbe9dd571c18","order_by":4,"name":"Zhengao Sun","email":"","orcid":"","institution":"The Affiliated Hospital of Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhengao","middleName":"","lastName":"Sun","suffix":""},{"id":581212781,"identity":"89b128f9-e579-4e2f-a128-aa101b358ab5","order_by":5,"name":"Ruihua Zhao","email":"","orcid":"","institution":"Guang'anmen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruihua","middleName":"","lastName":"Zhao","suffix":""},{"id":581212786,"identity":"16d26b16-1c56-4766-8068-3e8e1e092678","order_by":6,"name":"Yanyun Yin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACZhiDvbHhQEKFhJw88Vp4Dh888OCMhbFhA9HWSaQlH3zYVpHIcICAQoPjvMckfu44nLidIcfgQOI8iQTGBuaHj27g03KYL02y98zhxJ0NZ4BatknksTOwGRvn4NXCYybB23Y4ccPBHrCWYsYGHjZpQlok/4K0HOYBapkjkdhwgAgt0mBbjrElHEhsIEKL5GEeY2vZtnTjDWeYDxxIOCZhbNhMwC98588Y3nzbZi274f7D5o8/aurk5NmbHz7Gp0XhAAOLBANDM5IQM07FECDfwMD8gYGhjoCyUTAKRsEoGNEAAPdtViZMKpP7AAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yanyun","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2026-01-16 00:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8614148/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8614148/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101751613,"identity":"9e4e429e-8ac3-4561-93d1-4e8291c6bdae","added_by":"auto","created_at":"2026-02-03 10:21:46","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86205,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for OMA\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/05a9c46e051c1bae87e89d77.jpeg"},{"id":101404870,"identity":"1b3bd3cc-6dc6-4580-bbb7-b692bd68e6e2","added_by":"auto","created_at":"2026-01-29 10:38:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46520,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for OMA\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/5d8b0a25e7fc982519a02d9f.jpeg"},{"id":101404868,"identity":"1684f799-47e0-40e5-a4d6-e6ed201c9ed4","added_by":"auto","created_at":"2026-01-29 10:38:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86982,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of factors associated with clinical pregnancy rate\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/f8813fc10c0ae569128f528d.jpeg"},{"id":101404872,"identity":"78099c7a-addc-4d95-b658-439fc382aa1d","added_by":"auto","created_at":"2026-01-29 10:38:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81869,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for predicting clinical pregnancy rate\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/dc5dd9b0e4a02a6d54e262d9.jpeg"},{"id":101404866,"identity":"613751cd-7580-42e0-a05c-108db58b4a92","added_by":"auto","created_at":"2026-01-29 10:38:03","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100261,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting clinical pregnancy rate\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/4c7c1b01c9eeb9036ce37ac0.jpeg"},{"id":101751502,"identity":"7120433b-064b-41ac-9238-71bff8c036e9","added_by":"auto","created_at":"2026-02-03 10:20:49","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50342,"visible":true,"origin":"","legend":"\u003cp\u003eDCA of nomogram for predicting clinical pregnancy rate\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/655d4dc3b9680433af1b4495.jpeg"},{"id":101404871,"identity":"9f11ba59-3d0a-45fb-8982-9f916dc90459","added_by":"auto","created_at":"2026-01-29 10:38:03","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":44167,"visible":true,"origin":"","legend":"\u003cp\u003eROC of nomogram for predicting clinical pregnancy rate\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/2e946c5e8c83fc25cd574b70.jpeg"},{"id":101755167,"identity":"2fb89e05-ed00-4085-bdbf-3fbd72981766","added_by":"auto","created_at":"2026-02-03 10:49:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1311572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8614148/v1/4a01d947-6222-480e-a889-b0d36f7532f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic determinants for clinical pregnancy rate following IVF in women with ovarian endometriomas? A propensity score-matched retrospective cohort study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEndometriosis is a chronic condition characterized by heterogeneous clinical manifestations, encompassing not only variability in lesion distribution but also diversity in signs and symptoms, with core symptoms including pain and infertility\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.The exact prevalence of endometriosis remains unknown, but estimates vary from 2\u0026ndash;10% in women of reproductive age up to 50% in infertile women\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, while approximately 17\u0026ndash;44% of women with OMA\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.A chronic inflammatory process impairing ovarian, tubal, and endometrial has been proposed as a key mechanism underlying reduced fertility.\u003c/p\u003e \u003cp\u003eThe diagnosis of OMA in patients with endometriosis is clinically paramount, with TVUS and magnetic resonance imaging (MRI) recognized as optimal imaging modalities that exhibit notably high diagnostic accuracy for OMA. TVUS fulfills the predetermined criteria for the Specificity in Prevalence-adjusted Negative Predictive Value (SPIN) triage test, with a specificity of 95%, and is generally low-cost, widely accessible, and notably better tolerated compared with MRI\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn women with OMA undergoing surgery for infertility, excision of the endometrioma capsule confers an increase in the spontaneous postoperative pregnancy rate\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The utility of cystectomy prior to assisted reproductive technology (ART) for improving reproductive outcomes in women with OMA is assessed, with limited consistency in result interpretation given the lack of significant difference in pregnancy rates\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Whereas prior surgical intervention for endometriosis emerges as a definitive risk factor for this condition in the context of OMA per se\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSurgical management of ovarian endometriomas fails to consistently enhance reproductive outcomes in subfertile women undergoing ART, and for cases in which endometrioma surgery is contemplated, preoperative specialized ultrasound for the assessment of disease complexity is recommended, with ovarian stripping constituting the preferred surgical modality.\u003c/p\u003e \u003cp\u003eThe European Society of Human Reproduction and Embryology (ESHRE) guideline affirms the general consensus that OMA larger than 4 cm should be surgically removed to reduce pain and improve spontaneous conception rates, while no evidence indicates that cystectomy prior to ART enhances pregnancy rates in infertile women with OMA larger than 3 cm\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo date, no conclusive evidence exists to delineate the nature of the correlation between maximum OMA diameter and pregnancy outcomes in women undergoing IVF, thereby precluding definitive guidance for clinicians regarding the indication for laparoscopic ovarian cystectomy.\u003c/p\u003e \u003cp\u003eTherefore, the present study seeks to delineate the factors predictive of pregnancy outcomes in women affected by OMA undergoing IVF, and to characterize the association between maximum OMA diameter and pregnancy outcomes.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy subjects\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e for this study was granted by the Ethics Review Committee of the Jiangsu Provincial Hospital of Traditional Chinese Medicine (approval number 2021NL-045-03), and all participants provided written informed consent. Data from 2,168 IVF cycles treated between December 2019 and November 2024 were retrieved for potential inclusion.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTo homogenize the study population, patients were excluded if they had: (1) autoimmune, infectious, or inflammatory diseases within 3 months prior to oocyte retrieval; (2) malignancies of any type; (3) previous endometriosis, ovarian, or pelvic surgery; (4) history of prior IVF treatment; (5) polycystic ovary syndrome (PCOS); (6) severe male factor infertility; (7) follicle-stimulating hormone (FSH)\u0026thinsp;\u0026gt;\u0026thinsp;10 mIU/mL; (8) anti-M\u0026uuml;llerian hormone (AMH)\u0026thinsp;\u0026lt;\u0026thinsp;0.5 ng/mL; (9) history of abdomino-pelvic radiotherapy or chemotherapy; or (10) concomitant leiomyoma and adenomyosis.\u003c/p\u003e \u003cp\u003ePatients underwent two-dimensional transvaginal sonography (2D-TVS) on days 3\u0026ndash;5 of their menstrual cycle. Following bladder emptying, patients were placed in the lithotomy position. An ultrasound probe covered with a condom was inserted into the vagina to the cervix. The first operator conducted a routine assessment of the uterus and ovaries, including size, shape, and echogenicity, with data grouped based on the presence or absence of OMA. Subsequently, the second operator utilized two-dimensional ultrasound to measure the maximum diameter of OMA.\u003c/p\u003e \u003cp\u003eThe demographic data, clinical records and ultrasound characteristics of all the patients with endometriosis were collected through their clinical records.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eStatistical analyses used SPSS 26.0 and R 4.3.1. Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for normal distribution or median (25th\u0026ndash;75th percentiles) for non-normal distribution, and categorical variables as frequencies with percentages. Group comparisons employed chi-square test for categorical variables, Student\u0026rsquo;s t-test for parametric continuous variables, and Mann-Whitney U test for non-parametric ones. Univariable logistic regression identified potential predictors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) incorporated into a multivariable model guided by clinical and statistical relevance. A nomogram with integrated DCA and ROC analysis (R PROC/RMDA packages) was developed, with performance assessed via AUC (1,000 bootstraps) and Hosmer-Lemeshow test (SPSS). Significance was set at two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with results reported as ORs with 95% CIs.\u003c/p\u003e\n\u003ch3\u003ePropensity score matching\u003c/h3\u003e\n\u003cp\u003eIn the preliminary analysis of the matched cohort, the researchers sought to investigate the association between OMA and pregnancy outcomes. To mitigate potential confounding, propensity score matching (PSM) was utilized, with a 1:3 nearest-neighbor matching approach without replacement and a caliper width of 0.05 to generate the matched cohort. The propensity score was defined as the predicted probability of OMA exposure based on age, BMI, and COH protocols. To validate the PSM model, baseline characteristics were compared between the two groups in the matched cohort, followed by multivariate logistic regression analysis conducted in the PSM cohort.\u003c/p\u003e\n\u003ch3\u003eSubgroup analyses\u003c/h3\u003e\n\u003cp\u003e The cohorts were stratified into subgroups, with women with OMA further categorized into a clinical pregnancy group and a non-clinical pregnancy group according to clinical pregnancy status, with factors associated with pregnancy outcomes evaluated.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical data\u003c/h2\u003e \u003cp\u003eStatistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between the two groups for the following variables: type of infertility (primary/secondary), number of abortions, number of cesarean deliveries, tubal patency (left/right), and levels of reproductive hormones (prolactin[PRL], progesterone[P], estradiol[E2], anti-M\u0026uuml;llerian hormone[AMH], and testosterone[T]). Among women with ovarian endometriomas (OMA), secondary infertility was twice as prevalent as primary infertility, accounting for 68.15% versus 31.85% of cases. In contrast, primary and secondary infertility accounted for nearly equivalent proportions among women without OMA. Notably, patients with OMA exhibited significantly lower serum AMH levels (median: 2.79; IQR: 1.40\u0026ndash;4.26) than their non-OMA counterparts (median: 3.73; IQR: 1.75\u0026ndash;4.81). In contrast, no statistically significant differences were noted between the two groups for age, body mass index (BMI), age at menarche, antral follicle count (AFC), or reproductive hormone levels (follicle-stimulating hormone[FSH] and luteinizing hormone[LH]), with all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026gt;\u0026thinsp;0.05. Comprehensive results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-OMA group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;738) %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOMA group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;248) %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et/Z/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}\\)\u003c/span\u003e\u003c/span\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(30,35.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(29,37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.08\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at menarche(y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1,3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.13,3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428(57.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79(31.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310(42.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169(68.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of abortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cesarean deliveries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of sex hormones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.63(6.09,9.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.96(6.27,9.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65(3.19,6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.10(3.01,5.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(31,66.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(26.57,55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.12(32.65,42.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.01(37.39,42.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.91(18.08,18.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.09(16.09,16.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9(0.9,0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.69,0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-14.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73(1.75,4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79(1.40,4.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal patency (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405(54.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170(68.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial obstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206(27.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(20.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(17.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(11.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTubal patency (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e414(56.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190(76.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePartial obstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188(25.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(14.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136(18.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(9.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFC (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(5,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(4,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFC (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(5,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIVF information and pregnancy outcomes\u003c/h3\u003e\n\u003cp\u003eStatistically significant differences were noted between the two study cohorts for a panel of key IVF cycle parameters and early pregnancy outcomes (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including the number of aspirated follicles, retrieved oocytes, fertilized oocytes, cleaved embryos, 2PN embryos, frozen day 3 embryos, frozen day 5 embryos, clinical pregnancy rate, early miscarriage rate, and late miscarriage rate. Notably, the clinical pregnancy rate among women with OMA was only half that documented in their non-OMA counterparts, highlighting the specific impact of OMA on early pregnancy establishment, whereas the rates of ongoing pregnancy and live birth did not differ significantly between the two groups. By contrast, no statistically significant variations were detected across core treatment modalities and downstream pregnancy endpoints, which encompassed COH protocols, duration of gonadotropin (Gn) therapy, biochemical pregnancy rate, ongoing pregnancy rate, and live birth rate (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A detailed breakdown of all study metrics and outcomes is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of IVF information and pregnancy outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-OMA group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;738) %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOMA group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;248) %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e/\u003cem\u003eZ\u003c/em\u003e/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOH protocols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLong GnRH agonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231(31.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(31.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGnRH antagonist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e366(49.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(45.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgestin-Primed Ovarian Stimulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(12.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(14.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimal ovarian stimulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(8.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGn duration (d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.16\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of aspirated follicles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(6,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(5,14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of retrieved oocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(5,14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4,13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fertilized oocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(5,11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(4,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cleaved embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(4,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of 2PN embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(4,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(3,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of frozen Day 3 embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of frozen Day 5 embryos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical pregnancy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244(33.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(29.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical pregnancy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146(19.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(10.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly miscarriage rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(13.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOngoing pregnancy rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(6.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLate miscarriage rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive birth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(6.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariate logistic regression analysis\u003c/h3\u003e\n\u003cp\u003eUnivariable analysis identified statistically significant differences between the non-OMA and OMA cohorts for the following parameters (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05): type of infertility, number of abortions, number of cesarean deliveries, number of aspirated follicles, number of fertilized oocytes, clinical pregnancy rate, early miscarriage rate, and reproductive hormone levels (PRL and P).\u003c/p\u003e \u003cp\u003eParameters yielding a univariable significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were subsequently incorporated into the multivariable logistic regression model. Multivariable logistic regression analysis confirmed that type of infertility, number of abortions, clinical pregnancy rate, and reproductive hormone levels (PRL and P) were independently associated with OMA (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The corresponding odds ratios (ORs) and 95% confidence intervals (CIs) for these variables were as follows: PRL (OR\u0026thinsp;=\u0026thinsp;0.942, 95% CI: 0.908\u0026ndash;0.978, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002); P (OR\u0026thinsp;=\u0026thinsp;0.177, 95% CI: 0.097\u0026ndash;0.323, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); clinical pregnancy rate (OR\u0026thinsp;=\u0026thinsp;0.557, 95% CI: 0.344\u0026ndash;0.903, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017); number of abortions (OR\u0026thinsp;=\u0026thinsp;1.878, 95% CI: 1.549\u0026ndash;2.277, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and type of infertility (OR\u0026thinsp;=\u0026thinsp;2.159, 95% CI: 1.526\u0026ndash;3.056, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The statistically significant factors associated with OMA identified in the multivariable analysis were visualized using a forest plot, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate the predictive utility of the key independent correlates of OMA identified via multivariable logistic regression, a receiver operating characteristic (ROC) curve was constructed for the composite predictive model incorporating these variables. The results demonstrated that this integrated model yielded an area under the ROC curve (AUC) of 0.804, with a corresponding 95% confidence interval (95% CI) of 0.722\u0026ndash;0.835. This AUC value indicates that the model exhibits favorable discriminative capacity for differentiating between OMA-positive and OMA-negative patients. The ROC curve visualizing the discriminative efficacy of the predictive model is depicted graphically in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of OMA patients\u003c/h2\u003e \u003cp\u003eAll participants were OMA patients undergoing IVF treatment, stratified into pregnant and non-pregnant subgroups based on clinical pregnancy outcomes. Univariable logistic regression analysis detected statistically significant variations across the two subgroups in age, number of abortions, BMI, COH protocols, Gn duration, and maximum OMA diameter (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eCandidate variables that achieved statistical significance at the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level in univariable logistic regression analyses were thereafter subjected to multivariable logistic regression modeling. After adjusting for confounding factors, the analysis confirmed that age, number of abortions, COH protocols, and maximum OMA diameter were independent factors associated with clinical pregnancy outcomes in OMA patients undergoing IVF (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Significant factors linked to clinical pregnancy rates, as identified via multivariable analysis, were depicted in a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePredictive performance of the combined model and individual predictors derived from multivariable logistic regression was assessed via ROC curve analysis. The combined model yielded an AUC of 0.857 (95% CI: 0.784\u0026ndash;0.930), while individual factors exhibited moderate discriminative capacity, with respective AUC values of 0.727 (age), 0.635 (number of abortions), 0.713 (COH protocols), and 0.684 (maximum OMA diameter). ROC curve analysis identified optimal cutoff values of 32.5 years for age and 21.25 mm for OMA diameter, respectively. Corresponding ROC curves illustrating the discriminative efficacy of the combined model and its individual component predictors are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo enable individualized prediction of clinical pregnancy rates in OMA patients undergoing IVF, we developed a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) incorporating independent predictors (age, number of abortions, COH protocols, maximum OMA diameter) identified via multivariable logistic regression.\u003c/p\u003e \u003cp\u003eVariable-specific point distributions intuitively reflect the differential contribution of each factor to pregnancy outcomes. Notably, the nomogram reveals a clear gradient across COH protocol coding (1\u0026thinsp;=\u0026thinsp;Long GnRH agonist, 2\u0026thinsp;=\u0026thinsp;GnRH antagonist, 3\u0026thinsp;=\u0026thinsp;Progestin-Primed Ovarian Stimulation, 4\u0026thinsp;=\u0026thinsp;Minimal ovarian stimulation): the Long GnRH agonist protocol correlates with the highest points, while the Minimal protocol corresponds to the lowest. Both age and maximum OMA diameter exhibit a significant negative correlation with clinical pregnancy rate in OMA patients undergoing IVF.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the clinical utility of our nomogram (for predicting IVF clinical pregnancy outcomes in OMA patients, stratified into pregnant/non-pregnant groups), we performed decision curve analysis (DCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross all threshold probabilities, the nomogram\u0026rsquo;s net benefit curve consistently outperformed the \u0026ldquo;predict all as non-pregnant\u0026rdquo; strategy. Notably, between 0 and ~\u0026thinsp;0.2 threshold probability, it also exceeded the \u0026ldquo;predict all as pregnant\u0026rdquo; strategy. The nomogram achieved a maximum net benefit of 0.049, with a peak incremental benefit of 0.032 vs. \u0026ldquo;predict all as pregnant.\u0026rdquo; Shaded regions denote threshold ranges where the model\u0026rsquo;s net benefit surpasses these two non-discriminatory strategies. These findings confirm that using the nomogram for prognostic risk stratification (identifying high/low pregnancy risk) within the relevant threshold range yields greater clinical net benefit than binary, one-size-fits-all prediction\u0026mdash;validating the model\u0026rsquo;s practical value for outcome-focused risk assessment in this OMA cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe constructed a nomogram from the independent predictors and assessed its discriminative performance via ROC curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model\u0026rsquo;s AUC\u0026mdash;a gold-standard metric for discriminative performance\u0026mdash;was 0.744 (95% CI: 0.538\u0026ndash;0.95), with its ROC curve deviating markedly from the null diagonal reference line. These findings confirm the nomogram has moderate discriminative capacity, reliably distinguishing between pregnant and non-pregnant OMA patients undergoing IVF, and serving as a quantitative tool for clinical pregnancy outcome risk stratification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOvarian endometrioma is a cystic lesion arising within the ovaries secondary to endometriosis, a prevalent gynecological disorder characterized by the ectopic implantation and proliferation of endometrial tissue outside the uterine cavity\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. This condition typically manifests with clinical symptoms including dysmenorrhea, chronic pelvic pain, and infertility.\u003c/p\u003e \u003cp\u003eEndometriosis is well recognized to exert an adverse impact on fertility. While a causal relationship between endometriosis and infertility has not been definitively established, a clear association has been identified. Notably, 30\u0026ndash;50% of women with endometriosis suffer from fertility impairment, and ovarian endometriomas are commonly detected during infertility evaluations\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWomen with concomitant ovarian endometriomas, often require ART to achieve clinical pregnancy\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, it is widely postulated that mechanisms may impair IVF outcomes in women with this condition. These include blunted ovarian stimulation responsiveness, perturbed steroidogenesis, impaired oocyte quality, compromised fertilization and embryo developmental potential, and defective endometrial implantation\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e The 5-year retrospective review of medical records and in vitro fertilization (IVF) pregnancy outcomes was conducted. Propensity score matching and multivariate logistic regression analyses identified type of infertility, number of abortions and cesarean deliveries, and level of sex hormones profiles as independent risk factors for ovarian cysts. Additionally, subgroup analysis stratified by clinical pregnancy outcomes among OMA patients revealed that age, miscarriage number, COH protocols, and maximum OMA diameter are independent predictors of IVF-associated clinical pregnancy outcomes.\u003c/p\u003e \u003cp\u003eProgesterone, a steroid hormone synthesized by the ovaries, adrenal cortex, and placenta, exerts pivotal regulatory effects on the intricate cascade of female reproductive functions\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Ovarian endometriomas originate from ectopic endometrial implantation and cyclic hemorrhage within the ovarian parenchyma, processes that progressively erode ovarian cortical tissue and compromise follicular reserve. Given that folliculogenesis and subsequent corpus luteum formation represent the primary sources of progesterone secretion, structural and functional follicular damage in OMA patients impairs follicular maturation and induces luteal insufficiency, thereby directly reducing basal progesterone levels. Progesterone resistance is widely recognized to represent a key pathological hallmark of endometriosis, as an expanding body of evidence has demonstrated that the loss of progesterone signaling occurs in both eutopic and ectopic endometrial tissues\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eProlactin secretion exhibits diurnal variation and is highly sensitive to stress, nipple stimulation, and recent meal consumption. The present study strictly defined basal prolactin as concentrations measured in the fasting state between 8:00 and 10:00 a.m.\u0026mdash;a standardized protocol that ensured the reliability and comparability of hormone assay data. Prolactin levels serve as a promising prognostic biomarker to distinguish endometriosis stages III/IV from I/II and differentiate infertile women with endometriosis from non-endometriosis counterparts\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe association between abortion and endometriosis has remained inconsistent and contentious across the existing body of observational literature. A Mendelian randomization (MR) analysis was designed to elucidate the potential causal relationship between these two conditions, and the resultant findings did not support the existence of a robust, definitive causal link\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Notably, this controversy largely stems from the failure to distinguish between spontaneous abortion and induced abortion, which exert divergent effects on endometriosis pathogenesis.\u003c/p\u003e \u003cp\u003eIntrauterine manipulations performed during induced abortion procedures can induce endometrial injury, thereby elevating the risk of ovarian endometrioma development. Similarly, dilation and curettage procedures, along with other intrauterine surgical interventions including cesarean section and hysteroscopic procedures, may disrupt the endometrial-myometrial interface and facilitate the migration, invasion, implantation, shedding, and development of endometrial colonies within the myometrium\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBy virtue of these interconnected pathophysiological mechanisms, namely endometrial injury and endometrial-myometrial interface disruption induced by intrauterine procedures, women with a history of one or more miscarriages (including induced abortion) exhibit an elevated susceptibility to endometriosis. This risk is further amplified by intrauterine surgical procedures, which are widely recognized as well-established risk factors for this condition\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Subsequently, ovarian endometriosis arising from such intrauterine procedure-mediated pathways may exert deleterious effects on embryo quality, which in turn contributes to an increased likelihood of adverse pregnancy outcomes\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFollicular density in the ovarian tissue adjacent to endometriotic cysts has been consistently demonstrated to be significantly lower than that in healthy ovarian tissue, yet this pathological alteration does not appear to result from tissue stretching in the adjacent area induced by cyst presence.\u003c/p\u003e \u003cp\u003eEndometriomas harbor free iron, reactive oxygen species (ROS), proteolytic enzymes, and inflammatory molecules at concentrations tens to hundreds of times greater than those detected in peripheral blood or other benign cyst types\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Cyst fluid induces significant alterations in the endometriotic cells it bathes, ranging from gene expression modifications to genetic mutations. The physical barrier separating cyst contents from normal ovarian tissue is a thin wall consisting of the ovarian cortex per se or fibroreactive tissue. ROS that potentially permeate surrounding tissues and proteolytic substances that degrade adjacent tissues are prone to induce the replacement of normal ovarian cortical tissue with fibrous tissue, with a reduction in cortex-specific stroma\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. This fibrosis is associated with smooth muscle metaplasia and is subsequently accompanied by follicular loss and intraovarian vascular injury.\u003c/p\u003e \u003cp\u003eIn general, the influence of endometriosis on pregnancy is mediated by multiple distinct pathophysiological mechanisms\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. First, toxic pelvic microenvironmental factors impair oocyte and embryo quality via direct, sustained deleterious effects on gametes and embryos during their transit through the distal fallopian tube. Second, inflammation-dependent perturbations of the eutopic endometrium induce aberrant endometrial remodeling during the window of implantation (WOI), compromise predecidual transformation, and ultimately culminate in defective placentation. Such pathological alterations predispose affected individuals to a spectrum of obstetric complications, including early pregnancy loss and notably recurrent pregnancy loss (RPL)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, in the context of ART, endometriosis exerts deleterious effects exclusively on the eutopic endometrium, with a particular emphasis on defective placentation, thereby potentially predisposing to early pregnancy loss and RPL. Furthermore, chronic inflammation of the eutopic endometrium exhibits a strong association with chronic endometritis.\u003c/p\u003e \u003cp\u003eThe ESHRE guidelines\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e indicate that routine surgery for ovarian endometriomas prior to ART is not recommended to improve live birth rates. This is because current evidence does not demonstrate a beneficial effect, and surgery is likely to adversely affect ovarian reserve.\u003c/p\u003e \u003cp\u003eCurrently, surgical treatment for endometriomas typically involves stripping them away or draining and destroying the cysts using an electric current or laser. A meta-analysis from the Cochrane Library compared two surgical modalities for endometriomas and concluded that women who underwent ovarian cystectomy may be associated with a lower AFC and reduced ovarian response compared with those who underwent endometrioma ablation\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn infertile women, surgical intervention for endometriomas smaller than 3 cm does not enhance IVF success rates. Conversely, for endometriomas larger than 6 cm located within the ovary, cystectomy results in a thin rim of ovarian tissue with compromised vascularity. Certain uncertainty persists regarding the potential benefits of cystectomy prior to IVF. Therefore, a comprehensive assessment is warranted, incorporating the patient\u0026rsquo;s miscarriage history and sex hormone profiles.\u003c/p\u003e \u003cp\u003e The ESHRE guidelines further note that a specific protocol for ART in women with endometriosis cannot be recommended. Instead, both GnRH antagonist and agonist protocols may be utilized according to patient and physician preferences, as no significant difference in pregnancy rates or live birth rates has been demonstrated. Endometriosis exerts a negligible impact on ovarian response. Specifically, a 4 cm diameter threshold distinguishes endometriomas that do or do not interfere with ovarian response\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study is not without limitations, which should be taken into account when interpreting the findings. First, occult deep infiltrating endometriosis (DIE) was not excluded. Such lesions can indirectly compromise pregnancy outcomes by perturbing the pelvic microenvironment and impairing endometrial receptivity, potentially introducing bias into the accuracy of the study results.\u003c/p\u003e \u003cp\u003eSecond, only pregnancy outcomes of the first embryo transfer cycle were analyzed, with data from multiple transfer cycles not being captured. This precludes the evaluation of cumulative pregnancy rates, thereby limiting the generalizability of the conclusions to cohorts undergoing repeated embryo transfers.\u003c/p\u003e \u003cp\u003eThird, miscarriage history was not stratified with sufficient granularity, failing to distinguish between unplanned miscarriage and embryonic arrest. These two entities exhibit distinct pathophysiological etiologies: the former is often linked to maternal factors (e.g., endocrine disorders, immune dysfunction), while the latter is predominantly attributed to embryonic chromosomal abnormalities. This lack of stratification may introduce confounding bias into the analysis, compromising the precision of the assessment regarding the association between endometriosis and miscarriage.\u003c/p\u003e \u003cp\u003eFourth, endometrial preparation regimens for transfer cycles were not documented. Different clinical regimens (e.g., natural cycle, ovulation induction cycle, artificial cycle) exert differential regulatory effects on endometrial receptivity, and the absence of this variable precludes the elimination of its confounding influence on pregnancy outcomes.\u003c/p\u003e \u003cp\u003eFinally, embryo grading\u0026mdash;a key surrogate for embryo quality\u0026mdash;was not incorporated into the analysis. As embryo quality represents a core prognostic factor for transfer outcomes, failure to adjust for this variable may compromise the robustness of the analysis investigating the impact of endometriosis on pregnancy outcomes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eCollectively, this study delineates critical independent determinants of ovarian cysts and prognostic factors for IVF clinical pregnancy outcomes in patients with OMA. These findings enable precise clinical risk stratification for ovarian cyst development and refine individualized prognostic assessment and therapeutic regimens for OMA patients undergoing IVF, thereby offering actionable clinical implications to guide evidence-based reproductive medicine practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDINGS\u003c/h2\u003e \u003cp\u003eSupported by the National Key R\u0026amp;D Program of China\u003c/p\u003e \u003cp\u003eAward Number: 2024YFC3505800\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.W. collected clinical data, drafted the main manuscript text, and conducted preliminary data collation. Y.Z. assisted with clinical data verification and literature supplementation. X.W. designed the study protocol and performed statistical analyses including propensity score matching and multivariate logistic regression. N.D. sorted out follow-up data and prepared reference lists. Z.S. provided professional guidance on the interpretation of reproductive endocrinology indicators. R.Z. supervised the research implementation and revised the manuscript for important intellectual content. Y.Y. conceived the research framework, secured funding support, critically revised the manuscript, and approved the final version for submission. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBECKER C M, BOKOR A, HEIKINHEIMO O, et al. ESHRE guideline: endometriosis [J]. Hum Reprod Open. 2022;2022(2):hoac009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMEULEMAN C, VANDENABEELE B. High prevalence of endometriosis in infertile women with normal ovulation and normospermic partners [J]. Fertil Steril. 2009;92(1):68\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFARKAS A H, ABUMUSA H. Structural Gynecological Disease: Fibroids, Endometriosis, Ovarian Cysts [J]. Med Clin North Am. 2023;107(2):317\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKANTI FS, GORAK SAVARD R, BERGERON F, et al. Transvaginal ultrasound and magnetic resonance imaging in the diagnosis of endometrioma: a systematic review and meta-analysis of diagnostic test accuracy studies [J]. J Obstet Gynaecol. 2024;44(1):2311664.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHART R J, HICKEY M, MAOURIS P et al. Excisional surgery versus ablative surgery for ovarian endometriomata [J]. Cochrane Database Syst Rev, 2008, (2): Cd004992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBENSCHOP L, FARQUHAR C, VAN DER POEL N, et al. Interventions for women with endometrioma prior to assisted reproductive technology [J]. Cochrane Database Syst Rev. 2010;2010(11):Cd008571.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSANTULLI P, LAMAU M C MARCELLINL, et al. 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The impact of endometrioma on IVF/ICSI outcomes: a systematic review and meta-analysis [J]. Hum Reprod Update. 2015;21(6):809\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI PIANI L SOMIGLIANAE, PAFFONI A, et al. Endometriosis and IVF treatment outcomes: unpacking the process [J]. Reprod Biol Endocrinol. 2023;21(1):107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTARABORRELLI S. Physiology, production and action of progesterone [J]. Acta Obstet Gynecol Scand, 2015, 94 Suppl 161: 8\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG P. WANG G. Progesterone Resistance in Endometriosis: Current Evidence and Putative Mechanisms [J]. Int J Mol Sci, 2023, 24(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMIRABI P, ALAMOLHODA S H, GOLSORKHTABARAMIRI M, et al. Prolactin concentration in various stages of endometriosis in infertile women [J]. JBRA Assist Reprod. 2019;23(3):225\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUANG Y, ZHANG D, ZHOU Y, et al. Causal Relationship Between Abortion and Endometriosis: A Bidirectional Two-Sample Mendelian Randomization Study [J]. Am J Reprod Immunol. 2025;93(3):e70064.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKHAN K N FUJISHITAA, MORI T. Pathogenesis of Human Adenomyosis: Current Understanding and Its Association with Infertility [J]. J Clin Med, 2022, 11(14).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePARAZZINI F, VERCELLINI P. Risk factors for adenomyosis [J]. Hum Reprod. 1997;12(6):1275\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMO X, ZENG Y. The relationship between ovarian endometriosis and clinical pregnancy and abortion rate based on logistic regression model [J]. Saudi J Biol Sci. 2020;27(1):561\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSANCHEZ A M, VIGAN\u0026ograve; P. The distinguishing cellular and molecular features of the endometriotic ovarian cyst: from pathophysiology to the potential endometrioma-mediated damage to the ovary [J]. Hum Reprod Update. 2014;20(2):217\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKARUPUTHULA N B, CHATTOPADHYAY R. Oxidative status in granulosa cells of infertile women undergoing IVF [J]. Syst Biol Reprod Med. 2013;59(2):91\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePIRTEA P, CICINELLI E, DE NOLA R, et al. Endometrial causes of recurrent pregnancy losses: endometriosis, adenomyosis, and chronic endometritis [J]. Fertil Steril. 2021;115(3):546\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePALLACKS C, HIRCHENHAIN J, KR\u0026uuml;SSEL JS, et al. Endometriosis doubles odds for miscarriage in patients undergoing IVF or ICSI [J]. Eur J Obstet Gynecol Reprod Biol. 2017;213:33\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKALRA R, MCDONNELL R, STEWART F, et al. Excisional surgery versus ablative surgery for ovarian endometrioma [J]. Cochrane Database Syst Rev. 2024;11(11):Cd004992.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSOMIGLIANA E, PALOMINO M C, CASTIGLIONI M, et al. The impact of endometrioma size on ovarian responsiveness [J]. Reprod Biomed Online. 2020;41(2):343\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ovarian endometrioma, Female infertility, IVF-ET, Clinical pregnancy rate, Propensity score matching, Risk factor","lastPublishedDoi":"10.21203/rs.3.rs-8614148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8614148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOvarian endometrioma (OMA) is associated with reduced in vitro fertilization (IVF) success rates. Therefore, the purpose of the study is to investigate risk factors associated with pregnancy outcomes following IVF in women with OMA and assess the predictive utility of maximum OMA diameter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis propensity score-matched retrospective cohort study enrolled 2416 women who underwent IVF between December 2020 and November 2025. A total of 248 transvaginal ultrasound (TVUS)-confirmed OMA patients who underwent IVF were allocated to the OMA group. Conversely, 2,168 concurrently enrolled patients without ultrasonographically confirmed OMA who underwent IVF were assigned to the non-OMA group. A sample size of 738 was calculated to achieve a statistical power of 99.9% and a two-sided α of 0.05. Age, body mass index (BMI), and controlled ovarian hyperstimulation (COH) protocols were adjusted between the two groups using propensity score matching (PSM) at a 1:3 ratio. After PSM, 248 OMA patients were matched to 738 patients without OMA who underwent IVF. Additionally, 248 OMA patients were stratified into pregnant and non-pregnant groups according to clinical pregnancy outcome, and factors associated with clinical pregnancy among these patients were identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter PSM, multivariate logistic regression revealed that type of infertility, number of abortions and cesarean deliveries, and level of sex hormones were independent factors for OMA, and OMA independently influenced clinical pregnancy and early miscarriage rates (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In the OMA group, multivariate logistic regression identified age, miscarriage number, COH protocols, and maximum OMA diameter as independent predictors of clinical pregnancy outcomes. The integrated predictive model exhibited an area under the receiver operating characteristic curve (AUC) of 0.857, with optimal predicted cutoff values of 32.5 years for age and 21.25 mm for maximum OMA diameter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retrospective analysis delineated infertility type, obstetric history, and sex hormone profiles as independent risk factors for ovarian cysts. OMA subgroup analysis demonstrated age, miscarriage count, COH protocols, and maximum OMA diameter as key predictors of IVF outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistry\u003c/strong\u003e: the Ethics Review Committee of the Jiangsu Provincial Hospital of Traditional Chinese Medicine, approval number:2021NL-045-03, Registration date: 16 January 2021.\u003c/p\u003e","manuscriptTitle":"Prognostic determinants for clinical pregnancy rate following IVF in women with ovarian endometriomas? A propensity score-matched retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 10:37:58","doi":"10.21203/rs.3.rs-8614148/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"27240727413030986322104285826469693544","date":"2026-06-03T22:16:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T05:26:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249644135584892737023046867453866282644","date":"2026-04-08T13:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79351535012926368259929892627345140399","date":"2026-04-08T11:10:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269746112822791959066725727890418312310","date":"2026-04-06T12:09:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170769437671786182388229114896198657746","date":"2026-04-06T09:28:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-27T06:52:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T10:23:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T10:22:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-01-16T00:35:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9405dc9e-f819-4092-9676-79255d784ff3","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"27240727413030986322104285826469693544","date":"2026-06-03T22:16:06+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T10:37:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 10:37:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8614148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8614148","identity":"rs-8614148","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}