Risk factors and prediction model for early pregnancy loss in polycystic ovary syndrome - development and validation: A retrospective and observational study.

Polycystic ovary syndrome (PCOS) is one of the most prevalent endocrine disorders among women of reproductive age, affecting approximately 6% to 20% of this population depending on diagnostic criteria. [ 1 ] Characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology, PCOS is associated with a myriad of reproductive and metabolic complications, including infertility, insulin resistance, and obesity. Among its reproductive consequences, early pregnancy loss (EPL), defined as spontaneous miscarriage before 12 weeks of gestation, is a significant concern, with studies suggesting that women with PCOS have a 30% to 50% increased risk of EPL compared to those without the syndrome. [ 2 ] Despite advances in understanding PCOS-related infertility, the mechanisms underlying the heightened risk of EPL remain incompletely elucidated, and effective predictive tools are lacking. The pathophysiology of EPL in PCOS is multifactorial, involving endocrine, metabolic, and inflammatory disturbances. Hyperandrogenism, a hallmark of PCOS, has been implicated in impaired endometrial receptivity and defective decidualization, which may contribute to early pregnancy failure. [ 3 ] Insulin resistance, present in 50% to 70% of PCOS women, exacerbates hyperinsulinemia, which in turn increases ovarian androgen production and reduces glycodelin secretion, a key protein for embryo implantation. [ 4 ] Additionally, obesity, prevalent in up to 80% of PCOS patients, independently elevates the risk of EPL through chronic low-grade inflammation, altered adipokine secretion, and oxidative stress. [ 5 ] Other potential contributors include luteal phase defects, elevated luteinizing hormone (LH) levels, and thrombophilic tendencies due to altered coagulation factors. [ 6 ] However, the relative contribution of these factors to EPL in PCOS remains uncertain, necessitating further investigation. Current strategies for predicting EPL in PCOS rely on isolated clinical or biochemical markers, such as serum progesterone, thyroid function, or insulin levels, but these lack sufficient sensitivity and specificity. [ 7 ] While some studies have explored predictive models for miscarriage in general populations, few have focused specifically on PCOS, where unique risk factors may necessitate tailored approaches. [ 8 ] The integration of multi-dimensional data—including clinical, endocrine, metabolic, and ultrasonographic parameters—into a comprehensive predictive model could enhance early identification of high-risk pregnancies and guide personalized interventions. [ 9 ] Recently, machine learning (ML) and artificial intelligence techniques have shown promise in improving risk prediction for adverse pregnancy outcomes by handling complex, nonlinear interactions among variables. [ 10 ] For instance, ML models incorporating anti-Müllerian hormone (AMH), body mass index (BMI), and glycemic markers have been used to predict PCOS-related infertility but have not – yet been extended to EPL prediction. [ 11 ] The development and validation of such a model for PCOS-associated EPL could fill a critical gap in reproductive medicine, enabling clinicians to implement targeted therapies – such as metformin for insulin resistance, progesterone supplementation for luteal support, or low-dose aspirin for thromboprophylaxis – in a more evidence-based manner. [ 12 ] Given the high prevalence of PCOS and the emotional and physical toll of recurrent pregnancy loss, this study aims to identify key risk factors for EPL in PCOS patients through a systematic evaluation of clinical and biochemical parameters; and to develop and validate a robust predictive model using advanced statistical and ML techniques. By leveraging a large, prospective cohort with rigorous follow-up, this research seeks to provide actionable insights for reducing EPL in this vulnerable population, ultimately improving reproductive outcomes and patient counseling.

Conceptualization: Jingqi Wu, Yongquan Chen. Data curation: Jingqi Wu, Huifang Chen, Jie Zhang, Yongquan Chen. Formal analysis: Jingqi Wu, Huifang Chen, Jie Zhang, Yongquan Chen. Methodology: Jingqi Wu, Yongquan Chen. Project administration: Jingqi Wu, Huifang Chen, Jie Zhang, Yongquan Chen. Resources: Huifang Chen, Jie Zhang. Writing – original draft: Jingqi Wu, Yongquan Chen. Writing – review & editing: Jingqi Wu, Yongquan Chen.

Patients with PCOS who visited the Department of Obstetrics and Gynecology at our hospital from January 2023 to June 2025 were selected as the study subjects. Inclusion Criteria: meeting the diagnostic criteria for PCOS as outlined in the Chinese Guidelines for the Diagnosis and Treatment of Polycystic Ovary Syndrome [ 13 ] ; natural pregnancy or ovulation-induced pregnancy with a gestational age of < 12 weeks; age 22 to 40 years; normal mental and cognitive function, capable of cooperating with the study; normal chromosomal karyotype in both spouses; normal sperm quality and sexual function in the male partner; informed consent to the study protocol, with signed consent forms; complete clinical data. Exclusion Criteria: severe cardiac/hepatic dysfunction or thyroid disorders; hematologic diseases; autoimmune diseases; malignant tumors; endometriosis, uterine fibroids/malformations, or fallopian tube obstruction/malformations; chromosomal abnormalities; miscarriage due to male factors or other causes. From January 2023 to June 2025, a total of 191 PCOS patients visited our hospital. Among them, 14 patients had severe cardiac/hepatic dysfunction or thyroid disorders, 4 patients had hematologic diseases, 11 patients had autoimmune diseases, 3 patients had malignant tumors, 20 patients had endometriosis, uterine fibroids/malformations, or fallopian tube obstruction/malformations, 2 patients had chromosomal abnormalities, 1 patient experienced miscarriage due to male factors, and 2 patients had incomplete clinical data. All the above patients were excluded, leaving 134 patients included in the study. According to the study design, 60% of the patients were randomly assigned to the training cohort, and 40% were assigned to the validation cohort using a random number table. The training cohort data were used to establish the model, while the validation cohort data were used to verify the model’s performance. The patient screening process and grouping are illustrated in Figure 1 . This study was approved by the Ethics Committee of Xiamen Huli Renjun Hospital (Approval No.: XMHLRJYY – 20251003). As this was a retrospective and observational study, patients’ personal information and examination data were anonymized or de-identified during the research process, and the ethics committee granted an exemption from the requirement for informed consent. This research was conducted in agreement with the Declaration of Helsinki. Flowchart of patient screening and grouping process. Spontaneous embryonic loss before 12 weeks of gestation, confirmed by ultrasound examination showing no fetal vital signs. Blood samples (5 mL) were collected from patients between 8:00 and 10:00 a.m. during prenatal visits (blood samples for sex hormone measurement were obtained between day 2 and day 5 of the menstrual cycle). The samples were then centrifuged at 3500 r/min for 5 minutes to separate the serum for testing. FINS and sex hormone levels, including estradiol (E2), progesterone (Prog), LH, follicle-stimulating hormone, prolactin (PRL), and testosterone (Testo), were measured using the Kairun Kaeser 6800 fully automatic chemiluminescence immunoassay analyzer. Thyroid-stimulating hormone and AMH were measured using the Mindray CL-6000i fully automatic chemiluminescence immunoassay analyzer. fasting blood glucose, impaired glucose tolerance, triglycerides (TG), total cholesterol, and high-density lipoprotein cholesterol (HDL-C) were measured using the Mindray BS-1000M fully automatic biochemical analyzer. Vitamin D (VD) was measured using the Agilent liquid chromatography G6400 series triple quadrupole mass spectrometer. Data on potential factors influencing EPL in PCOS patients were collected, including: demographic and clinical factors: age, BMI, history of adverse pregnancy outcomes. 2) Laboratory test results during prenatal visits: fasting blood glucose, FINS, impaired glucose tolerance, TG, total cholesterol, HDL-C, thyroid-stimulating hormone, AMH, VD, and sex hormone levels (E2, Prog, LH, follicle-stimulating hormone, PRL, and Testo). IBM SPSS 25.0 (Chicago) was used for data analysis. Depending on the data type and distribution, t -tests, nonparametric rank-sum tests, or chi-square tests were employed for intergroup comparisons. Univariable and multivariable logistic regression analyses were used to screen for risk factors associated with EPL. Independent risk factors identified through multivariable logistic regression were used to construct a nomogram for predicting the risk of EPL in PCOS patients. The predictive performance of the model was evaluated using the C-index, area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). A P -value < 0.05 was considered statistically significant.

This study included 134 patients who underwent PCOS, with an average age of (31.1 ± 4.3) years old. Among them, 40 patients underwent EPL. According to the study design, 80 patients were randomly assigned to the training cohort, and 54 patients were assigned to the validation cohort. Statistical analysis showed no significant differences between the training and validation cohorts in terms of demographics, laboratory results, or surgical characteristics (Table 1 ). Balance test between training and validation cohorts (N = 134). AMH = anti-Müllerian Hormone, BMI = body mass index, E2 = estradiol, EPL = early pregnancy loss, FBG = fasting blood glucose, FINS = fasting insulin, FSH = follicle-stimulating hormone, HDL-C = high-density lipoprotein cholesterol, IGT = impaired glucose tolerance, LH = luteinizing hormone, PRL = prolactin, Prog = progesterone, SD = standard deviation, t =  t -test, Testo = testosterone, TC = total cholesterol, TG = triglyceride, TSH = thyroid-stimulating hormone, VD = vitamin D, χ 2  = Chi-square test. Among the 80 PCOS patients in the training cohort, 24 underwent EPL. Univariable logistic analysis revealed statistical differences between the EPL group and non-EPL group in BMI, FINS, LH, LH/FSH, PRL, Testo, AMH, and VD ( P  < .05, Table 2 ). Multivariable logistic analysis (to avoid overfitting, variables with P  < .1 in the univariable analysis were included) identified the following as independent risk factors for EPL in PCOS patients: FINS, LH, Testo, and AMH ( P  < .05, Table 2 ). Analysis of EPL risk factors for PCOS patients in training cohort. AMH = anti-Müllerian hormone, BMI = body mass index, E2 = estradiol, EPL = early pregnancy loss, FBG = fasting blood glucose, FINS = fasting insulin, FSH = follicle-stimulating hormone, HDL-C = high-density lipoprotein cholesterol, IGT = impaired glucose tolerance, LH = luteinizing hormone, PRL = prolactin, Prog = progesterone, TC = total cholesterol, Testo = testosterone, TG = triglyceride, TSH = thyroid-stimulating hormone, VD = vitamin D. ROC curve analysis showed that in the training cohort, the AUC values for Testo, LH, AMH, and FINS were 0.817, 0.752, 0.629 and 0.622, respectively. In the validation cohort, the AUC values for Testo, LH, AMH, and FINS were 0.770, 0.786, 0.642 and 0.649, respectively (Fig. 2 ). These 4 risk factors were used to construct a nomogram for predicting EPL in PCOS patients. The total risk score was derived by summing the individual scores, corresponding to the probability of EPL (Fig. 3 ). ROC curve analysis of risk factors of early pregnancy loss in patients with polycystic ovary syndrome. (Left: training group, Right: validation group). A nomogram predicting the probability of early pregnancy loss in patients with polycystic ovary syndrome. In this prediction model, the C-indices for the training and validation cohorts were 0.869 (95% CI: 0.800–0.939) and 0.860 (95% CI: 0.746–0.973), respectively. The AUC values for the ROC curves were 0.869 and 0.860, indicating excellent predictive accuracy and capability for EPL in PCOS patients (Fig. 4 ). The calibration curves for both cohorts showed that the bias-corrected lines were uniformly close to the ideal line, demonstrating the stability of the model’s predictive performance (Fig. 5 ). Furthermore, DCA revealed that the decision curves for both the training and validation cohorts were above the reference lines (“All” and “None”), indicating strong clinical applicability and net benefit of the model. This confirms that the prediction model is an effective tool for assessing the risk of EPL in patients with PCOS (Fig. 6 ). ROC curves and AUC of the prediction of early pregnancy loss in patients with polycystic ovary syndrome in the training cohort and the validation cohort. (left: training cohort, right: validation cohort). AUC = Area under the curve, ROC = Receiver operating characteristic. Calibration curves of prediction model for early pregnancy loss in patients with polycystic ovary syndrome in the training cohort and the validation cohort. (Left: training cohort, Right: validation cohort). Decision curve analysis of the prediction model for early pregnancy loss in patients with polycystic ovary syndrome in the training cohort and the validation cohort. (Left: training group, Right: validation group).

This study successfully identified fasting insulin (FINS), LH, testosterone (Testo), and AMH as independent risk factors for EPL in women with Polycystic Ovary Syndrome (PCOS) and developed a robust nomogram prediction model based on these parameters. [ 14 ] The model demonstrated excellent predictive accuracy and clinical utility, offering a practical tool for early risk stratification in this vulnerable population. [ 15 ] Our findings reinforce the pivotal role of hyperandrogenism and LH hypersecretion in the pathogenesis of EPL among PCOS patients. Elevated Testo levels have been implicated in impaired endometrial receptivity and defective decidualization, creating an unfavorable environment for embryo implantation and early development. [ 3 ] Similarly, high LH concentrations are associated with premature luteinization and compromised oocyte quality, potentially leading to luteal phase deficiency and subsequent pregnancy loss. [ 16 ] The confirmation of these established endocrine disturbances within our model underscores their fundamental contribution to adverse reproductive outcomes in PCOS. [ 17 ] A significant contribution of our study is the incorporation of metabolic and ovarian reserve markers into the predictive framework. Hyperinsulinemia, a hallmark of insulin resistance, exacerbates hyperandrogenemia and may directly impair endometrial function by reducing glycodelin secretion. [ 4 ] Furthermore, our results position AMH as an independent predictor of EPL, even after adjusting for other confounders. [ 11 ] While AMH is a well-established marker of ovarian reserve, its independent association with EPL suggests a potential, yet not fully elucidated, role in endometrial biology or embryo quality that extends beyond mere follicular quantification. [ 18 ] This integration of endocrine (LH, Testo), metabolic (FINS), and ovarian (AMH) dimensions provides a more holistic reflection of PCOS heterogeneity, thereby achieving superior predictive performance compared to any single biomarker. [ 12 ] The model’s strong performance, with C-indices of 0.869 and 0.860 in the training and validation cohorts respectively, attests to its discriminative power and generalizability. [ 19 ] The favorable calibration curves indicated a high degree of agreement between predicted probabilities and observed outcomes. [ 20 ] Most importantly, the DCA confirmed the model’s clinical value, demonstrating a consistent net benefit across a range of clinically reasonable risk thresholds. [ 21 ] This suggests that employing this nomogram for risk stratification can facilitate informed clinical decision-making. [ 22 ] From a translational perspective, this nomogram offers a user-friendly interface for clinicians to estimate an individual patient’s EPL risk by integrating 4 readily available parameters. [ 23 ] This enables the early identification of high-risk PCOS pregnancies, paving the way for personalized interventions. [ 24 ] For instance, patients flagged as high-risk could be candidates for intensified management, such as metformin for significant insulin resistance, [ 25 ] targeted luteal phase support with progesterone, [ 26 ] or closer monitoring during early gestation. [ 27 ] Despite its strengths, this study has certain limitations warranting consideration. Firstly, this was a single-center study with a modest sample size. [ 28 ] Future multi-center, large-scale prospective studies are essential to validate and refine the model’s general applicability. [ 29 ] Secondly, the model relies on biomarkers that require specific timing for measurement (preconception or early pregnancy), which might pose challenges for routine implementation in some healthcare settings. [ 30 ] Finally, the precise mechanistic pathways linking these biomarkers, particularly AMH, to EPL pathogenesis require further molecular investigation. [ 31 ]

This study developed and validated a comprehensible prediction model for assessing the risk of EPL in PCOS patients. The model demonstrates reasonable predictive accuracy and clinical applicability, offering promise for enhancing personalized risk assessment and guiding proactive management strategies to improve reproductive outcomes. However, due to the limitations of a single-center study design and a relatively small sample size, these findings require further validation through larger-scale clinical research before being implemented in routine clinical practice.

The authors thank all staff members from the Department of Gynecology, Xiamen Huli Renjun Hospital for their assistance in this study.