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Background <p>Polycystic ovary syndrome (PCOS) is associated with a significantly high risk of spontaneous abortion, yet there is a scarcity of clinical prediction models for early pregnancy loss in this patient population.</p> Objective <p>To identify risk factors for early spontaneous abortion in women with PCOS and to develop and validate a predictive model for assessing this risk.</p> Methods <p>Researchers collected clinical data including demographic statistics, medical, menstrual, obstetric, and biochemical data. Univariate logistic regression was initially used to identify potential factors associated with early spontaneous abortion. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection and dimension reduction. Multivariate logistic regression was then used to determine independent predictors, based which a nomogram was constructed. The model’s discriminative ability, clinical utility, and calibration were assessed using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration plots.</p> Results <p>This retrospective study included 619 PCOS patients from a maternity and gynecological hospital in China. Univariate analysis revealed that elevated testosterone, higher HOMA-IR, increased D-dimer, decreased serum iron, platelet (PLT) count ≥ 300 × 10⁹/L, and homocysteine (HCY) &gt; 7 µmol/L were significantly associated with an increased risk of spontaneous abortion, while early pregnancy supportive treatment reduced the risk. LASSO regression identified six key predictors: testosterone, D-dimer, serum iron, early pregnancy support, PLT count, and HCY. Multivariate analysis confirmed that elevated testosterone, increased D-dimer, PLT count ≥ 300 × 10⁹/L, and HCY &gt; 7 µmol/L were independent risk factors, while higher serum iron was a protective factor. The nomogram developed from these factors showed high predictive accuracy, with an area under the curve (AUC) of 0.92 (95% CI: 0.90–0.95) in the training set and 0.86 (95% CI: 0.80–0.93) in the vaildation set. Decision curve and calibration analyses further demonstrated favorable clinical applicability and model fit.</p> Conclusion <p>The constructed nomogram provides an accurate and clinically useful tool for predicting the risk of early spontaneous abortion in patients with PCOS, facilitating personalized treatment and early intervention strategies.</p>

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A nomogram to predict early spontaneous abortion in women with polycystic ovary syndrome: a retrospective cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Qian Han,
  • Mengyuan Li,
  • Guanshan Li,
  • Mingwei Xin,
  • Xiaodan Yin,
  • Junqin He

摘要

Background

Polycystic ovary syndrome (PCOS) is associated with a significantly high risk of spontaneous abortion, yet there is a scarcity of clinical prediction models for early pregnancy loss in this patient population.

Objective

To identify risk factors for early spontaneous abortion in women with PCOS and to develop and validate a predictive model for assessing this risk.

Methods

Researchers collected clinical data including demographic statistics, medical, menstrual, obstetric, and biochemical data. Univariate logistic regression was initially used to identify potential factors associated with early spontaneous abortion. Least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection and dimension reduction. Multivariate logistic regression was then used to determine independent predictors, based which a nomogram was constructed. The model’s discriminative ability, clinical utility, and calibration were assessed using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration plots.

Results

This retrospective study included 619 PCOS patients from a maternity and gynecological hospital in China. Univariate analysis revealed that elevated testosterone, higher HOMA-IR, increased D-dimer, decreased serum iron, platelet (PLT) count ≥ 300 × 10⁹/L, and homocysteine (HCY) > 7 µmol/L were significantly associated with an increased risk of spontaneous abortion, while early pregnancy supportive treatment reduced the risk. LASSO regression identified six key predictors: testosterone, D-dimer, serum iron, early pregnancy support, PLT count, and HCY. Multivariate analysis confirmed that elevated testosterone, increased D-dimer, PLT count ≥ 300 × 10⁹/L, and HCY > 7 µmol/L were independent risk factors, while higher serum iron was a protective factor. The nomogram developed from these factors showed high predictive accuracy, with an area under the curve (AUC) of 0.92 (95% CI: 0.90–0.95) in the training set and 0.86 (95% CI: 0.80–0.93) in the vaildation set. Decision curve and calibration analyses further demonstrated favorable clinical applicability and model fit.

Conclusion

The constructed nomogram provides an accurate and clinically useful tool for predicting the risk of early spontaneous abortion in patients with PCOS, facilitating personalized treatment and early intervention strategies.