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Objective <p>Preterm birth is a major global health issue, with higher rates observed in pregnancies following assisted reproductive technology (ART). This study aimed to develop and validate via the validation set a prediction model for preterm birth in singleton pregnancies following ART, by identifying key clinical and ART-related risk factors.</p> Methods <p>This retrospective study included 268 women who underwent ART and delivered singleton Live births at Sun Yat-sen Memorial Hospital between 2013 and 2021. Data on demographic characteristics, medical history, ART-related factors, pregnancy complications, and delivery outcomes were extracted from medical records. The study cohort was randomly divided into a training set (160 participants) and an internal validation set (108 participants). The training set was used to identify predictors and construct a nomogram using multivariable logistic regression. The model’s performance was evaluated through discrimination (area under the curve, AUC), calibration (calibration curves), and clinical usefulness (decision curve analysis). The relationship between cervical length and gestational age was also assessed using restricted cubic splines.</p> Results <p>The total preterm birth rate was 23.5% (63/268). Independent risk factors for preterm birth in singleton pregnancies after ART included the number of embryos implanted (OR = 3.54, 95% CI: 1.29–9.69, <i>p</i> = 0.014), gestational hypertensive disease (GHD) (OR = 3.07, 95% CI: 1.36–6.93, <i>p</i> = 0.007), premature rupture of membranes (PROM) (OR = 4.70, 95% CI: 1.90–11.61, <i>p</i> &lt; 0.001), polycystic ovary syndrome (PCOS) (OR = 2.27, 95% CI: 1.04–4.93, <i>p</i> = 0.039), and intrauterine adhesion (IA) (OR = 3.32, 95% CI: 0.67–16.59, <i>p</i> = 0.043). The nomogram developed from these factors demonstrated acceptable discrimination (AUC = 0.77 in the training set, AUC = 0.71 in the validation set) and calibration in both sets. Decision curve analysis showed that the model provided net benefits across a wide range of threshold probabilities (0.00 to 0.83). The analysis of cervical length indicated significant differences between the preterm and full-term groups, with a higher reduction rate in cervical length observed in the preterm group after 16 weeks of gestation.</p> Conclusions <p>The prediction model developed in this study is effective for predicting preterm birth risk in ART pregnancies. This model can help clinicians identify high-risk pregnancies early and implement targeted interventions. Cervical length monitoring may be a useful tool in predicting preterm birth, especially after the second trimester.</p>

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A prediction model of preterm birth in singleton pregnancy after assisted reproduction therapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月7日

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  • Zhaorui Wang,
  • Hanjie Mo,
  • Liqiong Zhu,
  • Jing Tang,
  • Jia Huang,
  • Zhuoyao Mai,
  • Xuedan Jiao,
  • Menglan Zhu,
  • Xiaohui Ji,
  • Nengyong Ouyang,
  • Hui Chen

摘要

Objective

Preterm birth is a major global health issue, with higher rates observed in pregnancies following assisted reproductive technology (ART). This study aimed to develop and validate via the validation set a prediction model for preterm birth in singleton pregnancies following ART, by identifying key clinical and ART-related risk factors.

Methods

This retrospective study included 268 women who underwent ART and delivered singleton Live births at Sun Yat-sen Memorial Hospital between 2013 and 2021. Data on demographic characteristics, medical history, ART-related factors, pregnancy complications, and delivery outcomes were extracted from medical records. The study cohort was randomly divided into a training set (160 participants) and an internal validation set (108 participants). The training set was used to identify predictors and construct a nomogram using multivariable logistic regression. The model’s performance was evaluated through discrimination (area under the curve, AUC), calibration (calibration curves), and clinical usefulness (decision curve analysis). The relationship between cervical length and gestational age was also assessed using restricted cubic splines.

Results

The total preterm birth rate was 23.5% (63/268). Independent risk factors for preterm birth in singleton pregnancies after ART included the number of embryos implanted (OR = 3.54, 95% CI: 1.29–9.69, p = 0.014), gestational hypertensive disease (GHD) (OR = 3.07, 95% CI: 1.36–6.93, p = 0.007), premature rupture of membranes (PROM) (OR = 4.70, 95% CI: 1.90–11.61, p < 0.001), polycystic ovary syndrome (PCOS) (OR = 2.27, 95% CI: 1.04–4.93, p = 0.039), and intrauterine adhesion (IA) (OR = 3.32, 95% CI: 0.67–16.59, p = 0.043). The nomogram developed from these factors demonstrated acceptable discrimination (AUC = 0.77 in the training set, AUC = 0.71 in the validation set) and calibration in both sets. Decision curve analysis showed that the model provided net benefits across a wide range of threshold probabilities (0.00 to 0.83). The analysis of cervical length indicated significant differences between the preterm and full-term groups, with a higher reduction rate in cervical length observed in the preterm group after 16 weeks of gestation.

Conclusions

The prediction model developed in this study is effective for predicting preterm birth risk in ART pregnancies. This model can help clinicians identify high-risk pregnancies early and implement targeted interventions. Cervical length monitoring may be a useful tool in predicting preterm birth, especially after the second trimester.