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Introduction and Hypothesis <p>Postpartum stress urinary incontinence (SUI) is a common pelvic floor disorder that impairs quality of life. Early identification of high-risk women is important for targeted interventions; however, prediction tools specific to delivery mode remain limited.</p> Methods <p>This retrospective cohort study enrolled women who underwent vaginal delivery (VD) or cesarean section (CS) at a single center. Two delivery-mode–specific prediction models for SUI at 1 year postpartum were developed using LASSO regression followed by multivariable logistic regression. Independent predictors were incorporated into nomograms to facilitate individualized risk estimation. Model performance was evaluated on the basis of discrimination (AUC), calibration, and clinical utility through decision curve analysis. Temporal internal–external validation was conducted to further assess model robustness.</p> Results <p>The incidence of postpartum SUI was 37.01% following VD and 28.10% following CS. For VD, independent predictors included SUI during pregnancy, parity, and manual placental removal. For CS, independent predictors were SUI during pregnancy, history of constipation, history of chronic cough or sneezing, history of cervical insufficiency, and twin pregnancy. Both models demonstrated good discrimination (training AUC VD 0.73, CS 0.75; internal testing VD 0.725, CS 0.748), robust calibration (VD <i>P</i> = 0.482; CS <i>P</i> = 0.884), and substantial clinical net benefit across wide threshold ranges. Temporal internal–external validation further supported the strong performance of both models.</p> Conclusions <p>Delivery mode–specific nomograms provide a reliable method for estimating the risk of postpartum SUI, enabling early and individualized intervention. Implementing these models may improve postpartum pelvic floor management, reduce SUI incidence, and support precision health strategies.</p>

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Mode-of-Delivery–Specific Nomograms for Predicting Stress Urinary Incontinence One Year After Childbirth

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • Yuan Wang,
  • Jingping Wang,
  • Lilan Yu,
  • Yihong Xu,
  • Lu Yao,
  • Hongying Huang,
  • Jianmei Wang,
  • Hongying Pan

摘要

Introduction and Hypothesis

Postpartum stress urinary incontinence (SUI) is a common pelvic floor disorder that impairs quality of life. Early identification of high-risk women is important for targeted interventions; however, prediction tools specific to delivery mode remain limited.

Methods

This retrospective cohort study enrolled women who underwent vaginal delivery (VD) or cesarean section (CS) at a single center. Two delivery-mode–specific prediction models for SUI at 1 year postpartum were developed using LASSO regression followed by multivariable logistic regression. Independent predictors were incorporated into nomograms to facilitate individualized risk estimation. Model performance was evaluated on the basis of discrimination (AUC), calibration, and clinical utility through decision curve analysis. Temporal internal–external validation was conducted to further assess model robustness.

Results

The incidence of postpartum SUI was 37.01% following VD and 28.10% following CS. For VD, independent predictors included SUI during pregnancy, parity, and manual placental removal. For CS, independent predictors were SUI during pregnancy, history of constipation, history of chronic cough or sneezing, history of cervical insufficiency, and twin pregnancy. Both models demonstrated good discrimination (training AUC VD 0.73, CS 0.75; internal testing VD 0.725, CS 0.748), robust calibration (VD P = 0.482; CS P = 0.884), and substantial clinical net benefit across wide threshold ranges. Temporal internal–external validation further supported the strong performance of both models.

Conclusions

Delivery mode–specific nomograms provide a reliable method for estimating the risk of postpartum SUI, enabling early and individualized intervention. Implementing these models may improve postpartum pelvic floor management, reduce SUI incidence, and support precision health strategies.