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Development and external validation of a LASSO-based nomogram to predict epidural-associated postpartum urinary
Rong Chen1, Ying Shao2,3, Junming Huang4
1Department of Anesthesiology, The Second Hospital of Sanming, Sanming, China.
Purpose:
To develop and validate a risk prediction model for postpartum urinary retention (PUR) among parturients receiving epidural analgesia (EA) during vaginal delivery, to support individualized bladder management.
Methods:
This dual-center retrospective cohort study analyzed electronic medical records from two hospitals (2022-2024). Least absolute shrinkage and selection operator (LASSO) regression was used to select independent predictors from demographic, labor, and analgesia-related variables. A clinical nomogram was developed. The model underwent internal and external validation; performance was assessed using calibration, discrimination (area under the receiver operating characteristic curve [AUC]), and decision curve analysis (DCA).
Results:
Among 1019 parturients receiving EA (PUR incidence 16.09%), prolonged analgesia duration and prolonged second-stage labor were independently associated with PUR, whereas higher intermittent catheterization frequency (ICF) was associated with a lower rate of recorded PUR. The model demonstrated excellent discrimination, satisfactory calibration, and clinical net benefit across validation cohorts.
Conclusion:
The validated nomogram provides real-time, individualized estimates of PUR risk during late labor. By identifying parturients at elevated risk, it offers an evidence-based basis for risk-adaptive bladder monitoring and individualized care; however, whether such model-guided management ultimately reduces PUR incidence remains to be established in prospective interventional studies.