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Updated: Jun 2, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a prognostic nomogram for severe postpartum hemorrhage after cesarean delivery:
Wanchang Yin1,2,3, Xinghe Wang2,3, Ruiman Li1
1Department of Obstetrics and Gynecology, The First Affiliate Hospital of Jinan University, Jinan University, Guangzhou, China.
Objectives:
To develop and validate a nomogram to predict severe postpartum hemorrhage following cesarean delivery.
Methods:
This is a two-center retrospective cohort study. Cesarean delivery patients from the First Affiliate Hospital of Jinan University were divided into a development cohort (n = 11 137) and an internal validation cohort (n = 4739). Cesarean delivery patients from the Dongguan Maternal and Child Health Care Hospital (n = 13 775) were enrolled in the external validation cohort. The nomogram was based on independent risk factors for severe postpartum hemorrhage obtained by multivariate logistic regression. We evaluated the discrimination and calibration of the nomogram in the development and validation cohorts.
Results:
The nomogram used data including previous cesarean delivery, pre-pregnancy weight, preterm birth, placenta previa, placenta accreta spectrum disorders, placental abruption, and mode of anesthesia. The area under the curves of the nomogram in the internal and external validation cohorts were 0.922 (95% confidence interval [CI] 0.897-0.947) and 0.813 (95% CI 0.785-0.841), respectively. Consistency between the predicted and actual probabilities was observed in both validation cohorts.
Conclusions:
The nomogram displayed good calibration and discrimination and can be used for screening in clinical practice to enable clinicians to intervene appropriately.
