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Updated: Aug 9, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Prognostic model on niche development after a first caesarean section: development and internal validation.

Sanne I Stegwee¹, L F Lucet van der Voet², Martijn W Heymans³

¹Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Obstetrics and Gynecology, Research Institute Amsterdam Reproduction & Development, Amsterdam, Netherlands.

European Journal of Obstetrics, Gynecology, and Reproductive Biology

|February 16, 2023

Summary

A predictive model for uterine niche development after a first cesarean section (CS) showed poor performance. Factors like gestational age, smoking, and surgical techniques influence healing, suggesting avenues for future prevention strategies.

Keywords:

Caesarean section Niche development Residual myometrium thickness Risk factors Transvaginal ultrasound

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Last Updated: Aug 9, 2025

An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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Published on: January 27, 2010

External Cephalic Version: Is it an Effective and Safe Procedure?

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Published on: June 6, 2020

Area of Science:

Reproductive Medicine
Gynecological Surgery
Medical Statistics

Background:

Uterine niche development, an indentation in the scar after cesarean section (CS), is a growing concern.
Understanding risk factors is crucial for preventing potential complications like abnormal uterine bleeding and infertility.

Purpose of the Study:

To develop and internally validate a prognostic prediction model for niche development after a first CS.
To identify patient- and surgery-related risk factors associated with uterine niche formation.

Main Methods:

Secondary analysis of a randomized controlled trial data from 32 Dutch hospitals.
Multivariable backward logistic regression and multiple imputation for missing data.
Internal validation using bootstrapping, assessing calibration and discrimination (AUC).

Main Results:

Two models were developed (total population and elective CS), identifying risk factors: gestational age, twin pregnancy, smoking, double-layer closure, and less surgical experience.
Protective factors included multiparity and Vicryl suture material.
Model performance was poor, with low Nagelkerke R² (0.01-0.05) and failed to poor discriminative ability (AUC 0.56-0.62).

Conclusions:

The developed prediction model is not suitable for accurately predicting niche development after a first CS.
Surgical experience and suture material are potential targets for future prevention strategies.
Further research is needed to identify additional risk factors to improve predictive model accuracy.