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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

Validating a predictive model for caesarean section in low-risk nulliparous pregnancies.

Linnea Ladfors¹, Patricia A Janssen²

¹Division of Clinical Epidemiology, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.

Women and Birth : Journal of the Australian College of Midwives

|August 4, 2023

Summary

A predictive model accurately identifies nulliparous women at high risk for caesarean birth (CB). This tool can guide targeted care to reduce rising CB rates.

Keywords:

Caesarean birth Nulliparity, Predictive Value of Tests, Risk Scores

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Area of Science:

Obstetrics and Gynecology
Maternal-Fetal Medicine
Clinical Prediction Models

Background:

Caesarean birth (CS) rates are increasing globally.
A 2017 model by Janssen et al. predicted CS in nulliparous women with 71% accuracy using admission data.

Purpose of the Study:

To validate the Janssen predictive model for caesarean birth risk in a new population of low-risk, nulliparous women.
To identify novel predictors to potentially improve the model's accuracy.

Main Methods:

Retrospective chart study of 348 healthy, nulliparous, term women in spontaneous labor.
Collected sociodemographic, pregnancy, and labor characteristics.
Validated the Janssen model and assessed its predictive performance (C-statistic, calibration, sensitivity, specificity).

Main Results:

The Janssen model achieved a C-statistic of 0.77 in the validation cohort.
No new predictors were identified to improve the model.
A cut-off score of 0.32 yielded 69% sensitivity and specificity, with acceptable calibration.

Conclusions:

The validated model effectively predicts caesarean birth risk in nulliparous women using readily available admission data.
Findings can inform targeted interventions for high-risk individuals, aiming to reduce overall caesarean birth rates.