Abstract
Objective This study aimed to develop a validated model to predict intrapartum cesarean in
nulliparous women and to use it to adjust for case-mix when comparing institutional
laboring cesarean birth (CB) rates.
Study Design This multicenter retrospective study used chart-abstracted data on nulliparous, singleton,
term births over a 7-year period. Prelabor cesareans were excluded. Logistic regression
was used to predict the probability of CB for individual pregnancies. Thirty-five
potential predictive variables were evaluated including maternal demographics, prepregnancy
health, pregnancy characteristics, and newborn weight and gender. Models were trained
on 21,017 births during 2011 to 2015 (training cohort), and accuracy assessed by prediction
on 15,045 births during 2016 to 2017 (test cohort).
Results Six variables delivered predictive success equivalent to the full set of 35 variables:
maternal weight, height, and age, gestation at birth, medically-indicated induction,
and birth weight. Internal validation within the training cohort gave a receiver operator
curve with area under the curve (ROC-AUC) of 0.722. External validation using the
test cohort gave ROC-AUC of 0.722 (0.713–0.731 confidence interval). When comparing
observed and predicted CB rates at 16 institutions in the test cohort, five had significantly
lower than predicted rates and three had significantly higher than predicted rates.
Conclusion Six routine clinical variables used to adjust for case-mix can identify outliers
when comparing institutional CB rates.
Keywords
case-mix - cesarean birth - computer model - prediction - nulliparous
