Highlights
- •Uterine atony is the most common cause of postpartum hemorrhage.
- •Accurately predicting uterine atony may decrease associated morbidity.
- •We developed risk prediction models to quantify patients’ risk of atony.
- •Antepartum model had moderate discriminatory ability, C-statistic 0.61 (0.60–0.62)
- •Intrapartum model had improved discriminatory ability, C-statistic 0.68 (0.67–0.69)
Abstract
Background
Uterine atony is the most common cause of postpartum hemorrhage and is associated
with substantial morbidity. Prospectively identifying women at increased risk of atony
may reduce the incidence of subsequent adverse events. We sought to develop and evaluate
clinical risk-prediction models for uterine atony following vaginal and cesarean delivery,
using prespecified risk factors identified from systematic review.
Methods
Using retrospective data from vaginal and cesarean deliveries occurring at a single
institution between 2010 and 2019, antepartum and intrapartum risk-prediction models
for uterine atony, defined by supplementary uterotonic administration in addition
to prophylactic oxytocin infusion, were developed using logistic regression. The C-statistic
quantified the ability of the model to discriminate between cases and controls.
Results
Data were available for 4773 atony cases and 23 933 controls. The antepartum model
included 20 risk factors and exhibited moderate discriminatory ability (C-statistic
0.61, 95% confidence interval 0.60 to 0.62). The intrapartum model included 27 risk
factors and showed improved discriminatory ability (C-statistic 0.68, 95% confidence
interval 0.67 to 0.69).
Conclusions
We identified antepartum and intrapartum risk-prediction models to quantify patients’
risk of uterine atony. Models performed similarly for all delivery modes, races, and
ethnic groups. Future work should further improve these models through inclusion of
more comprehensive prediction data.
Keywords
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Article info
Publication history
Published online: April 21, 2022
Accepted:
April 15,
2022
Identification
Copyright
© 2022 Elsevier Ltd. All rights reserved.
