Tweetable abstract
A model may guide the clinician in making appropriate management
decisions for primiparous women with clinical suspected cephalopelvic
disproportion.
INTRODUCTION
Cephalopelvic disproportion (CPD) is
a mismatch between the maternal pelvis and the fetus, which is the most
common cause of obstructed labor1. Dystocia and
subsequent emergency cesarean section are associated with maternal and
neonatal morbidity, including uterine rupture, postpartum hemorrhage,
chorioamnionitis, neonatal birth injuries, and even
mortality2, 3.
On
the other hand, the over-diagnosis of CPD is one of the main reasons for
a continuous increase in cesarean
deliveries4. If
obstetricians and midwives could identify patients at high risk for
cesarean delivery, it might avoid increased
complications by offering a
scheduled surgery, while those at low risk could be encouraged to pursue
vaginal delivery.
Clinical pelvimetry to estimate various pelvic dimensions using
systematic manual examinations of specific bony landmarks has been
considered poor accurate and unsatisfactory interobserver
agreement5. Hence,
radiological pelvimetry measurements using X-ray6,
computed tomography 7 and magnetic resonance imaging
(MRI)8 have been introduced to antenatal assess the
relationship between the maternal pelvis and fetus in order to choose
proper delivery methods. MRI pelvimetry is superior to other
radiological techniques, including X-ray and computed tomography, since
it can provide an
accurate
evaluation of pelvic dimensions while imaging soft tissue structures and
fetus without radiation exposure9, 10. The pelvimetric
errors of MRI are approximate 1% versus 10% for conventional X-ray
measurements 8. Although the MRI seemed promising to
evaluate the maternal pelvic capacity and fetal size, to our knowledge,
yet there is still no literature that has focused on the model using MRI
to predict the chance of successful vaginal delivery.
As such, the objective of our study was to prospectively assess the
clinical and MRI features to develop and validate a risk
prediction
model for estimating CPD in
primiparous women before the onset
of labor.