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.