Novillo-Del Álamo Blanca, Martínez-Varea Alicia, Satorres-Pérez Elena, Nieto-Tous Mar, Modrego-Pardo Fernando, Padilla-Prieto Carmen, García-Florenciano María Victoria, Bello-Martínez de Velasco Silvia, Morales-Roselló José
Department of Obstetrics and Gynecology, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain.
Department of Pediatrics, Obstetrics and Gynecology, Faculty of Medicine, University of Valencia, 46010 Valencia, Spain.
J Pers Med. 2024 May 9;14(5):502. doi: 10.3390/jpm14050502.
Labor induction is one of the leading causes of obstetric admission. This study aimed to create a simple model for predicting failure to progress after labor induction using pelvic ultrasound and clinical data. A group of 387 singleton pregnant women at term with unruptured amniotic membranes admitted for labor induction were included in an observational prospective study. Clinical and ultrasonographic variables were collected at admission prior to the onset of contractions, and labor data were collected after delivery. Multivariable logistic regression analysis was applied to create several models to predict cesarean section due to failure to progress. Afterward, the most accurate and reproducible model was selected according to the lowest Akaike Information Criteria (AIC) with a high area under the curve (AUC). Plausible parameters for explaining failure to progress were initially obtained from univariable analysis. With them, several multivariable analyses were evaluated. Those parameters with the highest reproducibility included maternal age ( < 0.05), parity ( < 0.0001), fetal gender ( < 0.05), EFW centile ( < 0.01), cervical length ( < 0.01), and posterior occiput position ( < 0.001), but the angle of descent was disregarded. This model obtained an AIC of 318.3 and an AUC of 0.81 (95% CI 0.76-0.86, < 0.0001) with detection rates of 24% and 37% for FPRs of 5% and 10%. A simplified clinical and sonographic model may guide the management of pregnancies undergoing labor induction, favoring individualized patient management.
引产是产科住院的主要原因之一。本研究旨在创建一个简单模型，利用盆腔超声和临床数据预测引产失败的进展情况。一项观察性前瞻性研究纳入了387名单胎足月妊娠、胎膜未破且因引产入院的孕妇。在宫缩开始前入院时收集临床和超声变量，并在分娩后收集分娩数据。应用多变量逻辑回归分析创建了多个模型，以预测因进展失败而进行剖宫产的情况。然后，根据最低赤池信息准则（AIC）和高曲线下面积（AUC）选择最准确、可重复的模型。解释进展失败的合理参数最初是从单变量分析中获得的。利用这些参数，评估了几个多变量分析。具有最高可重复性的参数包括产妇年龄（<0.05）、产次（<0.0001）、胎儿性别（<0.05）、胎儿估计体重百分位数（<0.01）、宫颈长度（<0.01）和枕后位（<0.001），但下降角度被忽略。该模型的AIC为318.3，AUC为0.81（95%可信区间0.76 - 0.86，<0.0001），假阳性率为5%和10%时的检测率分别为24%和37%。一个简化的临床和超声模型可能会指导引产妊娠的管理，有利于个体化的患者管理。
