Development and validation of a predictive model to identify the established labor

Abstract

BackgroundThe diagnosis of the established labor is a crucial clinical decision, and it requires an accurate midwifery assessment. Regular uterine contractions and a cervical dilatation of 4-6 cm are commonly considered to diagnose active labor with a lack of knowledge regarding the role of maternal signs. This study aimed to build and to validate a predictive model based on maternal signs and symptoms to identify a cervical dilatation ³4 . MethodsA prospective observational study was conducted from May to September 2018 in a II Level Maternity Unit (development data), and from May to September 2019 in a I Level Maternity Unit (validation data). Singleton, at term pregnant women with cephalic presentation and presence of uterine activity were enrolled consecutively at the maternity triage or at the antenatal ward during the initial assessment to diagnose the stage of labor. Women <18 years old, with language barrier and induction of labor were excluded. During the assessment midwife observed features of contractions (rhythm, frequency and duration) and 8 specific maternal signs. A nomogram for the calculation of the predictions of cervical dilatation ³ 4 on the ground of 11 maternal signs and symptoms was obtained from a multivariate logistic regression model. The model predictive performance was investigated by internal and external validation. Results288 assessments were analyzed (216 for development data). In the final logistic regression model “Rhythm” (OR 6.26), “Duration” (OR 8.15) of contractions and “show” (OR 4.29) had a significant impact on increasing the probability of a cervical dilatation ³ 4 cm. The area under the ROC curve in the model with only uterine activity was 0.865 (development data) and 0.927 (validation data), with an increment to 0.905 and 0.956, respectively when adding maternal signs. ConclusionOur predictive model based on maternal signs and symptoms has a good performance and could be useful in clinical practice. This may improve the quality of midwifery care, avoiding medicalization, aiming to promote a positive experience of childbirth.