Definition of Failed Induction of Labor and Its Predictive Factors: Two Unsolved Issues of an Everyday Clinical Situation

Baños, Núria; Migliorelli, Federico; Posadas, Eduardo; Ferreri, Janisse; Palacio, Montse

doi:10.1159/000433429

Cited by 253 publications

(50 citation statements)

References 60 publications

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“…Premature cervical remodelling and ripening usually precede a spontaneous preterm birth regardless of the initial etiology [3] , and an unripe cervix will probably lead to a failed induction of labor (IOL) [4] . With the lack of good tools to assess cervical remodelling [5] , there is growing scientific interest to learn more about cervical structure and behaviour.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age

Baños

Perez‐Moreno²,

Triginer³

et al. 2016

Fetal Diagn Ther

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Objectives: Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. Methods: This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Results: Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). Discussion: This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant.

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Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age

Baños

Perez‐Moreno²,

Triginer³

et al. 2016

Fetal Diagn Ther

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“…Then, we use the opposite of CF for evidence in rules that contain negations, CF(¬F) ; the minimum of CFs for conjunctions, CF(F 1 ˄ F 2 ) ; and the maximum of CFs for disjunctions, CF(F 1 ˅ F 2 ). In this project, priorities are calculated using CFs normalized with the equations (1), (2) and (3): (1)…”

Section: B Decision-making Rulesmentioning

confidence: 99%

“…Over the last two decades, the induction rate has doubled, turning into a fairly common procedure, used in over 20% of gestations [1], [2]. However, good predictive factors of the success of this procedure have not been identified yet, so there are not support tools for the experts' criteria so far.…”

Section: Introductionmentioning

confidence: 99%

“…However, good predictive factors of the success of this procedure have not been identified yet, so there are not support tools for the experts' criteria so far. Currently, the decision of inducing is made only on the basis of clinical knowledge itself consisting of protocols, guides and previous experience of certain characteristics of mother and foetus, but in case of making unwise decisions it would cause serious complications [2], [3]. Under these circumstances, it seems interesting to know beforehand the probability of success of the induction in order to dismiss the inductions having a high probability of failure and thus improving results on health, reducing costs derived from medication, hospitalization or qualified staff.…”

Section: Introductionmentioning

confidence: 99%

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Development of a Predictive Model for Induction Success of Labour

Pruenza¹,

Teurón²,

Lechuga³

et al. 2018

IJIMAI

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Induction of the labour process is an extraordinarily common procedure used in some pregnancies. Obstetricians face the need to end a pregnancy, for medical reasons usually (maternal or fetal requirements) or less frequently, social (elective inductions for convenience). The success of induction procedure is conditioned by a multitude of maternal and fetal variables that appear before or during pregnancy or birth process, with a low predictive value. The failure of the induction process involves performing a caesarean section. This project arises from the clinical need to resolve a situation of uncertainty that occurs frequently in our clinical practice. Since the weight of clinical variables is not adequately weighted, we consider very interesting to know a priori the possibility of success of induction to dismiss those inductions with high probability of failure, avoiding unnecessary procedures or postponing end if possible. We developed a predictive model of induced labour success as a support tool in clinical decision making. Improve the predictability of a successful induction is one of the current challenges of Obstetrics because of its negative impact. The identification of those patients with high chances of failure, will allow us to offer them better care improving their health outcomes (adverse perinatal outcomes for mother and newborn), costs (medication, hospitalization, qualified staff) and patient perceived quality. Therefore a Clinical Decision Support System was developed to give support to the Obstetricians. In this article, we had proposed a robust method to explore and model a source of clinical information with the purpose of obtaining all possible knowledge. Generally, in classification models are difficult to know the contribution that each attribute provides to the model. We had worked in this direction to offer transparency to models that may be considered as black boxes. The positive results obtained from both the information recovery system and the predictions and explanations of the classification show the effectiveness and strength of this tool.

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“…The main determinants are probably the differences in indications and risk factors for failure of induction that have been previously described, such as obesity, nulliparity, an unfavorable cervix and a previous cesarean delivery. [9][10][11] Disparities might also be due to obstetric practices that vary between centers and physicians. 12,13 It is therefore complex to interpret the results of those studies and compare them over time or between different maternity units.…”

Section: Introductionmentioning

confidence: 99%

Target populations to reduce cesarean rates after induced labor: A national population‐based cohort study

Blanc-Petitjean

Schmitz

Salomé³

et al. 2019

Acta Obstet Gynecol Scand

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Introduction Great variations in cesarean rates after induction of labor exist and reasons for these disparities remain unclear. They may be explained by individual characteristics or obstetric practices. Ten‐group classification systems have proved their utility to monitor cesarean rates in general population. We aimed to identify groups of women that account for most cesareans after induction of labor using the Nippita reproducible 10‐group classification, specifically designed for induced population. Material and methods A prospective population‐based cohort study was performed in 94 French maternity units, including 3042 women undergoing induction of labor. Women were sorted according to 10 mutually exclusive groups based on parity, weeks of gestation, number of fetuses, fetal presentation and previous cesarean delivery. Relative size, cesarean delivery rate and contribution to the overall cesarean rate were described for each group. Cesarean rates were compared according to the Bishop score at the onset of labor induction. Indications for cesarean delivery were also described in the groups that contributed most to the overall cesarean rate. The MEDIP protocol was registered in ClinicalTrial (NCT02477085). Results The overall cesarean rate was 21.0% among this population of induced women. Nulliparous women with a term singleton cephalic fetus (groups 1, 2 and 3; at 37‐38, 39‐40 and ≥41 weeks of gestation, respectively) accounted for two‐thirds of the overall cesarean rate because they were the largest group (relative size of 10.6, 16.6 and 18.1%, respectively) and had higher cesarean rates (27.2, 30.9 and 33.0%, respectively). When the Bishop score was <6 (n = 2270/3042), cesarean delivery rates were higher (24.1 vs 10.7% if Bishop score ≥6, P < 0.01), in particular for group 1 (29.1 vs 12.5%, P = 0.02), and group 2 (33.3 vs 19.3%, P = 0.01). In groups 1, 2 and 3, which contributed most to the overall cesarean rate, a significant part of the cesareans were performed before 6 cm of cervical dilation for dystocia only (40.0, 16.7 and 17.6%, respectively). Conclusions Nulliparous women with a term singleton cephalic fetus and an unfavorable cervix represent the population to target for auditing induction practices. Specific actions could be implemented among this population to weigh the benefits and risks of induction and improve the management of labor induction.

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Definition of Failed Induction of Labor and Its Predictive Factors: Two Unsolved Issues of an Everyday Clinical Situation

Cited by 253 publications

References 60 publications

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age

Development of a Predictive Model for Induction Success of Labour

Target populations to reduce cesarean rates after induced labor: A national population‐based cohort study

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