Risk Calculator to Predict Cesarean Delivery Among Women Undergoing Induction of Labor

External validation and updating of the Rossi nomogram for predicting cesarean delivery following induction: is the Bishop score valuable?

OBJECTIVE

This study sought to validate the Rossi nomogram in a Chinese population and then to include the Bishop score to see if it has an effect on the accuracy of the nomogram.

MATERIALS AND METHODS

The Rossi predictive model was applied and externally validated in a retrospective cohort from August 2017 and July 2023 in a Chinese tertiary-level medical center. For the revision and updating of the models, the regression coefficients of all the predictors (except race) were re-estimated and then the cervical Bishop score at the time of induction was added. Each model's performance was measured using the receiver-operating characteristic and calibration plots. Decision curve analysis determined the range of the probability threshold for each prediction model that would be of clinical value.

RESULTS

A total of 721 women met the inclusion criteria, of whom 183 (25.4%) underwent a cesarean delivery. The calibration demonstrated the underestimation of the original model, with an area under the curve (AUC) of 0.789 (95% confidence interval [CI] 0.753-0.825, p < 0.001). After recalibrating the original model, the discriminative performance was improved from 0.789 to 0.803. Moreover, the discriminatory power of the updated model was further improved when the Bishop score at the time of induction was added to the recalibrated multivariable model. Indeed, the updated model demonstrated good calibration and discriminatory power, with an AUC of 0.811. The decision curve analysis indicated that all the models (original, recalibrated, and updated) provided higher net benefits of between 0 and 60% of the probability threshold, which indicates the benefits of using the models to make decisions concerning patients who fall within the identified range of the probability threshold. The net benefits of the updated model were higher than those of the original model and the recalibrated model.

CONCLUSION

The nomogram used to predict cesarean delivery following induction developed by Rossi et al. has been validated in a Chinese population in this study. More specifically, adaptation to a Chinese population by excluding ethnicity and including the Bishop score prior to induction gave rise to better performance. The three models (original, recalibrated, and updated) offer higher net benefits when the probability threshold is between 0 and 60%.

Fast Healthcare Interoperability Resources-Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

BACKGROUND

The escalating prevalence of cesarean delivery globally poses significant health impacts on mothers and newborns. Despite this trend, the underlying reasons for increased cesarean delivery rates, which have risen to 36.3% in Portugal as of 2020, remain unclear. This study delves into these issues within the Portuguese health care context, where national efforts are underway to reduce cesarean delivery occurrences.

OBJECTIVE

This paper aims to introduce a machine learning, algorithm-based support system designed to assist clinical teams in identifying potentially unnecessary cesarean deliveries. Key objectives include developing clinical decision support systems for cesarean deliveries using interoperability standards, identifying predictive factors influencing delivery type, assessing the economic impact of implementing this tool, and comparing system outputs with clinicians' decisions.

METHODS

This study used retrospective data collected from 9 public Portuguese hospitals, encompassing maternal and fetal data and delivery methods from 2019 to 2020. We used various machine learning algorithms for model development, with light gradient-boosting machine (LightGBM) selected for deployment due to its efficiency. The model's performance was compared with clinician assessments through questionnaires. Additionally, an economic simulation was conducted to evaluate the financial impact on Portuguese public hospitals.

RESULTS

The deployed model, based on LightGBM, achieved an area under the receiver operating characteristic curve of 88%. In the trial deployment phase at a single hospital, 3.8% (123/3231) of cases triggered alarms for potentially unnecessary cesarean deliveries. Financial simulation results indicated potential benefits for 30% (15/48) of Portuguese public hospitals with the implementation of our tool. However, this study acknowledges biases in the model, such as combining different vaginal delivery types and focusing on potentially unwarranted cesarean deliveries.

CONCLUSIONS

This study presents a promising system capable of identifying potentially incorrect cesarean delivery decisions, with potentially positive implications for medical practice and health care economics. However, it also highlights the challenges and considerations necessary for real-world application, including further evaluation of clinical decision-making impacts and understanding the diverse reasons behind delivery type choices. This study underscores the need for careful implementation and further robust analysis to realize the full potential and real-world applicability of such clinical support systems.

Influence factor analysis and prediction model of successful application of high-volume Foley Catheter for labor induction

BACKGROUND

This study aimed to establish a clinical-based nomogram for predicting the success rate of high-volume Foley catheterization for labor induction.

METHODS

This retrospective study included 1149 full-term pregnant women who received high-volume Foley catheterization for labor induction from January 2019 to December 2021 in Changshu No.1 People's Hospital. Univariate and multivariate logistic regression analyses were performed, in which the labor induction success was set as dependent variables and the characteristics (including age, height, weight, BMI, gravidity, parity, gestational age, uterine height, abdominal circumference, cervical Bishop score, amniotic fluid index, cephalic presentation, neonatal weight, pregnancy complications, etc.) were set as independent variables. A nomogram scoring model was established based on these risk factors, and a calibration curve was plotted to verify the predictive accuracy of the model.

RESULTS

The success rate of labor induction was 83.55% (960/1149). Univariate analysis revealed that the risk factors associated with the success rate of high-volume Foley catheterization for labor induction were height, pregnancy, birth, age, weight, BMI, uterine height, abdominal circumference, and hypertension. Multivariate logistic regression analysis showed that age (OR = 0.950; 95% CI: 0.904 ~ 0.998), height (OR = 1.062; 95% CI: 1.026 ~ 1.100), BMI (OR = 0.871; 95% CI: 0.831 ~ 0.913), and parity (OR = 8.007; 95% CI: 4.483 ~ 14.303) were independent risk factors for labor induction success by high-volume Foley catheterization. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve in the prediction model was 0.752 (95% CI 0.716 ~ 0.788). A nomogram was constructed based on the final multivariate analysis with a corrected C-index of 0.748, which indicated that the model was calibrated reasonably.

CONCLUSION

Four risk factors were used to construct a nomogram to evaluate the success rate of high-volume Foley catheterization for labor induction. The nomogram provides a visual clinical tool to assist in the selection of the most appropriate mode of labor induction for pregnant women of different risk levels.

Recent trends in term trial of labor after cesarean by number of prior cesarean deliveries

BACKGROUND

Cesarean delivery is a major source of maternal morbidity, and repeat cesarean delivery accounts for 40% of cesarean delivery, but recent data on the trial of labor after cesarean and vaginal birth after cesarean are limited.

OBJECTIVE

This study aimed to report the national rates of trial of labor after cesarean and vaginal birth after cesarean by number of previous cesarean deliveries and examine the effect of demographic and clinical characteristics on these rates.

STUDY DESIGN

This was a population-based cohort study using the US natality data files. The study sample was restricted to 4,135,247 nonanomalous singleton, cephalic deliveries between 37 and 42 weeks of gestation, with a history of previous cesarean delivery and delivered in a hospital between 2010 and 2019. Deliveries were grouped by number of previous cesarean deliveries (1, 2, or ≥3). The trial of labor after cesarean (deliveries with labor among deliveries with previous cesarean delivery) and vaginal birth after cesarean (vaginal deliveries among trial of labor after cesarean) rates were computed for each year. The rates were further subgrouped by history of previous vaginal delivery. Year of delivery, number of previous cesarean deliveries, history of previous cesarean delivery, age, race and ethnicity, maternal education, obesity, diabetes mellitus, hypertension, inadequate prenatal care, Medicaid payer, and gestational age were examined concerning the trial of labor after cesarean and vaginal birth after cesarean using multiple logistic regression. SAS software (version 9.4) was used for all analyses.

RESULTS

The trial of labor after cesarean rates increased from 14.4% in 2010 to 19.6% in 2019 (P<.001). This trend was seen in all categories of number of previous cesarean deliveries. Moreover, vaginal birth after cesarean rates increased from 68.5% in 2010 to 74.3% in 2019. The trial of labor after cesarean and vaginal birth after cesarean rates were the highest for deliveries with a history of both 1 previous cesarean delivery and a vaginal delivery (28.9% and 79.7%, respectively) and the lowest for those with a history of ≥3 previous cesarean deliveries and no history of vaginal delivery (4.5% and 46.9%, respectively). Factors associated with the trial of labor after cesarean and vaginal birth after cesarean rates are similar, but several factors have different directions of effect, such as non-White race and ethnicity, which is associated with a higher likelihood of trial of labor after cesarean but a lower likelihood of successful vaginal birth after cesarean.

CONCLUSION

More than 80% of patients with a history of previous cesarean delivery deliver by repeat scheduled cesarean delivery. With vaginal birth after cesarean rates increasing among those who attempt a trial of labor after cesarean, emphasis should be put on safely increasing the trial of labor after cesarean rates.

Impacts of embracing 39-week elective induction across an entire labor and delivery unit

BACKGROUND

Induction of labor among low-risk, 39-week nulliparas increased significantly in the United States following publication of the outcomes of A Randomized Trial of Induction Versus Expectant Management trial. However, the rates of labor induction and outcomes in non-nulliparous patients and the wider impacts on the labor unit have not been reported widely.

OBJECTIVE

This study aimed to compare the induction of labor rates and outcomes before and after liberal implementation of 39-week elective induction at a single center.

STUDY DESIGN

This was a retrospective cohort study comparing the delivery characteristics of pregnancies 1 year before and 1 year after adoption of a new 39-week elective induction policy at a single, tertiary-care center. Notably, elective induction was not restricted to nulliparas. We examined all live, singleton, in-born deliveries ≥36 weeks gestation, excluding those with fetal anomalies and prolonged antenatal admission. Deliveries at ≥39 weeks gestation were further subcategorized as being high risk (diabetes mellitus, chronic hypertension, intrauterine growth restriction, history of fetal demise or cholestasis) or low risk, nulliparas vs multiparas, and with or without a previous cesarean delivery. Elective deliveries were those without a maternal, fetal, or obstetrical indication. Primary outcomes included gestational age and indications for delivery, rates of labor induction and elective induction, and time from admission to delivery. Secondary outcomes included the rate of cesarean deliveries, indications for cesarean deliveries, and maternal and newborn morbidities. The outcomes were compared using Wilcoxon rank-sum tests or chi-square tests as appropriate. The odds of cesarean delivery were analyzed using multivariate logistic regression and controlling for relevant confounders.

RESULTS

A total of 2672 pre-implementation and 2526 post-implementation deliveries were studied. Among patients at ≥39 weeks gestation, elective delivery increased (pre-implementation, 344/1788 [19.2%] vs post-implementation, 684/1710 [40.0%]; P<.01) and admission for labor or ruptured membranes decreased (pre-implementation, 920/1788 [51.5%] vs post-implementation, 579/1710 [33.9%]; P<.01). Labor induction in the 39th week of gestation increased among low-risk and high-risk nulliparas, multiparas, and those with a previous cesarean delivery (P<.05 for each pairwise comparison), and the rate of 39-week elective inductions increased in all low-risk subgroups. Deliveries at 36 to 38 weeks gestation were similar in the proportion, timing, indications for delivery, and rate of labor induction. The odds of cesarean delivery was unchanged overall (adjusted odds ratio, 0.97; 95% confidence interval, 0.83-1.14) and for low-risk, ≥39-week nulliparas (adjusted odds ratio, 0.90; 95% confidence interval, 0.66-1.23) and low-risk, ≥39-week multiparas (adjusted odds ratio, 1.18; 95% confidence interval, 0.71-1.98). Among all deliveries, the median (interquartile range) time from admission to delivery increased significantly (pre-implementation, 12.8 [6.0-21.6] hours vs post-implementation, 15.6 [7.1-25.1] hours; P<.01) and the total cumulative patient care time from admission to delivery increased by 15% (pre-implementation, 41,578 hours vs post-implementation, 47,605 hours) when normalized by delivery volume. Chorioamnionitis incidence increased, whereas other maternal and neonatal morbidities were unchanged.

CONCLUSION

Following adoption of a nonrestrictive, 39-week elective induction policy at a single, tertiary-care center, the rates of 39-week induction of labor and elective inductions increased among nulliparas, multiparas, and those with a previous cesarean delivery. The rate of cesarean delivery was unchanged, and the median time from admission to delivery and the cumulative admission to delivery hours increased significantly. Future studies are needed to further explore the full scope of the impacts on labor unit operations, costs, and patient experiences and outcomes.

Utilizing machine learning to predict unplanned cesarean delivery

OBJECTIVE

To develop a comprehensive machine learning (ML) model predicting unplanned cesarean delivery (uCD) among singleton pregnancies based on features available at admission to labor.

METHODS

A retrospective cohort study from a tertiary medical center. Women with singleton vertex pregnancy of 34 weeks or more admitted for vaginal delivery between March 2011 and May 2019 were included. The cohort was divided into training (80%) and validation (20%) data sets. A separate cohort between June 2019 and April 2021 served as a test data set. Features selection was performed using a Random Forest ML algorithm.

RESULTS

The study population included 73 667 women, of which 4125 (6.33%) underwent uCD. The final model consisted of 13 features, based on prediction importance. The XGBoost model performed best with areas under the curve for the training, validation, and test data sets of 0.874, 0.839, and 0.840, respectively. The model showed a 65% positive predictive value for uCD among women in the 100th centile group, and a 99% or more negative predictive value in the less than 50th centile group. Positive and negative predictive values remained high among subgroups with high pretest probability of uCD.

CONCLUSION

An ML model for the prediction of uCD provides clinically useful risk stratification that remains accurate across gestational weeks 34-42 and among clinical risk groups. The model may be clinically useful for physicians and women admitted for labor.

SYNOPSIS

A machine learning model predicts unplanned cesarean delivery and can inform women's individualized decision making.

Predictive factors for vaginal delivery by induction of labor in uncomplicated pregnancies at 40-41 gestational weeks: A Japanese prospective single-center cohort study

AIM

We investigated cervical parameters predictive of vaginal delivery in elective labor induction among women at 40-41 gestational weeks.

METHODS

This Japanese prospective single-center cohort study was conducted between July 2019 and June 2020. We enrolled women with an uncomplicated singleton pregnancy who underwent labor induction at 40-41 gestational weeks. We analyzed background characteristics and cervical parameters, including Bishop score, cervical length, posterior cervical angle, and changes in cervical parameters before and after cervical dilatation. The endpoint was the rate of vaginal delivery.

RESULTS

Of 142 eligible participants, all 24 multiparous women underwent vaginal delivery. Among the nulliparous women (n = 118), the following categories showed significantly higher rates of vaginal delivery: Bishop scores of ≥6 before and after dilatation, compared with Bishop score <6 (adjusted prevalence ratio (aPR) [95% confidence interval (CI)]; 1.58 [1.17-2.13] and 1.56 [1.13-2.14], respectively) and cervical length of <10 and 10-20 mm before dilation, compared with cervical length of >30 mm (aPR [95% CI]; 1.47 [1.00-2.15] and 2.13 [1.42-3.18], respectively). The posterior cervical angle and other background characteristics showed no significant associations. Furthermore, women with cervical lengths of ≥20 mm before and <20 mm after dilatation showed a higher rate of vaginal delivery, compared to cervical length of ≥20 mm even after dilatation (aPR [95% CI]; 1.95 [1.19-3.20]).

CONCLUSIONS

High Bishop score, short cervical length, and changes in cervical length with dilatation are potential independent predictors of vaginal delivery following elective labor induction in nulliparous women at 40-41 gestational weeks.

Deriving a prediction model for emergency cesarean delivery following induction of labor in singleton term pregnancies

OBJECTIVES

To derive a prediction model combining various clinical factors associated with increased risk of emergency cesarean section following induction of labor in women with unfavorable cervix.

METHODS

All women with singleton term pregnancies undergoing induction of labor and fulfilling inclusion criteria were included in this cross-sectional study after supplying consent. Women with a Bishop score of 6 or less were induced with dinoprostone gel. Multiple regression analysis was used to find the most significant independent predictive factors and these factors were used to develop the predictive model and calculator.

RESULTS

After multiple logistic regression, risk of emergency cesarean after induction of labor was significantly associated with the following variables: height (adjusted odds ratio [aOR] 0.955, P = 0.033), nulliparity (aOR 3.987, P < 0.001), closed cervix (aOR 2.030, P = 0.030), fetal station -3 above ischial spine (aOR 2.719, P = 0.043), firm or medium cervical consistency (aOR 2.028, P = 0.004), cervical length 3 cm or longer (aOR 3.090, P = 0.015), posterior cervix (aOR 2.112, P = 0.002).

CONCLUSION

Use of a prediction model would help to reduce the number of emergency cesarean sections secondary to unsuccessful inductions and help in the reduction of maternal and perinatal morbidity.

Prediction of successful labor induction with very unfavorable cervix: A comparison of six scores

OBJECTIVE

To compare the ability of six scoring systems to predict successful labor induction with cervical ripening among women with a Bishop score <3.

METHODS

Secondary analysis of data from a prospective, multicenter observational Methods of Induction of Labor and Perinatal Outcomes (MEDIP) cohort study in 94 obstetrical French units. We included women with a Bishop score <3 before cervical ripening. We compared six scores: Bishop, simplified Bishop, modified Bishop, simplified Bishop including parity, Hughey, and Levine scores. Vaginal delivery defined successful labor induction. The ability of each score to predict successful labor induction was evaluated by comparing their area under the curve (AUC).

RESULTS

Among the 600 eligible women in this study, 408 (68%) delivered vaginally. Body mass index (calculated as weight in kilograms divided by the square of height in meters; mean ± standard deviation [SD]: 24.7 ± 5.5 vs 26.0 ± 5.7; P = 0.01) and nulliparity (48.8% vs 85.4%; P < 0.001) were lower in the successful induction group, whereas height was higher (mean ± SD: 165.3 ± 6.0 cm vs 163.7 ± 6.0 cm; P = 0.002). The simplified Bishop including parity, Hughey, and Levine scores had the highest AUC (0.70, 95% confidence interval [CI] 0.65-0.73; 0.68, 95% CI 0.64-0.74; and 0.69, 95% CI 0.65-0.74, respectively).

CONCLUSION

In women with a very unfavorable cervix, scores that include parity predict successful labor induction more accurately, such as simplified Bishop including parity, Hughey, or Levine scores.

Quality Initiative to Reduce Failed Trial of Labor after Cesarean Using Calculated VBAC Success Likelihood

OBJECTIVE

 Vaginal birth after cesarean can reduce morbidity associated with multiple cesarean deliveries. Failed vaginal birth after cesarean is associated with increased maternal and neonatal morbidity. The Maternal-Fetal Medicine Units Vaginal Birth After Cesarean calculator is a validated tool to predict the likelihood of successful trial of labor after cesarean. Predicted likelihood < 60% has been associated with increased maternal and neonatal morbidity. We sought to determine if formal incorporation of calculated vaginal birth after cesarean likelihood into patient-centered counseling would reduce failed vaginal birth after cesarean.

STUDY DESIGN

 This is a quality improvement intervention at a single tertiary-care academic medical center, in which standardized patient counseling was implemented, facilitated by an electronic medical record template featuring patient-specific likelihood of vaginal birth after cesarean success. Term singleton pregnancies with history of one to two cesareans were included; those with contraindication to labor were excluded. Historical controls (January 2016-December 2018, n = 693) were compared with a postimplementation cohort (January 2019-April 2020, n = 328). Primary outcome was failed vaginal birth after cesarean.

RESULTS

 Fewer patients in the postintervention cohort had a history of an arrest disorder (PRE: 48%, 330/693 vs. POST: 40%, 130/326, p = 0.03); demographics were otherwise similar, including the proportion of patients with <60% likelihood of success (PRE: 39%, 267/693, vs. POST: 38%, 125/326). Following implementation, induction of labor in patients with a <60% likelihood of successful vaginal birth after cesarean decreased from 17% (45/267) to 5% (6/125, p < 0.01). The proportion of failed vaginal birth after cesarean decreased from 33% (107/329) to 22% (32/143, p = 0.04). Overall vaginal birth after cesarean rate did not change (PRE: 32%, 222/693, vs. POST: 34%, 111/326, p = 0.52).

CONCLUSION

 An intervention targeting provider counseling that included a validated vaginal birth after cesarean success likelihood was associated with decreased risk of failed trial of labor after cesarean without affecting overall vaginal birth after cesarean rate.

KEY POINTS

· Labored cesarean increases maternal morbidity.. · Application of the Maternal-Fetal Medicine Units (MFMU) calculator to antenatal counseling decreased labored cesarean.. · Application of the MFMU calculator to antenatal counseling did not decrease overall vaginal birth after cesarean rate..

Defining a Cesarean Delivery Rate for Optimizing Maternal and Neonatal Outcomes

After the global cesarean delivery rate nearly doubled between 2000 and 2015, cesarean deliveries now account for nearly one third of births in the United States. Although rates have plateaued, the high national cesarean delivery rate has garnered criticism from both lay and academic communities because it has not been associated with improvements in maternal or neonatal outcomes. Efforts are underway to lower the cesarean delivery rate through implementation of hospital-level and national guidelines. However, the cesarean delivery rate that optimizes maternal and neonatal outcomes is not known. Defining a cesarean delivery rate that optimizes perinatal outcomes and reduces morbidity seems simple. However, there are a host of challenges to such a task, including determining the outcomes that are most meaningful to use, deciding the population that should define the rate, and incorporating person-centered decision making, given that people place different value on different outcomes. Rather than a "call" for cesarean delivery rate reductions of a specific and arbitrary magnitude, we need further attention to defining an evidence-based optimal target. This commentary summarizes current national and international cesarean delivery rate targets, discusses the challenges of identifying an evidence-based national cesarean delivery rate target, and explores future considerations for best defining a cesarean delivery rate target.

A Model to Predict Vaginal Delivery and Maternal and Neonatal Morbidity in Low-Risk Nulliparous Patients at Term

OBJECTIVE

This study aimed to develop and validate a model to predict the probability of vaginal delivery (VD) in low-risk term nulliparous patients, and to determine whether it can predict the risk of severe maternal and neonatal morbidity.

METHODS

Secondary analysis of an obstetric cohort of patients and their neonates born in 25 hospitals across the United States (n = 115,502). Trained and certified research personnel abstracted the maternal and neonatal records. Nulliparous patients with singleton, nonanomalous vertex fetuses, admitted with an intent for VD ≥ 37 weeks were included in this analysis. Patients in active labor (cervical exam > 5 cm), those with prior cesarean and other comorbidities were excluded. Eligible patients were randomly divided into a training and test sets. Based on the training set, and using factors available at the time of admission for delivery, we developed and validated a logistic regression model to predict the probability of VD, and then estimated the prevalences of severe morbidity according to the predicted probability of VD.

RESULTS

A total of 19,611 patients were included. Based on the training set (n = 9,739), a logistic regression model was developed that included maternal age, body mass index (BMI), cervical dilatation, and gestational age on admission. The model was internally validated on the test set (n = 9,872 patients) and yielded a receiver operating characteristic-area under the curve (ROC-AUC) of 0.71 (95% confidence interval [CI]: 0.70-0.72). Based on a subset of 18,803 patients with calculated predicted probabilities, we demonstrated that the prevalences of severe morbidity decreased as the predicted probability of VD increased (p < 0.01).

CONCLUSION

In a large cohort of low-risk nulliparous patients in early labor or undergoing induction of labor, at term with singleton gestations, we developed and validated a model to calculate the probability of VD, and maternal and neonatal morbidity. If externally validated, this calculator may be clinically useful in helping to direct level of care, staffing, and adjustment for case-mix among various systems.

KEY POINTS

· A model to predict the probability of vaginal delivery in low-risk nulliparous patients at term.. · The model also predicts the risk of severe maternal and neonatal morbidity.. · The prevalences of severe morbidity decrease as the probability of vaginal delivery increases..

Magnetic Resonance Imaging-Based Nomogram to Antenatal Predict Cesarean Delivery for Cephalopelvic Disproportion in Primiparous Women

BACKGROUND

Cephalopelvic disproportion (CPD)-related obstructed labor is associated with maternal and neonatal morbidity and mortality. Accurate prediction of whether a primiparous woman is at high risk of an unplanned cesarean delivery would be a major advance in obstetrics.

PURPOSE

To develop and validate a predictive model assessing the risk of cesarean delivery in primiparous women based on MRI findings.

STUDY TYPE

Prospective.

POPULATION

A total of 150 primiparous women with clinical findings suggestive of CPD.

FIELD STRENGTH/SEQUENCE

T1-weighted fast spin-echo sequences, single-shot fast spin-echo (SSFSE) T2-weighted sequences at 1.5 T.

ASSESSMENT

Pelvimetry and fetal biometry were assessed independently by two radiologists. A nomogram model combined that the clinical and MRI characteristics was constructed.

STATISTICAL TESTS

Univariable and multivariable logistic regression analyses were applied to select independent variables. Receiver operating characteristic (ROC) analysis was performed, and the discrimination of the model was assessed by the area under the curve (AUC). Calibration was assessed by calibration plots. Decision curve analysis was applied to evaluate the net clinical benefit. A P value below 0.05 was considered to be statistically significant.

RESULTS

In multivariable modeling, the maternal body mass index (BMI) before delivery, bilateral femoral head distance, obstetric conjugate, fetal head circumference, and fetal abdominal circumference was significantly associated with the likelihood of cesarean delivery. The discrimination calculated as the AUC was 0.838 (95% confidence interval [CI]: 0.774-0.902). The sensitivity and specificity of the nomogram model were 0.787 and 0.764, and the positive predictive and negative predictive values were 0.696 and 0.840, respectively. The model demonstrated satisfactory calibration (calibration slope = 0.945). Moreover, the decision curve analysis proved the superior net benefit of the model compared with each factor included.

DATA CONCLUSION

Our study might provide a nomogram model that could identify primiparous women at risk of cesarean delivery caused by CPD based on MRI measurements.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Nomogram predicting cesarean delivery undergoing induction of labor among high-risk nulliparous women at term: a retrospective study

Background

Our aim was to create and validate a nomogram predicting cesarean delivery after induction of labor among nulliparous women at term.

Methods

Data were obtained from medical records from Nanjing Drum Tower Hospital. Nulliparous women with singleton pregnancies undergoing induction of labor at term were involved. A total of 2950 patients from Jan. 2014 to Dec. 2015 were served as derivation cohort. A nomogram was constructed by multivariate logistic regression using maternal, fetal and pregnancy characteristics. The predictive accuracy and discriminative ability of the nomogram were internal validated by 1000-bootstrap resampling, followed by external validation of a new dataset from Jan. 2016 to Dec. 2016.

Results

Logistic regression revealed nine predictors of cesarean delivery, including maternal height, age, uterine height, abdominal circumference, estimated fetal weight, indications for induction of labor, initial cervical consistency, cervical effacement and station. Nomogram was well calibrated and had an AUC of 0.73 (95% confidence interval [CI], 0.70-0.75) after bootstrap resampling for internal validation. The AUC in external validation reached 0.67, which was significantly higher than that of three models published previously (P<0.05).

Conclusions

This validated nomogram, constructed by variables that were obtained form medical records, can help estimate risk of cesarean delivery before induction of labor.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12884-022-04386-8.

Association between plasma leptin and cesarean section after induction of labor: a case control study

Background

Obesity in pregnancy is common, with more than 50% of pregnant women being overweight or obese. Obesity has been identified as an independent predictor of dysfunctional labor and is associated with increased risk of failed induction of labor resulting in cesarean section. Leptin, an adipokine, is secreted from adipose tissue under the control of the obesity gene. Concentrations of leptin increase with increasing percent body fat due to elevated leptin production from the adipose tissue of obese individuals. Interestingly, the placenta is also a major source of leptin production during pregnancy. Leptin has regulatory effects on neuronal tissue, vascular smooth muscle, and nonvascular smooth muscle systems. It has also been demonstrated that leptin has an inhibitory effect on myometrial contractility with both intensity and frequency of contractions decreased. These findings suggest that leptin may play an important role in dysfunctional labor and be associated with the outcome of induction of labor at term. Our aim is to determine whether maternal plasma leptin concentration is indicative of the outcome of induction of labor at term. We hypothesize that elevated maternal plasma leptin levels are associated with a failed term induction of labor resulting in a cesarean delivery.

Methods

In this case-control study, leptin was measured in 3rd trimester plasma samples. To analyze labor outcomes, 174 women were selected based on having undergone an induction of labor (IOL), (115 women with successful IOL and 59 women with a failed IOL). Plasma samples and clinical information were obtained from the UI Maternal Fetal Tissue Bank (IRB# 200910784). Maternal plasma leptin and total protein concentrations were measured using commercially available assays. Bivariate analyses and logistic regression models were constructed using regression identified clinically significant confounding variables. All variables were tested at significance level of 0.05.

Results

Women with failed IOL had higher maternal plasma leptin values (0.5 vs 0.3 pg, P = 0.01). These women were more likely to have obesity (mean BMI 32 vs 27 kg/m², P = 0.0002) as well as require multiple induction methods (93% vs 73%, p = 0.008). Logistic regression showed Bishop score (OR 1.5, p < 0.001), BMI (OR 0.92, P < 0.001), preeclampsia (OR 0.12, P = 0.010), use of multiple methods of induction (OR 0.22, P = 0.008) and leptin (OR 0.42, P = 0.017) were significantly associated with IOL outcome. Specifically, after controlling for BMI, Bishop Score, and preeclampsia, leptin was still predictive of a failed IOL with an odds ratio of 0.47 (P = 0.046). Finally, using leptin as a predictor for fetal outcomes, leptin was also associated with of fetal intolerance of labor, with an odds ratio of 2.3 (P = 0.027). This association remained but failed to meet statistical significance when controlling for successful (IOL) (OR 1.5, P = 0.50).

Conclusions

Maternal plasma leptin may be a useful tool for determining which women are likely to have a failed induction of labor and for counseling women about undertaking an induction of labor versus proceeding with cesarean delivery.

Maternal and fetal characteristics for predicting risk of Cesarean section following induction of labor: pooled analysis of PROBAAT trials

OBJECTIVE

Induction of labor (IOL) is one of the most widely used obstetric interventions. However, one-fifth of IOLs result in Cesarean section (CS). We aimed to assess maternal and fetal characteristics that influence the likelihood of CS following IOL, according to the indication for CS.

METHODS

This was a secondary analysis of pooled data from four randomized controlled trials, including women undergoing IOL at term who had a singleton pregnancy and an unfavorable cervix, intact membranes and the fetus in cephalic presentation. The main outcomes of this analysis were CS for failure to progress (FTP) and CS for suspected fetal compromise (SFC). Restricted cubic splines were used to determine whether continuous maternal and fetal characteristics had a non-linear relationship with outcome. Optimal cut-offs for those characteristics with a non-linear pattern were determined based on the maximum area under the receiver-operating-characteristics curve. Adjusted odds ratios (aOR) were computed, using multivariable logistic regression analysis, for the associations between optimally categorized characteristics and outcome.

RESULTS

Of a total of 2990 women undergoing IOL, 313 (10.5%) had CS for FTP and 227 (7.6%) had CS for SFC. The risk of CS for FTP was increased in women aged 31-35 years compared with younger women (aOR, 1.51 (95% CI, 1.15-1.99)), in nulliparous compared with parous women (aOR, 8.07 (95% CI, 5.34-12.18)) and in Sub-Saharan African compared with Caucasian women (aOR, 2.09 (95% CI, 1.33-3.28)). Higher body mass index (BMI) increased incrementally the risk of CS for FTP (aOR, 1.06 (95% CI, 1.04-1.08)). High birth-weight percentile was also associated with an increased risk of CS due to FTP (aOR, 2.66 (95% CI, 1.74-4.07) for birth weight between the 80.0^th and 89.9^th percentiles and aOR, 4.08 (95% CI, 2.75-6.05) for birth weight ≥ 90^th percentile, as compared with birth weight between the 20.0^th and 49.9^th percentiles). For CS due to SFC, higher maternal age (aOR, 1.09 (95% CI, 1.05-1.12)) and BMI (aOR, 1.05 (95% CI, 1.03-1.08)) were associated with an incremental increase in risk. The risk of CS for SFC was increased in nulliparous compared with parous women (aOR, 5.91 (95% CI, 3.76-9.28)) and in South Asian compared with Caucasian women (aOR, 2.50 (95% CI, 1.23-5.10)). Birth weight < 10.0^th percentile increased significantly the risk of CS due to SFC (aOR, 1.93 (95% CI, 1.22-3.05)), as compared with birth weight between the 20.0^th and 49.9^th percentiles. Bishop score did not demonstrate a significant association with the risk of CS for FTP or for SFC.

CONCLUSIONS

In women undergoing IOL, maternal age, BMI, parity, ethnicity and birth-weight percentile are predictors of CS due to FTP and of CS due to SFC, but the direction and magnitude of the associations differ according to the indication for CS. These characteristics should be considered in combination with the Bishop score to stratify the risk of CS for different indications in women undergoing IOL. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

The use of ultrasound, fibronectin and other parameters to predict the success of labour induction

Induction of labour is a common obstetrical procedure and is undertaken when the benefits of delivery are considered to outweigh the risks of continuation of pregnancy. However, more than one-fifth of induction cases fail to result in vaginal births and lead to unplanned caesarean deliveries, which compromise the birth experience and have negative clinical and resource implications. The need for accurate prediction of successful labour induction is increasingly recognised and many researchers have attempted to evaluate the potential predictability of different factors including maternal characteristics, Bishop score, various biochemical markers and ultrasound markers and derive predictive models to address this issue.

A Peripheral Immune Signature of Labor Induction

Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8⁺ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.

Risk of caesarean delivery in labour induction: a systematic review and external validation of predictive models

BACKGROUND

Despite the existence of numerous published models predicting the risk of caesarean delivery in women undergoing induction of labour (IOL), validated models are scarce.

OBJECTIVES

To systematically review and externally assess the predictive capacity of caesarean delivery risk models in women undergoing IOL.

SEARCH STRATEGY

Studies published up to 15 January 2021 were identified through PubMed, CINAHL, Scopus and ClinicalTrials.gov, without temporal or language restrictions.

SELECTION CRITERIA

Studies describing the derivation of new models for predicting the risk of caesarean delivery in labour induction.

DATA COLLECTION AND ANALYSIS

Three authors independently screened the articles and assessed the risk of bias (ROB) according to the prediction model risk of bias assessment tool (PROBAST). External validation was performed in a prospective cohort of 468 pregnancies undergoing IOL from February 2019 to August 2020. The predictive capacity of the models was assessed by creating areas under the receiver operating characteristic curve (AUCs), calibration plots and decision curve analysis (DCA).

MAIN RESULTS

Fifteen studies met the eligibility criteria; 12 predictive models were validated. The quality of most of the included studies was not adequate. The AUC of the models varied from 0.520 to 0.773. The three models with the best discriminative capacity were those of Levine et al. (AUC 0.773, 95% CI 0.720-0.827), Hernández et al. (AUC 0.762, 95% CI 0.715-0.809) and Rossi et al. (AUC 0.752, 95% CI 0.707-0.797).

CONCLUSIONS

Predictive capacity and methodological quality were limited; therefore, we cannot currently recommend the use of any of the models for decision making in clinical practice.

TWEETABLE ABSTRACT

Predictive models that predict the risk of cesarean section in labor inductions are currently not applicable.

The effects of smoking on pregnancy risks in women with polycystic ovary syndrome: a population-based study

STUDY QUESTION

Is there is an association between smoking and pregnancy complications in pregnant women with polycystic ovarian syndrome (PCOS)?

SUMMARY ANSWER

There is an increased risk of developing gestational diabetes mellitus (GDM) among women with PCOS who smoke.

WHAT IS KNOWN ALREADY

Smokers are at increased risk of developing Type 2 Diabetes Mellitus (DM). Given the common pathophysiology and shared risk factors between type 2 DM and GDM, we sought to assess whether an association between smoking and the development of GDM exists.

STUDY DESIGN, SIZE, DURATION

This is a retrospective population-based study utilizing data from the HCUP-NIS over 11 years from 2004 to 2014. Pregnant women with PCOS who did smoke were compared to pregnant women with PCOS who did not smoke. A second comparison was made between pregnant smokers with and without PCOS. Of the 443 590 women who smoked during pregnancy and the 14 882 women with PCOS, 631 women were both smokers and diagnosed with PCOS.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The Healthcare Cost and Utilization Project-Nationwide Inpatient Sample (HCUP-NIS) is the largest inpatient sample database in the USA and is composed of hospital inpatient stays submitted by hospitals throughout the entire country. Each year, the database provides information relating to 7 million inpatient stays, including patient characteristics, diagnosis and procedures. The data are representative of ∼20% of admissions to US hospitals across 48 states and the District of Columbia.

MAIN RESULTS AND THE ROLE OF CHANCE

There were no differences in the risks of preterm delivery (aOR1.2; CI 0.8-1.9), placental abruption (aOR1.1; CI 0.4-3.2), pregnancy induced hypertension (aOR1.0; CI 0.7-1.5), rate of operative vaginal delivery (aOR1.5; CI 0.9-2.5) and rates of cesarean section (C/S) (aOR1.0; CI 0.7-1.3) between smoking and non-smoking women with PCOS. A significant association between smoking and GDM was observed in women with PCOS (aOR1.5; CI1.01-2.1).

LIMITATIONS, REASONS FOR CAUTION

The limitations of our study are its retrospective nature and the fact that it relies on an administrative database. Data regarding smoking and PCOS diagnosis could be skewed due to patients' underreporting, lack of documentation and documentation differences.

WIDER IMPLICATIONS OF THE FINDINGS

The public health implications of confirming smoking as a risk for GDM among women with PCOS are many. This can lead to earlier screening in pregnancy of smokers for GDM. Earlier initiation of interventions could decrease fetal complications and possibly have an impact on the life and long-term health of the offspring. Future studies are needed in order to assess whether smoking cessation during pregnancy decreases the risk of GDM in that gestation.

STUDY FUNDING/COMPETING INTEREST(S)

No external funding was used. The authors report no competing interests.

TRIAL REGISTRATION NUMBER

N/A.

Reducing the cesarean delivery rate

BACKGROUND

The cesarean delivery rate has been rising in recent years, having associated maternal morbidities. Elective induction of labor has also been seen to rise during this same time period.

OBJECTIVE

This current study investigated the difference in the cesarean delivery rate between induction of labor and spontaneous labor among nulliparous, term, singleton, and vertex-presenting women.

STUDY DESIGN

A retrospective cohort in a single institution over a seven-year period was used for this analysis, observing the difference in cesarean delivery rate at different term gestational ages and neonatal morbidity using the 5-minute Apgar score < 5.

RESULTS

A statistically significant difference was found in cesarean delivery rate between those women whose labor was induced and those whose labor began spontaneously, at each term gestational age of labor initiation (P < 0.001). The proportion of indications for induction was described (i.e. elective vs. medically-indicated), and no difference was found for neonatal morbidity between the groups analyzed, using the 5-minute Apgar score as the perinatal outcome measure.

CONCLUSION

A comparison was made between spontaneous and induced labor regarding the resultant cesarean delivery rate, and a significant difference was found favoring spontaneous labor. This should be considered when electing to deliver using an induction methodology for nulliparous women, especially when there are no medical indications for it.

Implementation of a calculator to predict cesarean delivery during labor induction: a qualitative evaluation of the clinician perspective

BACKGROUND

We previously conducted a prospective cohort study (n=1610) demonstrating that the implementation of a validated calculator to predict likelihood of cesarean delivery during labor induction was associated with reduced maternal morbidity, reduced cesarean delivery rate, and improved birth satisfaction.

OBJECTIVE

To optimize future implementation, we used qualitative interviews to understand the clinician perspective on: (1) the cesarean delivery risk calculator implementation and (2) the mechanisms by which the use of the calculator resulted in the observed improved outcomes.

STUDY DESIGN

After completion of the prospective study (June 30, 2019), 20 trainees and attending clinicians (including nurse-midwives, obstetrical physicians, and family medicine physicians) at the study site participated in a single, brief semistructured interview from March 1, 2020, to June 30, 2020. Transcriptions were coded using a systematic approach.

RESULTS

Overall, clinicians had favorable perspectives regarding the cesarean delivery risk calculator. Clinicians described the calculator as offering "objective data" and a "standardized snapshot of the labor trajectory." Concerns were raised regarding "overreliance" on calculator output. Barriers to use included time for patient counseling and "awkwardness" around the interactions and perceived patient misunderstanding of the calculator result. Although most senior clinicians (n=8) reported that the calculator did not impact patient management, trainee clinicians (n=12) more often felt that the calculator influenced care at the extremes of cesarean delivery risk. Furthermore, more senior clinicians felt "neutral" regarding any impact of counseling patients on cesarean delivery risk compared with trainee clinicians, who felt that the counseling "built [patient-clinician] trust."

CONCLUSION

This qualitative evaluation characterized the generally positive clinician perspective around the cesarean delivery risk calculator, while identifying specific facilitators and barriers to implementation. In addition, we elucidated potential mechanisms by which the calculator may have been related to clinician decision making and patient-clinician interactions, leading to reduced maternal morbidity and improved patient birth satisfaction. This information is important as widespread implementation of the cesarean delivery risk calculator begins.

Maternal and fetal characteristics to predict c-section delivery: A scoring system for pregnant women

INTRODUCTION

Cesarean section is one of the most common obstetrical interventions that has been performed at an increasing rate globally, due to both medical and non-medical reasons. This study aims to develop a prediction tool for pregnant women potentially needing c-section, such that necessary preparations from the mothers, families, and health providers can be made.

METHODS

A total of 603 pregnant women were recruited in the first phase of c-section prediction tool development. The association between the maternal and fetal factors on the risk of c-section were analyzed, followed by a stepwise multivariate regression analysis. In the next phase, 61 pregnant women were enrolled for external validation. Discrimination was assessed using area under the curve. The calibration plot was then made and assessed using the Hosmer-Lemeshow test.

RESULTS

There were 251 (41.6%) cases of vaginal delivery and 352 (58.4%) of c-section assessed. Multivariate analysis showed that gestational age < 37 wg (OR: 1.66, 95% CI: 1.10-2.51), pre-pregnancy body mass index (underweight) (OR: 0.40, 95% CI: 0.22-0.76), no history of vaginal delivery (OR: 2.66, 95% CI: 1.76-4.02), history of uterine surgery (OR: 8.34, 95% CI: 4.54-15.30), obstetrical complications (OR: 5.61, 95% CI: 3.53-8.90), birthweight ⩾ 3500 g (OR: 4.28, 95% CI: 2.16-8.47), and non-cephalic presentation (OR: 2.74, 95% CI: 1.53-4.89) were independently associated with c-section delivery. Those parameters were included in a 7-item scoring tool, with consecutive predictive scores of 1,-1,2,3,3,2,2,1. The area under the curve result was 0.813 (95% CI: 0.779-0.847), indicating a good predictive ability. The external validation showed AUC: 0.806, 95% CI: 0.694-0.917, Hosmer-Lemeshow test p = 0.666 and calibration plot coefficient of r = 0.939.

CONCLUSION

A total of 7 maternal-fetal factors were found to be strongly associated with c-section delivery, including gestational age < 37, maternal underweight body mass index, previous uterine surgery, obstetrical complications, birthweight ⩾ 3500, history of vaginal delivery, and non-cephalic presentation. Using these factors, a prediction tool was developed and validated with good quality.

Maternal Morbidity and Birth Satisfaction After Implementation of a Validated Calculator to Predict Cesarean Delivery During Labor Induction

IMPORTANCE

A previously created and validated calculator provides an individualized cesarean delivery risk score for women undergoing labor induction. A higher predicted risk of cesarean delivery on the calculator has been associated with increased maternal and neonatal morbidity regardless of ultimate delivery mode. The effect of this calculator when implemented in clinical care has yet to be evaluated.

OBJECTIVE

To determine whether implementation of a validated calculator that predicts the likelihood of cesarean delivery at the time of labor induction is associated with maternal morbidity and birth satisfaction.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study used medical record review to compare the 1 year before calculator implementation (July 1, 2017, to June 30, 2018) with the 1 year after implementation (July 1, 2018, to June 30, 2019) at a US urban, university labor unit. Women admitted for labor induction with singleton gestation in cephalic presentation, intact membranes, and an unfavorable cervix were included. Data were analyzed from August 1, 2019, to September 13, 2020.

EXPOSURES

Patient and clinician knowledge of the calculated cesarean delivery risk score based on the validated calculator.

MAIN OUTCOMES AND MEASURES

The primary outcomes were (1) composite maternal morbidity defined by at least 1 of the following within 30 days of delivery: endometritis, postpartum hemorrhage (estimated or quantitative blood loss >1000 mL), blood transfusion, wound infection, venous thromboembolism, hysterectomy, intensive care unit admission, and readmission and (2) patient satisfaction assessed via Birth Satisfaction Scale-Revised (BSS-R) scores. Secondary outcomes included rate of cesarean delivery and neonatal morbidity.

RESULTS

A total of 1610 women were included in the analysis (788 in the preimplementation and 822 in the postimplementation periods) with a median age of 29 (interquartile range [IQR], 24-34) years. There were no significant baseline differences between groups except fewer inductions at a gestational age of 40 weeks or later in the postimplementation period (256 [31.1%] vs 298 [37.8%]). Calculator implementation was associated with decreased maternal morbidity overall, even when adjusting for confounders (141 [17.9%] vs 95 [11.6%]; adjusted absolute risk difference [aARD], -6.3%; 95% CI, -9.7% to -2.8%). Although there was no difference in birth satisfaction overall, calculator implementation was associated with improvements on items pertaining to quality of care provision (median BSS-R score, 19 [IQR, 16-20] vs 19 [IQR, 17-20]; P = .006). Calculator implementation was also associated with a decrease in cesarean delivery rate (228 [28.9%] vs 167 [20.3%]; aARD, -8.5% [95% CI, -12.6% to -4.5%]). There were no significant differences in neonatal morbidity.

CONCLUSIONS AND RELEVANCE

These findings suggest that implementation of a validated calculator to predict risk of cesarean delivery in clinical care is associated with reduced maternal morbidity. Implementation should occur broadly to determine whether calculator use improves national maternal outcomes.

Evidence-based labor management: induction of labor (part 2)

Induction of labor is indicated for many obstetrical, maternal, and fetal indications. Induction can be offered for pregnancy at 39 weeks' gestation. No prediction method is considered sensitive or specific enough to determine the incidence of cesarean delivery after induction. A combination of 60- to 80-mL single-balloon Foley catheter for 12 hours and either 25-μg oral misoprostol initially, followed by 25 μg every 2-4 hours, or 50 μg every 4-6 hours (if no more than 3 contractions per 10 minutes or previous uterine surgery), or oxytocin infusion should be recommended for induction of labor. Adding membrane stripping at the beginning of induction should be considered. Once 5-6 cm of cervical dilation is achieved during the induction of labor, consideration can be given to discontinue oxytocin infusion if in use at that time and adequate contractions are present. Induction with oxytocin immediately (as soon as feasible) or up to 12 hours of term prelabor rupture of membranes if labor is not evident is recommended. Outpatient Foley ripening can be considered for low-risk women. Cesarean delivery should not be performed before 15 hours of oxytocin infusion and amniotomy if feasible and ideally after 18-24 hours of oxytocin infusion.

Evaluation of shorter mifepristone to misoprostol intervals for second trimester medical abortion: a retrospective cohort study

OBJECTIVES

To assess shorter mifepristone-misoprostol intervals compared to current guidelines for second trimester medical abortion on total abortion time (mifepristone to fetal expulsion) and induction time (first misoprostol to fetal expulsion).

METHODS

This retrospective cohort study included women who elected for a second trimester medical abortion with mifepristone and misoprostol at an academic tertiary medical center in the United States from January 2008 to June 2018. We abstracted times of mifepristone administration, first dose of misoprostol, and fetal expulsion from the medical record. We assessed outcomes based on the shorter intervals <12 h and 12 to 24 h compared to the guideline mifepristone-misoprostol interval (24-48 h).

RESULTS

The study population included eighty-nine women, 47, 28, and 14 women in the <12 h, 12 24 h, guideline (24-48 h) groups, respectively. The cohort had a median gestational age of 22^0/7 weeks (range: 15^0/7-27^0/7) and parity of 1 (range: 0-5) with no differences observed between groups. Total abortion times were 20.7 h (range: 3.7-46.9), 30.6 h (16.7-48.0), and 42.8 h (32.7-62.6), respectively (p < 0.001). Induction times were 12.9 h (range: 1.2-36.6), 11.7 h (2.0-35.2), and 9.3 h (5.3-16.5), respectively. Fetal expulsion within 12 h of first misoprostol dose occurred in 22 (47%), 14 (50%), and 9 (64%), respectively (p = 0.52).

CONCLUSIONS

Shorter mifepristone-misoprostol intervals (less than 24 h) significantly decrease the total abortion time while maintaining a clinically similar induction time.

IMPLICATIONS

Shortening the mifepristone-misoprostol interval in second trimester medical abortion significantly decreases the total abortion time which may be preferable to some women or health systems.