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

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%.

Prediction of Failure to Progress after Labor Induction: A Multivariable Model Using Pelvic Ultrasound and Clinical Data

Objective: 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. Material and Methods: 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). Results: 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 (p < 0.05), parity (p < 0.0001), fetal gender (p < 0.05), EFW centile (p < 0.01), cervical length (p < 0.01), and posterior occiput position (p < 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, p < 0.0001) with detection rates of 24% and 37% for FPRs of 5% and 10%. Conclusions: A simplified clinical and sonographic model may guide the management of pregnancies undergoing labor induction, favoring individualized patient management.

Translation and validation of the Hungarian version of the Birth Satisfaction Scale-Revised (BSS-R)

BACKGROUND

Evidence relating maternal birth experience to a range of maternal and neonatal outcomes is increasingly compelling. Consequently valid and reliable self-report of birth experience from the mothers perspective is critical.

AIM

The current study sought to translate and validate a Hungarian-language version of the Birth Satisfaction Scale-Revised (BSS-R).

METHOD

Following forward and backwards translation into Hungarian, the Hungarian BSS-R (HU-BSS-R) was administered to women in a major Transylvanian hospital maternity unit within 72 h postpartum. Key psychometric characteristics were then examined in relation to factor structure, divergent and convergent validity, internal consistency, and known-groups discriminant validity.

RESULTS

Two-hundred and thirty-two women completed the HU-BSS-R. Confirmatory factor analysis revealed the HU-BSS-R to offer an excellent fit to data for the established tri-dimensional measurement model. The HU-BSS-R was also found to offer excellent convergent and divergent validity and known-groups discriminant validity. No significant differences were observed between internal consistency observations between the current study and the original UK validation study.

CONCLUSIONS

The HU-BSS-R is a valid and reliable translation of the original BSS-R, it has proved itself to have excellent psychometric properties and is suitable for use in the Hungarian maternity context.

The Relationship between Gestational Weight Gain and Cesarean Delivery among Patients undergoing Induction of Labor

OBJECTIVE

This study aimed to determine the impact of pregnancy weight gain above National Academy of Medicine (NAM) guidelines on cesarean delivery rates following induction.

STUDY DESIGN

This is a secondary analysis of a prospective cohort study of patients who underwent induction at a single tertiary care center between July 2017 and July 2019. Included in the primary study were patients undergoing term (≥37 weeks) labor induction with a singleton gestation, intact membranes, and unfavorable cervical examination (Bishop score of ≤6 and cervical dilation ≤ 2 cm). Eligibility for this analysis was limited to patients with a documented prenatal body mass index (BMI) < 20 weeks' gestation. The primary exposure was gestational weight gain greater than NAM guidelines (WGGG) for a patient's initial BMI category. The primary outcome was cesarean delivery for any indication.

RESULTS

Of 1,610 patients included in the original cohort, 1,174 (72.9%) met inclusion criteria for this analysis and 517 (44.0%) of these had weight gain above NAM guidelines. Of the entire cohort, 60.0% were Black and 52.7% had private insurance. In total, 160 patients (31%) with WGGG underwent cesarean compared with 127 patients (19.3%) without WGGG (p < 0.001), which equates to a 59% increased odds of cesarean when controlling for initial BMI category, parity, gestational diabetes, and indication for induction (Adjusted Odds Ratio [aOR] 1.58, 95% confidence interval [CI] 1.17-2.12). Among only nulliparous patients, WGGG was associated with an increased odds of cesarean (26.4 vs. 38.2%, aOR 1.50, 95% CI 1.07-2.10). In multiparous patients, however, there was no difference in cesarean between those with and without WGGG (8.8 vs. 14.1%, aOR 1.85, 95% CI 0.96-3.58).

CONCLUSION

This study demonstrates that weight gain above NAM guidelines is associated with more than a 50% increased odds of cesarean. Patients should be informed of this association as gestational weight gain may be a modifiable risk factor for cesarean delivery.

KEY POINTS

· Weight gain above NAM guidelines was associated with a 59% increased odds of cesarean delivery.. · Nulliparous patients with weight gain above NAM guidelines are at higher risk of cesarean delivery.. · No difference in cesarean delivery among multiparous patients regardless of gestational weight gain..

Validated Calculators Predicting Cesarean Delivery After Induction: Accuracy in an External Population

OBJECTIVE

To evaluate the performance of two previously published calculators in predicting cesarean delivery after induction of labor in an external population.

METHODS

This was a cohort study including all nulliparous pregnant patients with singleton, term, vertex fetuses; intact membranes; and unfavorable cervices who underwent induction of labor between 2015 and 2017 at an academic tertiary care institution. Individual predicted cesarean risk scores were calculated with two previously published calculators. For each calculator, patients were stratified into three risk groups (lower, middle, and upper thirds) of approximately equivalent size. Predicted and observed incidences of cesarean delivery were compared with two-tailed binomial tests of probability in the overall population and in each risk group.

RESULTS

A total of 846 patients met inclusion criteria, and 262 (31.0%) had cesarean deliveries, which was significantly lower than overall predicted rates of 40.0% and 36.2% with the two calculators (both P<.01). Both calculators significantly overestimated risk of cesarean delivery in higher risk tertiles (all P<.05). The areas under the receiver operating characteristic for both calculators were 0.57 or less in the overall population and in each risk group, suggesting poor predictive value. Higher predicted risk tertile in both calculators was not associated with any maternal or neonatal outcomes except wound infection.

CONCLUSION

Both previously published calculators had poor performance in this population, with neither calculator accurately predicting the incidence of cesarean delivery. Patients and health care professionals might be discouraged regarding trial of labor induction by falsely high predicted risk-of-cesarean scores. We caution against widespread implementation of these calculators without further population-specific refinement and adjustment.

Prediction of successful labor induction in persons with a low Bishop score using machine learning: Secondary analysis of two randomized controlled trials

BACKGROUND

The objective of this paper was to identify predictors of a vaginal birth in individuals with singleton pregnancies and a Bishop Score <4, following Induction of Labor (IoL) using dinoprostone vaginal insert (DVI). Secondarily, we sought to understand the association between oxytocin use for labor augmentation and IoL outcomes.

METHODS

We developed and internally validated a multivariate prediction model using machine learning (ML) applied to data from two Phase-III randomized controlled double-blind trials (NCT01127581, NCT00308711). The model was internally validated using 10-fold cross-validation.

RESULTS

This study included 1107 participants. Despite unfavorable cervical status and inclusion of high-risk pregnancies, 72% of participants had vaginal births. The model's area under receiver operating characteristic curve was 0.73. The following factors increased the chance of vaginal birth: being parous; being between 37 and 41 weeks of gestation; having a lower Body Mass Index; having a lower maternal age; having fewer maternal comorbidities; and having a higher Bishop score. Parity alone correctly predicted the outcome in ~50% of cases, at a ~10% false-negative rate. Participants whose labors progressed without requiring oxytocin had a higher probability of vaginal birth than those requiring oxytocin for either induction or augmentation (81% vs 70% vs 77%, respectively).

DISCUSSION

Even in high-risk pregnancies and with low Bishop scores, the use of DVI results in a high chance of vaginal birth. Parity is a critical predictor of success. The judicious use of oxytocin for labor induction or augmentation can increase the chance of vaginal birth. Our study validates the use of ML and predictive modeling for treatment response prediction when considering IoL.

Development and Validation of a Nomogram to Estimate Risk of Cesarean After Induction of Labor in Term Pregnancies with an Unfavorable Cervix in Iran

This study aimed to develop and validate a labor induction nomogram for nulliparous and multiparous women who were 38 weeks pregnant or more and had their labor induced by an unfavorable cervix. This prospective study was conducted on 300 individuals (200 for nomogram development and 100 for nomogram validation). Height, body mass index at delivery, parity, gestational age, adjusted bishop score, and cesarean section risk assessment were all recorded on a checklist. Participants were followed until they gave birth, and the type of delivery was noted in the checklist. Out of 300 labor inductions, 80 (26.7%) underwent a cesarean section. Cesarean risk estimation was the only predictor of delivery type based on multivariate logistic regression. The AUC (Area Under the Curve) in development group was 0.68 and in validation group was 0.71. The developed nomogram for predicting of cesarean section risk following labor induction has a relatively good predictive value among women.

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.

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.

Daily weekday audit and feedback to clinicians for an inpatient intervention in obstetrics: is there sustained impact over the weekend? A secondary analysis of a prospective cohort study

Background

Audit and feedback as an implementation strategy leads to small, but potentially important improvements in practice. Yet, audit and feedback is time and personnel intensive. Many interventions designed for inpatient care are meant to be utilized by care teams all days of the week, including weekends when research staff are at a minimum. We aimed to determine if audit and feedback regarding use of an evidence-based inpatient obstetric intervention performed only on weekdays could have a sustained impact over the weekend.

Methods

This study was performed as a secondary analysis of a prospective cohort study examining the impact of implementation of a validated calculator that predicts the likelihood of cesarean delivery during labor induction. During the 1 year postimplementation period, Monday through Friday, a member of the study team contacted clinicians daily to provide verbal feedback. While the same clinician pool worked weekend shifts, audit and feedback did not occur on Saturdays or Sundays. The primary outcome was intervention use, defined as documentation of counseling around the cesarean risk calculator result, in the electronic health record. Intervention use was compared between those with (weekdays) and without (weekends) audit and feedback.

Results

Of the 822 women meeting eligibility criteria during the postimplementation period (July 1, 2018–June 30, 2019), 651 (79.2%) were admitted on weekdays when audit and feedback was occurring and 171 (20.8%) on weekends without audit and feedback. The use of the cesarean risk calculator was recorded in 676 of 822 (82.2%) of eligible patient charts. There was no significant difference in cesarean risk calculator use overall by days when audit and feedback occurred versus days without audit and feedback (weekday admissions 82.0% vs. weekend admissions 83.0%, aOR 0.90 95% CI [0.57–1.40], p = 0.76). There was no significant trend in the relationship between calculator use and weekday versus weekend admission by month across the study period (p = 0.21).

Conclusions

Daily weekday audit and feedback for implementation of an evidence-based inpatient obstetric intervention had sustained impact over the weekends. This finding may have implications for both research staffing, as well as sustainability efforts. Further research should determine the lowest effective frequency of audit and feedback to produce implementation success.

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.