Multivariate Analysis of Determinants and Development of a Predictive Algorithm for Successful Labor Induction in Nulliparous Women

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Methods This retrospective cohort analysis included 1,000 term pregnant nulliparas who underwent labor induction in the Ultrasound Department of the National Traditional Chinese Medicine Hospital in Xiangxi Tujia and Miao Autonomous Prefecture between January 2017 and December 2024. We conducted univariate and multivariate logistic regression analyses after comparing various clinical parameters. The outcome variable was defined as the binary success of labor induction, while potential predictors included maternal demographics, obstetric characteristics, cervical conditions (e.g., Bishop score, endocervical impedance [ECI], internal os [IOS], external os [EOS]), duration of oxytocin administration, and presence of a nuchal cord. The model's performance was assessed via receiver operating characteristic (ROC) curve analysis, the concordance index (C-index), a calibration plot, and decision curve analysis (DCA). Results Labor induction was successful in 715 patients (71.5%) and failed in 285 patients (28.5%). Significant differences were observed in the Bishop score and cervical length (CL) between the two groups (P < 0.05). Notably, the ECI, IOS, and EOS were markedly lower in the successful induction group than in the failure group (P < 0.05), whereas heart rate was significantly elevated (P < 0.05). Stepwise selection identified the CL, Bishop score, ECI, and hardness ratio (HR) as significant predictors, which were incorporated into a final logistic regression model. The area under the ROC curve was 0.79 (95% CI: 0.71–0.85), with a corrected C-index of 0.753, indicating satisfactory discrimination and calibration. Conclusion Through comprehensive evaluation, we identified cervical length, the Bishop score, the ECI, and HR as pivotal determinants of successful labor induction. A nomogram incorporating these four factors was constructed to predict the likelihood of successful induction in term singleton nulliparous women. This visual clinical instrument serves as an adjunctive tool for guiding personalized induction strategies based on individual risk profiles. Labor induction electronic cervical elastography term singleton nulliparous women cervical length Bishop score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Induction of labor during pregnancy at term initiates the labor process through drugs to achieve delivery before natural labor. Labor induction is particularly common in maternity centers for critically ill pregnant women, accounting for more than 20% of all deliveries, and can significantly reduce the risk of continuing pregnancy [ 1 , 2 ]. The application of labor induction in obstetrics has been gradually expanded, especially for full-term singleton cephalic primiparas, but how to improve the effectiveness and efficiency of the induction of labor is an urgent problem to be solved [ 3 , 4 ]. Relevant studies have shown that cervical maturity can affect the success or failure of labor induction[ 5 , 6 ]. Therefore, a reliable and repeatable assessment method of cervical maturity is needed before the induction of labor, which offers useful counseling guidance for women who are considering the induction of labor. E-cervix elastography is a diagnostic technique that assesses the softness of tissues by measuring pressure changes caused by intrinsic branching arteriolar pulsations of the tissue[ 7 ]. Previous studies have demonstrated good reproducibility of e-cervical elastography in different pregnancy cycles, as well as its potential for clinical use[ 8 ]. In recent years, a number of studies have shown that E-cervix elastography technology has more advantages than traditional methods in assessing cervical insufficiency[ 9 ]. In this context, this study collected and analyzed the cervical elasticity parameters and clinical indicators of the cervix to study the factors related to the cervix in full-term singleton cephalic primiparas; establish a prediction model for the labor induction success of full-term singleton cephalic primiparas; and personalize the clinical management of the induction of labor, adequate trial delivery, and reduction of primary cesarean section. Materials and methods Subjects A retrospective cohort analysis was conducted on a total of 1000 cases of first full-term, singleton, cephalic primiparas who underwent induction of labor by oxytocin in the obstetrics department of our hospital from January 2017 to December 2024. The samples were obtained from nulliparous women with full-term pregnancies who met the criteria for labor induction at the Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture. They were divided into a successful induction group and a failed induction group. (Figures 1-2). Inclusion criteria Gestational age ≥37 weeks; ② Singleton, cephalic primiparas; ③ Vaginal trial conditions; ④ Intact fetal membranes; and ⑤ Complete medical records. Exclusion criteria The exclusion criteria were as follows: ① gestational age < 37 weeks; ② gestational age ≥ 42 weeks; ③ twin or multiple pregnancies; ④ scarred uterus; ⑤ cephalopelvic disproportion; ⑥ placenta previa or placental abruption; ⑦ acute inflammation of the reproductive tract; and ⑧ maternal or fetal diseases requiring immediate delivery. Case data collection The clinical data of the pregnant women were collected, including age, height, prenatal weight, gestational age, uterine height, abdominal circumference, premature rupture of membranes, the time of rupture of membranes, induction intervention (Foley balloon was placed in the uterine cavity before induction of labor), the cervical Bishop score, the number of days of oxytocin use, and the presence of an umbilical cord around the neck. Ultrasonic examination was performed with a Samsung W-10 color Doppler ultrasonic diagnostic instrument with an EV3-10B cavity content integration probe and a frequency of 17 MHz. E-cervix elastic imaging software was used. In all pregnant women, the bladder was empty before examination, the lithotomy position was taken, and sagittal cervical sections were obtained via transvaginal ultrasound while the patient was calmly breathing. The probe was then gently adjusted so that it did not compress the cervix. When the [E31] widths of the anterior and posterior cervical lips of the inner and outer cervix were clearly equal, the elastic imaging mode was switched to the stable fetal state and the probe was kept stationary until the data collection was completed. The probe automatically freezes and saves images. Image analysis : The obtained elastic images are described and analyzed, and the instrument software converts the cervical elastic strain parameters into the color spectrum from blue (soft) to red (hard). The four-point method was used to select the area of interest (ROI) and trace it from the inner opening of the cervix to the outer opening of the cervix so that the ROI wrapped around the entire cervical region and excluded adjacent tissues such as the bladder or vaginal wall. The measured parameters included the cervical length, hardness ratio (HR, the proportion of the more complex tissue in 30% of the pixels in the ROI), elastic contrast index (ECI), strain values of the inner and outer [E32] cervical orifice (IOS, EOS) and their ratio (IOS/EOS). The above operation was completed by the same doctor with more than 3 years of ultrasound experience. Each pregnant woman was measured 3 times, and the average value was taken. Relevant definitions and descriptions For the determination of gestational age, if the difference between the measured value of the early pregnancy ultrasound and the actual menstruation was more than 5 days, the gestational age was corrected according to the measured value of ultrasound. ② Prenatal body mass index (BMI) = weight (kg)/height (m) 2 ; ③ Induction of labor with oxytocin: This was performed according to the guidelines for Cervical Ripening and Induction of Labor in late pregnancy[10]. Statistical analysis SPSS 25.0 software and R4.1.2 software were used for statistical analysis. All statistical tests were two-sided probability tests, and P<0.05 was considered statistically significant. After classifying the variables, the potential predictors were analyzed by the chi-square test. The variables with statistically significant differences in univariate analysis were entered into the multivariate logistic regression model as candidate variables to establish the prediction model for the outcome of labor induction success. Discrimination and calibration were used to evaluate the predictive ability of the model. It [QCE3] is expressed as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and its value range is 0.5~1.0. The best critical value (cutoff value) was calculated via the Youden index (sensitivity + specificity-1). The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the prediction model; p>0.05 indicates that: there is no statistically significant difference between the predicted value of the model and the actual probability of the outcome, the goodness of fit is good, and the prediction model has good calibration ability. The bootstrap repeated sampling method (1000 samples) was used for internal validation of the model, and a calibration chart was constructed. In accordance with the prediction model, the rms package of R 4.1.2 software was used to establish the nomogram prediction model. 2.2.1 Comparative Analysis of Clinical Metrics Amongst Participants Based on the retrospective analysis of medical records from a cohort of 1000 nulliparous women, the success rate for elective pregnancy induction was determined to be 71.5% (n=715). A comparative assessment of clinical parameters, including maternal age, stature, pregestational weight, gestational duration, uterine height, abdominal girth, presence and duration of premature rupture of membranes (PROM), induction method (intrauterine Foley balloon placement), cervical Bishop score, duration of oxytocin administration, and nuchal cord status, revealed significant intergroup differences in Bishop scores and cervical length (CL) between the successful and unsuccessful induction groups (P<0.05). The detailed results are presented in Table 1. 2.2.2 Cervical Metric Comparison For the cervical metrics, the E-Cervix index (ECI [E34] ), internal os score (IOS), and external os score (EOS) were markedly lower in the group that experienced successful induction than in the failure group (P<0.05). Additionally, the heart rate demonstrated a significant increase in the successful induction group compared to the failure group [E35] (P<0.05). Comprehensive data are presented in Table 2. 2.2.3 Development of a Predictive Model for Successful Elective Induction Variables exhibiting statistical significance in univariate analysis were incorporated into the logistic regression multivariate analysis via stepwise selection. The dichotomous outcome variable represented the result of the induction procedure (failure=0, success=1). Ultimately, the CL, Bishop score, cervical ECI, and HR were identified as predictive factors and included in the model. Details are provided in Table 3. 2.2.4 Validation and Evaluation of the Predictive Model The performance of the predictive model was evaluated via receiver operating characteristic (ROC) curve analysis, yielding an area under the curve (AUC) of 0.79 (95% CI: 0.71-0.85), with a cutoff value of 0.73, a sensitivity of 0.70, and a specificity of 0.78 (see Figure 3). Decision curve analysis indicated high clinical utility, with a threshold probability range of approximately 0.04-0.96 and a net benefit of 0.14. Internal validation via bootstrap resampling (n=1000) resulted in a C-index of 0.76 (95% CI: 0.69-0.81) and a corrected C-index of 0.753. The calibration plots revealed good agreement between the predicted and observed probabilities (Figure 4). 2.2.5 Nomogram Construction for the Prediction of Induction Failure With R software's 'rms' package, a nomogram was constructed based on the multiple logistic regression model's influential factors (Figure 5). Total scores were calculated by summing individual scores for the CL, Bishop score, ECI, and HR. For example, for a pregnant woman presenting with cervical elasticity parameters of a HR of 56% and ECI of 3.2, CL of 3.3 cm, Bishop score of 6, and a nomogram total score of 108, the predicted probability of successful termination exceeded 80%, surpassing the model's cutoff value of 73%. Thus, it can be predicted that such patients are more likely to achieve successful induction (Figure 6). The nomogram developed in this study is a valuable tool for predicting the likelihood of successful labor induction in full-term singleton nulliparous women. The following are the step-by-step instructions for its clinical application: (1) Clinicians first need to measure the relevant parameters for each patient. These parameters include the cervical length (CL), Bishop score, elastic contrast index (ECI), and hardness ratio (HR). These measurements can be obtained through the methods described in the study, such as using ultrasound for CL and ECI, HR measurements and a clinical examination for the Bishop score. (2) Refer to the nomogram. For each measured value of the CL, Bishop score, ECI, and HR, the corresponding score on the respective scale of the nomogram was determined. For example, if a patient has a CL of 3.3 cm, a specific score can be read from the CL scale; the same applies to the other parameters. (3) The scores obtained from each parameter are summed. This total score represents the combined influence of these factors on the probability of successful labor induction. (4) Compare the total score with the cutoff value of the model (in this study, it is 73%). If the total score is higher than 73%, for example, if it is 108%, as in the example in the study, the predicted probability of successful termination exceeds 80%. In such cases, clinicians can consider vaginal induction of labor as a viable option. If the total score is close to or lower than the cutoff value, indicating a lower probability of successful induction, alternative delivery methods, such as cesarean section may be needed, or additional measures, such as further cervical ripening interventions, may be used to improve the chances of successful induction. This approach allows for personalized induction strategies based on individual patient risk profiles, ultimately aiming to optimize the delivery process and improve maternal–fetal outcomes. Discussion The induction of labor is a common obstetric procedure, particularly in critically ill pregnant women, accounting for more than 20% of all deliveries[11, 12]. It aims to initiate the labor process through pharmacological means to achieve delivery before natural labor commences. Despite its widespread use, optimizing the effectiveness and efficiency of labor induction remains an urgent challenge[13]. This study sought to develop a predictive nomogram capable of forecasting successful labor induction in nulliparous women by elucidating critical determinants influencing such success. Our retrospective cohort analysis included 1,000 term pregnant nulliparas who underwent labor induction at the Ultrasound Department of the National Traditional Chinese Medicine Hospital in Xiangxi Tujia and Miao Autonomous Prefecture between January 2017 and December 2024. Labor induction was successful in 715 patients (71.5%) and failed in 285 patients (28.5%). Significant differences in the Bishop score, cervical length (CL), endocervical impedance (ECI), internal os (IOS), external os (EOS), and heart rate were detected between the two groups. Specifically, the ECI, IOS, and EOS were markedly lower in the successful induction group than in the failure group, whereas the HR was significantly elevated. Through univariate and multivariate logistic regression analyses[14], we identified the CL, Bishop score, ECI, and HR as significant predictors of successful labor induction. These factors were incorporated into a final logistic regression model, which demonstrated satisfactory discrimination and calibration, with an area under the ROC curve of 0.79 (95% CI: 0.71-0.85) and a corrected C-index of 0.753. Previous studies have highlighted the importance of cervical maturity in determining the success or failure of labor induction[15]. For example, the Bishop score has been widely used as a predictor of labor induction success due to its correlation with cervical ripeness[16]. Our findings align with these studies, confirming that higher Bishop scores are associated with a greater likelihood of successful induction. Additionally, our results underscore the significance of cervical length, endocervical impedance, and cervical openings as key determinants. E-cervix elastography, which assesses the softness of tissues by measuring pressure changes caused by intrinsic branching arteriolar pulsations, has shown promise in evaluating cervical insufficiency[17]. Our study further validates this technique's utility in predicting labor induction outcomes. The observation that HR was significantly higher in the successful induction group suggests that maternal physiological parameters also play crucial roles in the induction process. The nomogram developed in this study offers a practical tool for clinicians to predict the likelihood of successful labor induction in full-term singleton nulliparous women. By incorporating the cervical length, Bishop score, ECI, and HR, this visual clinical instrument allows for personalized induction strategies based on individual risk profiles[18]. Clinicians can use this nomogram to counsel patients more effectively, tailor interventions, and potentially reduce the cesarean section rate among nulliparous women. For example, a pregnant woman presenting with a cervical elasticity parameter of a HR of 56%, an ECI of 3.2, a CL of 3.3 cm, and a Bishop score of 6 would have a predicted probability of successful induction exceeding 80%, surpassing the model's cutoff value of 73%. This information can guide clinicians in making informed decisions regarding induction methods and timing, ultimately improving patient outcomes. For example, consider a 28-year-old nulliparous woman with a gestational age of 39 weeks. Her cervical length (CL) was 3.0 cm, her Bishop score was 5, her elastic contrast index (ECI) was 3.5, and her hardness ratio (HR) was 48%. By referring to the nomogram and calculating the total score, her p [E31] redicted probability is 90, which is above the cutoff value of 73%. Based on the model, the predicted probability of successful induction is relatively high. In this case, the obstetrician can confidently proceed with labor induction, knowing that the likelihood of a successful outcome is favorable; this not only streamlines the delivery process, but also reduces the anxiety of both the patient and the medical team. One of the primary strengths of this study is its large sample size, which enhances the robustness of the predictive model. Additionally, the use of both univariate and multivariate logistic regression analyses allowed us to identify independent predictors of successful labor induction. The model's performance was rigorously evaluated via various metrics, including receiver operating characteristic (ROC) curve analysis, the concordance index, a calibration plot, and decision curve analysis, ensuring its reliability and clinical applicability. However, several limitations must be acknowledged. First, as a retrospective study, it is subject to inherent biases and confounding variables that may not have been fully accounted for. Second, while our model demonstrated good discrimination and calibration, its generalizability to other populations and settings needs to be tested. More data from multicenter experiments in the future are needed to validate the reliability of our model. Despite the valuable insights provided by this study, several limitations regarding the model's performance should be acknowledged. The C-index of 0.753 indicates that the model has moderate predictive power. While this suggests that the model can differentiate between successful and failed labor induction cases to a certain extent, there is room for improvement. A higher C-index closer to 1 implies a stronger predictive ability. This moderate value might limit the model's reliability in making highly accurate predictions for individual patients. Another limitation of this study is the lack of external validation for the developed predictive model. The model was internally validated via the bootstrap repeated sampling method, but it has not been tested in other populations or clinical settings. Therefore, future research should focus on validating this model in multiple centers with different patient populations. This would help determine the model's applicability across a broader range of clinical scenarios and enhance its reliability as a tool for predicting successful labor induction in nulliparous women. Future research should focus on validating our predictive model in different clinical settings and populations. Further studies with larger and more diverse samples will provide valuable insights into the model's external validity. Additionally, the integration of additional biomarkers and advanced imaging techniques may further increase the predictive accuracy of labor induction models. Future validation is vital. Multicenter studies should be conducted to test the model in diverse populations, ensuring its generalizability. Integrating the model into electronic medical records can streamline decision-making, with real-time calculations based on patient data. For improvement, the addition of biomarkers such as cytokines can refine predictions. These biomarkers can offer more insights into cervical ripening. However, challenges such as biomarker identification, measurement, and integration into the model need to be overcome. Machine learning algorithms, which can handle complex interactions and nonlinear relationships, offer promising avenues for future investigations. Moreover, understanding the underlying mechanisms linking cervical parameters and maternal physiological factors to labor induction outcomes could inform novel therapeutic strategies. For example, interventions aimed at optimizing cervical conditions or modulating maternal physiological responses may improve the success rates of labor induction. [E31] Please ensure that the intended meaning has been maintained in this edit. Conclusion In conclusion, this study developed and internally validated a model for predicting successful labor induction in full-term primiparas with singleton cephalic births by using real-world data. The meaningful screening parameters in the model were used to establish a nomogram model, including the clinical parameters of CL and the Bishop score and the cervical elasticity parameters of ECI and HR, which have not been studied in practice. On the one hand, according to the nomogram, it was simple and easy to perform a simple calculation with the meaningful screening parameters to predict the probability of successful induction of the primipara, which has certain clinical convenience. On the other hand, according to the independent factors that affect the outcome of induction in the prediction model, interventions can be used to advance pregnancy, such as a variety of ways to facilitate cervical ripening. We can take measures to improve the score of the prediction model and select the optimal mode of production, improve successful induction, and reduce the rate of cesarean section and the incidence of adverse maternal and child outcomes. However, the data of this study were applied to a narrow population, so further prospective studies using this method are needed to improve the practicability of the prediction model and expand the application of the model to the population. Declarations Ethics approval and consent to participate Statement on study approval: The protocol of this study was approved by the Institutional Review Board of the Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture. (date of approval, June 27, 2024, number 0012-22- NHR). Statement on informed consent: The need for informed consent was waived by the Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture. The main basis for this decision is as follows: This study is a retrospective cohort analysis, and all the data collected are sourced from patients' routine clinical diagnosis and treatment records. No additional interventions will be carried out on patients during the data collection process. Moreover, the research process strictly adheres to ethical norms, and strict confidentiality measures are implemented for patients' personal information, minimizing potential impacts on patients' privacy and rights to the greatest extent possible. Consent for publication We, the undersigned authors of the manuscript titled‘Multivariate Analysis of Determinants and Development of a Predictive Algorithm for Successful Labor Induction in Nulliparous Women’, confirm our consent for the publication of this article in BMC Pregnancy and Childbirth. Each of us has reviewed the final version of the manuscript and agrees to its submission for publication. Availability of data and material All data that support the findings of this study are available on reasonable request from the corresponding author. However, it should be noted that due to ethical and privacy considerations, the raw data cannot be disclosed in full to meet every demand for disclosure. The raw data contain sensitive patient information, including but not limited to personal identifiers, medical history details, and pregnancy - related data.Requests for data access will be carefully reviewed by the research team in accordance with the regulations of the Institutional Review Board of the Ultrasound Department of the National Traditional Chinese Medicine Hospital in Xiangxi Tujia and Miao Autonomous Prefecture. Competing interests All authors have no conflicts of interests to declare Funding This study did not receive funding from any source, commercial or otherwise. Authors' contributions NW conceived, designed, and super vised the research and wrote the manuscript; ZYW performed the research, and acquired and analyzed the data. Acknowledgements NW and ZYW was supported by Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture.. References Middleton P, Shepherd E, Crowther CA. 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Repeatability and reproducibility of quantitative cervical strain elastography (E-Cervix) in pregnancy. Sci Rep. 2021;11(1):23689. Nazzaro G, et al. Cervical elastography using E-cervix for prediction of preterm birth in singleton pregnancies with threatened preterm labor. J Matern Fetal Neonatal Med. 2022;35(2):330–5. Gill P, Lende MN. and J.W. Van Hook, Induction of Labor. 2024. Marconi AM. Recent advances in the induction of labor. F1000Res, 2019. 8. Shrem G, et al. Isolated Oligohydramnios at Term as an Indication for Labor Induction: A Systematic Review and Meta-Analysis. Fetal Diagn Ther. 2016;40(3):161–73. Thayer SM, et al. Optimizing induction of labor: the Birth Efficiency and Satisfaction Induction of Labor (BEST induction of labor) study. Am J Obstet Gynecol MFM. 2024;6(11):101507. Wang H, et al. Inconsistency Between Univariate and Multiple Logistic Regressions. Shanghai Arch Psychiatry. 2017;29(2):124–8. Liu Y, et al. An evaluation of cervical maturity for Chinese women with labor induction by machine learning and ultrasound images. BMC Pregnancy Childbirth. 2023;23(1):737. Kolkman DGE, et al. The Bishop score as a predictor of labor induction success: a systematic review. Am J Perinatol. 2013;30(8):625–30. Nazzaro G, et al. Cervical elastography using E-cervix for prediction of preterm birth in singleton pregnancies with threatened preterm labor. J Matern Fetal Neonatal Med. 2022;35(2):330–5. Kuba K et al. Reassessing the Bishop score in clinical practice for induction of labor leading to vaginal delivery and for evaluation of cervix ripening. Placenta Reprod Med, 2023. 2. Additional Declarations No competing interests reported. Supplementary Files Cervicalelastographytechnology.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Editor assigned by journal 18 Sep, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Submission checks completed at journal 10 Apr, 2025 First submitted to journal 30 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5990931","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443949681,"identity":"96a67713-b0b1-42a4-b894-ce13aab7ab56","order_by":0,"name":"Nei Fang 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Prefecture.","correspondingAuthor":false,"prefix":"","firstName":"Zhiyun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-02-09 07:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5990931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5990931/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-025-08315-3","type":"published","date":"2025-11-14T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81002626,"identity":"739f89d5-2583-49ab-9cb5-0a0b68f64b36","added_by":"auto","created_at":"2025-04-21 06:36:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178780,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of measurements.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/e528a10db198f31664a9e369.png"},{"id":81004130,"identity":"30059e91-9f2a-4187-a526-f6c2caa3c78c","added_by":"auto","created_at":"2025-04-21 06:44:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":785952,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing the number of subjects enrolled in this study.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/9f6c66ea3f9066ee242d6057.png"},{"id":81004129,"identity":"947eac57-fd05-4533-8ddf-7b2810596a30","added_by":"auto","created_at":"2025-04-21 06:44:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45460,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve of success induced labor prediction model in single cephalic primipara with full-term pregnancy\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/0596da8d17869140e9ea72aa.png"},{"id":81002636,"identity":"0563f23b-4af4-4c54-8adf-263bc45d054a","added_by":"auto","created_at":"2025-04-21 06:36:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249778,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A Decision Curve Analysis and Figure B Calibration Curve of the modeling group\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/16e61e48d6dbd3cdc4c3dd99.png"},{"id":81002621,"identity":"2d19d17c-eca7-4993-ab61-10d88bde8421","added_by":"auto","created_at":"2025-04-21 06:36:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32633,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram of prediction model of successful induced labor in single cephalic primipara with full-term pregnancy\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/272dcf69f6df18cc26ea8be1.jpg"},{"id":81004138,"identity":"90088c58-bd8d-4802-ac23-50531b5aa595","added_by":"auto","created_at":"2025-04-21 06:44:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":183988,"visible":true,"origin":"","legend":"\u003cp\u003eshows that a successful induction of labor pregnant women with the cervical elasticity parameters respectively HR and ECI of 56%, 3.2, a CL of 3.3cm, and a Bishop score of 5, the corresponding scores respectively were 32, 34, 22 and 20, and the total score was 108, and the corresponding probability of successful induction was more than 80%\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/e9a8a87e3ec500e3ee0e1807.jpg"},{"id":96104976,"identity":"6c6174c7-2762-48d5-859b-e3f585adef8e","added_by":"auto","created_at":"2025-11-17 16:05:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2061811,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/46ed417e-65c4-4e04-8463-f50c4a7bbdb5.pdf"},{"id":81004134,"identity":"b875e63f-1749-4c86-b7d2-da6bf258e89e","added_by":"auto","created_at":"2025-04-21 06:44:13","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":142749,"visible":true,"origin":"","legend":"","description":"","filename":"Cervicalelastographytechnology.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5990931/v1/25845a5d9e6d5c572ac5988a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multivariate Analysis of Determinants and Development of a Predictive Algorithm for Successful Labor Induction in Nulliparous Women","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eInduction of labor during pregnancy at term initiates the labor process through drugs to achieve delivery before natural labor. Labor induction is particularly common in maternity centers for critically ill pregnant women, accounting for more than 20% of all deliveries, and can significantly reduce the risk of continuing pregnancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The application of labor induction in obstetrics has been gradually expanded, especially for full-term singleton cephalic primiparas, but how to improve the effectiveness and efficiency of the induction of labor is an urgent problem to be solved [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Relevant studies have shown that cervical maturity can affect the success or failure of labor induction[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, a reliable and repeatable assessment method of cervical maturity is needed before the induction of labor, which offers useful counseling guidance for women who are considering the induction of labor. E-cervix elastography is a diagnostic technique that assesses the softness of tissues by measuring pressure changes caused by intrinsic branching arteriolar pulsations of the tissue[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Previous studies have demonstrated good reproducibility of e-cervical elastography in different pregnancy cycles, as well as its potential for clinical use[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent years, a number of studies have shown that E-cervix elastography technology has more advantages than traditional methods in assessing cervical insufficiency[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In this context, this study collected and analyzed the cervical elasticity parameters and clinical indicators of the cervix to study the factors related to the cervix in full-term singleton cephalic primiparas; establish a prediction model for the labor induction success of full-term singleton cephalic primiparas; and personalize the clinical management of the induction of labor, adequate trial delivery, and reduction of primary cesarean section.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective cohort analysis was conducted on a total of 1000 cases of first full-term, singleton, cephalic primiparas who underwent induction of labor by oxytocin in the obstetrics department of our hospital from January 2017 to December 2024. The samples were obtained from nulliparous women with full-term pregnancies who met the criteria for labor induction at the Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture. They were divided into a successful induction group and a failed induction group. (Figures 1-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion\u003c/strong\u003e\u003cstrong\u003e criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGestational age ≥37 weeks; ② Singleton, cephalic primiparas; ③ Vaginal trial conditions; ④ Intact fetal membranes; and ⑤ Complete medical records.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria were as follows: ① gestational age \u0026lt; 37 weeks; ② gestational age ≥ 42 weeks; ③ twin or multiple pregnancies; ④ scarred uterus; ⑤ cephalopelvic disproportion; ⑥ placenta previa or placental abruption; ⑦ acute inflammation of the reproductive tract; and ⑧ maternal or fetal diseases requiring immediate delivery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical data of the pregnant women were collected, including age, height, prenatal weight, gestational age, uterine height, abdominal circumference, premature rupture of membranes, the time of rupture of membranes, induction intervention (Foley balloon was placed in the uterine cavity before induction of labor), the cervical Bishop score, the number of days of oxytocin use, and the presence of an umbilical cord around the neck.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUltrasonic examination\u003c/strong\u003e was performed with a Samsung W-10 color Doppler ultrasonic diagnostic instrument with an EV3-10B cavity content integration probe and a frequency of 17 MHz. E-cervix elastic imaging software was used. In all pregnant women, the bladder was empty before examination, the lithotomy position was taken, and sagittal cervical sections were obtained via transvaginal ultrasound while the patient was calmly breathing. The probe was then gently adjusted so that it did not compress the cervix. When the \u003ca id=\"_anchor_1\" href=\"#_msocom_1\" language=\"JavaScript\" name=\"_msoanchor_1\"\u003e[E31]\u003c/a\u003e widths of the anterior and posterior cervical lips of the inner and outer cervix were clearly equal, the elastic imaging mode was switched to the stable fetal state and the probe was kept stationary until the data collection was completed. The probe automatically freezes and saves images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis\u003c/strong\u003e: The obtained elastic images are described and analyzed, and the instrument software converts the cervical elastic strain parameters into the color spectrum from blue (soft) to red (hard). The four-point method was used to select the area of interest (ROI) and trace it from the inner opening of the cervix to the outer opening of the cervix so that the ROI wrapped around the entire cervical region and excluded adjacent tissues such as the bladder or vaginal wall. The measured parameters included the cervical length, hardness ratio (HR, the proportion of the more complex tissue in 30% of the pixels in the ROI), elastic contrast index (ECI), strain values of the inner and outer \u003ca id=\"_anchor_2\" href=\"#_msocom_2\" language=\"JavaScript\" name=\"_msoanchor_2\"\u003e[E32]\u003c/a\u003e cervical orifice (IOS, EOS) and their ratio (IOS/EOS). The above operation was completed by the same doctor with more than 3 years of ultrasound experience. Each pregnant woman was measured 3 times, and the average value was taken.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelevant definitions and descriptions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFor the determination of gestational age, if the difference between the measured value of the early pregnancy ultrasound and the actual menstruation was more than 5 days, the gestational age was corrected according to the measured value of ultrasound. ② Prenatal body mass index (BMI) = weight (kg)/height (m) \u003csup\u003e2\u003c/sup\u003e; ③ Induction of labor with oxytocin: This was performed according to the guidelines for Cervical Ripening and Induction of Labor in late pregnancy[10].\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 25.0 software and R4.1.2 software were used for statistical analysis. All statistical tests were two-sided probability tests, and P\u0026lt;0.05 was considered statistically significant. After classifying the variables, the potential predictors were analyzed by the chi-square test. The variables with statistically significant differences in univariate analysis were entered into the multivariate logistic regression model as candidate variables to establish the prediction model for the outcome of labor induction success. Discrimination and calibration were used to evaluate the predictive ability of the model. It \u003ca id=\"_anchor_3\" href=\"#_msocom_3\" language=\"JavaScript\" name=\"_msoanchor_3\"\u003e[QCE3]\u003c/a\u003e is expressed as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and its value range is 0.5~1.0. The best critical value (cutoff value) was calculated via the Youden index (sensitivity + specificity-1). The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration of the prediction model; p\u0026gt;0.05 indicates that: there is no statistically significant difference between the predicted value of the model and the actual probability of the outcome, the goodness of fit is good, and the prediction model has good calibration ability. The bootstrap repeated sampling method (1000 samples) was used for internal validation of the model, and a calibration chart was constructed. In accordance with the prediction model, the rms package of R 4.1.2 software was used to\u003c/p\u003e\n\u003cp\u003eestablish the nomogram prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Comparative Analysis of Clinical Metrics Amongst Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the retrospective analysis of medical records from a cohort of 1000 nulliparous women, the success rate for elective pregnancy induction was determined to be 71.5% (n=715). A comparative assessment of clinical parameters, including maternal age, stature, pregestational weight, gestational duration, uterine height, abdominal girth, presence and duration of premature rupture of membranes (PROM), induction method (intrauterine Foley balloon placement), cervical Bishop score, duration of oxytocin administration, and nuchal cord status, revealed significant intergroup differences in Bishop scores and cervical length (CL) between the successful and unsuccessful induction groups (P\u0026lt;0.05). The detailed results are presented in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Cervical Metric Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the cervical metrics, the E-Cervix index (ECI\u003ca id=\"_anchor_4\" href=\"#_msocom_4\" language=\"JavaScript\" name=\"_msoanchor_4\"\u003e[E34]\u003c/a\u003e ), internal os score (IOS), and external os score (EOS) were markedly lower in the group that experienced successful induction than in the failure group (P\u0026lt;0.05). Additionally, the heart rate demonstrated a significant increase in the successful induction group compared to the failure group\u003ca id=\"_anchor_5\" href=\"#_msocom_5\" language=\"JavaScript\" name=\"_msoanchor_5\"\u003e[E35]\u003c/a\u003e (P\u0026lt;0.05). Comprehensive data are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Development of a Predictive Model for Successful Elective Induction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVariables exhibiting statistical significance in univariate analysis were incorporated into the logistic regression multivariate analysis via stepwise selection. The dichotomous outcome variable represented the result of the induction procedure (failure=0, success=1). Ultimately, the CL, Bishop score, cervical ECI, and HR were identified as predictive factors and included in the model. Details are provided in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 Validation and Evaluation of the Predictive Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe performance of the predictive model was evaluated via receiver operating characteristic (ROC) curve analysis, yielding an area under the curve (AUC) of 0.79 (95% CI: 0.71-0.85), with a cutoff value of 0.73, a sensitivity of 0.70, and a specificity of 0.78 (see Figure 3). Decision curve analysis indicated high clinical utility, with a threshold probability range of approximately 0.04-0.96 and a net benefit of 0.14. Internal validation via bootstrap resampling (n=1000) resulted in a C-index of 0.76 (95% CI: 0.69-0.81) and a corrected C-index of 0.753. The calibration plots revealed good agreement between the predicted and observed probabilities (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.5 Nomogram Construction for \u003c/strong\u003e\u003cstrong\u003ethe \u003c/strong\u003e\u003cstrong\u003ePrediction of Induction Failure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith R software's 'rms' package, a nomogram was constructed based on the multiple logistic regression model's influential factors (Figure 5). Total scores were calculated by summing individual scores for the CL, Bishop score, ECI, and HR. For example, for a pregnant woman presenting with cervical elasticity parameters of a HR of 56% and ECI of 3.2, CL of 3.3 cm, Bishop score of 6, and a nomogram total score of 108, the predicted probability of successful termination exceeded 80%, surpassing the model's cutoff value of 73%. Thus, it can be predicted that such patients are more likely to achieve successful induction (Figure 6). The nomogram developed in this study is a valuable tool for predicting the likelihood of successful labor induction in full-term singleton nulliparous women. The following are the step-by-step instructions for its clinical application: (1) Clinicians first need to measure the relevant parameters for each patient. These parameters include the cervical length (CL), Bishop score, elastic contrast index (ECI), and hardness ratio (HR). These measurements can be obtained through the methods described in the study, such as using ultrasound for CL and ECI, HR measurements and a clinical examination for the Bishop score. (2) Refer to the nomogram. For each measured value of the CL, Bishop score, ECI, and HR, the corresponding score on the respective scale of the nomogram was determined. For example, if a patient has a CL of 3.3 cm, a specific score can be read from the CL scale; the same applies to the other parameters. (3) The scores obtained from each parameter are summed. This total score represents the combined influence of these factors on the probability of successful labor induction. (4) Compare the total score with the cutoff value of the model (in this study, it is 73%). If the total score is higher than 73%, for example, if it is 108%, as in the example in the study, the predicted probability of successful termination exceeds 80%. In such cases, clinicians can consider vaginal induction of labor as a viable option. If the total score is close to or lower than the cutoff value, indicating a lower probability of successful induction, alternative delivery methods, such as cesarean section may be needed, or additional measures, such as further cervical ripening interventions, may be used to improve the chances of successful induction. This approach allows for personalized induction strategies based on individual patient risk profiles, ultimately aiming to optimize the delivery process and improve maternal–fetal outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe induction of labor is a common obstetric procedure, particularly in critically ill pregnant women, accounting for more than 20% of all deliveries[11, 12]. It aims to initiate the labor process through pharmacological means to achieve delivery before natural labor commences. Despite its widespread use, optimizing the effectiveness and efficiency of labor induction remains an urgent challenge[13]. This study sought to develop a predictive nomogram capable of forecasting successful labor induction in nulliparous women by elucidating critical determinants influencing such success.\u003c/p\u003e\n\u003cp\u003eOur retrospective cohort analysis included 1,000 term pregnant nulliparas who underwent labor induction at the Ultrasound Department of the National Traditional Chinese Medicine Hospital in Xiangxi Tujia and Miao Autonomous Prefecture between January 2017 and December 2024. Labor induction was successful in 715 patients (71.5%) and failed in 285 patients (28.5%). Significant differences in the Bishop score, cervical length (CL), endocervical impedance (ECI), internal os (IOS), external os (EOS), and heart rate were detected between the two groups. Specifically, the ECI, IOS, and EOS were markedly lower in the successful induction group than in the failure group, whereas the HR was significantly elevated.\u003c/p\u003e\n\u003cp\u003eThrough univariate and multivariate logistic regression analyses[14], we identified the CL, Bishop score, ECI, and HR as significant predictors of successful labor induction. These factors were incorporated into a final logistic regression model, which demonstrated satisfactory discrimination and calibration, with an area under the ROC curve of 0.79 (95% CI: 0.71-0.85) and a corrected C-index of 0.753.\u003c/p\u003e\n\u003cp\u003ePrevious studies have highlighted the importance of cervical maturity in determining the success or failure of labor induction[15]. For example, the Bishop score has been widely used as a predictor of labor induction success due to its correlation with cervical ripeness[16]. Our findings align with these studies, confirming that higher Bishop scores are associated with a greater likelihood of successful induction. Additionally, our results underscore the significance of cervical length, endocervical impedance, and cervical openings as key determinants.\u003c/p\u003e\n\u003cp\u003eE-cervix elastography, which assesses the softness of tissues by measuring pressure changes caused by intrinsic branching arteriolar pulsations, has shown promise in evaluating cervical insufficiency[17]. Our study further validates this technique's utility in predicting labor induction outcomes. The observation that HR was significantly higher in the successful induction group suggests that maternal physiological parameters also play crucial roles in the induction process.\u003c/p\u003e\n\u003cp\u003eThe nomogram developed in this study offers a practical tool for clinicians to predict the likelihood of successful labor induction in full-term singleton nulliparous women. By incorporating the cervical length, Bishop score, ECI, and HR, this visual clinical instrument allows for personalized induction strategies based on individual risk profiles[18]. Clinicians can use this nomogram to counsel patients more effectively, tailor interventions, and potentially reduce the cesarean section rate among nulliparous women. For example, a pregnant woman presenting with a cervical elasticity parameter of a HR of 56%,\u0026nbsp;an ECI of 3.2, a CL of 3.3 cm, and a Bishop score of 6 would have a predicted probability of successful induction exceeding 80%, surpassing the model's cutoff value of 73%. This information can guide clinicians in making informed decisions regarding induction methods and timing, ultimately improving patient outcomes. For example, consider a 28-year-old nulliparous woman with a gestational age of 39 weeks. Her cervical length (CL)\u0026nbsp;was\u0026nbsp;3.0 cm,\u0026nbsp;her\u0026nbsp;Bishop score\u0026nbsp;was\u0026nbsp;5,\u0026nbsp;her\u0026nbsp;elastic contrast index (ECI)\u0026nbsp;was\u0026nbsp;3.5, and\u0026nbsp;her\u0026nbsp;hardness ratio (HR)\u0026nbsp;was\u0026nbsp;48%. By referring to the nomogram and calculating the total score, her p\u003ca id=\"_anchor_1\" href=\"#_msocom_1\" language=\"JavaScript\" name=\"_msoanchor_1\"\u003e[E31]\u003c/a\u003e redicted probability is 90, which is above the\u0026nbsp;cutoff\u0026nbsp;value of 73%. Based on the model, the predicted probability of successful induction is relatively high. In this case, the obstetrician can confidently proceed with labor induction, knowing that the likelihood of a successful outcome is favorable; this not only streamlines the delivery process, but also reduces the anxiety of both the patient and the medical team.\u003c/p\u003e\n\u003cp\u003eOne of the primary strengths of this study is its large sample size, which enhances the robustness of the predictive model. Additionally, the use of both univariate and multivariate logistic regression analyses allowed us to identify independent predictors of successful labor induction. The model's performance was rigorously evaluated via various metrics, including receiver operating characteristic (ROC) curve analysis, the concordance index, a calibration plot, and decision curve analysis, ensuring its reliability and clinical applicability.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations must be acknowledged. First, as a retrospective study, it is subject to inherent biases and confounding variables that may not have been fully accounted for. Second, while our model demonstrated good discrimination and calibration, its generalizability to other populations and settings needs to be tested. More data from multicenter experiments in the future are needed to validate the reliability of our model. Despite the valuable insights provided by this study, several limitations regarding the model's performance should be acknowledged. The C-index of 0.753 indicates that the model has moderate predictive power. While this suggests that the model can differentiate between successful and failed labor induction cases to a certain extent, there is room for improvement. A higher C-index closer to 1 implies a stronger predictive ability. This moderate value might limit the model's reliability in making highly accurate predictions for individual patients.\u0026nbsp;Another limitation of this study is the lack of external validation for the developed predictive model. The model was internally validated\u0026nbsp;via the bootstrap repeated sampling method, but it has not been tested in other populations or clinical settings. Therefore, future research should focus on validating this model in multiple centers with different patient populations. This would help determine the model's applicability across a broader range of clinical scenarios and enhance its reliability as a tool for predicting successful labor induction in nulliparous women.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on validating our predictive model in different clinical settings and populations. Further studies with larger and more diverse samples will provide valuable insights into the model's external validity.\u0026nbsp;Additionally, the integration of additional biomarkers and advanced imaging techniques may further increase the predictive accuracy of labor induction models. Future validation is vital. Multicenter studies should be conducted to test the model in diverse populations, ensuring its generalizability. Integrating the model into electronic medical records can streamline decision-making, with real-time calculations based on patient data. For improvement, the addition of biomarkers such as cytokines can refine predictions. These biomarkers can offer more insights into cervical ripening. However, challenges such as biomarker identification, measurement, and integration into the model need to be overcome. Machine learning algorithms, which can handle complex interactions and nonlinear relationships, offer promising avenues for future investigations.\u003c/p\u003e\n\u003cp\u003eMoreover, understanding the underlying mechanisms linking cervical parameters and maternal physiological factors to labor induction outcomes could inform novel therapeutic strategies. For example, interventions aimed at optimizing cervical conditions or modulating maternal physiological responses may improve the success rates of labor induction.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003ca href=\"#_msoanchor_1\"\u003e[E31]\u003c/a\u003ePlease ensure that the intended meaning has been maintained in this edit.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study developed and internally validated a model for predicting successful labor induction in full-term primiparas with singleton cephalic births by using real-world data. The meaningful screening parameters in the model were used to establish a nomogram model, including the clinical parameters of CL and the Bishop score and the cervical elasticity parameters of ECI and HR, which have not been studied in practice. On the one hand, according to the nomogram, it was simple and easy to perform a simple calculation with the meaningful screening parameters to predict the probability of successful induction of the primipara, which has certain clinical convenience. On the other hand, according to the independent factors that affect the outcome of induction in the prediction model, interventions can be used to advance pregnancy, such as a variety of ways to facilitate cervical ripening. We can take measures to improve the score of the prediction model and select the optimal mode of production, improve successful induction, and reduce the rate of cesarean section and the incidence of adverse maternal and child outcomes. However, the data of this study were applied to a narrow population, so further prospective studies using this method are needed to improve the practicability of the prediction model and expand the application of the model to the population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatement on study approval: The protocol of this study was approved by\u0026nbsp;the Institutional Review Board of the Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture. (date of approval, June 27, 2024, number 0012-22- NHR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatement on informed consent: The need for informed consent was waived by the Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture.\u0026nbsp;The main basis for this decision is as follows: This study is a retrospective cohort analysis, and all the data collected are sourced from patients' routine clinical diagnosis and treatment records. No additional interventions will be carried out on patients during the data collection process. Moreover, the research process strictly adheres to ethical norms, and strict confidentiality measures are implemented for patients' personal information, minimizing potential impacts on patients' privacy and rights to the greatest extent possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe, the undersigned authors of the manuscript titled‘Multivariate Analysis of Determinants and Development of a Predictive Algorithm for Successful Labor Induction in Nulliparous Women’, confirm our consent for the publication of this article in BMC Pregnancy and Childbirth. Each of us has reviewed the final version of the manuscript and agrees to its submission for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data that support the findings of this study are available on reasonable request from the corresponding author.\u0026nbsp;However, it should be noted that due to ethical and privacy considerations, the raw data cannot be disclosed in full to meet every demand for disclosure. The raw data contain sensitive patient information, including but not limited to personal identifiers, medical history details, and pregnancy - related data.Requests for data access will be carefully reviewed by the research team in accordance with the regulations of the Institutional Review Board of the Ultrasound Department of the National Traditional Chinese Medicine Hospital in Xiangxi Tujia and Miao Autonomous Prefecture.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts of interests to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive funding from any source, commercial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNW conceived, designed, and super vised the research and wrote the manuscript; ZYW performed the research, and acquired and analyzed the data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNW and ZYW was supported by Ultrasound Department of National Traditional Chinese Medicine Hospital of Xiangxi Tujia and Miao Autonomous Prefecture..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMiddleton P, Shepherd E, Crowther CA. Induction of labour for improving birth outcomes for women at or beyond term. Cochrane Database Syst Rev. 2018;5(5):CD004945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarconi AM. Recent advances in the induction of labor. F1000Res, 2019. 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Verde M, et al. Is Uterine Myomectomy a Real Contraindication to Vaginal Delivery? Results from a Prospective Study. J Invest Surg. 2022;35(1):126\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez-Ramos L, et al. Methods for the induction of labor: efficacy and safety. Am J Obstet Gynecol. 2024;230(3S):S669\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoates D, et al. A systematic scoping review of clinical indications for induction of labour. PLoS ONE. 2020;15(1):e0228196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvars J, et al. Simplified Bishop score including parity predicts successful induction of labor. Eur J Obstet Gynecol Reprod Biol. 2016;203:309\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGultekin S, et al. Comparison of elastosonography and digital examination of cervix for consistency to predict successful vaginal delivery after induction of labor with oxytocin. J Matern Fetal Neonatal Med. 2017;30(23):2795\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMlodawski J, et al. Repeatability and reproducibility of quantitative cervical strain elastography (E-Cervix) in pregnancy. Sci Rep. 2021;11(1):23689.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazzaro G, et al. Cervical elastography using E-cervix for prediction of preterm birth in singleton pregnancies with threatened preterm labor. J Matern Fetal Neonatal Med. 2022;35(2):330\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill P, Lende MN. and J.W. Van Hook, Induction of Labor. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarconi AM. Recent advances in the induction of labor. F1000Res, 2019. 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrem G, et al. Isolated Oligohydramnios at Term as an Indication for Labor Induction: A Systematic Review and Meta-Analysis. Fetal Diagn Ther. 2016;40(3):161\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThayer SM, et al. Optimizing induction of labor: the Birth Efficiency and Satisfaction Induction of Labor (BEST induction of labor) study. Am J Obstet Gynecol MFM. 2024;6(11):101507.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, et al. Inconsistency Between Univariate and Multiple Logistic Regressions. Shanghai Arch Psychiatry. 2017;29(2):124\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, et al. An evaluation of cervical maturity for Chinese women with labor induction by machine learning and ultrasound images. BMC Pregnancy Childbirth. 2023;23(1):737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolkman DGE, et al. The Bishop score as a predictor of labor induction success: a systematic review. Am J Perinatol. 2013;30(8):625\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNazzaro G, et al. Cervical elastography using E-cervix for prediction of preterm birth in singleton pregnancies with threatened preterm labor. J Matern Fetal Neonatal Med. 2022;35(2):330\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuba K et al. Reassessing the Bishop score in clinical practice for induction of labor leading to vaginal delivery and for evaluation of cervix ripening. Placenta Reprod Med, 2023. 2.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Labor induction, electronic cervical elastography, term singleton nulliparous women, cervical length, Bishop score","lastPublishedDoi":"10.21203/rs.3.rs-5990931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5990931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe present investigation aims to develop an innovative predictive nomogram capable of predicting successful labor induction in nulliparous women, thereby elucidating critical determinants influencing such success and estimating the probability of successful induction in term singleton pregnancies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort analysis included 1,000 term pregnant nulliparas who underwent labor induction in the Ultrasound Department of the National Traditional Chinese Medicine Hospital in Xiangxi Tujia and Miao Autonomous Prefecture between January 2017 and December 2024. We conducted univariate and multivariate logistic regression analyses after comparing various clinical parameters. The outcome variable was defined as the binary success of labor induction, while potential predictors included maternal demographics, obstetric characteristics, cervical conditions (e.g., Bishop score, endocervical impedance [ECI], internal os [IOS], external os [EOS]), duration of oxytocin administration, and presence of a nuchal cord. The model's performance was assessed via receiver operating characteristic (ROC) curve analysis, the concordance index (C-index), a calibration plot, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLabor induction was successful in 715 patients (71.5%) and failed in 285 patients (28.5%). Significant differences were observed in the Bishop score and cervical length (CL) between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the ECI, IOS, and EOS were markedly lower in the successful induction group than in the failure group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas heart rate was significantly elevated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Stepwise selection identified the CL, Bishop score, ECI, and hardness ratio (HR) as significant predictors, which were incorporated into a final logistic regression model. The area under the ROC curve was 0.79 (95% CI: 0.71\u0026ndash;0.85), with a corrected C-index of 0.753, indicating satisfactory discrimination and calibration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThrough comprehensive evaluation, we identified cervical length, the Bishop score, the ECI, and HR as pivotal determinants of successful labor induction. A nomogram incorporating these four factors was constructed to predict the likelihood of successful induction in term singleton nulliparous women. This visual clinical instrument serves as an adjunctive tool for guiding personalized induction strategies based on individual risk profiles.\u003c/p\u003e","manuscriptTitle":"Multivariate Analysis of Determinants and Development of a Predictive Algorithm for Successful Labor Induction in Nulliparous Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 06:36:08","doi":"10.21203/rs.3.rs-5990931/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-18T15:25:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T15:22:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-03T17:10:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T13:32:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296623553665558316897639995546107380717","date":"2025-04-17T13:21:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21441226846453117194836553898420216158","date":"2025-04-16T15:41:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-16T04:36:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-10T23:23:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-03-30T08:24:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2543d2c-624e-4dbf-90e9-103d8f293c37","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T15:59:59+00:00","versionOfRecord":{"articleIdentity":"rs-5990931","link":"https://doi.org/10.1186/s12884-025-08315-3","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2025-11-14 15:57:12","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-04-21 06:36:08","video":"","vorDoi":"10.1186/s12884-025-08315-3","vorDoiUrl":"https://doi.org/10.1186/s12884-025-08315-3","workflowStages":[]},"version":"v1","identity":"rs-5990931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5990931","identity":"rs-5990931","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}