A simple bedside composite risk score (AB-100 rule) for predicting cesarean birth in nulliparous women: a historical cohort study with external validation

A simple bedside composite risk score (AB-100 rule) for predicting cesarean birth in nulliparous women: a historical cohort study with external validation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A simple bedside composite risk score (AB-100 rule) for predicting cesarean birth in nulliparous women: a historical cohort study with external validation Shu-Ying Chen, Shih-Peng Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9135505/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Cesarean birth rates continue to rise worldwide. Advanced maternal age and elevated body mass index (BMI) are well-established risk factors for intrapartum cesarean delivery in nulliparous women. However, translating these epidemiological risk factors into a simple clinical tool that can support individualized counseling and shared decision-making remains challenging. This study aimed to develop and externally validate a practical bedside composite risk score integrating maternal age and BMI (the AB-100 rule) for predicting cesarean birth. Methods We conducted a historical cohort study including nulliparous women with singleton term pregnancies undergoing trial of labor at a tertiary academic center. A composite score was calculated as maternal age (years) + 2 × BMI at delivery (kg/m²) (AB score). Logistic regression and receiver operating characteristic (ROC) curve analysis were used to assess predictive performance. A clinically practical threshold of AB score ≥ 100 was evaluated. External validation was performed using an independent cohort managed by multiple obstetricians. Decision curve analysis (DCA) was conducted to assess potential clinical utility. Results Higher composite scores were significantly associated with increased risk of cesarean birth. Women with AB score ≥ 100 had a markedly higher cesarean rate compared with those with lower scores (67.6% vs 17.3%). The model demonstrated moderate discrimination and consistent performance in the external validation cohort. Decision curve analysis indicated a positive net benefit across clinically relevant threshold probabilities. Conclusions Although maternal age and BMI are known determinants of cesarean birth risk, integrating them into a simple bedside composite score provides an intuitive framework for early risk communication. The AB-100 rule may support shared decision-making and individualized delivery planning in nulliparous women. Cesarean birth Maternal age Body mass index Risk prediction Nulliparous women Shared decision-making Figures Figure 1 Figure 2 Key Message A simple composite score based on maternal age and body mass index identifies nulliparous women at markedly increased risk of cesarean birth and demonstrates robust performance in external validation, supporting practical bedside risk stratification. BACKGROUND The global rate of cesarean birth has increased substantially over the past several decades and now exceeds the level considered optimal for population health outcomes. 1 Although cesarean delivery is a life-saving intervention when medically indicated, the growing proportion of procedures performed for intrapartum indications such as labor dystocia or failure to progress has raised significant clinical and public health concerns. 2 Contemporary discussions also emphasize the importance of informed counseling and shared decision-making regarding mode of delivery, particularly among low-risk nulliparous women. 3 Identifying women at increased risk of cesarean birth before or early in labor therefore remains a key challenge in modern obstetric practice. Maternal characteristics play an important role in determining labor outcomes. Advanced maternal age has been consistently associated with increased rates of operative delivery, possibly reflecting age-related changes in uterine function and obstetric management. 4 , 5 Similarly, maternal adiposity is strongly linked to prolonged labor, impaired uterine contractility, and higher cesarean rates. 6 – 8 Experimental and clinical studies suggest that obesity may alter myometrial contractility and labor dynamics, thereby increasing the likelihood of labor arrest. 6 These structural maternal factors may therefore represent an underlying physiological vulnerability that predisposes to intrapartum cesarean delivery even in otherwise uncomplicated pregnancies. Several studies have attempted to develop prediction models for intrapartum cesarean delivery using combinations of maternal characteristics, clinical findings, and biochemical markers. 9 – 11 More recently, machine learning approaches have been introduced to improve predictive performance. 10 – 12 However, many existing models require multiple variables or complex computational tools, limiting their practical use in routine clinical settings. In contrast, simple and transparent risk scores derived from readily available maternal characteristics may offer greater bedside applicability and facilitate rapid risk assessment during antenatal counseling or early labor evaluation. In addition to structural maternal factors, maternal hemodynamic status during pregnancy may also influence obstetric outcomes. Elevated blood pressure and cardiovascular adaptations during pregnancy have been associated with adverse maternal and perinatal outcomes. 13 , 14 Emerging evidence suggests that even blood pressure levels below traditional hypertensive thresholds may have prognostic implications for pregnancy outcomes. 14 However, the potential role of maternal hemodynamic markers in predicting intrapartum cesarean delivery remains incompletely understood. Inter-physician variation in labor management further complicates risk prediction. Differences in diagnostic thresholds for labor arrest and in clinical decision-making may obscure true biological associations between maternal characteristics and delivery outcomes. 15 Studying cohorts with standardized clinical management may therefore provide clearer insights into the relationship between maternal risk factors and cesarean birth. From a clinical perspective, obstetric decision-making often involves balancing maternal characteristics, obstetric risk factors, and parental preferences. Therefore, a straightforward and interpretable risk indicator may be useful in facilitating shared decision-making between clinicians and pregnant women when discussing delivery planning. In this study, we propose a simple composite score combining maternal age and BMI (age + 2 x BMI), referred to as the AB score. We further evaluated a clinically practical threshold (AB score ≥ 100), termed the AB-100 rule , to identify nulliparous women at higher risk of cesarean birth. Although maternal age and BMI are well-known risk factors individually, we hypothesized that their combination into a single composite measure could provide a practical and intuitive screening tool for early risk stratification in routine obstetric care. While maternal age and obesity have long been recognized as determinants of cesarean birth risk, these factors are rarely operationalized into simple clinical tools that can be readily applied in routine obstetric practice. Translating established epidemiological associations into intuitive bedside risk indicators may facilitate individualized counseling and shared decision-making regarding mode of delivery. Therefore, this study aimed not to identify new biological risk factors, but to develop and externally validate a practical composite score (AB-100 rule) that integrates maternal age and BMI into a clinically meaningful risk stratification framework. METHODS Study design and setting This historical cohort study was conducted to evaluate the predictive value of a composite score based on maternal age and body mass index for cesarean birth in nulliparous women. The primary cohort consisted of women managed in a standardized clinical environment at a tertiary academic medical center between January 2022 and December 2024. To minimize inter-physician variability in intrapartum decision-making, all deliveries in the primary cohort were supervised by a single attending obstetrician following a consistent labor management protocol. To assess the generalizability of the model, external validation was performed using an independent cohort of nulliparous women managed by multiple obstetricians at the same institution during the same period. Participants Women were eligible for inclusion if they were nulliparous with a singleton pregnancy, cephalic presentation, and term gestation (≥ 37 weeks) undergoing a trial of labor. Women were excluded if they had multiple gestations, pre-existing hypertension or hypertensive disorders diagnosed before admission, fetal distress, elective cesarean delivery without a trial of labor, or incomplete clinical data. A total of 544 women met the inclusion criteria for the primary derivation cohort. An additional independent cohort of 392 nulliparous women was used for external validation of the prediction model. Variables and measurements The primary outcome was mode of delivery, categorized as vaginal birth or cesarean birth. The primary predictor was a composite maternal risk score calculated as: Maternal age (years) + 2 × body mass index at delivery (kg/m²). This composite score was designed to capture the combined structural contribution of maternal age and adiposity to labor dynamics. Maternal systolic blood pressure was evaluated as a potential functional predictor. The maximum systolic blood pressure recorded during pregnancy was used as an indicator of hemodynamic stress. Additional clinical variables were obtained from electronic medical records, including fetal birth weight, glucose values from the 75-g oral glucose tolerance test, and estimated blood loss at delivery. Risk stratification To identify clinically relevant thresholds for the composite score and systolic blood pressure, decision tree analysis was performed. Optimal cutoff values were determined using the Youden index to maximize combined sensitivity and specificity. Based on these thresholds, participants were categorized into three risk groups: Low risk : composite score ≤ 100 and systolic blood pressure ≤ 134 mmHg Intermediate risk : elevation of either variable High risk : composite score > 100 and systolic blood pressure > 134 mmHg Statistical analysis Continuous variables are presented as mean ± standard deviation and were compared using the Student t test. Categorical variables are presented as number (percent) and were compared using the chi-square test or Fisher exact test as appropriate. Multicollinearity among predictors was assessed before model construction. Logistic regression analysis was performed to evaluate the association between maternal characteristics and cesarean birth. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. To evaluate the discriminatory performance of the prediction models, receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. ROC curves were constructed for the composite maternal score and for models incorporating additional clinical variables. Decision curve analysis (DCA) was conducted to assess the potential clinical usefulness of the prediction models across a range of threshold probabilities. Net benefit was calculated for each model and compared with default strategies of treating all women or treating none. External validation of the composite score was performed using the independent cohort managed by multiple obstetricians. Model discrimination in the validation cohort was evaluated using AUC, and the predictive performance of the score was compared between the derivation and validation cohorts. All statistical analyses were performed using Python (version 3.12.7) . Data processing and management were conducted using the pandas and numpy libraries. Logistic regression modeling and receiver operating characteristic (ROC) curve analyses were performed using the scikit-learn (version 1.5.1) package. Model discrimination was evaluated using the area under the ROC curve (AUC). Optimal cutoff values were determined using the Youden index , which maximizes the combined sensitivity and specificity. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the prediction models by estimating the net benefit across a range of threshold probabilities and comparing the model with default strategies of treating all womens or treating none. External validation of the prediction model was conducted using an independent cohort, and model performance in the validation dataset was evaluated using ROC analysis and AUC. A two-sided P value < 0.05 was considered statistically significant. RESULTS Maternal characteristics A total of 544 nulliparous women were included in the primary cohort. Among them, 401 (73.7%) achieved vaginal birth and 143 (26.3%) underwent cesarean birth . Baseline maternal characteristics are summarized in Table 1 . Women who underwent cesarean birth were generally older and had higher body mass index at delivery compared with those who achieved vaginal birth. Several metabolic and hemodynamic variables also differed between the two groups. In particular, the 1-hour glucose value from the oral glucose tolerance test (OGTT) was significantly higher among women who underwent cesarean delivery. Table 1 Comparison of Maternal Characteristics between Vaginal Birth and Cesarean Birth in Nullipara Variable Vaginal Birth (n = 401) Cesarean Birth (n = 143) P -value Maternal age (years) 32.5 ± 4.3 34.3 ± 5.0 < 0.001* BMI at Delivery (kg/m²) 26.4 ± 4.0 29.2 ± 5.7 < 0.001* BMI Change (kg/m²) 3.5 ± 2.3 3.4 ± 3.0 0.921 Fetal Birthweight (g) 3025.7 ± 402.9 3112.1 ± 641.8 0.133 Blood Loss (mL) 88.3 ± 119.2 826.1 ± 404.2 < 0.001* OGTT Fasting (mg/dL) 80.8 ± 6.8 82.2 ± 7.7 0.052 OGTT 1-hr (mg/dL) 132.0 ± 31.5 144.2 ± 32.8 < 0.001* OGTT 2-hr (mg/dL) 117.2 ± 25.3 125.6 ± 29.8 0.003* GDM (n, %) 60 (15.0%) 37 (25.9%) 0.005* Max SBP (mmHg) 127.2 ± 10.0 133.1 ± 17.9 < 0.001* SBP SD (mmHg) 7.8 ± 2.5 8.4 ± 3.8 0.092 * Note : Values are presented as Mean ± Standard Deviation or number (percentage). *Indicates statistical significance ( P < 0.05). Table 1. Maternal characteristics according to mode of delivery in the primary cohort. Comparison of demographic, metabolic, and hemodynamic variables between women who achieved vaginal birth and those who underwent cesarean birth. Continuous variables are presented as mean ± standard deviation and categorical variables as number (percentage). Although this difference reached statistical significance, the magnitude of discrimination was limited when evaluated using receiver operating characteristic analysis. This suggests that while OGTT 1-hour glucose values may be statistically associated with cesarean birth, their ability to meaningfully distinguish individual risk remains modest. Logistic regression analysis Multivariable logistic regression analysis was performed to evaluate the independent association between maternal characteristics and cesarean birth (Table 2 ). Table 2 Multivariable Logistic Regression Analysis for Predicting Cesarean Birth in Nullipara Variable Odds Ratio (95% CI) P-value Age (per year) 1.08 (1.03–1.14) < 0.001* BMI at Delivery (per kg/m²) 1.12 (1.07–1.18) < 0.001* Max SBP (per mmHg) 1.01 (0.99–1.03) 0.167 SBP Variability (CV) (per %) 1.02 (0.92–1.12) 0.756 OGTT 1-hr (per mg/dL) 1.00 (0.99–1.01) 0.364 OGTT 2-hr (per mg/dL) 1.00 (0.99–1.01) 0.390 * Note : CI = Confidence Interval; BMI = Body Mass Index; SBP = Systolic Blood Pressure; OGTT 1-hr, 2-hr = oral glucose tolerant test post-glucose-load 1 hour and 2 hours glucose level. Table 2. Multivariable logistic regression analysis of factors associated with cesarean birth. Odds ratios (OR) and 95% confidence intervals (CI) for maternal characteristics associated with cesarean delivery in the primary cohort. Maternal age and body mass index were independently associated with an increased likelihood of cesarean delivery. These findings support the importance of structural maternal factors in determining labor outcomes. Based on these observations, a composite maternal score combining maternal age and body mass index was constructed to represent the combined influence of these structural characteristics. Maternal systolic blood pressure also showed a positive association with cesarean birth, suggesting that maternal hemodynamic status may contribute additional physiological information beyond structural risk factors. Receiver operating characteristic analysis The discriminatory performance of the predictive variables was evaluated using receiver operating characteristic (ROC) curve analysis (Fig. 1 ). The composite maternal score demonstrated moderate discriminatory ability for predicting cesarean birth. In contrast, although OGTT 1-hour glucose values differed significantly between groups, their ROC curve showed relatively limited discriminatory performance. This highlights the distinction between statistical association and clinically meaningful prediction. The OGTT 1-hour glucose value was not included in the final prediction model because its discriminatory performance was limited despite statistical differences between groups. Overall, the ROC analysis supports the use of maternal age and body mass index as the core structural predictors in the proposed risk model. Risk stratification and external validation The predictive performance of the composite maternal score was further assessed in an independent validation cohort (Table 3 ). The association between the composite score and cesarean birth remained consistent in this cohort, indicating stable predictive performance across different clinical settings. Table 3 External Validation of the Composite Maternal Score for Predicting Cesarean Birth in a Multi-physician Nulliparous Cohort (N = 392) Risk Category AB-100 Score a Total Women (n) Cesarean Birth, n (%) Odds Ratio (95% CI) P -value Low Risk 100 34 23 (67.6%) 9.98 (4.63–21.54) < 0.001* Note : a AB-100 Score= Calculated as Maternal Age (years) + 2 × BMI at admission (kg/m 2 ); CI = Confidence Interval; OR = Odds Ratio; Ref = Reference group. Table 3. External validation of the composite maternal score in the independent cohort. Association between the composite maternal score and cesarean birth in the validation cohort. Odds ratios (OR) with 95% confidence intervals (CI) are presented. External validation represents a critical step in prediction model research because models developed within a single cohort may overestimate their predictive ability. The consistency observed in the validation cohort therefore supports the robustness and generalizability of the composite maternal score. Additional analyses examining risk stratification using systolic blood pressure thresholds and combined maternal score models are provided in the Supporting Information (Supplementary Tables S1 and S2) . Decision curve analysis Decision curve analysis was performed to evaluate the potential clinical usefulness of the prediction model (Fig. 2 ). Across a range of threshold probabilities, the composite maternal score demonstrated a positive net benefit compared with default strategies of treating all women or treating none . These findings suggest that the model may provide clinically meaningful decision support when applied to antenatal counseling or early labor risk assessment. Importantly, the presence of external validation further strengthens the clinical relevance of the model , indicating that the observed net benefit is not limited to the derivation cohort but may also be applicable in broader clinical settings. DISCUSSION The present study demonstrates that combining two universally available maternal characteristics into a simple composite score provides clinically meaningful information for predicting cesarean birth in nulliparous women. Although maternal age and BMI are established risk factors, our findings highlight the importance of translating these epidemiological determinants into an easily interpretable bedside tool that can support real-world obstetric decision-making. Maternal age and adiposity are well-recognized determinants of labor outcomes. Advanced maternal age has been associated with increased operative delivery rates, possibly reflecting both biological and clinical factors. 4 , 5 Age-related changes in uterine contractility and myometrial responsiveness may contribute to inefficient labor progress, while obstetric management practices may also differ in older mothers. Similarly, maternal obesity has been linked to altered myometrial contractility and prolonged labor. Experimental and clinical studies have suggested that obesity may affect uterine muscle function and metabolic signaling pathways, potentially increasing the risk of labor dystocia and cesarean delivery. 6 , 7 Our findings are consistent with this biological framework, supporting the concept that structural maternal characteristics can influence the mechanical and physiological processes of labor. Although the present composite score includes only two predictors, maternal age and body mass index were intentionally selected because they are universally available and represent structural determinants of labor mechanics. This simplicity may enhance bedside applicability and facilitate rapid clinical risk assessment without requiring complex data inputs or computational tools. Interestingly, although the 1-hour glucose value from the oral glucose tolerance test differed significantly between groups, its discriminatory performance in ROC analysis was limited. This observation highlights an important distinction between statistical association and predictive usefulness. While metabolic factors may contribute to overall pregnancy risk profiles, their individual ability to discriminate cesarean birth risk at the women level appears limited in comparison with structural maternal characteristics such as age and body mass index. This discrepancy underscores the difference between statistical significance and clinical prediction. Variables that demonstrate significant group differences do not necessarily improve the performance of prediction models designed for individual risk estimation. Previous studies have proposed a variety of prediction models for cesarean birth, often incorporating multiple clinical variables or complex algorithms. 8 More recently, machine learning approaches have been introduced to improve predictive performance. 9 , 10 However, many of these models require extensive input variables or computational tools that may limit their practicality in routine clinical settings. While machine learning approaches may achieve higher predictive accuracy in some contexts, their implementation often requires extensive datasets, specialized computational infrastructure, and complex model interpretation. In contrast, simple clinical risk scores may offer greater practicality in routine obstetric practice, particularly in settings where rapid clinical decision-making is required. External validation represents a critical component of prediction model development. Models derived from a single cohort frequently demonstrate optimistic predictive performance due to overfitting. In the present study, the composite score maintained consistent associations with cesarean birth in an independent validation cohort managed by multiple obstetricians. This finding suggests that the predictive value of the score is not solely dependent on the original derivation cohort and may be applicable across different clinical settings. Decision curve analysis further demonstrated that the composite maternal score provided a positive net benefit across a range of threshold probabilities compared with default strategies. These findings suggest that the model may offer clinically meaningful support when applied to antenatal counseling or early labor risk assessment. By identifying women at higher risk of cesarean birth, clinicians may be able to provide more individualized counseling and optimize intrapartum monitoring strategies. Another important consideration is the role of maternal hemodynamic status. Previous studies have suggested that blood pressure and cardiovascular adaptations during pregnancy may influence maternal and perinatal outcomes. 13 In the present study, maternal systolic blood pressure showed an association with cesarean birth, suggesting that functional maternal physiology may contribute additional information beyond structural risk factors. However, the predictive contribution of blood pressure was more modest than that of maternal age and body mass index. Further research may help clarify the interaction between structural maternal characteristics and maternal hemodynamic status in determining labor outcomes. Several limitations should be considered when interpreting these findings. First, this study was conducted at a single institution, which may limit the generalizability of the results to other populations or healthcare systems. Second, although external validation was performed, both cohorts were derived from the same institutional environment. Third, the model focused on readily available maternal characteristics and did not incorporate additional intrapartum variables that may further improve predictive accuracy. Future studies in larger multicenter populations may help refine and validate this approach. Beyond its statistical performance, the practical utility of the AB-100 rule lies in its capacity to facilitate Shared Decision Making (SDM) between clinicians and nulliparous women. Currently, counseling regarding the risk of intrapartum cesarean birth is often based on subjective clinical judgment or complex models that are difficult to communicate at the bedside. The AB-100 rule simplifies this by providing an objective, easily calculated threshold. For instance, our data demonstrates that women with a score exceeding 100 face a significantly higher risk of cesarean delivery (67.6% vs 17.3% in the validation cohort). By integrating such concrete figures into prenatal or early-labor consultations, clinicians can move away from vague risk descriptors and empower patients with evidence-based insights. This transparency allows for a more personalized birth plan, helping mothers align their expectations with their physiological risk profile and potentially reducing the psychological distress associated with unexpected surgical interventions. Ultimately, the AB-100 rule serves not merely as a predictive algorithm, but as a crucial communication bridge that supports the ethical and clinical goals of shared autonomy in obstetric care. Despite these limitations, this study also has several strengths. The study population consisted exclusively of nulliparous women undergoing trial of labor, providing a relatively homogeneous clinical cohort. The use of standardized labor management in the derivation cohort minimized variability in clinical decision-making. Furthermore, the inclusion of external validation and decision curve analysis strengthens the clinical relevance of the proposed model. This approach does not aim to introduce new biological predictors but instead focuses on enhancing the clinical usability of existing evidence. In clinical practice, this composite maternal score may serve as a simple bedside tool to identify nulliparous women at increased risk of cesarean birth, thereby supporting individualized counseling and potentially guiding intrapartum monitoring strategies. Overall, our findings suggest that a simple composite score based on maternal age and body mass index may serve as a practical tool for identifying nulliparous women at increased risk of cesarean birth. Such an approach may complement existing clinical assessment and support individualized obstetric counseling. Further prospective multicenter studies are warranted to confirm the clinical utility and implementation of this simple risk stratification approach. Importantly, the simplicity of the AB-100 rule may make it particularly suitable for use in low-resource settings where access to advanced obstetric technologies or complex prediction models is limited. Because the score relies only on routinely available maternal characteristics, it may facilitate early risk communication and basic risk stratification in diverse clinical environments, including primary care facilities and midwife-led maternity units. CONCLUSIONS The AB-100 rule translates well-known maternal risk factors into a simple and clinically applicable risk stratification tool. This approach may enhance individualized counseling and shared decision-making in nulliparous women considering vaginal birth. Further prospective multicenter studies are warranted to evaluate its impact on clinical outcomes and obstetric management strategies. Abbreviations BMI Body mass index CI Confidence interval IRB Institutional Review Board OR Odds ratio ROC Receiver operating characteristic Declarations Ethics approval and consent to participate : This study was approved by the Institutional Review Board of Taipei Medical University (TMU-JIRB No. N202512016). As this study involved a retrospective analysis of de-identified clinical data, the requirement for informed consent was waived by the ethics committee. All procedures were performed in accordance with the Declaration of Helsinki. Consent for publication : Not applicable. Availability of data and materials : The anonymized datasets supporting the findings of this study, encompassing the primary derivation cohort (n = 544) and the independent external validation cohort (n = 392), are available as Supporting Information (Table S3 and Table S4) uploaded with the manuscript. Competing interests : All authors declare that they have no competing interests related to this work. Funding : The authors received no financial support for the research, authorship, and/or publication of this article. Authors’ contributions : SPM and SYC conceptualized and designed the study. SPM was responsible for data collection and clinical management. SYC performed the statistical analysis and data visualization. SPM and SYC co-wrote the initial draft of the manuscript. All authors reviewed and approved the final manuscript. Acknowledgements : The authors would like to express their sincere gratitude to the medical and nursing staff involved in obstetric care at the study site for their dedication to maternal health and their assistance in maintaining high-quality clinical records. The authors also thank all individuals whose clinical data contributed to this study. Authors' information: SPM is an attending physician specializing in obstetrics and gynecology at Shuang Ho Hospital, with a research focus on labor management and clinical prediction models. SYC is a PhD and researcher in nursing science, specializing in physiology, hypertension and maternal health. References World Health O, WHO Statement on Caesarean Section Rates. World Health Organization; 2015. Report No.: WHO/RHR/15.02. 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Wilson MG, Bone JN, Mistry HD, Slade LJ, Singer J, von Dadelszen P, et al. Blood Pressure and Heart Rate Variability and the Impact on Pregnancy Outcomes: A Systematic Review. J Am Heart Assoc. 2024;13(5):e032636. Woolcock H, Parra N, Zhang Y, Reddy UM, Bello NA, Miller E, et al. Pregnancy Outcomes in Women Who Developed Elevated Blood Pressure and Stage I Hypertension after 20 Weeks, Gestation. Am J Perinatol. 2024;41(15):2135–43. Spong CY, Berghella V, Wenstrom KD, Mercer BM, Saade GR. Preventing the first cesarean delivery: summary of a joint Eunice Kennedy Shriver National Institute of Child Health and Human Development, Society for Maternal-Fetal Medicine, and American College of Obstetricians and Gynecologists Workshop. Obstet Gynecol. 2012;120(5):1181–93. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYTABLELEGENDS.docx SupplementaryTableS1BPrisk.docx SupplementaryTableS2AB100BPrisk.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 14 Apr, 2026 Editor invited by journal 19 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9135505","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623552045,"identity":"02e78a47-ef09-4700-900e-e47f27078a42","order_by":0,"name":"Shu-Ying Chen","email":"","orcid":"","institution":"Hungkuang University","correspondingAuthor":false,"prefix":"","firstName":"Shu-Ying","middleName":"","lastName":"Chen","suffix":""},{"id":623552046,"identity":"84831946-32d0-462f-9027-8cc5713fa521","order_by":1,"name":"Shih-Peng Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACNvnHxz9+qIBzidDCx5CWxixxhoGBB6aFh5AWOYYcMwbeNlK0sDEcMHsgOa+OwV7sjAHDh7LDDPYSCQS0MDakGxRuO8zAI51jwDjjHJBBUAszwwEJyW0HwFqYedtAeglpAVojwTunDqLlL1FaeJjZJHgbmCFaGInSIsHGbCxx7DAPz+20goM959J5eO4/wK9Ffgb/x4cfaurk2Gcnb3zwo8xajr3nAH4tMACOjQMMRETLKBgFo2AUjAIiAAANnjZL+sB0+gAAAABJRU5ErkJggg==","orcid":"","institution":"Shuang Ho Hospital, Taipei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shih-Peng","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2026-03-16 09:08:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9135505/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9135505/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107706254,"identity":"5e5e5189-6efd-48ca-812d-cad84f98d502","added_by":"auto","created_at":"2026-04-24 09:17:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":723187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curves for prediction of cesarean birth.\u003c/strong\u003e\u003cbr\u003e\nROC curves illustrating the discriminatory performance of maternal predictors for cesarean birth. The composite maternal score based on maternal age and body mass index demonstrated moderate predictive performance. Although the 1-hour oral glucose tolerance test value showed statistical differences between groups, its ROC discrimination was limited.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9135505/v1/45f0671e25db5cfb3076401c.png"},{"id":107535406,"identity":"07915486-a951-4c9c-b831-b2d271e9d557","added_by":"auto","created_at":"2026-04-22 11:12:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":420255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis of the composite maternal score.\u003c/strong\u003e\u003cbr\u003e\nDecision curve analysis demonstrating the clinical net benefit of the composite maternal score across a range of threshold probabilities. The model showed greater net benefit compared with default strategies of treating all women or treating none.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9135505/v1/695368dfb9d67876e8df1d78.png"},{"id":107709056,"identity":"950dc138-9a53-4fd5-be2b-57f4ac37564f","added_by":"auto","created_at":"2026-04-24 09:34:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1426371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9135505/v1/14dd8804-e458-4e41-a3ed-430fe4685474.pdf"},{"id":107535405,"identity":"e7ba4992-2675-4fe2-88eb-3b5d39d6bd05","added_by":"auto","created_at":"2026-04-22 11:12:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14617,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYTABLELEGENDS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9135505/v1/8af6a968b43dabbc0d2937d1.docx"},{"id":107535409,"identity":"ae891d00-e883-4775-9742-28dddbda25bf","added_by":"auto","created_at":"2026-04-22 11:12:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15506,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1BPrisk.docx","url":"https://assets-eu.researchsquare.com/files/rs-9135505/v1/1a3d22fd5220cf7635cecd94.docx"},{"id":107535408,"identity":"ad6a405b-184d-45bb-af1c-96afcb925b72","added_by":"auto","created_at":"2026-04-22 11:12:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17602,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2AB100BPrisk.docx","url":"https://assets-eu.researchsquare.com/files/rs-9135505/v1/0f644b4da150a40e1631f318.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A simple bedside composite risk score (AB-100 rule) for predicting cesarean birth in nulliparous women: a historical cohort study with external validation","fulltext":[{"header":"Key Message","content":"\u003cp\u003eA simple composite score based on maternal age and body mass index identifies nulliparous women at markedly increased risk of cesarean birth and demonstrates robust performance in external validation, supporting practical bedside risk stratification.\u003c/p\u003e"},{"header":"BACKGROUND","content":"\u003cp\u003eThe global rate of cesarean birth has increased substantially over the past several decades and now exceeds the level considered optimal for population health outcomes.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Although cesarean delivery is a life-saving intervention when medically indicated, the growing proportion of procedures performed for intrapartum indications such as labor dystocia or failure to progress has raised significant clinical and public health concerns.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Contemporary discussions also emphasize the importance of informed counseling and shared decision-making regarding mode of delivery, particularly among low-risk nulliparous women.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Identifying women at increased risk of cesarean birth before or early in labor therefore remains a key challenge in modern obstetric practice.\u003c/p\u003e \u003cp\u003eMaternal characteristics play an important role in determining labor outcomes. Advanced maternal age has been consistently associated with increased rates of operative delivery, possibly reflecting age-related changes in uterine function and obstetric management.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Similarly, maternal adiposity is strongly linked to prolonged labor, impaired uterine contractility, and higher cesarean rates.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Experimental and clinical studies suggest that obesity may alter myometrial contractility and labor dynamics, thereby increasing the likelihood of labor arrest.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These structural maternal factors may therefore represent an underlying physiological vulnerability that predisposes to intrapartum cesarean delivery even in otherwise uncomplicated pregnancies.\u003c/p\u003e \u003cp\u003eSeveral studies have attempted to develop prediction models for intrapartum cesarean delivery using combinations of maternal characteristics, clinical findings, and biochemical markers.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e More recently, machine learning approaches have been introduced to improve predictive performance.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, many existing models require multiple variables or complex computational tools, limiting their practical use in routine clinical settings. In contrast, simple and transparent risk scores derived from readily available maternal characteristics may offer greater bedside applicability and facilitate rapid risk assessment during antenatal counseling or early labor evaluation.\u003c/p\u003e \u003cp\u003eIn addition to structural maternal factors, maternal hemodynamic status during pregnancy may also influence obstetric outcomes. Elevated blood pressure and cardiovascular adaptations during pregnancy have been associated with adverse maternal and perinatal outcomes.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Emerging evidence suggests that even blood pressure levels below traditional hypertensive thresholds may have prognostic implications for pregnancy outcomes.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e However, the potential role of maternal hemodynamic markers in predicting intrapartum cesarean delivery remains incompletely understood.\u003c/p\u003e \u003cp\u003eInter-physician variation in labor management further complicates risk prediction. Differences in diagnostic thresholds for labor arrest and in clinical decision-making may obscure true biological associations between maternal characteristics and delivery outcomes.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Studying cohorts with standardized clinical management may therefore provide clearer insights into the relationship between maternal risk factors and cesarean birth.\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, obstetric decision-making often involves balancing maternal characteristics, obstetric risk factors, and parental preferences. Therefore, a straightforward and interpretable risk indicator may be useful in facilitating shared decision-making between clinicians and pregnant women when discussing delivery planning.\u003c/p\u003e \u003cp\u003eIn this study, we propose a simple composite score combining maternal age and BMI (age\u0026thinsp;+\u0026thinsp;2 x BMI), referred to as the AB score. We further evaluated a clinically practical threshold (AB score\u0026thinsp;\u0026ge;\u0026thinsp;100), termed the \u003cb\u003eAB-100 rule\u003c/b\u003e, to identify nulliparous women at higher risk of cesarean birth. Although maternal age and BMI are well-known risk factors individually, we hypothesized that their combination into a single composite measure could provide a practical and intuitive screening tool for early risk stratification in routine obstetric care.\u003c/p\u003e \u003cp\u003eWhile maternal age and obesity have long been recognized as determinants of cesarean birth risk, these factors are rarely operationalized into simple clinical tools that can be readily applied in routine obstetric practice. Translating established epidemiological associations into intuitive bedside risk indicators may facilitate individualized counseling and shared decision-making regarding mode of delivery. Therefore, this study aimed not to identify new biological risk factors, but to develop and externally validate a practical composite score (AB-100 rule) that integrates maternal age and BMI into a clinically meaningful risk stratification framework.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis historical cohort study was conducted to evaluate the predictive value of a composite score based on maternal age and body mass index for cesarean birth in nulliparous women. The primary cohort consisted of women managed in a standardized clinical environment at a tertiary academic medical center between January 2022 and December 2024. To minimize inter-physician variability in intrapartum decision-making, all deliveries in the primary cohort were supervised by a single attending obstetrician following a consistent labor management protocol.\u003c/p\u003e \u003cp\u003eTo assess the generalizability of the model, external validation was performed using an independent cohort of nulliparous women managed by multiple obstetricians at the same institution during the same period.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWomen were eligible for inclusion if they were nulliparous with a singleton pregnancy, cephalic presentation, and term gestation (\u0026ge;\u0026thinsp;37 weeks) undergoing a trial of labor. Women were excluded if they had multiple gestations, pre-existing hypertension or hypertensive disorders diagnosed before admission, fetal distress, elective cesarean delivery without a trial of labor, or incomplete clinical data. A total of 544 women met the inclusion criteria for the primary derivation cohort. An additional independent cohort of 392 nulliparous women was used for external validation of the prediction model.\u003c/p\u003e\n\u003ch3\u003eVariables and measurements\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was mode of delivery, categorized as vaginal birth or cesarean birth. The primary predictor was a composite maternal risk score calculated as:\u003c/p\u003e \u003cp\u003e \u003cb\u003eMaternal age (years)\u0026thinsp;+\u0026thinsp;2 \u0026times; body mass index at delivery (kg/m\u0026sup2;).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis composite score was designed to capture the combined structural contribution of maternal age and adiposity to labor dynamics. Maternal systolic blood pressure was evaluated as a potential functional predictor. The \u003cb\u003emaximum systolic blood pressure recorded during pregnancy\u003c/b\u003e was used as an indicator of hemodynamic stress. Additional clinical variables were obtained from electronic medical records, including fetal birth weight, glucose values from the 75-g oral glucose tolerance test, and estimated blood loss at delivery.\u003c/p\u003e\n\u003ch3\u003eRisk stratification\u003c/h3\u003e\n\u003cp\u003eTo identify clinically relevant thresholds for the composite score and systolic blood pressure, decision tree analysis was performed. Optimal cutoff values were determined using the \u003cb\u003eYouden index\u003c/b\u003e to maximize combined sensitivity and specificity.\u003c/p\u003e \u003cp\u003eBased on these thresholds, participants were categorized into three risk groups:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLow risk\u003c/b\u003e: composite score\u0026thinsp;\u0026le;\u0026thinsp;100 and systolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;134 mmHg\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntermediate risk\u003c/b\u003e: elevation of either variable\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigh risk\u003c/b\u003e: composite score\u0026thinsp;\u0026gt;\u0026thinsp;100 and systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;134 mmHg\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and were compared using the Student t test. Categorical variables are presented as number (percent) and were compared using the chi-square test or Fisher exact test as appropriate.\u003c/p\u003e \u003cp\u003eMulticollinearity among predictors was assessed before model construction. Logistic regression analysis was performed to evaluate the association between maternal characteristics and cesarean birth. Odds ratios (OR) and 95% confidence intervals (CI) were calculated.\u003c/p\u003e \u003cp\u003eTo evaluate the discriminatory performance of the prediction models, \u003cb\u003ereceiver operating characteristic (ROC) curve analysis\u003c/b\u003e was performed and the \u003cb\u003earea under the curve (AUC)\u003c/b\u003e was calculated. ROC curves were constructed for the composite maternal score and for models incorporating additional clinical variables.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDecision curve analysis (DCA)\u003c/b\u003e was conducted to assess the potential clinical usefulness of the prediction models across a range of threshold probabilities. Net benefit was calculated for each model and compared with default strategies of treating all women or treating none.\u003c/p\u003e \u003cp\u003eExternal validation of the composite score was performed using the independent cohort managed by multiple obstetricians. Model discrimination in the validation cohort was evaluated using AUC, and the predictive performance of the score was compared between the derivation and validation cohorts.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using \u003cb\u003ePython (version 3.12.7)\u003c/b\u003e. Data processing and management were conducted using the \u003cb\u003epandas\u003c/b\u003e and \u003cb\u003enumpy\u003c/b\u003e libraries. Logistic regression modeling and receiver operating characteristic (ROC) curve analyses were performed using the \u003cb\u003escikit-learn (version 1.5.1)\u003c/b\u003e package. Model discrimination was evaluated using the area under the ROC curve (AUC).\u003c/p\u003e \u003cp\u003eOptimal cutoff values were determined using the \u003cb\u003eYouden index\u003c/b\u003e, which maximizes the combined sensitivity and specificity.\u003c/p\u003e \u003cp\u003eDecision curve analysis (DCA) was performed to assess the clinical usefulness of the prediction models by estimating the net benefit across a range of threshold probabilities and comparing the model with default strategies of treating all womens or treating none.\u003c/p\u003e \u003cp\u003eExternal validation of the prediction model was conducted using an independent cohort, and model performance in the validation dataset was evaluated using ROC analysis and AUC.\u003c/p\u003e \u003cp\u003eA two-sided \u003cb\u003eP value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMaternal characteristics\u003c/h2\u003e \u003cp\u003eA total of \u003cb\u003e544 nulliparous women\u003c/b\u003e were included in the primary cohort. Among them, \u003cb\u003e401 (73.7%) achieved vaginal birth and 143 (26.3%) underwent cesarean birth\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eBaseline maternal characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Women who underwent cesarean birth were generally older and had higher body mass index at delivery compared with those who achieved vaginal birth. Several metabolic and hemodynamic variables also differed between the two groups. In particular, the \u003cb\u003e1-hour glucose value from the oral glucose tolerance test (OGTT)\u003c/b\u003e was significantly higher among women who underwent cesarean delivery.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Maternal Characteristics between Vaginal Birth and Cesarean Birth in Nullipara\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVaginal Birth (n\u0026thinsp;=\u0026thinsp;401)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCesarean Birth (n\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI at Delivery (kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Change (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.921\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFetal Birthweight (g)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3025.7\u0026thinsp;\u0026plusmn;\u0026thinsp;402.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3112.1\u0026thinsp;\u0026plusmn;\u0026thinsp;641.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.133\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Loss (mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e88.3\u0026thinsp;\u0026plusmn;\u0026thinsp;119.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e826.1\u0026thinsp;\u0026plusmn;\u0026thinsp;404.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOGTT Fasting (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e80.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e82.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.052\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOGTT 1-hr (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e132.0\u0026thinsp;\u0026plusmn;\u0026thinsp;31.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e144.2\u0026thinsp;\u0026plusmn;\u0026thinsp;32.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOGTT 2-hr (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e117.2\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e125.6\u0026thinsp;\u0026plusmn;\u0026thinsp;29.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDM (n, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e60 (15.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37 (25.9%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax SBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e127.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e133.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP SD (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.092\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*\u003cb\u003eNote\u003c/b\u003e: Values are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation or number (percentage). *Indicates statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1. Maternal characteristics according to mode of delivery in the primary cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Comparison of demographic, metabolic, and hemodynamic variables between women who achieved vaginal birth and those who underwent cesarean birth. Continuous variables are presented as mean \u0026plusmn; standard deviation and categorical variables as number (percentage).\u003c/p\u003e\u003cp\u003eAlthough this difference reached statistical significance, the magnitude of discrimination was limited when evaluated using receiver operating characteristic analysis. This suggests that while OGTT 1-hour glucose values may be statistically associated with cesarean birth, their ability to meaningfully distinguish individual risk remains modest.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLogistic regression analysis\u003c/h3\u003e\n\u003cp\u003eMultivariable logistic regression analysis was performed to evaluate the independent association between maternal characteristics and cesarean birth (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Analysis for Predicting Cesarean Birth in Nullipara\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI at Delivery\u003c/b\u003e\u0026nbsp;(per kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.07\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax SBP\u003c/b\u003e (per mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP Variability (CV)\u003c/b\u003e (per %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.92\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOGTT 1-hr (per mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOGTT 2-hr (per mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*\u003cb\u003eNote\u003c/b\u003e: CI\u0026thinsp;=\u0026thinsp;Confidence Interval; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; SBP\u0026thinsp;=\u0026thinsp;Systolic Blood Pressure; OGTT 1-hr, 2-hr\u0026thinsp;=\u0026thinsp;oral glucose tolerant test post-glucose-load 1 hour and 2 hours glucose level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2. Multivariable logistic regression analysis of factors associated with cesarean birth.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Odds ratios (OR) and 95% confidence intervals (CI) for maternal characteristics associated with cesarean delivery in the primary cohort.\u003c/p\u003e\n \u003cp\u003eMaternal age and body mass index were independently associated with an increased likelihood of cesarean delivery. These findings support the importance of structural maternal factors in determining labor outcomes. Based on these observations, a composite maternal score combining maternal age and body mass index was constructed to represent the combined influence of these structural characteristics.\u003c/p\u003e \u003cp\u003eMaternal systolic blood pressure also showed a positive association with cesarean birth, suggesting that maternal hemodynamic status may contribute additional physiological information beyond structural risk factors.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReceiver operating characteristic analysis\u003c/h2\u003e \u003cp\u003eThe discriminatory performance of the predictive variables was evaluated using receiver operating characteristic (ROC) curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe composite maternal score demonstrated \u003cb\u003emoderate discriminatory ability\u003c/b\u003e for predicting cesarean birth. In contrast, although OGTT 1-hour glucose values differed significantly between groups, their ROC curve showed relatively limited discriminatory performance. This highlights the distinction between statistical association and clinically meaningful prediction. The OGTT 1-hour glucose value was not included in the final prediction model because its discriminatory performance was limited despite statistical differences between groups.\u003c/p\u003e \u003cp\u003eOverall, the ROC analysis supports the use of maternal age and body mass index as the core structural predictors in the proposed risk model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk stratification and external validation\u003c/h2\u003e \u003cp\u003eThe predictive performance of the composite maternal score was further assessed in an independent validation cohort (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The association between the composite score and cesarean birth remained consistent in this cohort, indicating stable predictive performance across different clinical settings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExternal Validation of the Composite Maternal Score for Predicting Cesarean Birth in a Multi-physician Nulliparous Cohort (N\u0026thinsp;=\u0026thinsp;392)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB-100 Score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Women (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCesarean Birth, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLow Risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62 (17.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh Risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.98 (4.63\u0026ndash;21.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cb\u003eAB-100 Score=\u003c/b\u003e Calculated as Maternal Age (years)\u0026thinsp;+\u0026thinsp;2 \u0026times; BMI at admission (kg/m\u003csup\u003e2\u003c/sup\u003e); CI\u0026thinsp;=\u0026thinsp;Confidence Interval; OR\u0026thinsp;=\u0026thinsp;Odds Ratio; Ref\u0026thinsp;=\u0026thinsp;Reference group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3. External validation of the composite maternal score in the independent cohort.\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Association between the composite maternal score and cesarean birth in the validation cohort. Odds ratios (OR) with 95% confidence intervals (CI) are presented.\u003c/p\u003e\n\u003cp\u003eExternal validation represents a critical step in prediction model research because models developed within a single cohort may overestimate their predictive ability. The consistency observed in the validation cohort therefore supports the \u003cb\u003erobustness and generalizability\u003c/b\u003e of the composite maternal score.\u003c/p\u003e \u003cp\u003eAdditional analyses examining risk stratification using systolic blood pressure thresholds and combined maternal score models are provided in the \u003cb\u003eSupporting Information (Supplementary Tables S1 and S2)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDecision curve analysis\u003c/h2\u003e \u003cp\u003eDecision curve analysis was performed to evaluate the potential clinical usefulness of the prediction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross a range of threshold probabilities, the composite maternal score demonstrated a \u003cb\u003epositive net benefit compared with default strategies of treating all women or treating none\u003c/b\u003e. These findings suggest that the model may provide clinically meaningful decision support when applied to antenatal counseling or early labor risk assessment.\u003c/p\u003e \u003cp\u003eImportantly, the presence of \u003cb\u003eexternal validation further strengthens the clinical relevance of the model\u003c/b\u003e, indicating that the observed net benefit is not limited to the derivation cohort but may also be applicable in broader clinical settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study demonstrates that combining two universally available maternal characteristics into a simple composite score provides clinically meaningful information for predicting cesarean birth in nulliparous women. Although maternal age and BMI are established risk factors, our findings highlight the importance of translating these epidemiological determinants into an easily interpretable bedside tool that can support real-world obstetric decision-making.\u003c/p\u003e \u003cp\u003eMaternal age and adiposity are well-recognized determinants of labor outcomes. Advanced maternal age has been associated with increased operative delivery rates, possibly reflecting both biological and clinical factors.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Age-related changes in uterine contractility and myometrial responsiveness may contribute to inefficient labor progress, while obstetric management practices may also differ in older mothers. Similarly, maternal obesity has been linked to altered myometrial contractility and prolonged labor. Experimental and clinical studies have suggested that obesity may affect uterine muscle function and metabolic signaling pathways, potentially increasing the risk of labor dystocia and cesarean delivery.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Our findings are consistent with this biological framework, supporting the concept that structural maternal characteristics can influence the mechanical and physiological processes of labor.\u003c/p\u003e \u003cp\u003eAlthough the present composite score includes only two predictors, maternal age and body mass index were intentionally selected because they are universally available and represent structural determinants of labor mechanics. This simplicity may enhance bedside applicability and facilitate rapid clinical risk assessment without requiring complex data inputs or computational tools.\u003c/p\u003e \u003cp\u003eInterestingly, although the 1-hour glucose value from the oral glucose tolerance test differed significantly between groups, its discriminatory performance in ROC analysis was limited. This observation highlights an important distinction between statistical association and predictive usefulness. While metabolic factors may contribute to overall pregnancy risk profiles, their individual ability to discriminate cesarean birth risk at the women level appears limited in comparison with structural maternal characteristics such as age and body mass index.\u003c/p\u003e \u003cp\u003eThis discrepancy underscores the difference between statistical significance and clinical prediction. Variables that demonstrate significant group differences do not necessarily improve the performance of prediction models designed for individual risk estimation.\u003c/p\u003e \u003cp\u003ePrevious studies have proposed a variety of prediction models for cesarean birth, often incorporating multiple clinical variables or complex algorithms.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e More recently, machine learning approaches have been introduced to improve predictive performance.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e However, many of these models require extensive input variables or computational tools that may limit their practicality in routine clinical settings. While machine learning approaches may achieve higher predictive accuracy in some contexts, their implementation often requires extensive datasets, specialized computational infrastructure, and complex model interpretation. In contrast, simple clinical risk scores may offer greater practicality in routine obstetric practice, particularly in settings where rapid clinical decision-making is required.\u003c/p\u003e \u003cp\u003eExternal validation represents a critical component of prediction model development. Models derived from a single cohort frequently demonstrate optimistic predictive performance due to overfitting. In the present study, the composite score maintained consistent associations with cesarean birth in an independent validation cohort managed by multiple obstetricians. This finding suggests that the predictive value of the score is not solely dependent on the original derivation cohort and may be applicable across different clinical settings.\u003c/p\u003e \u003cp\u003eDecision curve analysis further demonstrated that the composite maternal score provided a positive net benefit across a range of threshold probabilities compared with default strategies. These findings suggest that the model may offer clinically meaningful support when applied to antenatal counseling or early labor risk assessment. By identifying women at higher risk of cesarean birth, clinicians may be able to provide more individualized counseling and optimize intrapartum monitoring strategies.\u003c/p\u003e \u003cp\u003eAnother important consideration is the role of maternal hemodynamic status. Previous studies have suggested that blood pressure and cardiovascular adaptations during pregnancy may influence maternal and perinatal outcomes.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e In the present study, maternal systolic blood pressure showed an association with cesarean birth, suggesting that functional maternal physiology may contribute additional information beyond structural risk factors. However, the predictive contribution of blood pressure was more modest than that of maternal age and body mass index. Further research may help clarify the interaction between structural maternal characteristics and maternal hemodynamic status in determining labor outcomes.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, this study was conducted at a single institution, which may limit the generalizability of the results to other populations or healthcare systems. Second, although external validation was performed, both cohorts were derived from the same institutional environment. Third, the model focused on readily available maternal characteristics and did not incorporate additional intrapartum variables that may further improve predictive accuracy. Future studies in larger multicenter populations may help refine and validate this approach.\u003c/p\u003e \u003cp\u003eBeyond its statistical performance, the practical utility of the AB-100 rule lies in its capacity to facilitate \u003cb\u003eShared Decision Making (SDM)\u003c/b\u003e between clinicians and nulliparous women. Currently, counseling regarding the risk of intrapartum cesarean birth is often based on subjective clinical judgment or complex models that are difficult to communicate at the bedside. The AB-100 rule simplifies this by providing an objective, easily calculated threshold. For instance, our data demonstrates that women with a score exceeding 100 face a significantly higher risk of cesarean delivery (67.6% vs 17.3% in the validation cohort). By integrating such concrete figures into prenatal or early-labor consultations, clinicians can move away from vague risk descriptors and empower patients with evidence-based insights. This transparency allows for a more personalized birth plan, helping mothers align their expectations with their physiological risk profile and potentially reducing the psychological distress associated with unexpected surgical interventions. Ultimately, the AB-100 rule serves not merely as a predictive algorithm, but as a crucial communication bridge that supports the ethical and clinical goals of shared autonomy in obstetric care.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study also has several strengths. The study population consisted exclusively of nulliparous women undergoing trial of labor, providing a relatively homogeneous clinical cohort. The use of standardized labor management in the derivation cohort minimized variability in clinical decision-making. Furthermore, the inclusion of external validation and decision curve analysis strengthens the clinical relevance of the proposed model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThis approach does not aim to introduce new biological predictors but instead focuses on enhancing the clinical usability of existing evidence.\u003c/b\u003e In clinical practice, this composite maternal score may serve as a simple bedside tool to identify nulliparous women at increased risk of cesarean birth, thereby supporting individualized counseling and potentially guiding intrapartum monitoring strategies.\u003c/p\u003e \u003cp\u003eOverall, our findings suggest that a simple composite score based on maternal age and body mass index may serve as a practical tool for identifying nulliparous women at increased risk of cesarean birth. Such an approach may complement existing clinical assessment and support individualized obstetric counseling.\u003c/p\u003e \u003cp\u003eFurther prospective multicenter studies are warranted to confirm the clinical utility and implementation of this simple risk stratification approach. Importantly, the simplicity of the AB-100 rule may make it particularly suitable for use in low-resource settings where access to advanced obstetric technologies or complex prediction models is limited. Because the score relies only on routinely available maternal characteristics, it may facilitate early risk communication and basic risk stratification in diverse clinical environments, including primary care facilities and midwife-led maternity units.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe AB-100 rule translates well-known maternal risk factors into a simple and clinically applicable risk stratification tool. This approach may enhance individualized counseling and shared decision-making in nulliparous women considering vaginal birth. Further prospective multicenter studies are warranted to evaluate its impact on clinical outcomes and obstetric management strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstitutional Review Board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was approved by the Institutional Review Board of Taipei Medical University (TMU-JIRB No. N202512016). As this study involved a retrospective analysis of de-identified clinical data, the requirement for informed consent was waived by the ethics committee. All procedures were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003cbr\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The anonymized datasets supporting the findings of this study, encompassing the primary derivation cohort (n = 544) and the independent external validation cohort (n = 392), are available as\u0026nbsp;\u003cstrong\u003eSupporting Information (Table S3 and Table S4)\u003c/strong\u003e uploaded with the manuscript.\u003cbr\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: All authors declare that they have no competing interests related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors received no financial support for the research, authorship, and/or publication of this article.\u003cbr\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e: SPM and SYC conceptualized and designed the study. SPM was responsible for data collection and clinical management. SYC performed the statistical analysis and data visualization. SPM and SYC co-wrote the initial draft of the manuscript. All authors reviewed and approved the final manuscript.\u003cbr\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: The authors would like to express their sincere gratitude to the medical and nursing staff involved in obstetric care at the study site for their dedication to maternal health and their assistance in maintaining high-quality clinical records. The authors also thank all individuals whose clinical data contributed to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information:\u0026nbsp;\u003c/strong\u003eSPM is an attending physician specializing in obstetrics and gynecology at Shuang Ho Hospital, with a research focus on labor management and clinical prediction models. SYC is a PhD and researcher in nursing science, specializing in physiology, hypertension and maternal health.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health O, WHO Statement on Caesarean Section Rates. World Health Organization; 2015. Report No.: WHO/RHR/15.02.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandall J, Tribe RM, Avery L, Mola G, Visser GH, Homer CS, et al. Short-term and long-term effects of caesarean section on the health of women and children. Lancet. 2018;392(10155):1349\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlperin M, Artsen A. Cesarean Delivery on Maternal Request. JAMA. 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttali E, Doleeb Z, Hiersch L, Amikam U, Gamzu R, Yogev Y, et al. The risk of intrapartum cesarean delivery in advanced maternal age. 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Cureus. 2025;17(5):e83655.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang M, Long D, Li Y, Liu X, Bai Z, Li Z. An explainable machine learning model in predicting vaginal birth after cesarean section. J Matern Fetal Neonatal Med. 2025;38(1):2546544.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson MG, Bone JN, Mistry HD, Slade LJ, Singer J, von Dadelszen P, et al. Blood Pressure and Heart Rate Variability and the Impact on Pregnancy Outcomes: A Systematic Review. J Am Heart Assoc. 2024;13(5):e032636.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolcock H, Parra N, Zhang Y, Reddy UM, Bello NA, Miller E, et al. Pregnancy Outcomes in Women Who Developed Elevated Blood Pressure and Stage I Hypertension after 20 Weeks, Gestation. Am J Perinatol. 2024;41(15):2135\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpong CY, Berghella V, Wenstrom KD, Mercer BM, Saade GR. 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Obstet Gynecol. 2012;120(5):1181\u0026ndash;93.\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":false,"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":"Cesarean birth, Maternal age, Body mass index, Risk prediction, Nulliparous women, Shared decision-making","lastPublishedDoi":"10.21203/rs.3.rs-9135505/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9135505/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCesarean birth rates continue to rise worldwide. Advanced maternal age and elevated body mass index (BMI) are well-established risk factors for intrapartum cesarean delivery in nulliparous women. However, translating these epidemiological risk factors into a simple clinical tool that can support individualized counseling and shared decision-making remains challenging. This study aimed to develop and externally validate a practical bedside composite risk score integrating maternal age and BMI (the AB-100 rule) for predicting cesarean birth.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted a historical cohort study including nulliparous women with singleton term pregnancies undergoing trial of labor at a tertiary academic center. A composite score was calculated as maternal age (years)\u0026thinsp;+\u0026thinsp;2 \u0026times; BMI at delivery (kg/m\u0026sup2;) (AB score). Logistic regression and receiver operating characteristic (ROC) curve analysis were used to assess predictive performance. A clinically practical threshold of AB score\u0026thinsp;\u0026ge;\u0026thinsp;100 was evaluated. External validation was performed using an independent cohort managed by multiple obstetricians. Decision curve analysis (DCA) was conducted to assess potential clinical utility.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigher composite scores were significantly associated with increased risk of cesarean birth. Women with AB score\u0026thinsp;\u0026ge;\u0026thinsp;100 had a markedly higher cesarean rate compared with those with lower scores (67.6% vs 17.3%). The model demonstrated moderate discrimination and consistent performance in the external validation cohort. Decision curve analysis indicated a positive net benefit across clinically relevant threshold probabilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAlthough maternal age and BMI are known determinants of cesarean birth risk, integrating them into a simple bedside composite score provides an intuitive framework for early risk communication. The AB-100 rule may support shared decision-making and individualized delivery planning in nulliparous women.\u003c/p\u003e","manuscriptTitle":"A simple bedside composite risk score (AB-100 rule) for predicting cesarean birth in nulliparous women: a historical cohort study with external validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 11:12:43","doi":"10.21203/rs.3.rs-9135505/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-14T14:35:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-19T19:22:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T11:54:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T11:54:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-03-16T09:04:16+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":"9107f450-e5ec-4390-ad64-3632606c72f0","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T11:12:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 11:12:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9135505","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9135505","identity":"rs-9135505","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}