Development and Validation of a Predictive Nomogram for Cesarean Delivery in Term Singleton Pregnancies Complicated by Small for Gestational Age Undergoing Labor Induction

Abstract

Objective: This study aimed to identify antenatal and intrapartum risk factors associated with cesarean delivery in term singleton pregnancies complicated by small for gestational age (SGA) and to develop a predictive model. Methods:We conducted a retrospective case-control study of 507 SGA patients who underwent labor induction between 2017 and 2022 at Fujian Maternity and Child Health Hospital.Comprehensive data on maternal demographics, obstetric complications, labor induction methods, and neonatal outcomes were collected. 354 (70%) experiencing SGA complications enrolled as the derivation cohort and 153 (30%) included in the validation set. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for cesarean delivery, and a predictive nomogram was developed based on these factors in the derivation cohort,and verified in the validation set. Results: A total of 134 (26.43%) women in the cohort underwent cesarean delivery following labor induction. Four significant independent risk factors for cesarean delivery were identified: maternal age(aOR1.08, 95%CI 1.01-1.15) , weightat admission (aOR 1.04, 95% CI 1.01 - 1.07), the use of dinoprostone for induction(aOR 2.08, 95% CI 1.13-3.81), and the Bishop score after cervical ripening(aOR0.65, 95% CI:0.54-0.80). The constructed nomogram displayed a discriminative ability with an area under the curve (AUC) of 0.78 in the training cohort and 0.77 in the validation cohort. Calibration curves indicated strong agreement（P＞0.05）between predicted probabilities and observed outcomes, while decision curve analysis confirmed significant net benefits across various various threshold probabilities. Conclusion:The developed nomogram provides clinicians with a reliable tool for predicting the likelihood of cesarean delivery in SGA pregnancies undergoing labor induction, aiding in informed decision-making and potentially optimizing clinical management strategies to improve perinatal outcomes.