Development and validation of a nomogram model for predicting postoperative complications of cesarean scar pregnancy based on clinical data

Background/objectives: Cesarean scar pregnancy (CSP) is a rare but increasingly prevalent form of ectopic pregnancy, often associated with severe postoperative complications. Current research lacks robust tools to predict these complications. This study aimed to develop and validate a clinical nomogram to assess the risk of postoperative complications in CSP patients using multidimensional clinical data. Methods: = 228). Multivariate logistic regression identified independent risk factors, which were used to construct a predictive nomogram. Model performance was evaluated by ROC curves, calibration plots, decision curve analysis, and clinical impact curves. Results: Four independent predictors of postoperative complications were identified: gestational age, interval since last cesarean section, residual myometrial thickness at the scar site, and intraoperative blood loss. The nomogram showed excellent discrimination with AUCs of 0.868 and 0.865 in the training and validation cohorts, respectively. Calibration and decision curve analyses confirmed good predictive accuracy and clinical utility. Conclusion: The developed nomogram effectively predicts postoperative complications in CSP patients and can guide early clinical interventions and personalized treatment strategies, enhancing patient safety and outcomes.