Background Cesarean scar pregnancy (CSP) is a condition closely associated with previous cesarean section scars, and improper diagnosis or treatment may result in massive hemorrhage and life-threatening risks. Currently, there is a lack of standardized treatment guidelines or consensus for CSP, leading to a chaotic array of treatment methods. The objective of this study is to formulate a novel CSP scorecard model to aid in the selection of treatment plans for CSP.
Methods A cohort comprising 1,248 patients diagnosed with CSP was examined over a period from January 2013 to January 2023. Univariate and multivariate logistic regression analyses were employed to identify high-risk factors predictive of CSP risk, which served as the foundation for constructing a nomogram. The predictive efficacy of the nomogram was assessed through the application of receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) curves.
ResultsSix risk factors—gestational age, gestational sac (GS) area, residual myometrial thickness, the relationship between the GS and scar, preoperative hemoglobin levels, and preoperative HCG—were evaluated as predictor variables in the nomogram. The nomogram demonstrated excellent discriminative ability, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of 0.84. Furthermore, the calibration curves and decision curve analysis indicated that the nomogram exhibited strong consistency and substantial clinical utility.
Conclusion This newly developed risk scoring system offers an effective tool for clinicians to tailor individualized CSP treatment plans.