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Abstract To analyze the risk factors of bleeding during cesarean scar pregnancy(CSP), and to construct a nomogram prediction model. Methods: 157 patients with CSP who received surgical treatment from February 2017 to April 2022 were selected in this study. These patients were divided into study group(52 patients with bleeding)and control group(105 patients without bleeding)according to the intraoperative bleeding. Logistic regression was used to analyze the risk factors affecting the intraoperative bleeding of the patients with CSP, and the nomograph model for predicting the intraoperative bleeding of the patients with CSP was constructed. The model was verified by the receiver operator characteristic(ROC)curve and the calibration curve. Results: There were no significant differences in age, number of cesarean sections, number of pregnancies, number of abortions, time to previous operation, duration of menopause, fetal heart rate, ultrasound classification, and blood flow situation by ultrasound of the patients between the two groups(P＞0.05). There were significant differences in scar thickness, preoperative blood hCG level, gestational weeks, and gestational sac diameter of the patients between the two groups(P＜0.05). The optimal cut-off values of scar thickness, preoperative blood hCG level, and gestational weeks for verifying the ROC model were 1.86mm, 80889.45mIU/ml, and 9 weeks, respectively. Logistic regression analysis showed that scar thickness, preoperative blood hCG level, gestational weeks, gestational sac diameter, and other abnormalities were the independent risk factors of intraoperative bleeding of the patients during CSP surgery(P＜0.05). ROC analysis showed that the area under the curve was 0.786(95%CI: 0.709-0.862), and the slope of the calibration curve was close to 1. Hosmer-lemeshow goodness-of-fit test χ2 was 6.015(P=0.305). Conclusion: The scar thickness, preoperative blood hCG level, gestational weeks, and gestational sac diameter are the risk factors of intraoperative hemorrhage of the patients during CSP surgery, and the nomogram prediction model based on which has reliable predictive ability for predicting intraoperative hemorrhage of the patients.
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