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标题：Evaluation of uterine scar healing by transvaginal ultrasound in 607 nonpregnant women with a history of cesarean section
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作者：Xingchen Zhou ; Tao Zhang ; Huayuan Qiao 等
期刊名称：BMC Women's Health
印刷版ISSN：1472-6874
电子版ISSN：1472-6874
出版年度：2021
卷号：21
期号：1
页码：1-8
DOI：10.1186/s12905-021-01337-x
出版社：BioMed Central
摘要：Caesarean scar defect (CSD) seriously affects female reproductive health. In this study, we aim to evaluate uterine scar healing by transvaginal ultrasound (TVS) in nonpregnant women with cesarean section (CS) history and to build a predictive model for cesarean scar defects is very necessary. A total of 607 nonpregnant women with previous CS who have transvaginal ultrasound measurements of the thickness of the lower uterine segment. The related clinical data were recorded and analyzed. All patients were divided into two groups according to their clinical symptoms: Group A (N = 405) who had no cesarean scar symptoms, and Group B (N = 141) who had cesarean scar symptoms. The difference in frequency of CS, uterine position, detection rate of CSD and the residual muscular layer (TRM) of the CSD were statistically significant between groups; the TRM measurements of the two groups were (mm) 5.39 ± 3.34 versus 3.22 ± 2.33, P < 0.05. All patients were divided into two groups according to whether they had CSDs: Group C (N = 337) who had no CSDs, Group D (N = 209) who had CSDs on ultrasound examination. The differences in frequency of CS, uterine position, TRM between groups were statistically significant (P < 0.05). In the model predicting CSDs by TRM with TVS, the area under the ROC curve was 0.771, the cut-off value was 4.15 mm. The sensitivity and specificity were 87.8% and 71.3%, respectively. Patients with no clinical symptoms had a mean TRM on transvaginal ultrasonography of 5.39 ± 3.34 mm, which could be used as a good reference to predict the recovery of patients with CSDs after repair surgery.
关键词：Cesarean section;Cesarean scar defect (CSD);Thickness of residual myometrial (TRM);Transvaginal ultrasound (TVS);Predictive model