Department of Radiology, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang 441000, China.
Department of Radiology, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, The First Clinical Medical College, 155 Hanzhong Road, Nanjing 210029, Jiangsu Province, China.
Int J Clin Pract. 2022 Jan 31;2022:9527412. doi: 10.1155/2022/9527412. eCollection 2022.
This study aimed to investigate the ability of ultrasound/magnetic resonance imaging (MRI) signature and clinical data-based model for preoperatively predicting the degree of placenta accreta spectrum disorders and develop combined prediction models.
The clinicopathological characteristics, prenatal ultrasound images, and MRI features of 132 pregnant women with placenta accreta spectrum disorders at Xiangyang No. 1 People's Hospital were retrospectively reviewed from January 2016 to December 2020. In the training set of 99 patients, the ultrasound/MRI features model, clinical characteristics model, and combined model were developed by multivariate logistic regression analysis to predict the degree of placenta accreta spectrum disorders. The prediction performance of different models was compared using the Delong test. The developed models were validated by assessing their prediction performance in a test set of 33 patients.
The multivariate logistic regression analysis identified history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder to construct a combined model for predicting the degree of placenta accreta spectrum disorders (area under the curve (AUC) = 0.931; 95% confidence interval (CI): 0.882-0.980). The AUC of the clinical characteristics model and ultrasound/MRI features model was 0.858 (95% CI 0.794-0.921) and 0.709 (95% CI 0.624-0.798), respectively. The AUC of the combined model was significantly higher than that of the ultrasound/MRI features model ( < 0.001) or clinical characteristics model ( < 0.0015) in the training set. In the test set, the combined model also showed higher prediction performance.
Ultrasound/MRI-based signature is a powerful predictor for the degree of placenta accreta spectrum disorders in an early stage. A combined model (constructed with history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder) can improve the accuracy for predicting the degree of placenta accreta spectrum disorders in an early stage.
本研究旨在探讨基于超声/磁共振成像（MRI）特征和临床数据的模型在术前预测胎盘植入谱系疾病程度方面的能力，并建立联合预测模型。
回顾性分析 2016 年 1 月至 2020 年 12 月在襄阳市第一人民医院就诊的 132 例胎盘植入谱系疾病孕妇的临床病理特征、产前超声图像和 MRI 特征。在 99 例患者的训练集中，采用多变量逻辑回归分析建立超声/MRI 特征模型、临床特征模型和联合模型，以预测胎盘植入谱系疾病的程度。采用 Delong 检验比较不同模型的预测性能。通过评估 33 例患者的测试集来验证所建立模型的预测性能。
多变量逻辑回归分析确定了流产史、子宫内膜损伤史和胎盘与子宫肌层/子宫浆膜与膀胱之间边界模糊这 3 个因素来构建联合模型以预测胎盘植入谱系疾病的程度（曲线下面积（AUC）=0.931；95%置信区间（CI）：0.882-0.980）。临床特征模型和超声/MRI 特征模型的 AUC 分别为 0.858（95%CI 0.794-0.921）和 0.709（95%CI 0.624-0.798）。在训练集中，联合模型的 AUC 显著高于超声/MRI 特征模型（<0.001）或临床特征模型（<0.0015）。在测试集中，联合模型也表现出较高的预测性能。
基于超声/MRI 的特征是早期预测胎盘植入谱系疾病程度的有力预测因子。联合模型（由流产史、子宫内膜损伤史和胎盘与子宫肌层/子宫浆膜与膀胱之间边界模糊构建）可提高早期预测胎盘植入谱系疾病程度的准确性。
